Cutting Parameters Optimization of Titanium Alloy Milling Process Based on Support Vector Machine and Particle Swarm Algorithm

XIANG Guoqi; YIN Guofu

doi:10.16080/j.issn1671-833x.2016.23/24.100

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Cutting Parameters Optimization of Titanium Alloy Milling Process Based on Support Vector Machine and Particle Swarm Algorithm

更新时间：2025-10-30

- Cutting Parameters Optimization of Titanium Alloy Milling Process Based on Support Vector Machine and Particle Swarm Algorithm
- Aeronautical Manufacturing Technology Vol. 59, Issue 23/24, (2016)
- 作者机构：
  
  1. 攀枝花学院资源与环境工程学院,攀枝花,617000
  2. 四川大学制造科学与工程学院,成都,610054
- 作者简介：
- 基金信息：
- DOI：10.16080/j.issn1671-833x.2016.23/24.100
  CLC：
- Published：2016
- 稿件说明：
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向国齐，殷国富. 基于支持向量机和粒子群算法的钛合金铣削加工参数优化[J]. 航空制造技术, 2016, 59(23/24): 100-103. XIANG Guoqi1， YIN Guofu2. Cutting Parameters Optimization of Titanium Alloy Milling Process Based on Support Vector Machine and Particle Swarm Algorithm. Aeronautical Manufacturing Technology, 2016, 59(23/24): 100-103.

XIANG Guoqi, YIN Guofu. Cutting Parameters Optimization of Titanium Alloy Milling Process Based on Support Vector Machine and Particle Swarm Algorithm[J]. Aeronautical Manufacturing Technology, 2016, 59(23/24).
向国齐，殷国富. 基于支持向量机和粒子群算法的钛合金铣削加工参数优化[J]. 航空制造技术, 2016, 59(23/24): 100-103. XIANG Guoqi1， YIN Guofu2. Cutting Parameters Optimization of Titanium Alloy Milling Process Based on Support Vector Machine and Particle Swarm Algorithm. Aeronautical Manufacturing Technology, 2016, 59(23/24): 100-103. DOI： 10.16080/j.issn1671-833x.2016.23/24.100.

XIANG Guoqi, YIN Guofu. Cutting Parameters Optimization of Titanium Alloy Milling Process Based on Support Vector Machine and Particle Swarm Algorithm[J]. Aeronautical Manufacturing Technology, 2016, 59(23/24). DOI： 10.16080/j.issn1671-833x.2016.23/24.100.

摘要

钛合金材料在加工过程中受铣削力影响易于产生变形而影响加工效果，为保证加工质量，提高生产效率及降低加工成本，研究切削加工参数的合理选择非常重要。对钛合金材料Ti6Al4V 铣削加工进行有限元数值计算，结合试验设计方法构建了基于支持向量机的切削力预测模型，以材料去除率为优化目标，提出了一种基于支持向量机和粒子群算法的优化方法，对钛合金铣削加工参数进行了优化。结果表明，该方法准确、高效、可行，为钛合金加工工艺参数优化提供一种新的方法，具有良好的推广价值。

Abstract

Titanium alloys are widely used in aviation fields

the processing quality of this materials will be affected by the milling force. In order to guarantee the machining quality

improve production efficiency and reduce cost

the cutting parameters of the titanium alloy are reasonable selected

which play an important role. In this paper

the titanium alloy Ti6Al4V milling process is analyzed by finite element method

a milling force prediction model is established based on support vector machine (SVM). The design methodology based on SVM and particle swarm optimization (PSO) is proposed for titanium alloy milling process cutting parameters. The results show that this methodology is feasible and highly effective

and thus can be used in the machining process parameters optimum and other material processing fields.

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