Development of a data-driven model for the evaluation and improvement of process robustness in the design of deep-drawing tools

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In industrial deep drawing processes, stochastic fluctuations and disturbances of the manufacturing conditions occur, which can cause uncontrolled deterioration of the product properties. The immunity to these negative influences is referred to as robustness. Robustness in deep drawing can be assessed by sensors integrated into the press line. This generates extensive amounts of data that have potential to be used for machine learning modelling and for analysing complex interactions. The field of explainable AI, which serves to explain such data-driven models is becoming increasingly relevant. As such, the aim of the research project is to describe the effects of stochastic fluctuations and disturbances on product quality in an explainable way using data-driven models. The scientific approach is based on the fact that the flange length of the first forming stage can be used as a significant quality criterion. As a metrological solution, a camera system will be used for non-contact measurement of the flange length. The research project is divided into two stages. The first stage is concerned with developing the modelling approach based on a cross die geometry. For this purpose, training data will be generated by experimental and numerical investigations. In the second stage, this modelling approach will be applied to industrial geometries using process data from series production. At the end of the research project, a unified explanatory model will be derived. It is hypothesized that the transformation of absolute process values into relative data will favour comparability between different geometries. The purpose of this model is to determine the process robustness in the design of deep drawing tools.