首页|基于PSO的FeCoNiCrMn高熵合金微铣削参数优化

基于PSO的FeCoNiCrMn高熵合金微铣削参数优化

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为了分析FeCoNiCrMn高熵合金的微铣削加工特性,采用正交试验法,探究主轴转速、每齿进给量、铣削深度等铣削参数对其微铣削力的影响.通过多元线性回归分析方法,建立FeCoNiCrMn高熵合金各向微铣削力的预测模型.以FeCoNiCrMn高熵合金加工时的铣削力和加工效率为优化目标,建立多目标优化模型,并通过粒子群优化算法对试验铣削参数进行优化.结果表明:采用粒子群算法优化后的铣削参数加工不仅降低了合金的铣削力,同时材料去除率提高了7.55%;在加工FeCoNiCrMn高熵合金时,应选择较大的主轴转速和每齿进给量以及较低的铣削深度,以有效降低铣削力并提高加工效率.
Optimization of Micro-milling Parameters for FeCoNiCrMn High Entropy Alloy Based on PSO
In order to analyze the micro-milling machining characteristics of FeCoNiCrMn high entropy alloy,orthogonal test meth-od was used to investigate the influence of milling parameters such as spindle speed,feed per tooth and milling depth on its micro-mill-ing force.The prediction model of micro-milling force of FeCoNiCrMn high entropy alloy in all directions was established by multiple linear regression analysis.Taking the milling force and machining efficiency during the machining of FeCoNiCrMn high entropy alloy as the optimization objectives,a multi-objective optimization model was established,and the experimental milling parameters were opti-mised by particle swarm optimization algorithm.The results show that the optimised milling parameters using particle swarm algorithm not only reduce the milling force of the alloy,but also increase the material removal rate by 7.55%;when machining FeCoNiCrMn high entropy alloy,a larger spindle speed and feed per tooth as well as a lower depth of milling should be selected in order to effectively re-duce the milling force and improve the machining efficiency.

FeCoNiCrMn high entropy alloymicro-millingparameter optimizationparticle swarm optimization algorithm

李潭、彭宝营、王鹏家、庞英杰

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北京信息科技大学机电工程学院,北京 100192

FeCoNiCrMn高熵合金 微铣削 参数优化 粒子群优化算法

2024

机床与液压
中国机械工程学会 广州机械科学研究院有限公司

机床与液压

CSTPCD北大核心
影响因子:0.32
ISSN:1001-3881
年,卷(期):2024.52(23)