Guest Editors
Dr. Mojtaba Ahmadieh Khanesar
Email: mojtaba.ahmadiehkhanesar@nottingham.ac.uk
Affiliation: Department of Mechanical, Materials and Manufacturing Engineering, Faculty of Engineering, University of Nottingham, Nottingham, United Kingdom
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Research Interests: robotics, control, metrology, mechatronics, machine learning
Summary
The use of advanced machine learning algorithms may increase the speed of industrial manufacturing, provide superior metrological characteristics for the manufactured parts, and contribute to higher production volume. Machine learning approaches can be used in parallel with advanced control to enable more advanced machine capabilities and make control algorithms resilient to production-line variations and factory-floor changes. The resilience and dexterity enabled by advanced machine learning approaches facilitate flexibility and customization in Industry 4.0 settings, providing real-time responses to market preferences. Enhanced collaboration between human workers and machines would be possible due to a safer operational environment and the extra dexterity added to machines in Industry 4.0. Waste reductions, energy-aware production, and time optimality are the main consequences of using advanced machine learning and control approaches in an industrial setting.
Motivated by the apparent need in Industry 4.0 for advanced machine learning approaches and high-performance industrial processes, this Special Issue of Computers, Materials & Continua will publish original research papers on techniques, methods, applications, and industrial use cases that report state-of-the-art advanced machine learning approaches. Applications of machine learning methods for maximizing flexibility and minimizing waste are highly appreciated. This Special Issue is intended to provide a platform for presenting advanced methodologies and cutting-edge techniques in machine learning for Industry 4.0 systems, including factory elements such as electric drives, mechatronic systems, and robotic systems. The following subtopics are the suggested themes of this special issue, including but not limited to:
· Machine Learning and Advanced Control Synergies
· Precision Metrology and Quality Assurance
· Flexibility and Customization in Manufacturing
· Human-Robot Collaboration
· Sustainable and Energy-Aware Production
· Smart Mechatronics and Electric Drives
· Robotic Autonomy and Dexterity
· Industrial Use Cases and Real-World Deployments
Keywords
improving industrial robot precision, surface characteristics, defect detection on the surface, human–machine collaboration and robotics, activity detection techniques in industry 4.0 settings, resilient process design for industrial robots, deep neural network applications within industrial settings, digital twin design and integration
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