Multi-objective optimization design for anti-fatigue lightweight of dump truck carriage combined with machine learning

IF 2.1 4区 工程技术
Kejun Lan, Wenyan Yu, Chengjie Huang, Yongjian Zhou, Zihang Li, Wei Huang
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Abstract

As urbanization continues to accelerate, dump trucks assume an increasingly important role in the transportation and construction of infrastructure. The carriage represents a critical structural assembly of dump trucks. One of the primary failure modes of the carriage is weld fatigue failure, which frequently gives rise to the problem of weld fatigue cracking during transportation. To increase the fatigue life of welds and enhance the degree of structural lightweight of a heavy dump truck carriage, a method for anti-fatigue lightweight design based on machine learning and multi-objective optimization is proposed. A high-fidelity finite element model of the carriage is established for static simulation analysis of the typical conditions. Based on the virtual reliability simulation test of the dump truck and the equivalent structural stress method, the fatigue life of the critical welds in the carriage is calculated. The important part thicknesses are selected as design variables through the comprehensive contribution analysis method. The maximum displacement and maximum stress under the dangerous condition are considered as constraints. The mass of the carriage and the minimum fatigue life of the critical welds are considered as optimization objectives. The GA-XGBoost machine learning approximation models (GA-XGBoost-MLAM) and NSGA-II algorithm are employed for multi-objective optimization design of the carriage. The entropy weighted TOPSIS method is utilized for multi-objective decision-making of Pareto solutions. The design after optimization and decision-making shows that, while satisfying the requirements of static structural performance, the minimum fatigue life mileage of the critical welds of the carriage is increased by 157,570 km, representing an increase of 36.58%. Additionally, the mass of the carriage is reduced by 295.69 kg, representing a decrease of 9.47%. Therefore, the proposed design method achieves a good effect in the anti-fatigue lightweight of dump truck carriage.
结合机器学习的自卸车车厢抗疲劳轻量化多目标优化设计
随着城市化进程的不断加快,自卸车在基础设施的运输和建设中发挥着越来越重要的作用。车厢是自卸车的关键结构组件。车厢的主要失效模式之一是焊缝疲劳失效,在运输过程中经常出现焊缝疲劳开裂的问题。为了提高焊缝的疲劳寿命,提高重型自卸车车厢的结构轻量化程度,提出了一种基于机器学习和多目标优化的抗疲劳轻量化设计方法。建立了车厢的高保真有限元模型,用于典型工况的静态仿真分析。基于自卸车虚拟可靠性模拟试验和等效结构应力法,计算了车厢关键焊缝的疲劳寿命。通过综合贡献分析方法,选择重要零件厚度作为设计变量。危险条件下的最大位移和最大应力被视为约束条件。滑块质量和关键焊缝的最小疲劳寿命被视为优化目标。采用 GA-XGBoost 机器学习近似模型(GA-XGBoost-MLAM)和 NSGA-II 算法对滑块进行多目标优化设计。采用熵权 TOPSIS 法对帕累托方案进行多目标决策。优化和决策后的设计结果表明,在满足静态结构性能要求的同时,车厢关键焊缝的最小疲劳寿命里程增加了 157570 公里,增加了 36.58%。此外,车厢质量减少了 295.69 千克,降低了 9.47%。因此,所提出的设计方法在自卸车车厢抗疲劳轻量化方面取得了良好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Advances in Mechanical Engineering
Advances in Mechanical Engineering Engineering-Mechanical Engineering
自引率
4.80%
发文量
353
期刊介绍: Advances in Mechanical Engineering (AIME) is a JCR Ranked, peer-reviewed, open access journal which publishes a wide range of original research and review articles. The journal Editorial Board welcomes manuscripts in both fundamental and applied research areas, and encourages submissions which contribute novel and innovative insights to the field of mechanical engineering
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