Improving the production quality and robustness of a SO16 sensor package by a simulation based digital twin approach

To fulfil actual and future requirements of xMR based current measurement devices the knowledge of possible deviations during the fabrication process and their impacts is necessary. The digital twin approach for a SO16 sensor device presented in this paper helps to understand the measurement offset change after manufacturing and improving the final product quality. The created FE based virtual prototype model included relevant nonlinear and temperature dependent material descriptions like metal plasticity and viscoelasticity. Beside model uncertainties regarding available data for the specific materials, the properties can fluctuate naturally due to several reasons like different batch qualities, changed ingredients etc. All these issues can have an impact on the resulting intrinsic stresses in the AMR sensor and were considered within the digital twin. An extensive design of experiments (DoE) study with about 40 free input parameters at process and component level was therefore conducted to quantify the uncertainties and their impact on the sensor behavior. With the usage of industrial state-of-the-art meta-modeling algorithms, a massive shrinkage of the initial input parameter space was possible. The parallel generation of a prognostic behavioral model, a so-called compact digital twin, for the resulting stresses in the AMR sensor enables the further usage and a future optimization of the remaining determining parameters.