Industrial Machine Learning Application

Dear Colleagues,

With the advent of Industry 4.0 and Smart Manufacturing paradigms, data have become a valuable resource, and very often an asset, for every manufacturing company. Data from the market, from machines, from warehouses, and from many other sources are now cheaper than ever to collect and store. A study from Juniper Research has identified industrial Internet of Things (IIoT) as a key growth market over the next five years, accounting for an increase in the global number of IIoT connections from 17.7 billion in 2020 to 36.8 billion in 2025, representing an overall growth rate of 107%. With such an amount of data produced every second, classical data analysis approaches are not useful, and only automated learning methods can be applied to produce value, a market estimated at more than 200 billion USD worldwide. Using machine learning techniques, manufacturers can exploit data to significantly impact their bottom line by greatly improving production efficiency, product quality, and employee safety.

The introduction of ML to industry has many benefits that can result in advantages well beyond efficiency improvements, opening doors to new opportunities for both practitioners and researchers. Some direct applications of ML in manufacturing include predictive maintenance, supply chain management, logistics, quality control, human–robot interaction, process monitoring, anomaly detection, and root cause analysis, to name a few.

Topics of Interest

This is an open call for papers, soliciting original contributions considering recent findings in theory, methodologies, and applications in the field of industrial machine learning. Potential topics include but are not limited to:

• Robustness-oriented learning algorithms;
• Machine learning for robotics (e.g., learning from demonstration);
• Continuous and lifelong learning for industrial applications;
• Transfer learning and domain adaptation;
• Anomaly detection and process monitoring;
• ML applications to predictive maintenance;
• ML applications to supply chain and logistics;
• ML applications to quality control;
• ML for flexible manufacturing;
• Deep learning for industrial applications;
• Learning from big data;
• Inference in real-time applications;
• Machine learning on embedded and edge computing hardware;
• Multimodal learning for industrial applications;
• Semantic representation of machine learning models.

All contributions are expected to focus on applications to the industrial sector, possibly with real-world case studies. Position papers presenting new industrial systems and case studies, possibly reporting preliminary validation studies, are also encouraged.

Dr. Paolo Rota
Prof. Dr. Miguel Angel Guevara Lopez
Dr. Francesco Setti
Guest Editors

Manuscript Submission Information

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