Technical Paper
A Combined Data Science and Simulation-Based Methodology for Efficient and Economic Prediction of Thermoplastic Performance for Automotive Industry
2023-04-11
2023-01-0936
There are significant predictive tool usages by design engineers in automotive industry to capture material composition and manufacturing process-induced variables. In specific, an accurate modeling of material behavior to predict the mechanical performance of a thermoplastic part is an evolving subject in this field as one needs to consider multiple factors and steps to achieve the right prediction accuracies. The variability in prediction comes from different factors such as polymer type (filled vs. unfilled, amorphous vs semi crystalline etc.), design and manufacturing features (weldline, gate locations, thickness, notches etc.), operating conditions (temperature, moisture etc.) and finally load states (tension, compression, flexural, impact etc.). Using traditional numerical simulation-based modelling to study and validate all these factors requires significant computational time and effort.