About this Research Topic

Manuscript Submission Deadline 02 November 2022

The mechanical engineering domain is witnessing a massive digital transformation across sectors such as materials, energy, mining, product design, manufacturing, supply chain, automotive and aerospace. All industrial functions are being interconnected in digital frameworks for real time monitoring, control and optimization to maximize productivity, operational efficiency, flexibility to meet dynamic market demands and minimize costs, rework and pollution. Machine learning (ML) and artificial intelligence (AI) techniques are being applied in almost every sphere of modern industries to analyze, classify, automate, optimize and predict processes in ever more effective and meaningful ways. In fact, ML and AI can now make decisions for maintenance, material requisitions, material flow plans and much more in modern factories and industrial automation systems. This research topic aims to collect relevant articles that address various aspects of ML and AI applications in the domain of industrial automation and robotics.

This Research Topic would welcome article contributions that focus of ML and AI to solve industrial automation and robotics domain problems with regards to (not limited to): product design, analysis, supply chain, manufacturing, control, automation, robotics, automotives, electric vehicles, aerospace, materials, materials processing, maintenance, waste reduction, green / sustainable manufacturing, industry 4.0 and more. The goal of this Research Topic is to provide ML and AI based technological solutions to the problems and challenges being faced by the industry, society and environment in the context of automation and robotics domain. This Research Topic would also foster interdisciplinary research efforts with ML, AI and industrial automation and robotics at its core.


Authors can submit original research articles based on experimental trials, novel theoretical frameworks, systematic reviews (including detailed bibliometric analyses) and industrial case studies incorporating ML and/or AI in the following (not limited to) areas:
• Industry 4.0
• Automation for sustainable manufacturing
• Automation in supply chain
• Digitalization of mechanical systems
• Robotics, automated guided vehicles, unmanned aerial vehicles, automated storage and retrieval systems
• Automation strategies for industrial systems
• System identification and control architectures in industrial systems
• Automotives – digital transformation and self driven vehicles
• Automated processing of advanced materials
• Automated energy generation and management in industries
• Automated industrial waste minimization, processing, recycling
• Predictive maintenance
• Autonomous industrial systems
• CAD/CAM
• CAE
• Additive manufacturing
• Digital twins
• Optimization and metaheuristics in industrial automation and robotics
• Fuels and automated combustion systems
• Bio mechanical devices in industrial automation and robotics / automation in bio mechanical device manufacturing
• Acoustics, vibrations and fluid structure analyses in industrial automation and robotics
• Augmented / virtual reality in industrial automation and robotics

Keywords: Robotics, Automation and Control, Smart Manufacturing, Predictive Maintenance, Industry 4.0, Machine Learning, Artificial Intelligence, Mechanical Engineering, Design, Optimization, Supply Chain Management


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The mechanical engineering domain is witnessing a massive digital transformation across sectors such as materials, energy, mining, product design, manufacturing, supply chain, automotive and aerospace. All industrial functions are being interconnected in digital frameworks for real time monitoring, control and optimization to maximize productivity, operational efficiency, flexibility to meet dynamic market demands and minimize costs, rework and pollution. Machine learning (ML) and artificial intelligence (AI) techniques are being applied in almost every sphere of modern industries to analyze, classify, automate, optimize and predict processes in ever more effective and meaningful ways. In fact, ML and AI can now make decisions for maintenance, material requisitions, material flow plans and much more in modern factories and industrial automation systems. This research topic aims to collect relevant articles that address various aspects of ML and AI applications in the domain of industrial automation and robotics.

This Research Topic would welcome article contributions that focus of ML and AI to solve industrial automation and robotics domain problems with regards to (not limited to): product design, analysis, supply chain, manufacturing, control, automation, robotics, automotives, electric vehicles, aerospace, materials, materials processing, maintenance, waste reduction, green / sustainable manufacturing, industry 4.0 and more. The goal of this Research Topic is to provide ML and AI based technological solutions to the problems and challenges being faced by the industry, society and environment in the context of automation and robotics domain. This Research Topic would also foster interdisciplinary research efforts with ML, AI and industrial automation and robotics at its core.


Authors can submit original research articles based on experimental trials, novel theoretical frameworks, systematic reviews (including detailed bibliometric analyses) and industrial case studies incorporating ML and/or AI in the following (not limited to) areas:
• Industry 4.0
• Automation for sustainable manufacturing
• Automation in supply chain
• Digitalization of mechanical systems
• Robotics, automated guided vehicles, unmanned aerial vehicles, automated storage and retrieval systems
• Automation strategies for industrial systems
• System identification and control architectures in industrial systems
• Automotives – digital transformation and self driven vehicles
• Automated processing of advanced materials
• Automated energy generation and management in industries
• Automated industrial waste minimization, processing, recycling
• Predictive maintenance
• Autonomous industrial systems
• CAD/CAM
• CAE
• Additive manufacturing
• Digital twins
• Optimization and metaheuristics in industrial automation and robotics
• Fuels and automated combustion systems
• Bio mechanical devices in industrial automation and robotics / automation in bio mechanical device manufacturing
• Acoustics, vibrations and fluid structure analyses in industrial automation and robotics
• Augmented / virtual reality in industrial automation and robotics

Keywords: Robotics, Automation and Control, Smart Manufacturing, Predictive Maintenance, Industry 4.0, Machine Learning, Artificial Intelligence, Mechanical Engineering, Design, Optimization, Supply Chain Management


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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