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基于多目标遗传算法的爬壁小车底板优化
Optimization of Wall-climbing Car Floor Based on Multi-objective Genetic Algorithm
【摘要】 为保证大型立式储油罐处于安全运行状态,需使用爬壁小车对其进行定期检查,而小车底板的自重将影响小车的吸附力及爬行高度。为实现小车底板轻量化设计,应用Solidworks建立了小车底板的多目标优化数学模型,基于多目标遗传算法对模型进行计算,求解出最优设计点,实现了爬壁小车模型的轻量化设计。
【Abstract】 To ensure the safe operation of large vertical storage tanks, regular volume inspections are required and accomplished by a wall-climbing trolley with load capacity, but trolly’s bottom plate weight affectes adsorption forcec and crawling height. To achieve the lightweight design of the wall-climbing trolley floor, a multi-objective optimization mathematical model for the trolley floor was established by Solidworks and caculated based on multi-objective genetic algorithm to solve the optimal design point and realize the model lightweight.
【关键词】 爬壁小车;
轻量化;
多目标优化;
遗传算法;
【Key words】 wall-climbing car; lightweight; multi-objective optimization; genetic algorithm;
【Key words】 wall-climbing car; lightweight; multi-objective optimization; genetic algorithm;
- 【文献出处】 机械制造与自动化 ,Machine Building & Automation , 编辑部邮箱 ,2021年04期
- 【分类号】TH18;TH122
- 【下载频次】381