The Fourth Industrial Revolution, also known as Industry 4.0, represents the technological evolution from traditional manufacturing systems to cyber-physical systems, which leads to improvement of overall productivity and reductions of environmental impact, thus promoting sustainable economic development. Industry 4.0 has been driven by emerging technology developments in the field of digital twin, artificial intelligence, robotic and automation, Internet of Things (IoT), cloud computing, and edge/fog computing, and has been a hot topic in both academia and industry. Implementation of IoT connects the physical assets to cyber networks and captures

a significant amount of data. The resulting big data are fed to AI-based mission-critical systems to perform effectively production monitoring, quality inspection, root cause analysis, quality prediction, and process control. The proper adoption of relevant industry 4.0 technologies should lead to significant efficiency improvement and cost reduction in various industrial sectors.

The goal of this special issue is to bring together researchers and practitioners from academia and industry to provide a forum for discussing industrial automation research on smart manufacturing and machine learning. It addresses the needs and challenges for integration with efficient machine learning algorithms and engineering solutions. Besides, it provides a vision for future research and development in the area of intelligent automation. The main theme of the special issue is machine learning for Industry 4.0, where digital factories, additive manufacturing, digital twins, cognitive and collaborative robots, freight transportation, process control, plant-wide systems, and broad aspects and issues will be well discussed.

Topics of interest include, but are not limited to:

• Machine learning for advanced automation

• Incremental and transfer learning

• Smart and digital factories

• Smart logistics and warehouses

• Robot vision and applications in automation

• Fault diagnosis, prediction and prognostics

• Industrial Internet of Things

• Edge computing-based machine learning for automation

• Big data analytics for forecasting and planning

• Integrated productivity and quality analysis

• Production planning, scheduling and control algorithms

• Digital twin for automation

• Data mining and data-driven decision making

• AI methods customized for different industries

• Online real-time data anomaly detection framework

• Sustainable manufacturing and remanufacturing

• 3D printing and additive manufacturing

• Industry 4.0 case studies

• 15 Oct. 2022 – Submission deadline

• 1 Dec. 2022 – First decision to authors

• 15 Jan. 2023 – Resubmission

• 1 Mar. 2023 – Final decision

• 10 Mar. 2023 – Final manuscripts upload

• 15 Mar. 2023 – Guest editorial due

• Jun. 2023 – Publication

Prof. MengChu Zhou

New Jersey Institute of Technology,

 

Prof. Yan Qiao

USA Macau University of Science and Technology, China

Prof. Bin Liu

IKAS Industries, China

Prof. Birgit Vogel-Heuser 

Technical University of Munich, Germany 

Prof. Heeyoung Kim

Korea Advanced Institute of Science and Technology, Korea

 heeyoungkim@kaist.ac.kr