基于粒子群算法的模具工艺优化

Huagang Liu, Zhixin Feng, Ruican Hao
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引用次数: 0

摘要

刀具铣削过程中切削参数的优化有利于生产过程的质量控制。提出了一种基于粒子群算法的压铸工艺参数优化方法。在总结了相对体积、铣削力和变形控制参数对传统算法的优点的基础上,采用二分迭代法,分别对角铣削工艺进行进给参数优化,并考虑了三种进给参数优化算法在粗加工、精加工中对加工效率和铣削力限制的约束条件;优化后选择最大包络参数和最小包络曲线进料进行进料。最后,将粒子群优化算法引入到传统优化结果的优化中,得到两次最优进给量。仿真结果表明,优化粗加工进给参数后,加工时间缩短了32.6%,且铣削力最大值始终在铣削工艺允许范围内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of Die Mold Process Based on Particle Swarm Optimization
The optimization of cutting parameters in the milling process of cutting tool is helpful to the quality control of the production process. Based on particle swarm optimization (PSO) algorithm, a method for optimizing the parameters of die casting process is presented. Based on the summary of the relative volume, the milling force and the deformation control parameters on the advantages of the traditional algorithm, using dichotomy iteration, respectively on the corner milling process for feed parameter optimization, and considering the three kinds of feed parameter optimization algorithms for rough machining, finish machining in machining efficiency and milling force limit the constraint conditions, select the maximum envelope parameters and minimum envelope curve feed for feeding after optimization. Finally, the particle swarm optimization algorithm is introduced into the optimization of the traditional optimization results, and the optimal feed quantity of two times is obtained. The simulation results show that the processing time is reduced by 32.6% after the optimization of the feed parameters of rough machining, and the maximum of milling force is always within the allowable range of the milling process.
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