Harnessing Artificial Intelligence for Optimum Performance in Industrial Automation ^†

Abstract

Artificial intelligence (AI) has become a powerful force in the ever-changing industrial automation field. The subject of this research paper focuses on the diverse applications related to artificial intelligence (AI) for enhancing performance in modern industrial settings. This paper starts by examining the historical background and basic principles of AI. Afterwards, fundamental techniques and algorithms based on machine and deep learning are discussed. The review classifies and analyzes practical implementations in which AI has played a crucial role in improving efficiency, accuracy, and flexibility in industrial operations. The report examines case examples to emphasize successful implementations, providing insights into the real advantages and knowledge gained from these efforts. Moreover, it tackles the inherent difficulties, like the complexities of integration, concerns about data privacy, and ethical considerations, that come with the use of AI in industrial operations. This article offers a thorough examination of the latest and next developments of artificial intelligence (AI) in industrial automation, as enterprises strive to meet the increasing need for improved efficiency. The review seeks to provide guidance to researchers, practitioners, and policymakers in navigating the dynamic convergence of artificial intelligence and industrial optimization by assessing the possible advancements that lie ahead. In essence, this highlights the crucial significance of AI in determining the future of industrial automation, providing unmatched prospects for attaining optimal performance and operational superiority.

1. Introduction

The use of artificial intelligence (AI) in industrial automation signifies a substantial transformation in the manufacturing sector. AI revolutionizes operations by employing advanced algorithms for deep learning and machine learning to improve and expedite procedures. Optimization results in enhanced efficiency, as AI-powered systems have the capacity to analyze extensive information to identify patterns, make informed decisions, and adapt to evolving production circumstances. The increasing interconnectedness of manufacturing processes has raised questions regarding security of data, as it subjects them to potential cyber risks and weaknesses. In the 1950s and 1960s, the main focus was on creating artificial intelligence (AI) systems and expert systems that relied on rules and aimed to replicate the cognitive processes used in human decision making. In the 1980s and 1990s, there was notable progress in control technology and the implementation of artificial intelligence approaches in industrial environments. The beginning of the twenty-first century saw a significant change due to the combination of improved processing capabilities, large datasets, and more sophisticated algorithms. Machine learning techniques, particularly those pertaining to neural networks, have gained significant recognition, leading to an increased utilization for artificial intelligence in industrial automation. The objective of this article is to thoroughly investigate the various uses and progressions of artificial intelligence (AI) in industrial automation. The aim is to clarify the historical context, core ideas, and practical applications of AI, highlighting its role in improving efficiency and adaptability. This study emphasizes the concrete advantages and valuable insights obtained from the incorporation of AI, using successful case studies. It also tackles issues such as the complexities of integration and ethical considerations. In essence, it offers direction for researchers, practitioners, and policymakers to effectively navigate the ever-changing field of AI in industrial optimization and promote future progress.

2. Fundamental Concepts

The core principles of artificial intelligence are essential in influencing the field of industrial automation, providing intelligent solutions to improve efficiency and streamline processes. Machine learning, also referred to as ML, is a foundational concept that allows systems to gain understanding through data without requiring explicit programming. Machine learning algorithms in the field of industrial automation leverage past data to identify patterns, trends, and connections, enabling the use of analytical forecasting, anomaly detection, and decision making based on learned knowledge. Deep learning, a specialized field within machine learning, plays a crucial role in industrial settings. Deep neural networks are composed of interconnected nodes arranged in layers, mimicking the anatomical structure of the human brain. Neural networks are used in industrial automation for various tasks, such as problem detection and process optimization. They are valuable because they can effectively capture complex correlations within data. Reinforcement training is a core concept where an AI agent gains knowledge by active interaction with the surroundings and receives response in the form of rewards or penalties based on its actions. Reinforcement learning is utilized in industrial automation to enhance autonomous devices and robotic control. This allows machines to adjust and improve their performance as time progresses. NLP, or natural language processing, facilitates the communication of information among machines and humans by interpreting and creating human language. In industrial environments, natural language processing (NLP) can be utilized to perform many tasks like voice commands, analysis of text, and human–machine interfaces. This application of NLP improves the interaction and cooperation between operators and artificial intelligence (AI) systems. By incorporating these essential AI principles into industrial automation, we can potentially develop adaptable and intelligent systems that have the ability to learn, optimize, and evolve in order to adapt to changing production conditions. These concepts collectively amplify the transformative potential of AI, profoundly altering the way industries approach automation, and pave the way for a more streamlined and intelligent future.

3. Literature Review

According to Masani et al. [1], there is an increasing acknowledgment of the crucial role that machine learning methods play in addressing the problems related to the absence of a precise automation system for manufacturing machinery, especially in the area of predictive maintenance and tracking of industrial machines. The authors suggest addressing the limitations of current automation systems by promoting the use of supervised machine learning techniques, notably the binary decision tree in conjunction with the CART (classification and regression trees) technique. Carvahlo et al. [2] highlight the growing volume of data produced by industrial processes, production systems, and equipment in the field of predictive maintenance (PdM). Machine learning (ML) techniques such as KNN and CART, etc., have become highly effective tools for predictive maintenance (PdM) applications, providing a proactive strategy to anticipate and prevent equipment malfunctions in industrial settings. Kroll et al. [3] mention a growing emphasis on the role of cyber physical production systems in addressing the challenges presented by rising energy carrier costs and the goal of enhanced industrial efficiency. To promote energy efficiency, it is crucial to integrate anomaly detection systems with ML (k-means, fuzzy clustering), specifically for wear-level detection, in order to improve maintenance cycles. The field of study on predictive maintenance in the context of the fourth industrial revolution, often known as Industry 4.0, has undergone substantial growth and conceptual advancement. This study enhances the comprehension of academic progress in failure prediction, with a specific focus on the creation of decision support systems for predictive maintenance and design support systems. The investigation focuses on frameworks that integrate machine learning and reasoning approaches designed specifically to meet the unique demands of Industry 4.0. This study conducts a thorough examination of 38 selected publications from a pool of 562, specifically addressing the challenges related to the application of machine learning and informatics in the field of predictive maintenance. The research in [4] examines the present advancements of machine learning methods like decision tree and CART specifically employed for predictive maintenance within the framework of Industry 4.0. The focus encompasses the categorization of research according to machine learning algorithms, classifications, types of machinery and equipment, and data-gathering devices, as well as methodologies, size, and types of data classification. This study aims to provide a comprehensive analysis of the notable contributions made by scholars in the domain of intelligent manufacturing throughout the Industry 4.0 era. In the ever-changing field of maintenance modeling, recent advancements [5] driven by data-driven methodologies and especially machine learning (ML) have generated prospects for a diverse array of purposes. Predictive maintenance (PdM) has been extensively adopted by the automotive industry to address the challenge of ensuring functional safety over the course of the product’s life while effectively managing maintenance expenses. Teoh et al. [6] propose a novel approach that integrates genetic algorithm (GA)-based resource management with machine learning to facilitate predictive maintenance in fog computing. The study evaluates the efficacy of the genetic algorithm (GA) related to time, cost, and energy by conducting simulations with FogWorkflowsim. The research in [7] examines the shift from traditional maintenance to the use of Industrial Internet of Things (IIoT) technology, particularly through the adoption of a condition monitoring system (CMS). The CMS is comprised of an experimental arrangement, an IIoT-driven condition monitoring application (CMA), and machine learning (ML) models. The study evaluates the efficacy of popular machine learning methods, specifically support vector machine (SVM), linear discrimination analysis (LDA), random forest (RF), decision tree (DT), and k-nearest neighbor (kNN). The DT model is regarded as the optimal selection for CMS because of its capacity to quickly determine threshold values and obtain excellent classification rates. Reference [8] explores the field of cloud manufacturing, presenting a machine learning method for enhancing awareness of reality and optimizing processes. More precisely, the emphasis is on anticipatory production planning, which includes scheduling operations and allocating resources, as well as anticipating maintenance needs. By developing a hybrid control system that blends Big Data methods and machine learning algorithms, this research contributes significantly. The study introduces a novel approach that utilizes LSTM neural networks alongside deep learning in current time to accurately forecast energy consumption during production. Reference [9] applies the DQN algorithm to production scheduling in order to coincide with the Industry 4.0 goal for improved control in manufacturing. It positions itself at the junction of these achievements. Agents of cooperative DQN, which employ deep neural networks, are strategically trained in the framework of RL to optimize scheduling methods based on user-defined objectives. The study’s validation is conducted by simulating a small plant that represents an abstracted front-end-of-line semiconductor fabrication facility. The study in [10] investigates the utilization of deep learning through reinforcement learning (DRL) in industrial sectors, including robotics, job shop organizing, and management of supply chain. The primary emphasis lies on employing deep reinforcement learning (DRL) to achieve intelligent allocation of resources in industrial edge equipment. Kuhnle et al. [11] contribute to the subject by specifically examining the design of reinforcement learning (RL) based on development of a control system of an adaptive nature. They provide an example of this system in action by employing the actual situation of order sending in a complicated shop environment. The fundamental nature of RL algorithms is sometimes perceived as opaque, which limits full comprehension. However, their promise in highly dynamic and complex production systems, particularly in cases where domain knowledge is restricted, is stressed. The research in [12] overcomes the restrictions by incorporating deep reinforcement learning (DRL) into the scheduling domain. This approach eliminates the requirement for manually extracted features and successfully tackles the difficulties caused by the lack of structured datasets. This paper makes significant contributions in multiple critical aspects. In [13], the authors examine the future trajectory of leading manufacturing organizations, emphasizing the vital importance of digital and information viability in the automation production process. The emergence of machine learning (ML), specifically the evolution of reinforcement learning (RL), highlights the considerable potential for revolutionary effects on the manufacturing industry. This paper aims to deliver a thorough examination of recent real-world uses of reinforcement learning (RL) in several areas, particularly focusing on its application in the industrial sector. The analysis explores the techniques of reinforcement learning (RL) in industrial applications, clarifying their distinct performances. Moreover, the study examines the obstacles and possibilities for using RL in automated production. It discusses the future direction of RL development to meet the changing requirements of intelligent manufacturing. The research critically examines the current body of knowledge and employs an organizational assessment to explore the practicality of implementing reinforcement learning, or RL, in manufacturing settings. The study primarily examines the utilization of reinforcement learning (RL) in manufacturing process structures, human–machine-assisted oversight and control, process inspection, preventive and post-processing, and complete material process management. The objective is to determine the suitability of RL in the intricate procedures and regulatory systems of the automated manufacturing sector, thus facilitating improved productivity and informed decision making. This literature review offers a thorough examination of the latest advancements in the use of RL in industrial settings. It gives a logical and consistent narrative that depicts the possible trajectory for intelligent manufacturing processes in the future. In the realm of chip manufacturing, the necessity for flexible planning has grown significantly crucial. This phenomenon is propelled by a growing assortment of things being manufactured and the diminishing duration that each product is available for purchase. The issue of scheduling is intrinsically intricate, characterized by intricate limitations and a demand for expeditious decision making, rendering it a formidable challenge. The aim of this study is to meet these practical requirements by providing a new method that effectively combines a hybrid algorithm rooted in deep reinforcement learning (DRL) and genetics. The main objective is to address the issue of scheduling independent parallel machines in semiconductor manufacturing, where setup time is dependent on the sequence of tasks and poses a significant challenge. The scheduling agent efficiently performs assigning tasks while adhering to the limits of dynamic scheduling by employing a deep Q network (DQN), which combines deep learning with Q learning. Furthermore, the study utilizes a combination of genetic algorithm techniques to enhance the efficiency and effectiveness of the agent’s training search process. In order to determine the precision of the proposed solution, comprehensive scenarios were developed to carry out comparative evaluations with dispatching rules and other knowledge-dependent methods. The testing results not only validate the practical viability of the technology that was created but also verify its implementation in an actual semiconductor manufacturing facility. This overview of the literature places the research in the broader context of semiconductor production scheduling, highlighting the significance of addressing dynamic challenges and the innovative integration of deep reinforcement training and hybridized genetic algorithms. This study improves the advancement of scheduling methodologies in semiconductor production, providing a valuable structure for practical execution. Esteso et al. [14] extensively analyze the application and real-world use of reinforcement learning (RL) techniques in the field of production management and planning (PPC). The study provides a thorough analysis of several aspects of PPC, including as facility resource planning, capacity planning, procurement and supply chain management, production scheduling, and inventory management. This paper provides a comprehensive examination of key components of RL, such as methodology, its context, states, behaviors, and rewards, emphasizing their significance. The review of the literature presented this paper in the broader context of RL applications in PPC, highlighting common trends, successful techniques, and the capacity of RL to offer innovative solutions when faced with complex planning and control challenges. Energy-efficient machining has become increasingly important in the manufacturing industry [15], as it aligns with the crucial objectives of conserving energy, reducing emissions, and saving costs. In order to address this gap, this study proposes a cohesive meta-reinforcement learning (MRL) approach for optimizing machining parameters. The objective is to detect commonalities in optimization approaches and use this comprehension to promptly adjust to novel machining assignments. Reference [16] investigate the cutting-edge methodologies and procedures employed in surface fault inspection, specifically in the domains of semiconductors, steel, and fabric manufacturing processes. ML has made substantial progress in the last ten years. This progress is notably evident in the development of deep learning (DL) algorithms used in autonomous driving automobiles and electronic strategy games. Researchers are actively investigating the possibilities of ML in the industrial sector, viewing it as a crucial catalyst for the progress of old manufacturing settings towards Industry 4.0. Although there is increasing interest, the practical use of machine learning in industry is still somewhat restricted, mostly limited to a small number of multinational corporations. Bertolini et al. [17] examine this field, offering a thorough analysis to clarify the true capabilities and possible limitations for algorithms of ML in the field of operation management. Subject review has been methodically designed to aid practitioners’ orientation, categorizing works from 2000 to the present based on the applicable algorithm and application domain. Within the field of industrial duties [18], quality control emerges as an area that is highly suitable for enhancement through technological advancements. Among these developments, machine vision appears as a breakthrough technology, enabling reliable and efficient 24/7 inspections that boost industrial processes’ overall efficiency. The paper presents a machine vision model that surpasses defect diagnosis by incorporating continuous improvement into manufacturing processes. The literature review in [19] situates the paper within the wider range of technical breakthroughs in quality control, highlighting the significant influence of vision and ML algorithms on improving the efficiency and effectiveness of industrial processes. Significantly, the review examines current works that employ deep learning methods for AOI. The article finishes by emphasizing prevailing patterns and delineating prospective avenues for further investigation. In their study, Huang [20] focuses on creating an advanced machine learning-based method for detecting defects in metal products. The system’s structure incorporates picture preprocessing with the computer vision library of OpenCV. Subsequently, training techniques are implemented, employing machine learning algorithms like generative adversarial networks (GANs) and CNN plus chunk-max pooling. These techniques are employed to address the issue of insufficient datasets for surface defects. The evaluation processes are executed utilizing the Python programming language and GPU-accelerated embedded hardware to accomplish effective defect identification. The incorporation of machine learning (ML) [21,22] into the industrial sector, under the Industry 4.0 framework, has brought about significant and revolutionary alterations. Industry 4.0 focuses on implementing intelligent factories that utilize smart sensors, gadgets, and machines to constantly gather data on production operations. Hao [23] undertakes a thorough assessment of optical inspection methods in the semiconductor sector, classifying existing research according to inspection algorithms and the items being examined. The visual inspection technologies employ a variety of vision-based algorithms, such as projection techniques, filtering-based methods, learning-based approaches, and hybrid methods. Automated identification of flaws [24] on hot-rolled steel surfaces poses significant difficulty because of considerations such as the need to locate them on large surfaces, changes in their appearance, and the rare incidence of flaws. This study aims to tackle the difficulty by focusing on extracting a collection of superior defect characteristics from surface photos. Reference [21] principally concentrates on the advancement of cutting-edge technology, namely in the domain of automated fault inspection for concrete buildings. The proposed framework employs a multi-stage methodology encompassing gathering data, fault recognition, scene rebuilding, flaw evaluation, and data integration. The use of automated fabric flaw detection [25] has become an essential aspect of quality control in the textile production industry, leading to an increase in research efforts to develop efficient systems. This research thoroughly examines current progress in fabric defect detection, classifying them into non-motif-based and motif-based methods. Aforementioned research studies mostly discuss the role of AI in a single domain related to the modern industry. This article aims to fill a significant research gap by bringing together multiple studies on predictive maintenance, production optimization, and quality control to create an exhaustive resource. By filling this void, it offers academics a consolidated platform for conducting literature reviews across many fields and their corresponding AI implementations concurrently in Table 1.

4. Research Synthesis

Table 1 explained the state of the algorithms research synthesis which has been adopted and applied in many industries. Many power predictive analysis algorithms named as LSTM, KNN, K-means, Decision tree, Deep reinforcement learning, support vector machine and convolutional neural network have been used for the optimal regression predictive and classification applications in engineering automation industries. These applications have proved that the industrial automation has been revolutionized by the machine learning algorithms.

Table 1. Research Synthesis.

References	Key Focus	Methods/Approaches	Application Area
[1]	Predictive Maintenance, Monitoring	BCT, CART	Industrial Machine
[2]	Literature Review in Predictive Maintenance	KNN, CART, LSTM	General
[3]	Anomaly Detection, Predictive Maintenance, System Modeling	K-means, Fuzzy Clustering, GA	Industrial Plants
[4]	Predictive Maintenance, Industry 4.0, Sustainability	Decision Tree, CART	Industry 4.0
[5]	Predictive Maintenance, Automotive Industry	KNN, CART, LSTM	Automotive Industry
[6]	Predictive Maintenance, Industry 4.0, IoT, fog Computing	K-means, Fuzzy Clustering, GA	Industry 4.0
[7]	Predictive Maintenance, IIoT, Condition Monitoring	Machine Learning	Condition Monitoring
[8]	Predictive Scheduling, Resource Allocation	LSTM	Manufacturing Systems
[9]	Production Scheduling, Deep Reinforcement Learning	DQN, RL	Production Scheduling
[10]	Resource Allocation, Industrial IoT, Deep Reinforcement Learning	Deep Reinforcement Learning	IIoT
[11]	Adaptive Production Control, Reinforcement Learning	Reinforcement Learning	Production Control
[12]	Scheduling, Discrete Automated Production Line, Deep Reinforcement Learning	Deep Reinforcement Learning	Automated Production Line
[13]	Reinforcement Learning, Industrial Automation	Reinforcement Learning	Industrial Automation
[26]	Dynamic Scheduling, Industry 3.5, Deep RL, Hybrid Genetic Method	Deep Reinforcement Learning, Hybrid Genetic Algorithm	Smart Production
[14]	Reinforcement Learning, Production Planning, Control	Reinforcement Learning	Production Planning and Control
[15]	Meta-Reinforcement Learning, Machining Parameters, Energy-Efficient Process Control	Meta-Reinforcement Learning	Process Control
[16]	Surface Defect Inspection, Deep Learning	Deep Learning	Industrial Products
[17]	Machine Learning, Industrial Applications	Literature Review	Industrial Applications
[18]	Machine Vision, Defective Product Inspection	KNN, CNN	Defective Product Inspection
[19]	AOI, Quality Monitoring, Electronics Industry	Review, Analysis	Electronics Industry
[20]	Intelligent Defect Detection, Machine Learning	CNN, GAN	General
[22]	Machine Learning, Industry 4.0	Machine Learning	Industry 4.0
[23]	Automated Visual Inspection, Semiconductor Industry	Survey	Semiconductor Industry
[24]	Automatic Fault Detection, Steel Products	SVM, DT	Steel Products
[21]	Automated Defect Inspection, Concrete Structures	CNN, LiDAR	Concrete Structures
[25,27]	Fabric Defect Detection	Review	Textile Manufacturing

Table 1. Research Synthesis.

References	Key Focus	Methods/Approaches	Application Area
[1]	Predictive Maintenance, Monitoring	BCT, CART	Industrial Machine
[2]	Literature Review in Predictive Maintenance	KNN, CART, LSTM	General
[3]	Anomaly Detection, Predictive Maintenance, System Modeling	K-means, Fuzzy Clustering, GA	Industrial Plants
[4]	Predictive Maintenance, Industry 4.0, Sustainability	Decision Tree, CART	Industry 4.0
[5]	Predictive Maintenance, Automotive Industry	KNN, CART, LSTM	Automotive Industry
[6]	Predictive Maintenance, Industry 4.0, IoT, fog Computing	K-means, Fuzzy Clustering, GA	Industry 4.0
[7]	Predictive Maintenance, IIoT, Condition Monitoring	Machine Learning	Condition Monitoring
[8]	Predictive Scheduling, Resource Allocation	LSTM	Manufacturing Systems
[9]	Production Scheduling, Deep Reinforcement Learning	DQN, RL	Production Scheduling
[10]	Resource Allocation, Industrial IoT, Deep Reinforcement Learning	Deep Reinforcement Learning	IIoT
[11]	Adaptive Production Control, Reinforcement Learning	Reinforcement Learning	Production Control
[12]	Scheduling, Discrete Automated Production Line, Deep Reinforcement Learning	Deep Reinforcement Learning	Automated Production Line
[13]	Reinforcement Learning, Industrial Automation	Reinforcement Learning	Industrial Automation
[26]	Dynamic Scheduling, Industry 3.5, Deep RL, Hybrid Genetic Method	Deep Reinforcement Learning, Hybrid Genetic Algorithm	Smart Production
[14]	Reinforcement Learning, Production Planning, Control	Reinforcement Learning	Production Planning and Control
[15]	Meta-Reinforcement Learning, Machining Parameters, Energy-Efficient Process Control	Meta-Reinforcement Learning	Process Control
[16]	Surface Defect Inspection, Deep Learning	Deep Learning	Industrial Products
[17]	Machine Learning, Industrial Applications	Literature Review	Industrial Applications
[18]	Machine Vision, Defective Product Inspection	KNN, CNN	Defective Product Inspection
[19]	AOI, Quality Monitoring, Electronics Industry	Review, Analysis	Electronics Industry
[20]	Intelligent Defect Detection, Machine Learning	CNN, GAN	General
[22]	Machine Learning, Industry 4.0	Machine Learning	Industry 4.0
[23]	Automated Visual Inspection, Semiconductor Industry	Survey	Semiconductor Industry
[24]	Automatic Fault Detection, Steel Products	SVM, DT	Steel Products
[21]	Automated Defect Inspection, Concrete Structures	CNN, LiDAR	Concrete Structures
[25,27]	Fabric Defect Detection	Review	Textile Manufacturing

5. Conclusions

This article examines the incorporation of artificial intelligence (AI) into industrial automation, focusing on the utilization of advanced algorithms for predictive maintenance, process optimization, and fault diagnosis. The text offers a comprehensive examination of the past, explores essential ideas, and presents new studies aimed at improving results in various fields. Inevitably, the development of AI will continue to enhance industrial automation.