Version 1
: Received: 7 February 2024 / Approved: 8 February 2024 / Online: 8 February 2024 (07:16:52 CET)
How to cite:
Liao, S.; Zhong, L.; Yi, L. Enhancing Vehicle Lightweight Design Process with Genetic Algorithm-Optimized BP Neural Network and Topology Optimization: A Comprehensive Study. Preprints2024, 2024020474. https://doi.org/10.20944/preprints202402.0474.v1
Liao, S.; Zhong, L.; Yi, L. Enhancing Vehicle Lightweight Design Process with Genetic Algorithm-Optimized BP Neural Network and Topology Optimization: A Comprehensive Study. Preprints 2024, 2024020474. https://doi.org/10.20944/preprints202402.0474.v1
Liao, S.; Zhong, L.; Yi, L. Enhancing Vehicle Lightweight Design Process with Genetic Algorithm-Optimized BP Neural Network and Topology Optimization: A Comprehensive Study. Preprints2024, 2024020474. https://doi.org/10.20944/preprints202402.0474.v1
APA Style
Liao, S., Zhong, L., & Yi, L. (2024). Enhancing Vehicle Lightweight Design Process with Genetic Algorithm-Optimized BP Neural Network and Topology Optimization: A Comprehensive Study. Preprints. https://doi.org/10.20944/preprints202402.0474.v1
Chicago/Turabian Style
Liao, S., Li Zhong and Liu Yi. 2024 "Enhancing Vehicle Lightweight Design Process with Genetic Algorithm-Optimized BP Neural Network and Topology Optimization: A Comprehensive Study" Preprints. https://doi.org/10.20944/preprints202402.0474.v1
Abstract
Amid the swift progression of the global automobile industry, the aspiration for lightweight vehicle design has surfaced as a crucial approach to boost fuel efficiency and curtail environmental impact. The objective of this study revolves around achieving holistic optimization of vehicle structure via detailed scrutiny of vehicle process characteristics, amalgamated with a Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) and a topology optimization (TO) method.The process initiates with an examination of the vehicle's technical characteristics, succeeded by an optimization protocol that couples GA with BPNN. This exhaustive optimization procedure derives the best-fit values of design parameters and identifies the globally optimal lightweight design approach. Subsequently, a comprehensive structural optimization is actualized by modifying the topological distribution of the structure.A simulation experiment of the conceptual car body beam skeleton is conducted as a conclusive step, and the anti-collision capacity of the car body beam skeleton is assessed by measuring deformation intrusion. The findings reveal that in the vehicle topology optimization, a comparative analysis of pre and post-optimization results indicates an improved topological density across diverse sections. The topological density of the vehicle's frontal structure escalates from 0.75 to 0.8. Following optimization, the stiffness assessment records a marginal increase (within the range of 0.02–0.05), implying that the lightweight design does not compromise the general rigidity of the structure.The optimized body structure manifests superior crashworthiness in the crash test: the deformation intrusion diminishes by 27% in front-end collisions. In side impacts, the deformation and intrusion at critical points are reduced by 13.15% to 10% respectively. The comprehensive method proposed herein proves to be an efficient strategy to actualize the lightweight design of vehicles.
Keywords
lightweight design of vehicle technology; genetic algorithm; BP neural network; topology optimization; design parameter optimization
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
