Experimental Study on Multi-objective Optimization of EDM Small Hole Machining for TC4 Titanium Alloy

ZHANG Wenchao; WANG Shuai

doi:10.13433/j.cnki.1003-8728.20200598

Volume 42 Issue 1

Jan. 2023

Turn off MathJax

Article Contents

Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2023 > 42(1): 113-118

ZHANG Wenchao, WANG Shuai. Experimental Study on Multi-objective Optimization of EDM Small Hole Machining for TC4 Titanium Alloy[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 113-118. doi: 10.13433/j.cnki.1003-8728.20200598

Citation:

ZHANG Wenchao, WANG Shuai. Experimental Study on Multi-objective Optimization of EDM Small Hole Machining for TC4 Titanium Alloy[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 113-118. doi: 10.13433/j.cnki.1003-8728.20200598

Citation:

PDF( 2144 KB)

Experimental Study on Multi-objective Optimization of EDM Small Hole Machining for TC4 Titanium Alloy

doi: 10.13433/j.cnki.1003-8728.20200598

School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116034, Liaoning, China

Received Date: 2021-03-28
Publish Date: 2023-01-25

Abstract

Abstract

To improve the surface machining quality and machining efficiency of TC4 titanium alloy in EDM (Electrical discharge machining), the copper cylindrical electrode was selected to carry out EDM small hole machining experiment of TC4 titanium alloy. The orthogonal experiment method was adopted. Taking the relative electrode wear rate, surface roughness and material removal volume of workpiece as optimization objectives, the influences of the peak current, discharge voltage and discharge pulse width on the optimization objectives were analyzed. RBF (Radial basis function) neural network was used to train with the experimental data, and the prediction model between the discharge parameters and the optimization objectives was established. Taking the prediction model as the fitness function, the multi-objective optimization simulation was carried out by combining the genetic algorithm with the Skyline selection algorithm, and the optimal technical index was obtained. Finally, the multi-objective optimization verification experiment was carried out. The results show that when the peak current is 14 A, the maintenance voltage is 39 V/42 V, and the discharge pulse width is 102 μs/108 μs, the optimal machining results can be obtained, and the error between the optimal value and the experimental value is small.
- EDM,
- RBF neural network,
- genetic algorithm,
- Skyline selection algorithm,
- multi-objective optimization

FullText(HTML)

References(16)

References

[1]	PRADHAN B B, MASANTA M, SARKAR B R, et al. Investigation of electro-discharge micro-machining of titanium super alloy[J]. The International Journal of Advanced Manufacturing Technology, 2009, 41(11-12): 1094-1106 doi: 10.1007/s00170-008-1561-y
[2]	ZHANG S F, ZHANG W C, LIU Y, et al. Corrigendum to Study on the gap flow simulation in EDM small hole machining with Ti alloy[J]. Advances in Materials Science and Engineering, 2017, 2017: 7961763
[3]	王续跃, 胡辉, 梁延德, 等. 钛合金小孔电火花加工有限元仿真研究[J]. 中国机械工程, 2013, 24(13): 1738-1742+1748 doi: 10.3969/j.issn.1004-132X.2013.13.008 WANG X Y, HU H, LIANG Y D, et al. Finite element simulation of small-hole on titanium alloy drilled by EDM[J]. China Mechanical Engineering, 2013, 24(13): 1738-1742+1748 (in Chinese) doi: 10.3969/j.issn.1004-132X.2013.13.008
[4]	马文宇, 何云, 李志慧, 等. 整硬麻花钻刃型结构对加工钛合金切削性能的影响[J]. 硬质合金, 2021, 38(2): 133-140 https://www.cnki.com.cn/Article/CJFDTOTAL-YZHJ202102010.htm MA W Y, HE Y, LI Z H, et al. Influence of edge structure of solid carbide twist drill on cutting performance of machining titanium alloy[J]. Cemented Carbide, 2021, 38(2): 133-140 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YZHJ202102010.htm
[5]	李明辉. 电火花加工理论基础[M]. 北京: 国防工业出版社, 1989 LI M H. Fundamentals of EDM theory[M]. Beijing: National Defense Industry Press, 1989 (in Chinese)
[6]	宗晓明, 高飞, 权超健, 等. GCr15轴承钢电火花线切割工艺参数优化[J]. 轴承, 2020(7): 9-14 https://www.cnki.com.cn/Article/CJFDTOTAL-CUCW202007003.htm ZONG X M, GAO F, QUAN C J, et al. Optimization of WEDM process parameters for GCr15 bearing steel[J]. Bearing, 2020(7): 9-14 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CUCW202007003.htm
[7]	SHARMA N, RAJ T, JANGRA K K. Parameter optimization and experimental study on wire electrical discharge machining of porous Ni₄₀Ti₆₀ alloy[J]. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2017, 231(6): 956-970 doi: 10.1177/0954405415577710
[8]	王蕾, 郭鲁荻, 戴恩成. 基于灰色关联分析法的GH4169合金电火花线切割加工参数优化[J]. 航空精密制造技术, 2020, 56(2): 31-34 doi: 10.3969/j.issn.1003-5451.2020.02.008 WANG L, GUO L D, DAI E C. Parameter optimization of GH4169 alloy in WEDM based on gray correlation[J]. Aviation Precision Manufacturing Technology, 2020, 56(2): 31-34 (in Chinese) doi: 10.3969/j.issn.1003-5451.2020.02.008
[9]	王天姝. 钛合金电火花沉积涂层微观组织及工艺优化研究[D]. 大连: 大连交通大学, 2019 WANG T S. Study on microstructure and process optimization of electro-spark deposition coating on Titanium alloy[D]. Dalian: Dalian Jiaotong University, 2019 (in Chinese)
[10]	李兴莘, 张靖, 何宇, 等. 基于改进粒子群算法的微电网多目标优化调度[J]. 电力科学与工程, 2021, 37(3): 1-7 doi: 10.3969/j.ISSN.1672-0792.2021.03.001 LI X S, ZHANG J, HE Y, et al. Multi-objective optimization dispatching of Microgrid based on improved particle swarm algorithm[J]. Electric Power Science and Engineering, 2021, 37(3): 1-7 (in Chinese) doi: 10.3969/j.ISSN.1672-0792.2021.03.001
[11]	ASSARZADEH S, GHOREISHI M. Neural-network-based modeling and optimization of the electro-discharge machining process[J]. The International Journal of Advanced Manufacturing Technology, 2008, 39(5-6): 488-500 doi: 10.1007/s00170-007-1235-1
[12]	关书怀, 沈艳霞. 基于粒子群优化径向基函数神经网络的电力负荷预测[J]. 传感器与微系统, 2021, 40(5): 128-131 https://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ202105036.htm GUAN S H, SHEN Y X. Power load forecasting based on PSO RBF-NN[J]. Transducer and Microsystem Technologies, 2021, 40(5): 128-131 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ202105036.htm
[13]	丁小艳, 程俊, 丁德林. 基于神经网络和遗传算法的电沉积Ni-TiB₂复合镀层工艺参数优化[J]. 电镀与精饰, 2020, 42(11): 20-24 https://www.cnki.com.cn/Article/CJFDTOTAL-DYJI202011005.htm DING X Y, CHENG J, DING D L. Optimization of process parameters of electrodepositing Ni-TiB₂ composite coating based on neural network and genetic algorithm[J]. Plating & Finishing, 2020, 42(11): 20-24 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DYJI202011005.htm
[14]	MARTÍNEZ-ALVARADO R, ALBA G G H, LEYVA- BRAVO J, et al. Radial basis function neural network for modelling an electrical discharge machining drilling process[C]//2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). Ixtapa: IEEE, 2018: 1-6
[15]	王海艳, 王倩. 支持偏好度动态适应的Skyline服务选择方法[J]. 华中科技大学学报(自然科学版), 2014, 42(11): 91-96 https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG201411017.htm WANG H Y, WANG Q. Skyline service selection approach with dynamic adaptation to user preference degree[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2014, 42(11): 91-96 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG201411017.htm
[16]	HEIDARI M, EMADI S. Services composition in multi-cloud environments using the skyline service algorithm[J]. International Journal of Engineering, 2021, 34(1): 56-65