The Integration of Advanced Mechatronic Systems into Industry 4.0 for Smart Manufacturing

Abstract

In recent years, the rapid advancement of digital technologies has driven a profound transformation in both individual lives and business operations. The integration of Industry 4.0 with advanced mechatronic systems is at the forefront of this digital transformation, reshaping the landscape of smart manufacturing. This article explores the convergence of digital technologies and physical systems, with a focus on the critical role of mechatronics in enabling this transformation. Using technologies such as advanced robotics, the Internet of Things (IoT), artificial intelligence (AI), and big data analytics, industries are developing intelligent and interconnected systems capable of real-time data exchange, distributed decision making, and automation. The paper further explores two case studies: one on a smart plastic injection moulding machine and another on soft robots. These examples illustrate the synergies, benefits, challenges, and future potential of integrating mechatronics with Industry 4.0 technologies. Ultimately, this convergence fosters the development of smart factories and products, enhancing manufacturing efficiency, adaptability, and productivity, while also contributing to sustainability by reducing waste, optimising resource usage, and lowering the environmental impact of industrial production. This marks a significant shift in industrial production towards more sustainable practices.

1. Introduction

In the era of digital transformation, the integration of Industry 4.0 and advanced mechatronic systems has emerged as a key driver for the evolution of smart manufacturing [1]. Industry 4.0, characterised by the convergence of digital technologies and physical systems, is reshaping traditional manufacturing processes. Mechatronics, an interdisciplinary field that combines mechanical engineering, electronics, computer science, and control engineering, plays a pivotal role in achieving the seamless integration of cyber–physical systems within smart manufacturing environments [2].

Industry 4.0 is the fourth revolution in industrial production, which is characterised by the incorporation of digital technologies into the manufacturing process to create interconnected and intelligent systems. This paradigm shift involves the use of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data analytics, and cyber–physical systems. These technologies enable real-time data exchange, distributed decision making, and automation, leading to a more efficient and productive manufacturing environment [3,4].

Employing Industry 4.0 technologies in manufacturing leads to the development of intelligent manufacturing production systems, commonly referred to as smart factories or smart production. A smart production system uses sensors, actuators, and equipment that are connected to digital networks and the digital world to control and integrate production processes [5]. Mechatronics is a basic concept in the design of manufacturing systems and plays a vital role in the manufacturing industry by bridging the gap between the physical and digital worlds [6]. Furthermore, the use of advanced mechatronic systems, including cutting-edge technologies in sensors, actuators, information communication technology (ICT), intelligent control, etc., is essential in implementing the principles of Industry 4.0, as they offer the hardware and software infrastructure required for an intelligent manufacturing environment [7].

Mechatronics and Industry 4.0 can be integrated to achieve smart manufacturing by leveraging smart systems, connected technologies, and ICT capabilities [8]. The fusion of mechatronics and Industry 4.0 is revolutionising the manufacturing industry, bringing about significant transformations. Mechatronics encompasses the necessary hardware and software components to facilitate the automation and control of manufacturing processes. Industry 4.0 offers the means to connect and analyse data, allowing real-time monitoring and control of these processes. Through the integration of these two disciplines, manufacturers have the ability to develop smart factories that exhibit increased efficiency, adaptability, and productivity. Furthermore, the integration of ecomechatronics with Industry 4.0 represents a powerful approach to achieving sustainability in modern manufacturing. Ecomechatronics combines ecological principles with mechatronic systems, focusing on minimising environmental impact while maintaining high levels of efficiency and productivity. When coupled with Industry 4.0 technologies, this integration enables the development of smart and energy-efficient systems that optimise resource use, reduce waste, and reduce carbon emissions [9,10].

This paper explores the transformative integration of mechatronics and Industry 4.0 through the lens of two case studies focused on a smart plastic injection moulding machine and soft robotics. These technologies are quintessential examples of mechatronic systems. The case studies serve as a practical illustration of the synergies, benefits, challenges, and future prospects associated with the integration of mechatronics and Industry 4.0 in the context of cutting-edge applications. The main contributions of the paper are:

• Comprehensive and unified framework: Our work provides a comprehensive overview of mechatronics and Industry 4.0, encompassing state-of-the-art technologies, applications, and emerging trends, including sustainability. This unified framework serves as a valuable resource for researchers and practitioners in the field.
• This paper is the first to dive into the intricacies of integrating mechatronics and Industry 4.0, exploring their application domains and synergies and challenges. By offering a detailed analysis, we contribute significantly to the advancement of this emerging field.
• Practical case studies: We present two concrete case studies that exemplify the successful integration of mechatronics into Industry 4.0. These important real-world examples demonstrate the practical applications and benefits of our proposed approach, providing valuable insights for future developments and the inclusion of additional advanced technologies.

The remainder of this paper is organised as follows. Section 2 and Section 3 provide a comprehensive review of the literature on the state of the art in mechatronics and Industry 4.0. Section 4 investigates the integration of mechatronics into Industry 4.0. The first case study (injection moulding machine) is presented in Section 5 and the soft robotics case study is discussed in Section 6. The opportunities and future directions of integration are presented in Section 7. Finally, Section 8 concludes the paper.

2. Mechatronics

2.1. Definition

Mechatronics is a multidisciplinary field of engineering and technology that integrates mechanical engineering, electrical and electronic engineering, computer science, and control engineering to design and create intelligent systems and products. It involves the synergistic combination of mechanical components, electronic control systems, and software to develop advanced and automated devices. At first, the goal of mechatronics was to improve the functionality and performance of mechanical systems by incorporating smart technologies, and it has been expanding in a wide range of applications, including manufacturing, automotive, consumer products, robotics, transportation, and healthcare.

There are multiple perspectives and debates around the definition of mechatronics as a scientific field. Born as a neologism merging “mecha” (from mechanics) and “tronics” (from electronics), its journey has been marked by constant transformation, adapting to technological advancements, and embracing intelligent systems [11]. Most definitions of mechatronics in the literature emphasise the integration of mechanical, electronic, control engineering, and information technology. This integration aims to achieve the optimal solution for a specific technological problem, resulting in the creation of a product or a system. Figure 1 shows the core components of mechatronics, detailed below [12,13,14].

2.1.1. Mechanical Components

The physical components and systems that generate, transmit, and control forces and motion. These are the physical backbone of the entire system and are responsible for generating and transmitting forces, motion, and energy. Some common mechanical elements include gears, levers, linkages, sensors, and actuators (motors, solenoids, etc.).

• Actuators: These are devices that convert electrical signals into mechanical motion. Examples include electric motors, and hydraulic and pneumatic actuators.
• Sensors: Although sensors are often associated with electronics, many mechatronic systems involve mechanical sensors. These can include devices such as encoders, accelerometers, and force sensors that provide feedback about the mechanical state of the system.
• Transmissions and gears: Mechanical systems often require the use of transmissions and gears to control and actuate motion. These elements help to transmit power efficiently and adjust speed and torque. Mechatronic systems may also include various linkages and mechanisms to achieve specific motions or functionalities. Examples include levers, gears, cams, and sliders. Furthermore, bearings are essential for reducing friction and facilitating smooth motion in mechanical components, while joints are crucial for connecting different parts and allowing relative motion, which is widely utilised in the field of robotics.
• Frames and structures: The physical structure that holds and supports the various components of a mechatronic system is also a mechanical element. It ensures stability and durability. In many mechatronic applications, especially in robotics, a mechanical chassis or housing provides a protective and structural framework for the electronic and mechanical components.
• Mechanical springs: Springs are often used in mechatronic systems to store and release mechanical energy. They find applications in damping vibrations, providing suspension, and controlling forces.

2.1.2. Electronic/Electrical Components

These handle power conversion, signal processing, monitoring, and communication between the various elements of the system.

• Microcontrollers and microprocessors: Integrated circuits that serve as the brain of mechatronic systems, processing input data and generating output signals. Examples: Arduino, Raspberry Pi, PIC microcontrollers.
• Sensors: Devices that detect physical or environmental changes and convert them into electrical signals for processing. Examples: Infrared sensors, ultrasonic sensors, temperature sensors, accelerometers.
• Actuators: Devices that convert electrical signals into mechanical motion, allowing control over the physical aspects of a mechatronic system. Examples: Servo motors, stepper motors, solenoids, piezoelectric actuators.
• Power electronics: The study and application of electronic devices to control and convert electrical power. Examples: Power inverters, voltage regulators, motor drives.
• Communication modules: Components that enable communication between different parts of a mechatronic system or with external devices. Examples: Wi-Fi modules, Bluetooth modules, and Zigbee modules. I2C, SPI, and USB are some common communication protocols.
• Circuits and components: Flat boards that hold and connect electronic components, providing a physical structure for the electronic system. Examples: Printed circuit boards (PCBs), integrated circuits (ICs), and MEMS (miniaturised electronic circuits that can include various functions, such as amplification, signal processing, or microcontroller capabilities).
• Power supplies: Devices that provide the electrical power necessary for the operation of various mechatronic elements. Examples: Batteries, power adapters, and voltage regulators.

2.1.3. Computer Science/Engineering

This is a core component in mechatronics, playing a crucial role in the integration of computational algorithms and software systems with mechanical and electronic elements. It implements algorithms, control laws, and data processing within the controller to interpret sensor data, generate control signals, and adapt the behaviour of the system.

• Programming and software development: The creation and implementation of software to control the behaviour of mechatronic systems. Example: Writing code in languages such as C, C++, Python, or Java to program microcontrollers/processors or develop control algorithms.
• Data structures: Organised formats for storing and managing data, crucial for efficient information processing in mechatronic applications. Example: Arrays, linked lists, queues, and trees used to organise and access data in mechatronic software.
• Machine learning and artificial intelligence: Techniques that enable mechatronic systems to learn from data and make decisions without explicit programming. Example: Neural networks for image recognition in robotics or reinforcement learning for adaptive control.
• Modelling and simulation: Creating virtual representations of mechatronic systems to analyse and optimise their behaviour before physical implementation. Example: Simulation software such as MATLAB/Simulink, LabView, or SolidWorks is used to model and test mechatronic designs.
• Embedded systems: Specialised computing systems designed to perform specific functions within mechatronic devices. Example: Embedded microcontrollers or processors that control the operation of robotic arms, drones, or smart devices.
• Communication protocols: Standards and rules that govern the exchange of data between different components of mechatronic systems. Example: CAN (controller area network), I2C (inter-integrated circuit), and SPI (Serial Peripheral Interface) for communication between sensors, actuators, and controllers.
• Graphical user interfaces (GUIs): Interfaces that allow users to interact with and monitor mechatronic systems through visual elements. Example: Design GUIs to control and monitor robotic systems or industrial automation processes (SCADA).

2.1.4. Control Systems

These serve as the central nervous system within mechatronics, ensuring that these complex systems operate precisely, reliably, and intelligently. They constantly monitor, regulate, and adjust the behaviour of mechatronic systems to achieve the desired output, despite disturbances and changing conditions. They act as a bridge between the physical world (sensors and actuators) and the computational world (software). They retrieve sensor data, compare them with desired outputs, and generate control signals to adjust the behaviour of the actuators, ensuring accuracy and efficiency. Here are key aspects of control systems in mechatronics along with examples of related elements:

• Controller: The component responsible for computing the control signal based on input data and the desired system behaviour. Example: Proportional–integral–derivative (PID) controllers.
• Industrial control system: A specialised type of control system used in industrial settings to monitor, control, and automate various processes and machinery. Examples: Supervisory control and data acquisition (SCADA) systems, programmable logic controllers (PLCs), distributed control system (DCS).
• Types/classifications: there are various types of control system, each designed for specific applications and industries: Open-loop control system, closed-loop (feedback) control system, digital control system, linear control system, nonlinear control system, adaptive control system, robust control system, optimal control system. Recently, intelligent control systems have emerged based on data-driven approaches that use AI, machine learning, and big data [15].

2.2. Evolution of Mechatronics

This review of the literature traces the historical development of mechatronics, highlighting key milestones and breakthroughs that have shaped this interdisciplinary field. By examining the convergence of mechanical engineering, electronics, and computer science, this review highlights pivotal moments and influential contributions. The purpose of this section is to provide a chronological exploration of these developments, tracing the origins and key breakthroughs that define the field today. In 1969, the term mechatronics was coined by Yaskawa Electric Corporation to denote the expansion of mechanical component functionalities through the integration of electronics.

2.2.1. Early Origins (1960s–1970s)

The term “mechatronics” was first coined by Tetsuro Mori, a senior engineer at the Japanese company Yaskawa, in 1969. Mori recognised the growing integration of mechanical and electronic components in industrial systems [16]. This period witnessed the foundation of mechatronics as an interdisciplinary field, with a focus on high-performance servo technology and early applications in automated manufacturing, automatic door openers, vending machines, and autofocus cameras [11].

The application of microprocessors to control electromechanical systems led to notable progress in mechatronics technology. This application improved the intelligence of mechatronic systems, surpassing their mechanical counterparts in terms of speed, usability, and efficiency [17].

2.2.2. Rise of CNC and Robotics (1980s)

The 1980s brought about an increased pressure in the manufacturing industry to produce more complex and high-demand products with greater flexibility and accuracy. Fortunately, there were significant advances in computer technology that facilitated the implementation of “computer support in the product development process” [18] by using computer numerical control (CNC), as well as computer-aided design (CAD) and computer-aided manufacturing (CAM) technologies.

The 1980s marked a significant turning point for the field of mechatronics. The advent of robotics not only showcased the practical applications of this interdisciplinary field, but also laid the groundwork for future advancements in automation, control systems, and intelligent machines.

2.2.3. Advances in Industrial Automation and Control Systems (1990s)

The acceleration of automation in industrial plants gained momentum following the invention of programmable logic controllers (PLCs), alongside other controllers like PID (proportional–integral–derivative), SCADA (supervisory control and data acquisition), and distributed control systems (DCSs). These technologies played a crucial role in supporting enterprise resource planning (ERP) systems [1]. During the 1990s, industrial automation and intelligent control emerged as a significant paradigm, introducing mechatronic systems to control and optimise industrial processes. The implementation of industrial automation transformed manufacturing, contributing to higher levels of efficiency, productivity, and product quality.

2.2.4. Integration of Information Technology (2000s)

Information and communications technology (ICT) played a crucial role in the evolution of advanced mechatronics by providing the necessary infrastructure for seamless integration and communication among diverse components. The convergence of mechatronics with ICT has led to the development of intelligent and interconnected systems, enabling enhanced functionality and adaptability. For instance, in [19], advancements in sensors, embedded processors, and wireless communication enable real-time data acquisition, processing, and decision making within mechatronic systems, enhancing their adaptability, precision, and efficiency. Furthermore, in Ref. [20] they point out that ICT facilitates seamless integration of diverse mechatronic components, enabling distributed control and collaboration between (mechatronics subsystems/components) excluding robots and machines, paving the way for intelligent factories and autonomous systems.

2.2.5. Emergence of Cyber–Physical Systems (2010s)

The concept of cyber–physical systems (CPSs) gained prominence in the 2010s, emphasising the integration of computational elements with physical processes. According to the classical NSF definition [21], CPSs are ‘physical and engineered systems whose operations are monitored, coordinated, controlled and tightly integrated by a computing and communication core at all scales and levels’. The cyber subsystem is responsible for computation, communication, and control, and is discrete, logic-based, and event-orientated, while the physical subsystem incorporates natural and human-made components that are bound by the laws of physics and that operate in continuous time. An analogue definition to cyber–physical systems, as in [22], is ‘advanced mechatronic systems that gain their intelligence by connecting to the Industrial Internet of Things (IIoT).

Although CPSs and mechatronics are distinct entities, they share a symbiotic relationship [2,23]: mechatronics forms the fundamental building blocks of many CPS, integrating mechanical, electrical, and software components into functional units. These units often include sensors, actuators, and embedded controllers, allowing CPSs to sense, process, and react to physical environments in real time. However, CPSs provide mechatronics with a broader network context, linking individual mechatronic systems into larger, interoperable ecosystems. This allows centralised control, distributed intelligence, and collaborative execution of complex tasks, propelling the development of autonomous robots, smart factories, and interconnected cities.

2.3. Applications of Mechatronics

Mechatronics is a crucial field that is leading innovation in various areas. Figure 1 illustrates the various uses of mechatronics in different fields. The subsequent discussion explores current trends and applications of mechatronics, providing insight into its wide range of uses.

2.3.1. Robotics and Automation [24,25]

One of the most well-known applications of mechatronics is in robotics and automation. This interdisciplinary field enables the creation of intelligent machines capable of performing complex tasks with precision, speed, and reliability. At its core, robotics involves the design, construction, operation, and application of robots. Mechatronics provides the essential tools and knowledge to bring these robots to life. By integrating mechanical systems with electronic and computer control, mechatronics engineers can create robots with sophisticated sensory capabilities, advanced manipulation skills, and autonomous decision-making abilities. Robotics applications span across various fields such as manufacturing, where robots play a crucial role in assembly, material handling, quality control, welding, and hazardous environments. There are various types of robots used in industry, each with specific capabilities and applications, including industrial robots, collaborative robots (cobots), soft robots, and autonomous mobile robots. Across all these categories, robotics leverages mechatronic principles to develop robots that provide advanced functionality and performance.

2.3.2. Agriculture [26]

Mechatronics has made significant contributions to the agricultural sector by providing automated solutions for farming and harvesting. Automated tractors, harvesters, and drones equipped with sensors and GPS technology have made farming more efficient and precise. These systems can collect data such as soil properties, weather conditions, and crop health, allowing farmers to make informed decisions. Mechatronics has also enabled the development of advanced irrigation systems and robots for sorting and packaging crops, reducing labour costs and increasing productivity. Mobile agricultural robots have been widely used for planting, seeding, cropping, cleaning, spraying, fertilising, and monitoring.

2.3.3. Automotive [27,28]

The field of automotive mechatronics is rapidly expanding, driven by the increasing complexity of contemporary vehicles and the need to make vehicles intelligent, autonomous, connected, efficient, and safe. Mechatronics has improved the automotive sector by introducing electric power steering (EPS) systems, antilock braking systems (ABSs), and adaptive cruise control (ACC), while advancements in the field of mechatronics contribute to creating advanced driver assistance apparatuses, energy management systems, and advanced motor control. It has also played a crucial role in the development of hybrid and electric vehicles (e-mobility), making them more energy efficient and environmentally friendly.

2.3.4. Healthcare [29,30]

Medical mechatronics is emerging as a crucial technology to improve healthcare by providing innovative solutions for diagnosis, treatment, and rehabilitation. It has enabled the development of advanced medical devices such as surgical robots, prosthetics, exoskeletons, wearable health monitoring devices, and drug delivery systems. These devices use sensors and actuators to perform intricate movements or to assist clinicians by suppressing vibrations of surgical tools. Mechatronics has also improved the accuracy and precision of medical procedures, reducing the risk of human error and enhancing patient care with the development of robots for minimally invasive surgery and endoscopy, which can also be improved using advanced control designs, and machine and deep learning techniques. In addition, assistive and rehabilitation robotics (e.g., bioinspired collaborative robots) hold promise to improve rehabilitation from various conditions (e.g., stroke) and assistance of daily living for, e.g., the ageing population.

2.3.5. Consumer Electronics [31,32]

Mechatronics serves as the principal catalyst for the innovation and functionality of contemporary consumer electronics. Notable devices include smartphones, intelligent appliances, wearable technology, virtual reality (VR) and augmented reality (AR) systems, home automation solutions, and gaming consoles. The integration of microcontrollers and sensors within these technologies facilitates advanced features such as motion control, sophisticated user interfaces, and feedback mechanisms, thus augmenting the overall user experience. Furthermore, mechatronics has enabled the miniaturisation of microelectromechanical systems (MEMSs) in electronic devices, enhancing their portability and usability.

Mechatronics has emerged as a pivotal interdisciplinary domain, catalysing innovation across diverse sectors. In aerospace and defence, it includes applications such as unmanned aerial vehicles (UAVs), guidance and control systems, military drones, and aircraft avionics systems [33]. Within the realm of energy and renewable resources, its applications extend to wind turbine control systems, solar tracking systems, energy-efficient HVAC systems, and hybrid and electric vehicle technology [34]. Ecomechatronics is a new concept that has recently emerged that represents a multidisciplinary approach that combines principles of mechanical engineering, electronics, computer science, and environmental science to design and develop sustainable and environmentally friendly mechatronic systems [9]. One of the main focuses of ecomechatronics is to enhance energy efficiency in systems through the adoption of intelligent mechatronics components, tools, controllers, and the whole design.

3. Industry 4.0

Industry 4.0, also known as the Fourth Industrial Revolution, is a term that refers to the integration of advanced digital technologies into manufacturing and production processes [35]. It is revolutionising the way industries operate and is considered a key driver of economic growth and competitiveness in the 21st century. The term Industry 4.0 was first introduced in 2011 by the German government as part of its high-tech strategy to promote the computerisation of manufacturing processes. It is characterised by the fusion of physical and digital systems, resulting in smart factories that can monitor, analyse, and make decisions on their own. Industry 4.0 is built on advances in technologies such as artificial intelligence (AI), the Internet of Things (IoT), big data analytics, and automation, to create a highly interconnected and intelligent network of machines, products, and people [36]. This integration of modern technologies enables industries to achieve greater efficiency, flexibility, and productivity, ultimately driving innovation, cost reduction, and customer satisfaction along with sustainability.

3.1. History and Evolution [3]

Industry 4.0 has its roots in the third industrial revolution, which introduced computers and automation to the manufacturing industry. The Fourth Industrial Revolution builds upon this foundation by integrating advanced technologies to create smarter and more efficient factories. The shift towards Industry 4.0 began in the early 2000s, with the emergence of IoT and big data analytics. Below is a detailed overview of the history and evolution of Industry 4.0, illustrated in Figure 2:

• Industry 1.0: (late 18th to early 19th centuries): The advent of steam and water power led to the rise of mechanisation in production, changing from handcrafting to machine-assisted fabrication.
• Industry 2.0 (late 19th to early 20th centuries): The use of electricity enabled mass production and assembly lines. This revolution drastically increased production capabilities.
• Industry 3.0 (late 20th century): The introduction of computers, automation, and early robotics led to greater efficiency and precision. This revolution is also sometimes called the “digital revolution”.
• Industry 4.0 (the current era): Industry 4.0 builds upon the third revolution, emphasising the following core elements:
  • Interconnectivity: The first pillar of Industry 4.0 is connectivity, which refers to seamless communication and data exchange between machines, devices, sensors, and systems. This is made possible by the Internet of Things (IoT), which enables machines and devices to connect and interact with each other and make decentralised decisions creating a network of intelligent systems. This real- time connectivity allows industries to track the performance of machines, monitor inventory levels, and optimise production processes.
  • Data analytics and artificial intelligence: Data analytics and AI play a crucial role in Industry 4.0 by providing real- time insights and predictive analysis for decision making. The massive amounts of data collected by connected machines and devices are analysed using advanced algorithms and techniques to gain valuable insights. This helps industries to identify trends, predict failures, and optimise processes for improved efficiency and productivity
  • Cyber–physical systems: The physical world merges with digital systems. Smart machines and sensors monitor physical processes with the virtual world of software used for simulation and modelling.
  • Automation and flexibility: The use of advanced robotics and machines to automate production processes. This not only reduces human error but also increases the speed and precision of manufacturing processes and allows for highly customised production and on-demand adaptability.

Key Stages in the Evolution of Industry 4.0 [20,35]

The key stages in the evolution of Industry 4.0 can be traced back to the early 2010s, when the term was first introduced by the German government. This stage is known as digitalisation of manufacturing, where companies started implementing digital technologies to improve their production processes. The next stage, known as smart manufacturing, saw the integration of IoT and data analytics into the production process. This allowed real-time monitoring of machines and processes, leading to increased efficiency and productivity. The third stage, cyber–physical systems, involved the use of advanced sensors and actuators to create a network of interconnected machines that can communicate with each other and make decisions without human intervention. The current stage, Industry 4.0, is characterised by the integration of artificial intelligence, cloud computing, and machine learning to enable autonomous decision making and optimisation of processes. This stage is still in its early days, but is expected to bring about significant changes in the manufacturing industry, leading to increased efficiency, cost savings, and innovation. Pandemic and climate concerns have driven even faster adoption of Industry 4.0, with an emphasis on resilient supply chains and environmental sustainability [38].

3.2. At the Heart of Industry 4.0: Foundational Technological Pillars

Industry 4.0 is transforming manufacturing and other industries through the convergence of digital technologies and physical systems. As shown in Figure 3, at the heart of this transformation lies a set of foundational technological pillars that drive innovation and efficiency [4,39]:

• Cyber–physical systems: CPSs combine physical and computational components to create intelligent systems that can sense, analyse, and respond to changes in their environment. They are the backbone of Industry 4.0, enabling real-time monitoring, control, and optimisation of industrial processes.
• Internet of Things: IoT connects physical devices and objects to the internet, allowing them to share data and communicate with each other and remote systems. This enables the collection of vast amounts of data from sensors and devices that can be used for analysis and decision making.
• Big data analytics: This involves the processing and analysis of large, complex datasets to extract meaningful insights. In Industry 4.0, big data analytics helps identify patterns, trends, and anomalies in production processes, enabling predictive maintenance, quality control, and process optimisation.
• Artificial intelligence: AI uses algorithms and models to mimic human intelligence and automate tasks. In Industry 4.0, AI is used for machine learning, predictive analytics, natural language processing, and robotics control, improving decision making, automating processes, and improving product quality.
• Cloud computing: This provides access to on-demand computing resources over the internet. In Industry 4.0, cloud computing enables the storage, processing, and analysis of large datasets, providing flexibility, scalability, and cost-effectiveness.
• Robotics: Modern robots, especially collaborative robots (cobots), can be easily reprogrammed and adapted for new tasks or product changes. This allows for agile manufacturing processes that can rapidly respond to market demands. Industrial robots, cobots, and mobile robots are increasingly being used for automation, precision manufacturing, and material handling, improving efficiency, safety, and product quality.
• Additive manufacturing (3D printing): In Industry 4.0, 3D printing enables the production of complex and customised parts, reducing lead times, minimising waste, and expanding design possibilities.

These fundamental technological pillars are interconnected and synergistic, driving the transformation of industry. Using these technologies, businesses can improve productivity, reduce costs, improve product quality, and gain a competitive edge in the evolving digital landscape.

The future of Industry 4.0 is promising, with the potential to revolutionise industries and bring about significant changes in the global economy. The integration of advanced technologies will continue to accelerate, making machines and factories more intelligent, connected, and autonomous. With the ongoing development of 5G networks, the connectivity between machines and devices will become even faster and more reliable, paving the way for real-time data analysis and decision making [40]. The reliance on artificial intelligence, machine learning, and deep learning will become more prevalent, leading to the creation of self-learning systems capable of adapting and improving on their own [41]. In the future, Industry 4.0 is expected to not only transform the manufacturing industry but also impact other sectors such as healthcare, logistics, and agriculture.

4. The Integration of Mechatronics and Industry 4.0

The Fourth Industrial Revolution has introduced a wave of transformative technologies that fundamentally redefine the manufacturing landscape. At the heart of this revolution lies the profound integration of mechatronics with the advanced concepts of Industry 4.0. This interdisciplinary fusion promises to drive unprecedented levels of automation, flexibility, efficiency, sustainability, and intelligence across industrial processes [42].

Mechatronics is considered the backbone of smart systems as it has its roots in the desire to create intelligent and multifunctional products that go beyond the capabilities of traditional mechanical systems [43]. By seamlessly incorporating sensors, actuators, microcontrollers, and intelligent control algorithms, mechatronic systems can perceive their environment, make informed decisions, and execute actions with precision. This foundation of intelligence and adaptability makes mechatronics the ideal enabler for smart connected systems envisioned within Industry 4.0 [6].

4.1. Mechatronics vs. Industry 4.0

Mechatronics and Industry 4.0 are distinct but closely interconnected concepts that both play pivotal roles in modern engineering and manufacturing. Mechatronics focuses on the design and development of automated systems and smart devices, such as robots, actuators, and sensors, which can function autonomously or semi-autonomously in various applications. However, Industry 4.0 emphasises the integration of connectivity and data exchange between machines, systems, and humans, facilitating more efficient, adaptive, and flexible production environments. The key distinctions between these two fields are summarised in

Table 1. The primary differences between mechatronics and Industry 4.0.

	Mechatronics	Industry 4.0
Focus	The synergistic integration of mechanical, electrical, and computer engineering to create intelligent systems.	A broader industrial transformation characterised by the convergence of physical and digital technologies.
Scope	At the component or device level.	More comprehensive in the entire industrial ecosystem.
Application	Applies to the design and development of specific machines and devices, such as automated robots, precision tools, and smart appliances.	Applies to broader manufacturing and industrial processes. It encompasses the entire production environment, integrating various technologies and systems to enable smart manufacturing, predictive maintenance, real-time monitoring, and supply chain optimisation.
Technologies	Robotics, embedded systems, sensors, actuators, control systems, and automation components.	CPSs, IoT, big data analytics, AI, cloud computing, advanced robotics, and digital twins.

.

4.2. The Mechatronics and Industry 4.0 Symbiosis

As thoroughly investigated in [1], rapid progress in cyber–physical domains, cloud computing, cloud edge platforms, as well as the development of AI techniques, big data analytics, and advanced wireless communication technologies in the context of Industry 4.0 are compelling mechatronics designers, practitioners, and educators to reevaluate the perception, design, manufacturing, and advancement of mechatronic systems. The integration of mechatronics and the concepts of Industry 4.0 creates a powerful synergy that propels manufacturing into the future through the following.

4.2.1. The Formation of Integration

To understand the integration of mechatronics within Industry 4.0, it is crucial to introduce the intelligent automation pyramid (IAP) [4]. The IAP is a conceptual framework designed to align various levels of automation in industrial settings, specifically within the context of Industry 4.0. Unlike traditional pyramids, the IAP facilitates dynamic information exchange and interaction among the hierarchical levels of industrial processes through advanced computing, cloud technologies, networking, and smart devices.

As illustrated in Figure 4, the IAP spans three key dimensions of automation: the physical dimension, the virtual/digital dimension, and the analytical dimension. These dimensions are distributed across the five layers of the IAP, which are the physical layer, control layer, supervisory layer, management layer, and enterprise resource planning (ERP) layer. Among these layers, the integration of mechatronics into Industry 4.0 takes place within the first two layers: (1) The physical layer, which serves as the cornerstone of automation, where individual devices and machines are interconnected within the factory’s network. It involves the integration of sensors, actuators, and various other devices to facilitate fundamental automation/manufacturing functions. (2) The control level, which is a critical layer where real-time monitoring and control of industrial processes take place. At this level, control systems, such as PLCs and DCSs, manage and regulate the operations of machines, devices, and production lines.

In the context of advanced mechatronics, the integrated hardware and software serve distinct yet interrelated functions that are vital for modern manufacturing. Hardware components, including mechanical parts, electronic devices, and sensors, provide the physical foundation of a mechatronic system, enabling automation and control of manufacturing processes. On the other hand, software is responsible for processing the data collected by these hardware components, guiding their actions, and enabling intelligent decision making through algorithms and machine learning techniques. This combination of hardware and software not only enhances efficiency but also empowers the creation of smart factories as envisioned in Industry 4.0, where intelligence is embedded within manufacturing processes to allow for adaptive and responsive operations.

Mechatronics integration within Industry 4.0 manifests in multiple forms, influenced by various factors and implementation strategies. These forms encompass [20,43]:

• Horizontal integration: This involves the seamless connection of machines, systems, and processes across different stages of production within a factory or across multiple factories. Horizontal integration ensures that each component in the production process communicates and collaborates effectively, leading to more streamlined operations and reduced downtime.
• Vertical integration: This integration focuses on connecting all levels of production, from the physical machinery on the shop floor to the enterprise management systems. This integration allows for real-time data flow between the operational, control, and management levels, enabling better decision making and enhanced operational efficiency
• End-to-end integration: This integrates all stages of the product lifecycle, from design and manufacturing to maintenance and service. It ensures that data and insights are shared across the entire lifecycle of the product.
• System integration: This involves combining various hardware and software systems into a cohesive framework. It includes integrating mechatronic systems with other automation technologies, such as VR and IoT devices.
• Information integration: This pertains to integrating data sources and information systems to provide a unified view of data across the organisation. It includes the use of data analytics and digital twins to enhance insights and decision making.

The following subsections present examples of how mechatronics integrates with the core pillars of Industry 4.0, along with various applications.

4.2.2. Advanced Robotics and Automation

Robotic systems have been used for decades in various industries, with industrial robots (manipulators) being the most commonly used. Robotics, already essential for modern industry, is rapidly advancing due to Industry 4.0. This next generation of robotics and related technologies promises to transform manufacturing, driving greater collaboration and intelligence within the frameworks of Industry 4.0 and the Industrial Internet of Things (IIoT). The interconnection and interdependence between robotics and Industry 4.0 have given rise to new concepts such as Robotics 4.0 [44], IoT-aided robotics, also known as the Internet of Robotic Things (IoRT) [45].

Recent developments in industrial robotics have introduced various cutting-edge technologies, including collaborative robots (cobots), autonomous mobile robots (AMRs), and industrial drones. Cobots, for example, significantly increase both productivity and safety by seamlessly collaborating with human workers and are equipped with state-of-the-art object tracking and obstacle detection sensors [46]. AMRs are another notable addition, capable of independently navigating factory floors, reducing workflows, and reducing the need for manual labour. In addition, industrial drones have emerged as valuable tools for various tasks such as inspection, surveillance, and inventory management, offering improved efficiency and accuracy [47].

To successfully adjust to Industry 4.0 and smart manufacturing, robotics technology, under the guidance of mechatronics engineers/designers, needs to address several critical transformations:

• Increased connectivity and interoperability [48]: There is a need for increased connectivity and interoperability between robotic systems through ‘networked robotics’ and with other components of the manufacturing ecosystem, such as sensors, actuators, and control systems. This would enable seamless communication and collaboration within the production environment via IoT and ICT, facilitating real-time data exchange and decision making.
• Enhanced flexibility and agility: Robotics technology should evolve towards greater flexibility to accommodate the dynamic demands of modern manufacturing. This involves developing modular and reconfigurable robotic systems that can easily adapt to changing production requirements with diversity of products [49].
• Advancements in artificial intelligence and machine learning: Robotics must take advantage of AI and machine learning techniques to improve autonomy and intelligence. By learning from experience and optimising performance, robots can make autonomous decisions for complex tasks and environments. This requires a new paradigm for robot learning and training towards smart manufacturing that encompasses a multidisciplinary research topic that contains robotics, machine learning, optimisation, and manufacturing science [50]. AI-enabled robotic systems can analyse large amounts of data in real time, enabling predictive maintenance and optimising production schedules to minimise downtime and maximise performance.
• Cloud robotics: Cloud robotics integrates cloud computing with robotics to enhance their capabilities within the Industry 4.0 paradigm. By transferring computationally demanding tasks to the cloud, robots gain access to robust processing capabilities and extensive data repositories, thereby alleviating the computational burden on the robots’ embedded processors. This substantial offloading of tasks not only frees up the microprocessor resources of the robot but also enables them to operate more efficiently, facilitating smoother and more responsive performance [48]. Exchanging services and information between various robots or agents is simplified, promoting better cooperation and coordination, thus advancing swarm robotics [44]. This facilitates real-time data analysis, sophisticated decision making, and increased adaptability in dynamic environments [1]. Cloud robotics becomes increasingly feasible, particularly with the advancements in edge and fog computing technologies, alongside the availability of leading cloud service providers like Amazon Web Services, Elastic Compute Cloud (EC2), Google Compute Engine, and Microsoft Azure [51].
• In contrast to conventional robots, advanced robotic systems exhibit enhanced capabilities in perception, autonomy, integrability, adaptability, and mobility [52]. Mechatronics engineers play a pivotal role in leveraging state-of-the-art technologies to design such advanced robots, ensuring their seamless integration into Industry 4.0 frameworks. These advances enable faster setup, commissioning, and reconfiguration, along with more efficient and stable operations. By embracing these enhancements, robotics technology can effectively facilitate the transition towards Industry 4.0 and propel the evolution of smart manufacturing.
• The effectiveness of these factories depends on the intelligent integration of their systems and subsystems. In this context, mechatronics plays a vital role in facilitating automation and control in manufacturing processes by utilising a highly beneficial combination of a manipulator, sensors, and controls. This integration enables the creation of sophisticated robotics, automated machinery, and intelligent manufacturing systems that can carry out tasks with accuracy and effectiveness. In addition, mechatronics facilitates the integration of advanced sensing and actuation mechanisms, enabling machines to dynamically adjust to varying conditions and improve their performance in real time [53].

4.2.3. Cyber–Physical Systems

Mechatronics and cyber–physical systems are complementary fields that blend physical and digital technologies. Together, they form the foundation for advanced technologies in automation, smart manufacturing, and Industry 4.0.

CPS represents the convergence of physical processes with computational and communication capabilities, resulting in interconnected systems capable of monitoring, analysing, and controlling physical processes in real time. Mechatronics, which integrates various engineering principles, plays a crucial role in the development and operation of CPSs. Mechatronic systems provide the foundation for sensing, actuation, and control within CPSs, enabling seamless interaction between the physical and digital components of these systems [54]. By incorporating sensors and actuators into mechanical structures and integrating them with embedded control systems, mechatronics enables the collection of real-time data from the physical environment and facilitates the execution of intelligent control strategies to optimise system performance.

Mechatronics and CPS share many similarities, including the use of embedded systems, control theory, and communication technologies. However, there are also some key distinctions between the two fields [18,55]

• Scope: Mechatronics is primarily concerned with the integration of physical systems with embedded computing and control, while CPSs encompass a broader range of systems that integrate physical components with computational and communication capabilities. Thus, mechatronics is deeply integrated and embedded in a centralised architecture, whereas CPSs typically include communication distributed networks and refer to a decentralised organisation with highly dispersed decision making.
• Complexity: CPSs are typically more complex than mechatronic systems and often involve the use of distributed computing, networking, big data, and AI. That is, the intended physical integration, which is linked to volume reduction in some mechatronic systems, may result in multiple physical couplings, whereas the predominance of IT in CPSs to control physical processes necessitates large and continuous real-time communication processing between these two worlds, resulting in different types of behavioural laws mixing continuous and discrete, and deterministic and stochastic behaviours.

The integration of mechatronics and cyber–physical systems (CPSs) brings about significant advantages, as exemplified by concepts such as cyber–physical mechatronic systems (CPMSs) [56] and information communication control–mechanical systems [57]. This integration contributes significantly to improving the adaptability and responsiveness of the system. It enables the seamless incorporation of advanced sensing and actuation capabilities into CPS, thereby enhancing perception and manipulation of the physical environment. This integration has led to numerous successful applications, including autonomous mobile robots, smart manufacturing, and industrial automation [58].

The true power of Industry 4.0 lies in the convergence of mechatronics and CPSs. Smart machines designed with mechatronic principles become nodes within a vast network of CPSs. They share data, collaborate, and continuously learn, driving greater efficiency, flexibility, and responsiveness across the production floor. This interplay leads to the emergence of fully fledged smart factories, where intelligent systems interact seamlessly to drive self-optimisation and adaptability.

4.2.4. IoT

As thoroughly discussed in [7,43], historically, mechatronic systems were primarily defined by the interaction between their mechanical, electrical, and control engineering components. The focus was on the physical interconnection of these elements, with the data serving the purpose of facilitating precise control actions. However, a profound shift has materialised in recent years, driving mechatronics toward a model where information itself takes centre stage and is dynamically managed by interconnected “smart objects” within the vast network of the IoT.

The Traditional Mechatronic Model

In the traditional model, a simplified representation of a mechatronic system or subsystem could be visualised in the following manner:

• Sensors: They gather data from the operational environment, such as temperature readings, vibration signals, or positional measurements.
• Actuators: They receive control signals, allowing for the physical manipulation of components (motors, valves, etc.).
• Controller: E.g., a microcontroller or a programmable logic controller, processes sensor data and sends calculated control commands to the actuators.

This system emphasises the flow of data with the purpose of achieving direct control of physical elements. It is a framework suitable for tasks where precision and deterministic responses are paramount.

The Emergent Information-Centric Model

The growing complexity of the Internet of Things and its related technologies has caused a significant change in the field of mechatronics. We are currently observing a shift towards a model that can be most accurately described as follows [1,6,43]:

• Smart objects: Devices are no longer mere sensors and actuators. They become “smart objects”, embedded with computational capacity, communication capabilities, and the ability to process data locally with real-time interaction. An example is the IOT sensor in Figure 5 that consists of three main components: a data capture sensor, a microprocessor that performs computations on the sensor’s output using programming, and communication capabilities to send data to the internet through a gateway.
• The IoT network: The IoT provides a decentralised communication network, linking countless smart objects (could be CPSs or advanced mechatronic systems) and establishing a vast information ecosystem.
• Information as a commodity: Within this ecosystem, the information itself gains value beyond simple control purposes. Deploying edge computing enables real-time data streams, data processing, data storage, and insights derived from analytics to become commodities exchanged and used for various optimisations and control, leading to faster responses and enhanced efficiency.
• Contextual intelligence: Smart objects can leverage information from across the network to make contextualised decisions, adapting their behaviour dynamically rather than solely relying on pre-programmed control instructions. These devices will incorporate AI and machine learning, enabling autonomous decision making, advanced analytics, and predictive capabilities.

The evolution of mechatronics from traditional systems based on physical components to information-centric systems facilitated by IoT represents a significant paradigm shift with far-reaching implications. By placing information at the heart of mechatronic systems, IoT technologies enable enhanced connectivity, intelligence, and adaptability, driving innovation across various domains including manufacturing, healthcare, transportation, and beyond. As we continue to harness the power of IoT, the future of mechatronics holds immense promise for creating smarter, more efficient, and more responsive systems that will shape the world of tomorrow.

Other examples of the integrations of Industry 4.0 and advanced mechatronics include:

• Digital twins and simulation: In [59], a framework was suggested to develop a coherent, multilevel, parameterised, and comprehensive digital twin model for manufacturing processes incorporating complex mechatronic systems that include mechanics, electronics, hydraulics, and control systems. The development of digital twin technology for educational purposes in remote mechatronics laboratories was introduced in [60]. Industry 4.0 allows the creation of digital twins—virtual representations of mechatronic systems and entire production facilities. These digital replicas, constantly fed with real-time data, enable virtual commissioning, testing, and optimisation before physical systems are deployed. This reduces risk, speeds up development, and allows for continuous improvement throughout a system’s lifecycle.
• AI: A review on the feasibility of artificial intelligence in mechatronics engineering was discussed in [61], and the authors in [62] used the inextricable link between control theory and artificial intelligence to develop intelligent control for applications of robotics and mechatronic systems. In the vast literature, artificial intelligence has emerged as a valuable asset in diverse domains, including mechatronics. AI techniques enable the achievement of goals and resolution of tasks related to the control of mechatronic systems that were previously considered unattainable. This is accomplished with improved computational efficiency and ease of implementation. The wealth of data generated by mechatronic systems coupled with AI and machine learning in Industry 4.0 empowers predictive maintenance strategies. Algorithms can detect subtle anomalies, predict failures before they occur, and trigger proactive maintenance, maximising uptime and reducing unexpected disruptions. In addition, mechanical systems, guided by Industry 4.0-data-driven insights become remarkably adaptable, capable of producing smaller batches of highly customised products with the same efficiency and quality control as traditional mass production lines. This capability addresses the increasing consumer need for personalised products, with mass personalisation in Industry 4.0 focusing on the production of uniquely customised items for individuals on a large scale [63].

4.3. Challenges of the Integration

Integrating mechatronics into Industry 4.0 presents several challenges that stem from technological, educational, managerial, and security dimensions. These challenges must be addressed to fully exploit the potential of this industrial revolution. The main challenges are summarised below [1,6,20,64]:

• Technological compatibility: The successful integration of mechatronics with Industry 4.0 technologies depends on the compatibility of legacy systems with modern digital solutions. Incompatibilities may require significant upgrades or replacements, impacting the overall integration process. Furthermore, the rapid evolution of disruptive technologies, such as ICT, IoT, and AI, requires continuous adaptation and integration into existing systems, which can overwhelm current infrastructures.
• Data management and security: The vast amount of data generated through integrated systems requires robust data management and cybersecurity measures. Ensuring data integrity and protecting against cyber threats are critical for maintaining the reliability of the integrated system.
• Workforce skills and training: Workforce skills and training are critical to integrating mechatronics and Industry 4.0. The process requires a workforce proficient in both traditional engineering disciplines and advanced digital technologies. A shortage of skilled people with expertise in mechatronics, automation, and data science can impede the integration process. Ensuring adequate training and upskilling is essential to address these gaps and facilitate successful integration.
• Organisational readiness: Successful integration also depends on the readiness of the organisation, including its culture, structure, and willingness to adopt new technologies. Resistance to change can be a significant barrier. This requires alignment of mechatronics integration with the overall business goals and strategy.
• Scalability: The integration solution should be scalable to accommodate future growth and changes in technology. This includes considering how new technologies or systems can be integrated into the existing framework, thus fostering innovation, improving efficiency, and promoting sustainability in modern manufacturing.
• Educational gaps: There is a pressing need for innovative educational models to equip mechatronic engineers with the competencies required for Industry 4.0, focusing on a shift from traditional technical skills to a more interdisciplinary approach. The curriculum must evolve to include social creativity and crowd-based applications, reflecting the need for customisation and responsiveness in product development, as well as considering sustainable applications.
• Cost and ROI: The financial investment required for integration can be significant. Factors such as the cost of new technology, training, potential downtime, and the expected return on investment must be considered.
• Regulatory compliance: The integration’s adherence to industry standards and regulations is crucial during integration. This ensures that integrated systems meet legal and safety requirements.

4.4. The Role of Logistics and Management in the Integration of Mechatronics and Industry 4.0

The advent of Industry 4.0 has profoundly transformed traditional manufacturing and production systems, introducing unprecedented levels of connectivity, automation, and data exchange. Central to this revolution is the integration of mechatronics, an interdisciplinary field that combines mechanics, electronics, and computing with advanced manufacturing practices. Effective logistics and management are critical to facilitate this integration, ensuring that resources are optimised, processes are streamlined, and technological advances are fully leveraged [65,66].

Logistics, the management of the flow of goods and services, plays a crucial role in the integration of mechatronics and Industry 4.0. It encompasses various activities, such as procurement, production planning, inventory management, and distribution, that contribute to the seamless movement of materials and information throughout the supply chain. With the integration of mechatronics and Industry 4.0, logistics becomes even more critical, as it enables real-time monitoring, tracking, and control of goods and processes. For example, through the use of sensors and RFID technology (related to the use of blockchain for supply chain management), logistics systems can provide real-time data on the location, condition, and status of goods, allowing companies to optimise inventory, reduce stock shortages, and improve customer satisfaction [67].

Effective logistics management is essential to ensure the smooth integration of mechatronics and Industry 4.0. Advanced planning and scheduling systems, powered by artificial intelligence and algorithms, can optimise production processes by dynamically adjusting production plans based on real-time demand-and-supply information. This not only improves operational efficiency, but also reduces lead times and enables businesses to meet customer demands more effectively. For example, AI is highly effective in identifying patterns within data collected by IoT sensors, particularly in ‘track-and-trace’ applications [68]. In addition, logistics management systems can enable predictive maintenance, using machine learning algorithms to monitor equipment performance and identify potential failures before they occur. By proactively addressing maintenance issues, businesses can reduce downtime, improve productivity, and enhance overall equipment effectiveness.

Furthermore, effective warehouse management is crucial in the integration of mechatronics and Industry 4.0. Automated storage and retrieval systems, enabled by mechatronics, can significantly improve warehouse operations by increasing picking accuracy, reducing errors, and optimising space utilisation. Advanced warehouse management systems, integrated with IoT devices and smart mobile warehouse robots and drones, can provide real-time visibility into warehouse operations, enabling businesses to track inventory levels, monitor order fulfilment, and streamline logistics processes [69]. This improves supply chain transparency, enhances decision making, and facilitates timely and accurate order fulfilment.

In addition to logistics, effective management is equally important in the integration of mechatronics and Industry 4.0. Managers must have a comprehensive understanding of the technologies involved, as well as the organisational and cultural changes required to support the integration process. They must ensure the alignment of mechatronics and Industry 4.0 strategies with overall business objectives and drive the necessary changes in organisational structure, workforce skills, and processes [66].

The interplay between the disruptive technologies of mechatronics and Industry 4.0 and operations management is transforming traditional manufacturing processes, leading to increased efficiency and flexibility in production systems. Mechatronics plays a crucial role in enhancing logistics efficiency through automation, robotic systems, and smart warehousing solutions. Automated guided vehicles (AGVs), drones for inventory management, and smart conveyor systems exemplify how mechatronic technologies facilitate seamless logistics operations within an Industry 4.0 context characterised by the widespread adoption of disruptive technologies such as AI, robotics, blockchain, 3D printing, 5G, IoT, digital twins, and augmented reality.

4.5. How Is Industry 4.0 Linked with the Sustainable Development Goals of UN Vision 2030?

Industry 4.0, marked by the integration of advanced technologies such as IoT, big data analytics, and cyber–physical systems, plays a crucial role in advancing the United Nations’ Sustainable Development Goals (SDGs) as described in the 2030 Agenda. These technologies are essential to drive sustainable economic growth, foster social equity, and ensure environmental responsibility.

The SDGs, adopted by all UN member states in 2015, encompass 17 goals aimed at addressing global challenges such as poverty, inequality, climate change, environmental degradation, peace, and justice [70]. Industry 4.0 technologies can significantly contribute to achieving these goals by improving operational efficiency and promoting sustainable practices in various sectors. For example, the deployment of IoT and big data analytics can optimise resource management and reduce waste, thus supporting goals related to responsible consumption and production (Goal 12) [71].

In addition, the concept of sustainable development emphasises the need for a balanced approach that integrates economic growth, social inclusion, and environmental protection. Industry 4.0 facilitates this integration by enabling smart manufacturing processes that not only improve productivity, but also minimise environmental impacts. For example, the use of advanced manufacturing technologies can lead to energy savings and lower greenhouse gas emissions, aligning with the climate action goal (Goal 13).

The 2030 vision for Industry 4.0, as articulated by various stakeholders, emphasises the creation of digital ecosystems that prioritise sustainability [72]. This vision aligns with the SDGs by advocating for the development of policies and practices that promote innovation while ensuring environmental sustainability. For example, the integration of sustainable practices into supply chains can improve resilience and adaptability, which are crucial for achieving multiple SDGs. The following discusses the contributions of Industry 4.0 to the main SDGs.

• SDG 3: Good Health and Well-Being [73]: Industry 4.0 has significant potential to contribute to SDG 3 by improving healthcare care delivery, including remote monitoring and diagnostics through IoT-enabled devices, allowing remote monitoring and early detection of health problems. This can reduce the burden on healthcare systems and improve access to care in remote areas. AI algorithms can analyse vast amounts of patient data to develop personalised treatment plans, tailoring interventions to individual needs and improving outcomes. In robotic surgery, advanced robotic systems can perform minimally invasive surgeries and endoscopies with greater precision and reduced risk of complications, leading to faster recovery times and better patient outcomes. This topic is further discussed in case II: soft robotics.
• SDG 7: Affordable and Clean Energy [74]: Industry 4.0 technologies play a crucial role in advancing SDG 7 by driving efficient energy management in smart grids. IoT-enabled devices optimise energy distribution and consumption, minimising energy losses and enhancing grid reliability. AI algorithms analyse consumption patterns to identify opportunities for demand-side management, which reduces peak loads and strengthens grid stability. Additionally, big data and AI-powered predictive maintenance can detect and address potential equipment failures before they happen, minimising downtime and increasing energy efficiency.
• SDG 8: Decent Work and Economic Growth [75]: Industry 4.0 is driving the creation of new industries and sectors, such as data analytics, autonomous robotics, and cybersecurity, which in turn are generating new job opportunities. The demand for new skills and competencies in this era also creates avenues for reskilling and upgrading, helping workers prepare for the jobs of the future. In addition, Industry 4.0 improves productivity and competitiveness by automating repetitive and time-consuming tasks, thereby reducing costs for businesses. It also accelerates innovation and product development, enabling companies to maintain a competitive edge in the global marketplace.
• SDG 9: Industry, Innovation, and Infrastructure [76]: Industry 4.0 fosters innovation by enabling the development of new products and services that align with sustainability goals. The digital transformation of industries drives research and development initiatives focused on sustainable technologies, leading to breakthroughs in energy efficiency and resource management. Additionally, Industry 4.0 technologies automate manufacturing processes, reduce waste, and enhance overall efficiency, resulting in increased productivity and competitiveness. They also improves product quality through real-time monitoring and control of manufacturing processes, ensuring adherence to high standards. The technology enables mass customisation, allowing manufacturers to produce tailored products that meet individual customer needs. By accelerating innovation, Industry 4.0 facilitates rapid prototyping, testing, and analysis of new products and processes. Data-driven decision making is another key advantage, as Industry 4.0 generates vast amounts of data that can be analysed to identify trends, optimise decision making, and drive further innovation. Moreover, it enhances collaboration and knowledge sharing among researchers, businesses, and other stakeholders, fostering a culture of innovation and continuous development. In terms of infrastructure development, Industry 4.0 supports the creation of smart infrastructure, such as smart cities and transportation systems, that are more efficient, sustainable, and resilient. Predictive maintenance enabled by these technologies reduces downtime and improves the reliability of infrastructure. Furthermore, Industry 4.0 plays a pivotal role in modernising existing infrastructure, making it more efficient and sustainable.
• SDG 11: Sustainable Cities and Communities [77]: Smart city initiatives powered by Industry 4.0 technologies are key to promoting sustainable urban development by enhancing resource management and public services. Developing smart cities requires a multidimensional approach that integrates technology, sustainability, and citizen well-being. The following Industry 4.0 technologies enable the creation of smart cities:
  • IoT: IoT sensors and devices collect diverse data on energy consumption, traffic, equipment performance, material flows, and more. When shared with municipal management systems, these data become a critical input for urban planning.
  • Big data analytics: The vast datasets generated by Industry 4.0 and the broader smart city environment require advanced analytical tools. Big data analytics processes this information in real time, converting it into actionable insights for decision-makers at the city level.
  • Cloud computing: Smart cities benefit from the scalability and flexibility of cloud-based computing infrastructure. Cloud platforms offer the necessary storage for large datasets, computing power for complex analytics, and the potential for shared service models that connect all operations with municipal services.
  • AI: AI and machine learning and pattern recognition techniques optimise production processes, detect anomalies or inefficiencies early, and generate predictions to improve resource allocation and service delivery.
• SDG 12: Responsible Consumption and Production [78]: Industry 4.0 smart factories enable more efficient manufacturing processes by reducing waste and minimising resource consumption. AI-powered predictive maintenance identifies and addresses potential equipment failures before they occur, reducing downtime and enhancing resource efficiency. Industry 4.0 technologies also optimise supply chains, lowering transportation costs and emissions. In addition, Industry 4.0 plays a crucial role in supporting the circular economy and product lifecycle management. These technologies allow for the tracking and management of products throughout their lifecycle, promoting reuse, repair, and recycling. By accelerating the development and adoption of sustainable materials and production processes, Industry 4.0 significantly reduces the environmental impact of manufacturing.

5. Case Study 1: The Smart Injection Moulding Machine

The integration of mechatronics and Industry 4.0 represents a natural evolution in manufacturing, combining the physical and digital realms to create intelligent and interconnected production systems. The integration of mechatronics and Industry 4.0 has revolutionised manufacturing processes, and the electric smart plastic injection moulding machine serves as a prime example of this synergy. Plastic injection moulding is one of the most prevalent manufacturing processes worldwide, offering unparalleled versatility and efficiency in the production of a wide range of plastic products. This method involves melting plastic granules and injecting the molten material into a mould cavity, where it solidifies to form the desired shape. Its widespread adoption can be attributed to its ability to produce complex geometries with high precision, rapid production cycles, and cost-effectiveness [79]. Furthermore, plastic injection moulding finds applications across various industries, including automotive, electronics, packaging, and healthcare, driving its significance in modern manufacturing [80].

5.1. The Design of the System

Our design involves the Electric Smart Plastic Injection Moulding Machine, which marks a substantial improvement over conventional hydraulic injection moulding machines. These machines incorporate advanced mechatronic components and utilise Industry 4.0 technologies, resulting in higher precision, greater energy efficiency, and better data monitoring for process optimisation. As shown in Figure 6, the dimensions of the main structure are 1246 mm in length, 290 mm in height and 250 mm in width, and there are two fixed plates connected to each other by two splinters. This structure was assembled on a base table that also housed the electric motors, the control unit, and other mechanical components including (as shown in Figure 1 with the numbers): (1) the barrel, (2) the injection screw inside the barrel, (3) nozzle plate, (4) sliding plate, (5) ball screw, (6) linear joints, (7) springs, (8) mould, and (9) encoder. Furthermore, the structure was made of aluminium 7075 which has excellent mechanical properties and exhibits good ductility, high strength, toughness, and good resistance to fatigue. The tie rods were made of 30 mm rolled steel capable of withstanding the high stresses that come from clamping and injection forces. The smart plastic IMM in Figure 6 has the following main modules:

• Injection unit: This component consists of a screw or plunger that is responsible for injecting the plastic material into the mould. In electric machines, the injection process is driven by electric motors for precise control of speed and pressure.
• Clamping unit: The clamping unit holds the mould in place during the injection process. It comprises a stationary platen and a moving platen, which are driven by electric motors or hydraulic systems to apply the necessary force to keep the mould closed during injection.
• Heating and cooling system: To maintain the temperature of the mould and the plastic material, electric heaters and cooling systems are employed. These systems ensure proper melting of the plastic and efficient cooling of the moulded part.
• Control system: At the heart of the smart plastic injection moulding machine lies the control system, which orchestrates the operation of various components. It includes sensors for monitoring parameters such as temperature, pressure, and position, as well as actuators for controlling the movement of the injection and clamping units.
• Sensors: Various sensors are deployed throughout the machine to collect real-time data on process parameters. These sensors may include temperature sensors, pressure transducers, position encoders, and flow meters. The data collected by these sensors are utilised for process monitoring, quality control, and predictive maintenance.
• Actuators: Actuators are responsible for translating control signals into physical movements within the machine. In electric smart moulding machines, actuators such as electric motors, servo motors, and linear actuators are used for precise and dynamic control of the injection and clamping processes.

5.2. Functionality of the Electric Smart Plastic Injection Moulding Machine

• Material preparation (plasticising): Plastic pellets are fed into a heated barrel where a rotating screw melts and mixes the material into a homogeneous molten state.
• Injection: Once the plastic material reaches the desired temperature and consistency, the screw of the injection unit injects it into the mould cavity under high pressure. The screw acts like a plunger, rapidly injecting the molten plastic into the mould cavity through a nozzle, sprue, and runner system. The precise control of speed and pressure ensures the accurate filling of the mould and the production of high-quality parts.
• Cooling: After injection, the mould is cooled rapidly to solidify the plastic material. The cooling system maintains the temperature of the mould at an optimal level, ensuring fast cycle times and consistent part quality. Furthermore, continuous pressure is applied to compensate for the shrinkage of the plastic as it cools. This ensures the part fills the mould completely.
• Ejection: Once the moulded part has sufficiently cooled and solidified, the clamping unit opens the mould and the ejector system pushes the part out of the mould cavity.
• Control and monitoring: Throughout the entire moulding process, the control system monitors key parameters such as temperature, pressure, speed, and position using the data collected from sensors. Any deviations from the set parameters can be detected in real time, allowing for immediate adjustments to ensure product quality and process efficiency.

5.3. How the Integration of Mechatronics with Industry 4.0 Technologies Can Improve Plastic Injection Moulding

The plastic injection moulding process is highly automated using advanced mechatronics technology, but there are still opportunities to improve efficiency and quality through the implementation of Industry 4.0 technologies. Some of the key Industry 4.0 technologies that can be applied to plastic injection moulding include:

• AI [81,82]: AI algorithms can analyse vast amounts of sensor data to identify optimal process parameters for temperature, pressure, injection speed, and more. This leads to better part quality, reduced waste, and faster cycle times. AI models can predict machine component failures before they occur, enabling predictive maintenance strategies which minimise downtime and costly repairs. AI-powered vision systems can detect defects in real time, triggering automatic adjustments or rejection of faulty parts.
• Robotics [80]: Robots can handle repetitive tasks like part removal, insert loading, and post-moulding assembly. Also, collaborative robots (cobots) work alongside human operators, combining human flexibility with robotic precision. This enhances safety and productivity.
• IoT [80,83]: IoT sensors on injection moulding machines provide real-time monitoring of continuous data on machine health, part quality, and production status, enabling immediate corrective actions. IoT connectivity allows remote monitoring and control of multiple injection moulding machines from a single platform, optimising production across the factory

Moreover, advances in digital twin technology allow manufacturers to create virtual replicas of their injection moulding systems, allowing virtual simulations for testing and optimisation without disrupting physical production [84]. Big data analytics that uses large-scale data collected from IoT devices fuels long-term trend analysis, driving continuous improvements in the injection moulding process. Cloud computing is another technology that offers scalable computing resources for AI-powered data analysis and process optimisation, as well as energy savings [85].

The characteristics of components manufactured through injection moulding are affected by a range of processing conditions and dynamic variations, including injection temperatures and injection pressures. The adoption of cyber–physical systems and intelligent manufacturing within smart injection moulding technologies enables the development of highly automated systems, adaptive process control, and enhanced production optimisation [86]. These technologies employ real-time sensing to monitor and optimise the injection moulding process parameters, leading to improved product quality and less waste. We introduced the initial phase of the design in [87].

In this section, we present a new model that embodies the fusion of mechatronics with Industry 4.0 through an IoT-enabled Smart Desktop Injection Machine. This compact and energy-efficient device for plastic injection moulding incorporates Internet of Things (IoT) technology and cloud computing. This integration provides the ability for users to remotely monitor and manage processes, perform real-time data analytics to enhance operational efficiency, leverage sophisticated automation capabilities, and seamlessly incorporate it into Industry 4.0 environments.

5.4. IoT Platform and Cloud Computing

The objective of smart injection moulding is to create a real-time production optimisation system through the use of sensor data extraction, computer optimisation techniques, and the implementation of control strategies. This approach is intended to improve the efficiency and precision of the injection moulding process.

In this paper, we used the Arduino IoT cloud platform, which offers a variety of features for building modern IoT and cloud computing solutions. Figure 7 shows the dashboard that displays data from connected devices to the IoT platform (Ubidots) for analysis, and Figure 8 illustrates the cloud variable interface, which automatically loads and updates data such as sensor readings. The data are downloaded from the cloud and fed into a statistical platform such as the Weibull analysis, enabling real-time failure detection/prediction.

For this particular project, we used the Arduino microcontroller with the ESP32 module. When monitoring the whole process, IoT sensors were used to send the information via Wi-Fi to the Arduino IoT cloud platform to analyse and control the machine.

The primary objective achieved in this case study is the development and implementation of a novel model that seamlessly integrates mechatronics with Industry 4.0 technologies. This is accomplished through the incorporation of advanced sensors and actuators, intelligent control systems, and the utilisation of IoT and cloud computing. We developed an IoT-enabled smart desktop injection machine that combines advanced sensor systems with cloud-based analytics to optimise real-time production. This is achieved by continuously monitoring key process parameters—such as temperature, pressure, and injection speed—and feeding these data into cloud platforms for real-time analysis and optimisation. The integration of Arduino IoT cloud platforms enables remote monitoring and real-time adjustments, greatly enhancing precision, minimising waste, and boosting overall system efficiency. This offers a more sustainable solution.

In addition to the hardware design, we developed a cloud-based dashboard that provides a comprehensive view of machine operations, enabling real-time decision making. This dashboard not only tracks operational efficiency but also utilises statistical methods for failure detection and process optimisation. We also developed the control software for the machine, incorporating algorithms for automatic regulation of the process, real-time data analysis, process optimisation, and fault detection. This software enabled the machine to operate efficiently and autonomously.

Additionally, the design is optimised to support and leverage advanced technologies like AI for predictive maintenance and machine learning algorithms to identify optimal process parameters. Robots and automation can also be employed to change moulds for different products, significantly enhancing machine flexibility and reducing downtime. By integrating robotic arms and automated systems, mould changes can be executed with high precision, minimising manual intervention, and ensuring consistent production. Furthermore, robotics and automation can be extended across the entire production cycle, from part removal to quality inspection and post-processing, thereby further streamlining operations. Collaborative robots (cobots) can work alongside human operators, performing repetitive tasks such as loading plastic into the barrel, removing finished parts, or assembling components, while ensuring high levels of safety and productivity. This integration of robotics and automation not only increases throughput, but also enhances production scalability, making it easier to adapt to changing manufacturing demands.

Thus, this case study exemplifies how the fusion of mechatronics with Industry 4.0 technologies can elevate traditional manufacturing processes, providing a smarter, more adaptive and efficient production environment.

6. Case Study 2: Soft Robotics

Soft robotics is an emerging field that uses soft and deformable materials in robotic systems, offering unique capabilities compared to traditional rigid robots [88]. It has emerged with the objective of increasing safety and flexibility in a human–robot interaction environment due to the traditional limitations posed by conventional robots, such as the high rigidity and weight that make them dangerous when working closely with humans [89]. Soft robots can interact with uncertain and unstructured environments, manipulate unknown objects, navigate rough terrains, and even engage in flexible interactions with living organisms or human bodies [90]. Soft robots, in contrast to conventional rigid robots, are infinite-dimensional systems with many degrees of freedom and highly nonlinear dynamics [91].

Soft continuum robots can perform complex movements inspired by natural systems and biological examples such as octopus, snakes, and elephant trunks, allowing them to handle diverse shapes, complex surfaces, and crowded environments effectively and conform to curvilinear paths with a higher degree of maneuverability [92]. They are ideally suited for a variety of tasks, especially those involving interaction with delicate environments in healthcare applications (minimally invasive surgery, endoscopic interventions, rehabilitation, and exoskeletons) [93], and a wide range of industrial applications (flexible grippers) [94], and in the assembly, inspection, and maintenance of aeroengine, nuclear, and aircraft facilities [95].

6.1. Advancements in Soft Robotics through Mechatronics and Industry 4.0

The field of soft robotics is inherently interdisciplinary, necessitating collaboration among experts in design, manufacturing, and control systems. The key technologies and research areas driving advances in soft robotics include:

• Materials [96]: The fabrication of soft robots primarily relies on materials like elastomers, including silicone and rubber, which offer the essential flexibility and deformability needed for their function. These materials enable soft robots to adapt to complex shapes and environments, enhancing their functionality in applications such as medical devices and wearables. In addition, smart materials such as shape-memory alloys and hydrogels are being explored for their ability to change properties in response to stimuli, offering further versatility.
• Assembly and fabrication process [97]: The assembly and fabrication process for soft robots typically involves advanced techniques like 3D printing, soft lithography, and moulding to create intricate and flexible structures. These processes allow for precise control over the robot’s shape and properties, enabling the integration of various soft materials and embedded actuators and sensors. Other methods used include bonding, shape deposition manufacturing, and thin-film manufacturing.
• Sensing [94,98]: Soft robotics benefit from various sensors to enhance functionality and interaction capabilities. The enhanced dexterity and mechanical compliance of soft robots necessitate precise control over their position and shape. To achieve this, soft robots must be equipped with advanced sensors that provide accurate perception of their surroundings, including location, force, temperature, shape, and other stimuli. Common sensor types include resistive sensors, piezoresistive sensors, capacitive sensors, optical sensors, inertial measurement units, encoders, and cameras.
• Actuating mechanisms [99,100]: Unlike their rigid counterparts, soft robots require compliant and adaptable actuators to mimic living organisms. Common types include pneumatic and hydraulic systems that utilise pressurised fluids to induce motion. Electroactive polymers offer another promising type, harnessing electrical energy to achieve deformation. These materials exhibit large strains, making them ideal for soft robots. Additionally, shape memory alloys and polymers provide actuation through controlled temperature changes. The choice of actuator depends on factors such as desired motion, power requirements, and environmental conditions.
• Motion and control [101,102]: Motion planning and control in soft robotics present unique challenges due to the inherent flexibility and nonlinearity of these systems. Unlike rigid robots with precise joints and actuators, soft robots often rely on complex deformations for locomotion and manipulation and can undergo a wide range of motion, including bending, twisting, stretching, compressing, buckling, and wrinkling. Additionally, the structure of soft robots is made up of a series of actuation elements, allowing them to exhibit continuum behaviour. This continuity and elasticity give the robots an infinite number of degrees of freedom, making motion planning and control highly challenging. Researchers have been exploring various approaches, including biologically inspired control strategies, nonlinear control designs, learning-based methods, and model-predictive control.

Although robotics as a whole is intrinsically linked to mechatronics and Industry 4.0, the core technologies underpinning soft robotics are especially well-suited for development through the integration of advanced mechatronics and Industry 4.0 principles. The following demonstrates these relationships.

6.1.1. Mechatronics and Soft Robotics

• Mechatronics plays a crucial role in the development and functionality of soft robotics, particularly in biomedical applications. The advent of robust and efficient soft mechatronic components has facilitated a paradigm shift in rehabilitation and assistive robotics. Systems have evolved from rigid structures linked by bioinspired actuators to fully compliant wearable devices [93]. These devices feature adaptable elements designed to maintain contact with the user, enhancing comfort and effectiveness.
• The advancement of soft robotics highlights the importance of material compliance and is closely related to the development of multifunctional components. Mechatronics encourages exploration of how various components, such as actuators and sensors, can be designed to perform multiple functions, enhancing the overall capabilities of soft robotic systems. Mechatronics also contributes to the implementation of the embodied intelligence principles in soft robotics [89]. By understanding how the body of a robot can simplify tasks and adapt through interaction with the environment, mechatronics helps to design systems that can evolve and improve their functionalities over time.
• The modular design of soft robotic systems is another area where mechatronics is vital. Using a modular architecture, soft robots can be easily reconfigured for different tasks. Mechatronic principles support this modularity by facilitating the integration of various components. For minimally invasive surgery, creating a fully integrated soft module requires modular mechatronic parts, such as a wireless microcontroller built into a PCB, and fluid supply lines and sensors to work properly [103].

6.1.2. Industry 4.0 and Soft Robotics

Soft robots are being increasingly adopted across various industrial applications due to their unique capabilities and advantages. Here are some key points that emphasise this relationship.

• Enhanced gripping and manipulation [90,104]: Soft grippers, a type of soft robot, are designed to perform precise and fast movements, which are essential for high productivity in industrial settings. They can adapt to different shapes and sizes of objects, making them versatile for various tasks. This flexibility is a key aspect in Industry 4.0 technology.
• Soft actuators are being explored for a range of applications, including soft grippers, artificial muscles, and sensor-integrated robots. These applications can improve productivity and safety in industries by allowing a more delicate handling of fragile objects and improving human–robot interaction [90].
• Soft robots, particularly those that use fluid-driven artificial muscles (FAMs), have a significant and growing relationship with various industrial applications. The simple structure of FAMs allows easy installation and disassembly, which is advantageous in industrial environments where equipment may need to be frequently reconfigured or maintained [94]. Moreover, the adaptability of these soft robots is particularly advantageous for grasping irregularly shaped or delicate items, significantly improving the efficiency of material handling processes in the Industry 4.0 environment.
• AI and machine learning, fundamental pillars of Industry 4.0, play a vital role in enhancing the functionality and efficiency of adaptive grippers. Unstructured and dense environments, such as those in agriculture, food processing, or home settings, present significant challenges for robotic systems. Grasping objects in these settings is particularly difficult due to the uncertainties in modelling both the robotic hand and the objects themselves [105]. These challenges are further compounded by unknown factors, such as the type of contact, shape, material, and stiffness properties of the objects being handled. AI and machine learning are integral to the development of adaptive grippers, enabling them to learn from data and improve their performance in real-time grasping tasks.
• In [106], a soft growing robot design has been demonstrated as a practical example of a cyber–physical system, which is another key pillar of Industry 4.0. The proposed robot has been designed for measurement and monitoring applications in confined and constrained environments. It includes a robotic base, positioned outside the remote site, and a soft expandable body that navigates the site by growing, with controllable length and steering capabilities.
• Soft continuum robots are increasingly being used for in situ inspections in industrial settings, including aircraft, nuclear, and aeroengine facilities [95]. Their flexibility and adaptability allow them to navigate complex and confined spaces that are difficult or dangerous for humans or traditional robots to access. In these environments, soft robots can perform detailed inspections, detecting structural issues or defects without requiring extensive disassembly, thus improving safety, efficiency, and reducing downtime [107].

Human–robot collaboration is a critical area that has the potential to revolutionise the future of robotic systems. As the demand for safe, collaborative robots continues to increase, the impact of soft robotics becomes increasingly significant. Soft robotics presents technological challenges in actuation, sensor integration, and control, but also opens new opportunities in various fields such as healthcare, manufacturing, and exploration tasks.

6.2. The Design of a Soft Continuum Robot

The proposed soft robot represents a novel integration of advanced mechatronics and the principles of Industry 4.0. This design merges the inherent flexibility and adaptability of soft robotics with the precision and intelligence offered by Industry 4.0 technologies, resulting in a system capable of unprecedented performance and versatility.

Figure 9 shows the prototype of the soft robot which was designed and fabricated at the Hamlyn Centre, Imperial College London [108], and has been successfully optimised and developed in different works [30,109,110].

6.2.1. Core Design Principles

• As shown in Figure 10, the robot is constructed of three segments connected in series, which collectively provide a total of six degrees of freedom (DOF). Each segment can bend significantly, allowing for a wide range of motion.
• Casting: The prototype is fabricated using a 3D printing lost-wax silicone-casting technique, which allows for precise shaping of the soft materials used in the manipulator.
• Material selection: Polydimethylsiloxane (PDMS) is the primary material used for the manipulator, chosen for its biocompatibility and flexibility.
• Mechatronic components: Including actuators, sensors, and embedded controllers, these components work in synergy to enable the robot’s complex movements and interactions with its environment:
  • Electromagnetic 5DOF sensor: The manipulator is equipped with an electromagnetic sensor (NDI, Aurora, 10002320) that is inserted into the central working channel. This sensor is positioned at the distal end and is used in conjunction with an Aurora NDI tracking system to measure the position and orientation of the manipulator during operation. This capability is essential for precise control and navigation in surgical environments.
  • Pneumatic actuation: The manipulator operates by pneumatic actuation, which involves the use of pressurised air to create movement. This method allows for smooth and flexible motion, which is crucial for delicate operations and tasks.
  • Controller: A digital microcontroller (mbed NXP LPC1768) is used to regulate the pressure in the internal chambers of the robot.
• Experiments were conducted to regulate the motion of the robot, successfully achieving consistent and repeatable rotation and bending at the tip. Figure 10 illustrates the motion of the tip in one of the desired poses. Figure 11 illustrates the motion of the tip in different desired poses.

6.2.2. Boosting Soft Robotics with Mechatronics and Industry 4.0 Technologies

Advanced mechatronics and Industry 4.0 technologies can significantly enhance the capabilities and applications of soft robots in several ways:

• The integration of IoT sensors to enhance the connectivity of the whole ecosystem. This enables real-time data collection, analysis, and feedback loops, optimising performance and decision making. This requires that the digital controller be connected with ESP8266, which enables Wi-Fi connection, or using another type of microcontroller with advanced capabilities such as Wi-Fi, IoT, AI cloud, etc. This integration of soft robots with the Internet of Things facilitates smooth communication between robots and various other devices or systems. This connectivity enables real-time data exchange, remote monitoring, and coordinated operations, which can be particularly useful in complex environments such as manufacturing floors or medical settings.
• Intelligent control: Develop advanced control system designs and AI-driven control focusing on self-learning algorithms to improve soft robot autonomy and adaptability. For example, a soft robot can learn to handle delicate objects with greater care or adjust its movements to navigate more efficiently; or considering a hybrid control strategy of both data-driven and closed-loop controllers of a soft robot for potential applications in endoluminal surgery and inspections in confined environments [101].
• Digital twin: Create virtual replicas of soft robots for virtual testing and simulation of different scenarios before deploying the robots in real-world applications. This technology can be used to optimise robot design, predict performance, and troubleshoot issues without the need for physical prototypes.

Our contributions primarily lie in the design and development of a novel soft continuum robot that integrates advanced mechatronics principles with the technologies of Industry 4.0. This integration is crucial as it enhances the robot’s adaptability and functionality, particularly in applications such as minimally invasive surgery and industrial automation. The design process involved utilising advanced materials, such as polydimethylsiloxane (PDMS), which are essential for achieving the desired flexibility and biocompatibility necessary for medical applications. Furthermore, our approach to using pneumatic actuation systems allows for smooth and precise movements, which are critical in delicate operations.

In our case study, we have highlighted the importance of modular design in soft robotics, which facilitates the reconfiguration of robotic systems for various tasks. This modularity is supported by mechatronic principles that enable the seamless integration of special types of sensors and actuators, enhancing the overall capabilities of the soft robotic systems. Our contributions also extend to the development of intelligent control systems, including closed-loop control systems, and facilitate future deployment of AI and machine learning, which are pivotal in improving the autonomy and efficiency of soft robots in dynamic environments.

Moreover, we have demonstrated the practical application, through experiments, of our soft robot prototype, which was fabricated at the Hamlyn Centre, Imperial College London. This prototype showcases the successful optimisation of soft robotics technologies. The integration of IoT sensors into our design further exemplifies our contributions, as it enables real-time data collection and analysis, optimising performance in industrial settings. Finally, in Section 6.2.2 we suggested further improvements in the prototype by implementing more technologies such as AI, digital twin, and big data analytics.

7. Opportunities and Future Directions of the Integration of Industry 4.0 and Mechatronics

The integration of mechatronics and Industry 4.0 technologies in smart manufacturing presents numerous future research directions and potential applications. As the manufacturing landscape evolves, the emphasis on intelligent systems that leverage advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics becomes increasingly critical. These technologies facilitate real-time data processing, improve operational efficiency, and support informed decision making, which are essential for maintaining competitiveness in the global market [111].

7.1. Opportunities from Integration

7.1.1. Enhanced Automation and Control

The adoption of Industry 4.0 technologies can significantly improve the automation capabilities of mechatronic systems. Intelligent algorithms and AI can analyse real-time data from sensors embedded in mechatronic devices, enabling adaptive control strategies. This can lead to:

• Self-optimising systems: Systems that continuously improve their performance by learning from operational data.
• Predictive maintenance: By real-time monitoring, the prediction of failures can minimise downtime and maintenance costs.

7.1.2. Customisation and Flexibility

Industry 4.0 promotes the shift towards mass customisation of products. Mechatronic systems equipped with adaptive capabilities can facilitate this customisation through:

• Reconfigurable manufacturing systems: Rapid modification of production lines to accommodate varying product specifications.
• User-centric design: Integrating customer feedback into the design process enables the creation of customised solutions that better meet user needs. This approach is equally critical in the context of smart cities, where personalised services and infrastructure are essential to enhance the quality of urban life and ensure the effective adoption of smart technologies.

7.1.3. Improved Data Utilisation

The integration of big data analytics into mechatronic systems can unlock significant value. Enhanced data collection and analysis enable:

• Smart decision making: Data-driven insights can guide operational strategies and improve efficiency.
• Quality control: Implementing real-time quality monitoring through IoT sensors, digital twins, and data analytics can reduce defects and improve product reliability.

7.1.4. Sustainability and Resource Efficiency

The drive for sustainability aligns with the principles of Industry 4.0. Mechatronics can enhance resource efficiency through:

• Energy management systems: These intelligent systems monitor and optimise energy consumption, significantly reducing environmental impact. A key example is industrial energy management software, such as Siemens EnergyIP and Schneider Electric EcoStruxure. These tools provide real-time monitoring and data analytics in industrial settings, providing insight to optimise energy usage, reduce waste, and improve operational efficiency. By automating energy control and providing actionable feedback, these systems contribute to more sustainable and cost-effective industrial processes.
• Waste reduction: Optimising manufacturing processes to minimise material waste.

7.2. Future Directions

The integration of Industry 4.0 and mechatronics creates substantial opportunities for both businesses and engineers. This document aims to identify future directions in this integration, as also emphasised in the recent literature.

7.2.1. Advances in AI and Machine Learning

Research in integrating advanced AI methodologies into mechatronics will likely produce superior adaptive systems. Potential areas of exploration include:

• Autonomous robotics: Developing robots that learn and adapt to their environments without significant human intervention.
• Cognitive automation: Implementing systems capable of reasoning and decision making based on contextual data.

The development of hybrid systems that combine various technologies to enhance smart manufacturing capabilities, for example, integrating AI with IoT, can lead to more adaptive and responsive manufacturing processes. Real-time data-driven methods, including machine learning and deep learning, can analyse the vast amounts of data generated by IoT devices to optimise production schedules, predict maintenance needs, and improve quality control. In addition, the application of edge computing and blockchain technology can improve data security and processing speed, enabling more efficient and secure manufacturing operations [112].

7.2.2. Collaborative Robots

Collaborative robots (cobots) play a crucial role in smart manufacturing environments, facilitating the adoption of collaborative and autonomous robotics in Industry 5.0, the next industrial revolution. Cobots can work alongside human operators, enhancing productivity and safety while allowing for more flexible production lines. Areas of focus include:

• Human–robot interaction: Research into human–robot interaction and the ergonomic design of these systems is essential to maximise their effectiveness and acceptance on the job.
• Task allocation: Developing algorithms to optimise task distribution between humans and cobots based on skill and capability.

The mechatronic-based development of advanced automated guided vehicles (AGVs) that can seamlessly integrate into factory architecture through advanced ICT and 5G networks is crucial to achieve a fully automated and efficient manufacturing system [67].

7.2.3. Cybersecurity in Mechatronics

As mechatronic systems become more interconnected, ensuring cybersecurity will be paramount. Research should focus on:

• Resilient systems: Developing robust mechatronic systems that can withstand cyberattacks.
• Data privacy measures: Implementing protocols to protect sensitive operational data.

7.2.4. Education and Workforce Development

As technology evolves, so must the workforce. Future educational models should consider:

• Interdisciplinary curriculum: Programmes that combine mechatronics with data analytics, AI, and cybersecurity.
• Hands-on training: Developing practical skills through simulations and real-world applications.

The sustainability aspect of smart manufacturing has gained traction in recent years. Future research should focus on how smart manufacturing systems can contribute to environmental sustainability through optimisation of resource usage and waste reduction. The implementation of green technologies and practices, supported by smart manufacturing principles, can lead to more sustainable production processes. This includes the use of renewable energy sources, green hydrogen technology, smart materials, and recycling initiatives, which are increasingly important in the increasingly environmentally conscious market of today [77].

Lastly, the role of small and medium enterprises (SMEs) in the adoption of smart manufacturing technologies cannot be overlooked. Research should aim to identify barriers to adoption and develop tailored strategies that can help SMEs leverage these technologies effectively. This includes understanding the unique challenges facing SMEs, such as limited resources and expertise, and providing scalable and cost-effective technology integration frameworks [66].

8. Conclusions

The convergence of mechatronics and Industry 4.0 is driving a transformative shift in manufacturing. Using advanced robotics, IoT, AI, and big data, industries are creating intelligent, interconnected systems that optimise production processes. The case studies presented in this article exemplify the tangible benefits of this integration, from enhanced efficiency in plastic injection moulding to innovative applications of soft robotics. Although challenges such as data security and workforce adaptation persist, the potential for increased productivity, flexibility, and sustainability is undeniable. Mechatronics is widely regarded as the driving force behind Industry 4.0. As we advance, ongoing research and development in this field will be crucial to fully realising the vision of smart factories and products, ultimately shaping the future of industrial production and other sectors. By also embracing ecomechatronics within the Industry 4.0 framework, industries can create sustainable manufacturing processes that not only meet economic goals but also actively contribute to environmental preservation. Future work will include the integration of quantitative data, such as performance metrics, cost–benefit analyses, and case studies with measurable results, to provide robust evidence supporting the advantages of combining mechatronics with Industry 4.0 technologies.