Development of a Machine Learning Model that represents the characteristics of a Manufacturing Systems

• Published in: Procedia CIRP Vol. 122; pp. 175 - 180
• Main Authors: Klar, Matthias, Rüdiger, Patrick, Scheidt, Marcel, Hussong, Marco, Glatt, Moritz, Ravani, Bahram, Aurich, Jan C.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2024
• Subjects: anormalie detection; forecasting model; machine learning; manufacturing system; material flow; anormalie detection; manufacturing system; material flow; machine learning; forecasting model
• ISSN: 2212-8271; 2212-8271
• DOI: 10.1016/j.procir.2024.01.026

The performance of a manufacturing system is a result of the interrelated characteristics of all functional units within the system, including material flow. Therefore, the operation of a manufacturing system requires harmonization of its elements, resulting in an optimized material flow, reduced operating costs, and energy consumption. Existing methods approach this problem by applying analytic formulations or material flow simulations that represent the behavior of a manufacturing system. However, building such a model is time-consuming and requires making assumptions and simplifications, which can lead to inaccurate results. As a consequence, this paper proposes two novel machine learning (ML) models that are trained to represent the characteristics of manufacturing systems. The first model is trained to predict the properties of the material flow and the functional units used for production. The second model predicts whether the manufacturing system is in a regular state or whether anomalies are present. The capabilities of the ML models are investigated with three application scenarios of increasing complexity which are used to generate synthetic training data generated in a discrete event simulation. The results show that the ML models can describe the characteristics of a manufacturing system with high accuracy which offers various possibilities for future research and application.