Finite Element Modelling and Optimisation of Structural Components for Lightweight Automotive Design

Abstract

Lightweight design in the automotive industry plays an important role in improving fuel efficiency and reducing greenhouse gas emissions. This study uses Finite Element Modeling (FEM) to analyze and optimize vehicle structural components to achieve weight reduction without sacrificing mechanical strength. Simulation results show that topology optimization can reduce component weight from 5.2 kg to 4.3 kg, equivalent to a reduction of 18%, while maintaining a safety factor above 1.5. In addition, the use of lightweight materials such as aluminium alloy (Al 7075-T6) and carbon fiber composites results in a weight reduction of up to 30%, while the maximum stress is reduced from 390 MPa to 380 MPa, and the maximum deformation is reduced from 1.82 mm to 1.75 mm. The validation of the FEM model shows a high level of accuracy, with the difference between simulation and experimental results being less than 5%, making this method reliable for structural performance prediction. However, challenges in the application of FEM include high computational costs and limitations in handling complex operational conditions. As a further development, Machine Learning (ML) based approaches can improve the efficiency of the optimization process, with previous studies showing that the combination of FEM and ML can reduce computational time by up to 40%. Thus, this research provides strategic insights in the development of lighter, more efficient and sustainable vehicles.