湖南大学学报（自然科学版）2024，Vol.51Issue(2)：163-176,14.DOI:10.16339/j.cnki.hdxbzkb.2024155
基于BS-TabNet和LSSA的车架智能轻量化设计
Intelligent Lightweight Chassis Design Based on BS-TabNet and LSSA
摘要
Abstract
In order to address the issues of long design cycles,high design complexity,and excessive reliance on engineer experience in traditional lightweight design of tractor chassis,an intelligent lightweight design method is proposed.Firstly chassis performance data is obtained through Design of Experiments(DOE)combined simulation.Then,the BS-TabNet model is constructed based on the TabNet algorithm,Bayesian optimization algorithm,and SHapley Additive exPlanation(SHAP)theory.This model is used to learn from the chassis performance data and generate a surrogate model for the chassis.Finally,the Levy flight strategy is applied to improve the Sparrow Search Algorithm(SSA),resulting in the Levy Sparrow Search Algorithm(LSSA),which is used to solve the lightweight design task and find the optimal structural parameters for the chassis.Compared with traditional machine learning algorithms,the BS-TabNet model shows higher accuracy,stability,and interpretability.Its accuracy reaches around 0.98,stability is improved by over 50％,and it has stronger interpretability,addressing the poor performance of deep learning on tabular data.Compared with traditional swarm intelligence optimization algorithms,the LSSA algorithm can obtain better optimization results.While meeting other performance requirements,it achieves a 5.64％reduction in chassis weight.The intelligent lightweight design method combines artificial intelligence with chassis lightweight design,and it can save a significant amount of design time and improve design efficiency.关键词
深度学习/贝叶斯优化/车架设计/TabNet/SHAP/SSAKey words
deep learning/Bayesian optimization/chassis design/TabNet/SHAP/SSA分类
交通工程引用本文复制引用
聂昕,刘文涛,陈少伟,张承霖,陈勇,杨昊..基于BS-TabNet和LSSA的车架智能轻量化设计[J].湖南大学学报（自然科学版）,2024,51(2):163-176,14.基金项目
广西区科技计划重大专项(桂科AA23062072),Guangxi Science and Technology Major Program(AA23062072) （桂科AA23062072）
柳州市科技计划重大专项(2022ABA0104 （）
2022ABB0101),Liuzhou Science and Technology Major Program(2022ABA0104 （）
2022ABB0101) （）
国家重点研发计划资助项目(2019YFB1706504),National Key Research and Development Program of China(2019YFB1706504) （2019YFB1706504）
