These slides were presented at the NAFEMS World Congress 2025, held in Salzburg, Austria from May 19–22, 2025.

Abstract

Sustainability is a critical challenge for industries today, primarily due to the extensive use of plastic in component design. Companies are actively seeking ways to minimize plastic consumption by optimizing component thickness through Finite Element Analysis (FEA). Fuel tank is an important component of a vehicle that undergoes through different loading conditions. The key challenge lies in determining the ideal thickness distribution that can withstand desired loading conditions while achieving this optimization in shortest time possible. Many design iterations might be performed during early concept design stage that increases the go to market time of any product. Extrusion blow molding is very a non-linear and un-predictive simulation that requires Finite Element Analysis expertise. All the subsequent simulations are dependent on thickness distribution achieved at the end of Extrusion Blow Molding simulation. Recent advancements in data-driven technologies, particularly machine learning (ML), offer a promising solution to expedite the design exploration process while conserving computational resources. To address the challenge of exploring wider design space in short span of time, Dassault Systemes has developed a novel methodology that employs a parametric machine learning (ML) physics model to efficiently predict the thickness distribution after blow molding and stress contours of subsequent structural load cases. By leveraging a neural network-based model, the richness of 3D simulation results is maintained while significantly reducing execution time, facilitating quasi-interactive design exploration and optimization. The ML physics model undergoes an optimization loop to identify the optimum thickness distribution of a fuel tank to maintain sufficient thinning in EBM while maintaining safety standards of structural loading. Machine learning can help predict Finite Element Analysis validation results faster in early concept design stage to reduce overall product development cycle. This paper highlights the potential of combining advanced ML techniques with traditional simulation methods to achieve design optimization quickly and efficiently.