Applications of Machine Learning and Computer Vision in Industry 4.0
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 15992
Special Issue Editors
Interests: computer vision; machine vision; camera systems; discrete simulation; production system modeling and simulation; convolution neural networks; Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Interests: virtual and augmented reality; Internet of Things; cloud computing; computational intelligence; Petri Nets
Special Issues, Collections and Topics in MDPI journals
Interests: robust control; optimization; LMI; decentralized control
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Industry 4.0, also referred to as the fourth industrial revolution, is a complex concept representing the highest degree of current automation, interoperability, data exchange and decentralization in manufacturing systems and technologies. It is defined as an integrating concept for technologies, systems and the concept of value chain organization. It combines Cyber–Physical Systems (CPS), Internet of Service (IoS) and Internet of Things (IoT).
The development and implementation of CPS, IoS and IoT in smart factories requires the development of new methods and approaches. However, the new tasks will also bring new challenges that can only be solved by applying and developing the latest knowledge from a range of engineering disciplines: machine perception systems, intelligent sensing, intelligent human–machine and machine-to-machine communication, computer vision, and machine learning, etc. New insights from these fields will enable modeling and subsequent replacement of human decision-making activities by machine activities.
Artificial intelligence (AI) will play a key role in the new tasks and challenges in the context of Industry 4.0 and smart factories. This discipline offers solutions that will be as efficient as possible, provide flexibility, be able to fully exploit shared data storage (cloud solutions) and fully exploit the concept of the Internet of Things.
As a result of digitalization and the advent of the Industry 4.0 concept, it is undergoing a significant transformation and AI is one of the tools in bringing about this change. AI has evolved in its capabilities over the years and has found application in various areas of industrial manufacturing and automation. The integration of AI with other advanced technologies in the industrial ecosystem will enable manufacturers to gain strong support in the Industry 4.0 concept.
Computer and machine vision is able to replace humans in monotonous repetitive vision activities because of their ability to see and understand the environment. When considering the sensory perception of objects, it is often visual perception that is aimed at, as about 90–95% of the information is drawn through this sensory channel. The application is mainly in the field of quality control, where the human factor very often fails or is not able to meet the requirements for productivity or accuracy of the checks performed. Machine vision is an essential element of automation and smart systems. No other aspect of the production line captures more information or is more valuable in assessing products and detecting defects, as well as gathering data to guide operations and optimize the productivity of robots and other equipment. Unlike simple sensors, vision sensors generate large amounts of image data, increasing their usefulness in Industry 4.0 environments.
Deep learning-based image analysis combines the specificity and flexibility of human visual inspection with the reliability, consistency and speed of a computer system. Deep learning models can repeatedly and iteratively solve challenging computer vision applications that would be difficult to develop programmatically and that are often impossible to solve using traditional machine vision approaches. Deep learning models can distinguish unacceptable errors while tolerating natural variations in complex patterns. Moreover, they can be easily adapted to new examples without reprogramming their underlying algorithms.
Dr. Oto Haffner
Dr. Erik Kučera
Prof. Dr. Danica Rosinová
Guest Editors
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Keywords
- machine learning and computer vision for control, monitoring, inspection and quality control in Industry 4.0 applications
- big data in digital and smart factories
- intelligent transportation and communication systems in digital and smart factories
- virtual, augmented and mixed reality in Industry 4.0 applications and services
- simulation and optimization in Industry 4.0 applications
- advanced control methods based on machine learning
- new human–machine interfaces for mechatronic, robotic and production systems
- education approaches for machine learning, computer vision and digital twins
- predictive maintenance based on machine learning and digital twins
- artificial intelligence in sustainable and human-centric solutions leading to Industry 5.0
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