Design Optimization in Automotive Product Development Using AI/ML Algorithms

The use of physical testing and simulation process for product development of effective design is a well-established practice in industry. Engineers rely on this process to explore the feasible design space and optimize product for future design configurations. Designers and researchers can use the data from existing designs to develop future designs. In this context, this paper presents an effective solution by using physical test and simulation data for day-to-day design problems such as Plastic Snap design and Chassis Section deflection. A high degree of accuracy is achieved in validating test data using machine learning algorithms. By using ML algorithms in product design, we can optimize the parameters in a shorter time & reduce development cost.