CAE Performance Prediction Using Machi Learning Model Based on Historical Data

Machine Learning applications are developed to disrupt product design methodology across all industries. Every design engineer would like to optimize his design at the concept stage only considering a few critical and essential load cases. The major challenge for the design engineer has not much simulation expertise required to prepare the CAE model, apply material properties, load case, solve and post-process to understand the CAE performance. Even, when the engineer has CAE expertise, it will take a considerable amount of time to prepare the CAE model, solve and post-process it. The objective of this work to predict the simulation results (stress/strain/ displacement/modal frequencies/fatigue life etc.) for a new design using a machine learning predictive model built based on historical simulation data, without going through traditional CAE simulation workflow. This also helps the engineer to understand the effect of each geometry/thickness/material parameter change influencing the CAE performance. We have developed a methodology to capture the geometric shape of any part/assembly with a unique shape vector to represent that geometry for machine learning model building. We have tries different supervised learning models (Linear Regressing, Deep Learning, Random Forest) based on historical simulation data (with variations in geometry, material parameters, thickness) for a component for modal analysis and Frequency Response load case. Our application will efficiently extract the shape vector from the geometry, extract the thickness and material property from the finite element solver deck, extract modal frequencies from the solver result file and finally, it will create a CSV file for the training machine learning model. The machine learning scored model will be used to predict the modal frequencies for any new design model. This same methodology can be extended to multiple domains and load cases to predict different results. This will significantly reduce the design iterations and improves the product development process to reach the final design.