孙瑶, 巩亚东, 刘寅
东北大学 机械工程与自动化学院， 辽宁 沈阳 110819
收稿日期: 2015-06-23
基金项目: 国家自然科学基金资助项目( 51375082) ; 国家自然科学基金青年基金资助项目( 51205053).
作者简介: 孙 瑶 (1990-),女,辽宁锦州人,东北大学博士研究生;
巩亚东(1958-),男,辽宁本溪人,东北大学教授，博士生导师。

摘要: 以慢走丝电火花线切割加工钛合金TC4为试验对象，在正交试验的基础上，通过信噪比(signal to noise ratio, SNR)方法研究峰值电流、开路电压、脉冲宽度、走丝速度和丝张力对加工时间、切缝宽度和表面粗糙度的影响规律.采用灰色关联度分析方法将多目标参数优化转化为单目标灰关联度的优化，得到慢走丝电火花线切割TC4在多项工艺指标要求下的最优参数组合.多目标优化结果表明：在峰值电流为30 A，开路电压为100 V，脉冲宽度为20 μs，走丝速度为105 mm/s，丝张力为12 N时，表面粗糙度减小了8.35%，切缝宽度减少了2.59%，加工时间减小了26.06%.
关键词：慢走丝电火花线切割TC4信噪比灰色关联分析参数优化
Experimental Research and Parameter Optimization for Low Speed Wire Electrical Discharge Machining TC4
SUN Yao, GONG Ya-dong, LIU Yin
School of Mechanical Engineering&Automation, Northeastern University, Shenyang 110819, China.
Corresponding author: SUN Yao, E-mail:sy547515291@163.com
Abstract: LS-WEDM (low speed wire electrical discharge machining) TC4 was regarded as the research object, the influence of peak current, open circuit voltage, pulse width, wire speed and wire tension’s effects on process time, kerf width and surface roughness was studied by SNR (signal to noise ratio) method. The gray correlation analysis was applied to realize the transformation from multi-objective parameter optimization to signal objective optimization of grey correlation degree and the optimum machining parameter combination of LS-WEDM were obtained under the multiple technical index requirements. Multi-objective parameter optimization results show that when the peak current is 30 A, the open circuit voltage is 100 V, the pulse width is 20 μs, the wire speed is 105 mm/s and wire tension is 12 N, the surface roughness, kerf width and process time were reduced by 8.35%, 2.59% and 26.06%, respectively.
Key Words: LS-WEDMTC4SNRgray correlation analysisparameter optimization
慢走丝电火花线切割以其加工精度高、表面质量好和不受工件材料限制的加工特点成为机械加工领域不可替代的加工手段，但也存在加工效率低的弊端.钛合金TC4因具有高温力学性能优异、比强度高和失稳临界值高等优点而广泛应用于航空航天领域，但其强度高、导热性差和易变形特点使其成为一种典型难加工材料^[1].
慢走丝电火花线切割加工钢、铜和硬质合金等材料已相当成熟，但对于其他材料的线切割研究相对较少^[2].目前慢走丝电火花线切割机床系统中没有针对TC4材料的加工参数.本文在试验基础上,利用灰色关联分析和信噪比的方法探究各电参数和非电参数对慢走丝电火花线切割TC4的影响规律，从而得到兼顾加工效率、加工稳定性和加工精度的多目标工艺参数最优组合，并用试验验证这一最优组合的正确性.为提高慢走丝电火花线切割机床的加工效率和其他型号钛合金材料加工参数的选择提供参考依据.
1 试验设计1.1 试验原理、设备与材料慢走丝电火花线切割是利用脉冲放电去除多余材料以达到对零件尺寸和加工形状要求的非接触式加工^[3].在微观上大致分为去离子水介质击穿和形成放电通道、在TC4试件和黄铜丝间进行能量转换和传递、两极材料抛出和极间介质消电离等几个阶段.本文所用机床为阿奇夏米尔CA20，见图 1.厚度为(10±0.01)mm的TC4试件接正极，黄铜丝(直径d=0.2 mm)接负极并以一定速度(30~330 mm/min)沿着电极丝轴线方向根据预定的切割轨迹进行单向移动，不断进入和离开TC4试件窄缝内的放电加工区.工作液为去离子水，有利于排除熔融的蚀除物，从而提高加工速度^[4].加工时上导丝嘴与工件上表面距离为0.1 mm.
图 1(Fig. 1)

图 1 CA20试验加工过程Fig.1 The test process of CA20

1.2 试验设计切缝宽度决定慢走丝线切割的加工尺度范围和最终加工尺寸的精度，其受电极丝直径、电极丝横向振动和两侧放电间隙的影响^[5].采用基恩士公司的超景深三维立体显微系统VHX-1000E对加工后的切缝宽度进行测量，为准确测量出切缝宽度，本文进行二维测量，见图 2a，首次提出三维面间距的方法更准确测量出切缝宽度,见图 2b.
图 2(Fig. 2)

图 2 TC4切缝宽度Fig.2 Kerf width of TC4

在冲液压力为0.8 MPa，脉冲间隔为25 μs的条件下，以峰值电流(I)、脉冲宽度(t_i)、开路电压(U_HP)、走丝速度(v_AW)和丝张力(F_W)为慢走丝线切割加工TC4的5个工艺参数，每个参数选择4个水平，见表 1，以材料加工时间、表面粗糙度和切缝宽度为工艺指标，正交试验结果如表 2所示.
表 1(Table 1)

表 1 正交试验的因素和水平Table 1 Factors and levels of the orthogonal test 因素 符号 水平 1 2 3 4 I/A A 30 35 40 45 UHP/V B 50 65 85 100 ti/μs C 5 10 15 20 vAW/(mm·s-1) D 75 90 105 120 FW/N E 10 12 14 16

表 1 正交试验的因素和水平 Table 1 Factors and levels of the orthogonal test

表 2(Table 2)

表 2 L16(54)正交表和试验结果Table 2 L16 (54) orthogonal Table and test results 序号 A B C D E RS 切缝宽度 加工时间 γj 实测/μm SNR ξij 实测/μm SNR ξij 实测/min SNR ξij 1 1 1 1 1 1 1.072 -0.604 1 238.869 -47.563 0.636 22.983 -27.228 1 0.879 2 1 2 2 2 2 1.112 -0.922 0.817 238.986 -47.567 0.638 18.533 -25.359 0.901 0.785 3 1 3 3 3 3 1.193 -1.533 0.605 238.73 -47.558 0.633 12.867 -22.19 0.772 0.67 4 1 4 4 4 4 1.217 -1.706 0.564 242.363 -47.689 0.705 11 -20.828 0.727 0.665 5 2 1 2 3 4 1.208 -1.641 0.579 226.713 -47.11 0.47 12.867 -22.19 0.772 0.607 6 2 2 1 4 3 1.203 -1.605 0.587 225.671 -47.07 0.459 8.05 -18.116 0.652 0.566 7 2 3 4 1 2 1.223 -1.749 0.554 239.351 -47.581 0.645 7.1 -17.025 0.626 0.608 8 2 4 3 2 1 1.32 -2.411 0.441 241.131 -47.645 0.679 4.617 -13.287 0.55 0.557 9 3 1 3 4 2 1.207 -1.634 0.58 236.123 -47.463 0.59 8.867 -18.956 0.673 0.614 10 3 2 4 3 1 1.19 -1.511 0.611 235.481 -47.439 0.58 7.217 -17.167 0.629 0.607 11 3 3 1 2 4 1.203 -1.605 0.587 236.257 -47.468 0.592 3.533 -10.963 0.512 0.564 12 3 4 2 1 3 1.207 -1.634 0.58 242.76 -47.704 0.714 2.983 -9.493 0.49 0.595 13 4 1 4 2 3 1.253 -1.959 0.512 230.886 -47.268 0.517 7.217 -17.167 0.629 0.553 14 4 2 3 1 4 1.233 -1.819 0.54 234.583 -47.406 0.567 4.283 -12.635 0.539 0.548 15 4 3 2 4 1 1.193 -1.533 0.605 243.267 -47.722 0.726 3.1 -9.827 0.495 0.609 16 4 4 1 3 2 1.3 -2.279 0.459 252.073 -48.031 1 2.283 -7.17 0.459 0.64

表 2 L16(54)正交表和试验结果 Table 2 L16 (54) orthogonal Table and test results

慢走丝线切割加工后的表面不同于传统加工的表面，由无数个无方向性的凸边和凹坑组成.采用基于白光干涉原理的法国STIL三维轮廓仪对TC4试件表面进行非接触测量，得出表面粗糙度(R_s)，图 3为表面形貌和表面粗糙度值在线检测.
图 3(Fig. 3)

图 3 表面粗糙度检测Fig.3 The measurement of surface roughness

参考文献

[1]	KuriachenB, Somashekhar K P, Mathew. Multiresponse optimization of micro-wire electrical discharge machining process[J].International Journal of Advanced Manufacturing Technology, 2015, 76(1/2/3/4): 91–104.(0)
[2]	宋满仓, 解廷利, 杜立群, 等. 薄镍板上Meso尺度结构的电火花线切割[J].光学精密工程, 2013, 21(7): 1748–1754. ( Song Man-cang, Xie Ting-li, Du Li-qun, et al. WEDM of Meso-scale structure on thin nickel plate[J].Optics and Precision Engineering, 2013, 21(7): 1748–1754.)(0)
[3]	MukherjeeR, Chakraborty S, Samanta S. Selection of wire electrical discharge machining process parameters using non-traditional optimization algorithms[J].Applied Soft Computing, 2012, 12(8): 2506–2516.(0)
[4]	狄士春, 黄瑞宁, 迟关心, 等. 微细电火花线切割加工关键技术[J].机械工程学报, 2006, 42(1): 221–226. ( Di Shi-chun, Huang Rui-ning, Chi Guan-xin, et al. Key technology of micro wire electrical discharge machining[J].Chinese Journal of Mechanical Engineering, 2006, 42(1): 221–226.)(0)
[5]	Di S C, Chu X Y, Wei D B, et al. Analysis of kerf width in micro-WEDM[J].International Journal of Machine Tools&Manufacture, 2009, 49(10): 788–792.(0)
[6]	马致远, 杨洪英, 陈国宝, 等. 基于田口法的生物浸出液萃铜工艺的优化[J].东北大学学报(自然科学版), 2013, 34(7): 990–993. ( Ma Zhi-yuan, Yang Hong-ying, Chen Guo-bao, et al. Optimization of extraction of copper from bio-leaching solution based on the Taguchi methods[J].Journal of Northeastern University(Natural Science), 2013, 34(7): 990–993.)(0)
[7]	Tosun N, Cogunb C, Tosun G A. Study on kerf and material removal rate in wire electrical discharge machining based on Taguchi method[J].Journal of Materials Processing Technology, 2004, 152(3): 316–322.(0)
[8]	Shivade A S, Shinde V D. Multi-objective optimization in WEDM of D3 tool steel using integrated approach of Taguchi method&grey relational analysis[J].The International Journal of Industrial Engineering, 2014, 10(4): 149–162.(0)
[9]	Varun A, Venkaiah N. Simultaneous optimization of WEDM responses using grey relational analysis coupled with genetic algorithm while machining EN353[J].International Journal of Advanced Manufacturing Technology, 2015, 76(1/2/3/4): 675–690.(0)
[10]	Chiang K T, Chang F P. Optimization of the WEDM process of particle-reinforced material with multiple performance characteristics using grey relational analysis[J].Journal of Materials Processing Technology, 2006, 180(1/2/3): 96–101.(0)
[11]	Lin J L, Lin C L. The use of the orthogonal array with grey relational analysis to optimize the electrical discharge machining process with multiple performance characteristics[J].International Journal of Machine Tools&Manufacture, 2002, 42(2): 237–244.(0)