Abstract
Digital shadows in industrial IT environments are virtual copies of production or manufacturing processes based on historical or real-time data obtained from physical sensors, control or automation systems. There is only one-way interaction between the shadowed process and its virtual copy, which differentiates a digital shadow from a digital twin exchanging the data in both directions. For many industrial applications, however, building a digital shadow using historical data is a sufficient, but quite challenging, task requiring the deployment of the entire data analytics pipeline.
The presented paper demonstrates how machine learning and some related AI-based approaches can assist in develo** and effective usage of intelligent IT applications. Thermal spray coating has been chosen as a use-case for demonstrating the applicability and feasibility of the chosen methodology for enhancing the operation of an industrial IT system supporting the coating process. The outcome of a comparative experimental studies demonstrated that artificial neural networks provide the most robust, versatile and generalisable technique for engineering data analytics in the chosen problem domain.
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Acknowledgement
The work has been completed as part of the research project funded by UKRI (Fundamental Research and Feasibility Studies) on Digitalised Surface Manufacturing led by Dr Anil Prathuru, School of Engineering, Robert Gordon University, U.K. The authors would like to thank their colleagues and the funding body for the opportunity to carry out this work and for the data provided in the course of our research endeavour.
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Petrovskiy, A., Arifeen, M., Petrovski, S. (2023). The Use of Machine Learning for Digital Shadowing in Thermal Spray Coating. In: Kovalev, S., Kotenko, I., Sukhanov, A. (eds) Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23). IITI 2023. Lecture Notes in Networks and Systems, vol 776. Springer, Cham. https://doi.org/10.1007/978-3-031-43789-2_32
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