New Challenges in Machine Learning for Industrial Applications

Dear Colleagues,

Few other topics are as prominent in the digital transformation as artificial intelligence. More precisely, it is machine learning (ML) and, subsumed within it, deep learning, which are central to the realization of essential functions in industrial processes (e.g., predictive maintenance, predictive quality, visual quality control, etc.) and can contribute to the optimization and sustainability of the same. Hardly a day goes by without new publications dealing with the application of new ML methods in an industrial context. Likewise, articles can be found in all forms of journalistic media that deal with artificial intelligence. In the application field, too, more and more companies are abandoning their initial skepticism and deciding to tap into these promised potentials for themselves. However, it is becoming increasingly apparent that the transfer of individual machine learning methods to applications in an industrial context is inadequate. Capabilities such as robustness, transparency, and traceability, as well as transferability must also be available in technical systems that rely on ML-based processes. However, common, traditional approaches to creating such capabilities in engineering systems are not usable in the ML context. In this Special Issue, we address research papers that deal precisely with these new challenges arising from the application and successive establishment of ML-based systems in industrial applications. We will only consider papers that address both the application of machine learning in an industrial context alongside specific challenges of the kind mentioned above.

Prof. Dr. Tobias Meisen
Guest Editor

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