Improving Expert Knowledge in Dynamic Process Monitoring by Symbolic Regression

To avoid destructive testing methods in the evaluation of final process quality results in production environments, we monitor and evaluate the process dynamics to make assumptions about the associated final process quality. This information can again be used in process control to adapt the process parameters according to the observed and given reference quantities. In our approach, we propose a method for modeling the observable process dynamics. This can be modeled by parametric functions which can either be determined solely by expert knowledge and Nonlinear Curve Fitting using an additional correction term that is found via Symbolic Regression. The obtained model parameters characterize the process dynamics and can be used to detect abnormal process behavior in order to adapt the process parameters by a control unit. For a proof of concept, we have applied the proposed approach to experimental resistance spot welding data.