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作 者:赵子豪 林述温[1] 赵国朋 Zhao Zihao;Lin Shuwen;Zhao Guopeng
出 处:《工程机械》2022年第7期74-83,I0017,共11页Construction Machinery and Equipment
基 金:福建省科技重大专项资助项目(2016HZ01010004)。
摘 要:针对现有挖掘机铲斗结构参数优化设计中,优化变量主要凭设计者经验选取导致优化效果欠佳,优化设计过程中需反复调用有限元软件进行结构应力分析,致使优化效率低等问题,提出一种基于知识引导的铲斗结构参数优化设计方法。以综合考虑铲斗结构轻量化和综合多工况下结构等强度最大化为优化目标,利用结构参数对铲斗结构件体积和综合多工况下最大应力值的灵敏度知识,指导优化变量的选择,采用基于样本的应力普查方法确定铲斗应力特征截面,并建立铲斗结构神经网络应力预测模型;在此基础上,针对现有遗传算法的局限性,构建优化过程知识引导的遗传寻优算法,并通过实例验证该方法的可行性。实例验证优化结果表明,与优化前相比,铲斗体积下降了14.14%,且铲斗各部位均满足结构强度要求。In the optimization design of bucket structure parameters of existing excavators,the optimization variables are mainly selected based on the designer’s experience,which leads to poor optimization results,and the optimization design process requires repeated calls to finite element software for structural stress analysis,resulting in low optimization efficiency,so a knowledgedirected optimization design method for bucket structure parameters is proposed.The objective of the optimization is to lightweight the bucket structure and to maximize the structural strength under comprehensive multiple working conditions,the sensitivity knowledge of the structural parameters to the volume of the bucket structure and the maximum stress value under comprehensive multiple working conditions is used to guide the selection of optimization variables,and the sample-based stress census method is used to determine the bucket stress characteristic section and to construct a neural network prediction model of the bucket structure.On this basis,due to the limitations of the existing genetic algorithms,a knowledge-directed genetic optimization algorithm for optimization process is constructed,and the feasibility of the method is verified by examples.The optimization results show that the bucket volume is reduced by 14.14%compared with that before the optimization,and all parts of the bucket meet the structural strength requirements.
关 键 词:挖掘机铲斗结构 代理模型 知识引导 智能优化设计
分 类 号:TU621[建筑科学—建筑技术科学]
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