线切割加工中切缝宽度与材料去除率的优化研究

张艳明; 张国军; 徐中; 陈志; 黄浩

doi:10.13433/j.cnki.1003-8728.2017.0812

线切割加工中切缝宽度与材料去除率的优化研究

doi: 10.13433/j.cnki.1003-8728.2017.0812

张艳明¹,
张国军^1,2, ,,
徐中¹,
陈志¹,
黄浩¹

1.
华中科技大学机械科学与工程学院, 武汉 430074;
2.
广东省制造装备数字化重点实验室, 广东东莞 523000

基金项目:

国家自然科学基金项目面上项目（51175207）与广东省省级科技科技计划项目（2013B091602001）资助

详细信息

作者简介:
张艳明(1993-),硕士研究生,研究方向为电火花线切割,优化算法,13554259541@163.com

通讯作者:
张国军(联系人),教授,博士,zgj@mail.hust.edu.cn

计量
- 文章访问数: 306
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- PDF下载量: 15
- 被引次数: 0
出版历程
- 收稿日期: 2016-03-31
- 刊出日期: 2017-08-05

Study on Optimization of Cutting Width and Material Removal Rate in WEDM

1.
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;
2.
Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Guangdong Dongguan 523000, China

摘要

摘要: 在精密制造业中，切缝宽度对尺寸精度的影响尤为显著，而材料去除率是影响加工效率的最重要指标，其与切缝宽度之间关系复杂且相互制约，一组加工参数难以同时获得较小的切缝宽度和较高的材料去除率。针对此问题，运用BP神经网络与粒子群算法（PSO）的混合算法建立多目标预测优化模型；以Ti6Al4V合金为实验对象，以水压、脉冲时间、脉冲间隙、伺服电压和电极丝张力为工艺参数，以切缝宽度（Kerf）和材料去除率（MRR）为工艺目标，设计田口实验。结果显示，Kerf和MRR的预测平均相对误差分别为5.32%和6.14%，优化得到单目标和多目标最优工艺参数，Kerf同比降低11.10%，MRR同比提高27.37%，表明对切缝宽度和材料去除率的预测与参数优化效果显著。
- 慢走丝线切割 /
- 切缝宽度 /
- BP神经网络 /
- 粒子群算法 /
- 多目标优化
Abstract: Cutting width and the material removal rate in LS-WEDM (low speed wire electrical discharge machining) is important processing indicators. In precision manufacturing, the cutting width particularly influences on the dimensional accuracy, and the material removal rate is the most important indicator influencing the processing efficiency, which has a complex and mutual restraint relationship with cutting width, a set of processing parameters is difficult to obtain smaller cutting width and higher material removal at the same time. To solve this problem, multi-objective optimization model is established by using BP (Back Propagation)neural network and particle swarm optimization (PSO), and in which Ti6Al4V alloy is took as testing object, water pressure, pulse-on time, pulse-off time, servo voltage and wire tension are took as testing parameters, the material removal rate (MRR) and the cutting width (Kerf) are took as processing indicators via Taguchi experiment. The results illustrate the average relative error of Kerf and MRR are of 5.32% and 6.14% respectively. Meanwhile the single-objective and multi-objective optimal processing parameters are obtained, and Kerf decreases of 11.10%, MRR increases of 27.37% comparing with the previous results. The present multi-objective optimization model has a significant effect for predicting and optimizing cutting width and material removal rate.
- LS-WEDM /
- cutting width(Kerf) /
- BP neural network /
- particle swarm optimization (PSO) /
- multi-objective optimization

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参考文献(15)

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