Training a Machine Learning Model for representing Manufacturing Systems towards optimizing Resilience

Studies affecting manufacturing system resilience have been of rising interest in recent years due to increased rates of naturally caused, and socio-economic disturbances. The non-linearity in modern manufacturing systems, attributed to a host of internal and external complexities in structure and operations cannot be properly modeled using conventional white-box techniques. This paper focuses on building a black-box description of a manufacturing system as a solution, addressing the inherent non-linearity, and further supporting fast investigations on a larger solution space with the example of an application scenario. This work acts as a precursor towards the implementation of a conceptual workflow describing a cross-domain application of machine learning (ML) for optimizing manufacturing system resilience.