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Volume optimization of gear trains with spur gears using genetic algorithm

机译:采用遗传算法与齿轮齿轮齿轮训练量优化

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Gear train volume optimization presents a complex problem tied to practical application in gear train manufacturing. This paper is oriented on the analysis of the problem of gear train volume minimization from a shaft axes positioning aspect. An original mathematical model has been developed where the objective function gives a minimum volume with changed shaft (spur gear) axes positions, while at the same time complying with all physical constraints. An original optimization software has also been developed using RCGA (Real Coded Genetic Algorithm) optimization methods. The general mathematical model was applied to three real conceptions of gear train as well as a comparative analysis of initial and optimal values. The results show a decrease of volume being directly linked to a decrease of not only space but material used to make the housing, costs, documentation formulation rate, etc.
机译:齿轮系卷优化呈现出与齿轮系制造业的实际应用相关的复杂问题。本文以轴轴定位方面的齿轮卷积最小化问题分析。已经开发了一个原始的数学模型,其中目标函数给出了轴(正齿轮)轴位置的最小体积,同时遵守所有物理约束。还使用RCGA(实际编码遗传算法)优化方法开发了原始优化软件。将军数学模型应用于齿轮系的三个真正的概念以及对初始和最佳值的比较分析。结果表明,卷的减少直接与不仅是空间的减少,而是用于制造房屋,成本,文件制定率等的材料。

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