| Issue |
EPJ Web Conf.
Volume 367, 2026
Fifth International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2026)
|
|
|---|---|---|
| Article Number | 01005 | |
| Number of page(s) | 10 | |
| Section | Robotics Design and Control | |
| DOI | https://doi.org/10.1051/epjconf/202636701005 | |
| Published online | 29 April 2026 | |
https://doi.org/10.1051/epjconf/202636701005
Integrated FEA framework for chassis design and failure prediction in SAE supra vehicles
1 Undergrad student, Department of Mechanical Engineering, Presidency University, Bengaluru
2 Assistant Professor, Department of Mechanical Engineering, Presidency University, Bengaluru
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 29 April 2026
Abstract
This paper will offer a unified strategy to the optimization of the chassis of a Formula SAE Supra car and predict structural failures using machine learning and Finite Element Analysis (FEA). The work demonstrates that computer aided design and analysis used simultaneously are more effective in comparison with the old-fashioned trial and error approach, and they do not affect the high safety standards. The proposed framework evaluates the chassis under the frontal, side, and rear impact conditions under systematic FEA when 2000 N (2 kN) loads are applied to it and then the results of this assessment are used to evaluate the variability of various design variants. The three candidate steels considered in the analysis include AISI 1018, AISI 4130, and AISI 4140 so that the most appropriate material to use in the structure can be identified. The choice of AISI 4130 chromoly steel can be explained by the opportunity to provide maximum Von Mises stress of 367.81 MPa, peak equivalent strain of mm/mm and maximum deformation of 1.2004 mm during the specified load cases, which met the requirements of the strengths and deformations.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

