Physical testing and finite element simulation are widely used in the industry to develop effective product designs. Engineers rely on these procedures to explore feasible design options and optimize products for future configurations. Designers and researchers can also create even better solutions by leveraging data from previous designs.

In this free webinar, experts from KPIT demonstrate how to use physical testing and simulation data to address everyday design challenges in automotive applications, such as plastic snap design and chassis section deflection. The experts also explain how the incorporation of machine learning algorithms ensures a high degree of accuracy in validating test data, allowing for the optimization of design parameters in a shorter time and at a lower cost.

Key topics and takeaways:

• Gain insight into the application of machine learning in the product development lifecycle
• Understand how to utilize AI/ML algorithms for performance prediction
• Learn about the role of design optimization during the early stages of product development
• Discover use cases in automotive subsystem design