Automated Parameterization of CAE Model Based on Test Data Response Bands

This study presents a data-driven approach aimed at enhancing the correlation between physical test data and Computer-Aided Engineering (CAE) simulations, with an emphasis on adapting the standard CAE model's response to minimize any gaps relative to the response of a given test specimen. Leveraging historical test data, machine learning techniques are used to categorize responses into distinct bands, effectively capturing the inherent variability observed in real-world scenarios. This categorization step recognizes patterns across a wide range of test data, forming the foundation for closely matching and adapting CAE models to new, unseen hardware data. In typical automotive simulation workflows, tuning a standard CAE model to match new hardware test data involves iterative parameter adjustments and simulations. This process can be time-consuming and often lacks predictive insight into the necessary modifications. The approach developed in this study addresses this challenge by systematically modifying a baseline CAE model to represent the test response of new hardware. Our approach leverages machine learning algorithms in two key stages: • Classification: Identify the closest matching response band for incoming new test data based on historical patterns. • Parameter optimization: Integrate Design of Experiment (DOE) with machine learning to extract optimal set of parameters from the standard CAE model, ensuring alignment with the identified test response category. This targeted adaptation enables the generation of a modified CAE model that closely replicates the behaviour observed in new test data. By reducing the gap between predicted and actual test responses, this process enhances the accuracy and efficiency of simulation-driven engineering. The result is a more reliable and automated method for aligning CAE models with physical test data, supporting faster development cycles and improved decision-making in product design and validation.