Experimental Research and Optimization of Process Parameters in the Electrical Discharge Machining of Monocrystalline Silicon

doi:10.3969/j.issn.1000-1093.2017.09.024

Abstract

Abstract: In order to solve the problem that the material removal rate, the surface roughness and the electrode loss cannot be simultaneously taken into account in electrical discharge machining, the influences of peak current, pulse width and pulse interval on the material removal rate, surface roughness and electrode loss in the electrical discharge machining of P-type monocrystalline silicon are analyzed through central composite design experiments. The response surface method is used to establish a second-order relational model of material removal rate, surface roughness and electrode loss. The results of variance analysis indicate that the proposed model has good fitting degree and adaptability. A process parameter optimization model is established by analyzing the constraints of the actual processing conditions on the process parameters to improve the material removal rate in the electrical discharge machining of monocrystalline silicon, and reduce both the surface roughness and the electrode loss, and the NSGA- II–based algorithm is designed to solve the optimization problems. The average relative errors of validation results of material removal rate, surface roughness and electrode loss under the condition of the optimal solution are 4.9%, 5.2% and 5.7%, respectively, compared with the theoretical optimal values. The verification tests show that the proposed algorithm can achieve the process parameters optimization of silicon materials in the electrical discharge machining. Key

Key words: manufacturingtechnologyandequipment, electricaldischargemachining, P-typemonocrystallinesilicon, materialremovalrate, surfaceroughness, electrodeloss, geneticalgorithm

CLC Number:

TG661

XIN Bin， LI Shu-juan， LI Yu-xi. Experimental Research and Optimization of Process Parameters in the Electrical Discharge Machining of Monocrystalline Silicon[J]. Acta Armamentarii, 2017, 38(9): 1854-1861.

References

［1］ Xia H，Kunieda M，Nishiwaki N. Removal amount difference between anode and cathode in EDM process[J]. International Journal of Electrical Machining，1996（1）：45-52.
[2］ DiBitonto D D，Eubank P T，Patel M R，et al. Theoretical models of the electrical discharge machining process. I. A simple cathode erosion model [J]. Journal of Applied Physics，1989，66（9）：4095-4103.
[3］ Snoeys R， Dauw D， Kruth J P. Improved adaptive control system for EDM processes[J]. CIRP Annals-Manufacturing Technology, 1980, 29(1): 97-101.
[4］ Snoeys R， Dauw D， Jennes M. Survey of EDM adaptive control and detection systems[J]. CIRP Annals-Manufacturing Technology, 1982, 31(2): 483-489.
[5］ Snoeys R， Dauw D， Kruth J P. Survey of adaptive control in electro discharge machining[J]. Journal of Manufacturing Systems, 1983, 2(2): 147-164.
[6］ Rajurkar K P， Wang W M， LindRay R P. A new model reference adaptive control of EDM[J]. CIRP Annals-Manufacturing Technology, 1989, 38(1): 183-186.
[7］郭成波，狄士春，韦东波，等.TC4钛合金电火花高效铣削加工效率研究[J].兵工学报,2015,36(11):2149-2156.
GUO Cheng-bo,DI Shi-chun,WEI Dong-bo,et al. Research on efficient electrical discharge milling of TC4 titanium alloy[J].Acta Armamentarii, 2015,36(11):2149-2156.(in Chinese)
[8］ Shabgard M R， Badamchizadeh M A， Ranjbary G，et al. Fuzzy approach to select machining parameters in electrical discharge machining (EDM) and ultrasonic-assisted EDM processes[J]. Journal of Manufacturing Systems, 2013, 32(1): 32-39.
[9］ Sengottuvel P， Ratishkumar S， Dinakaran D. Optimization of multiple characteristics of EDM parameters based on desirability approach and fuzzy modeling[J]. Procedia Engineering, 2013, 64(4): 1069-1078.

[10］ AsRarzadeh S， Ghoreishi M. Neural-network-based modeling and optimization of the electro-discharge machining process[J]. The International Journal of Advanced Manufacturing Technology, 2007, 39(5/6): 488-500.
[11］ Joshi S N， Pande S S. Intelligent process modeling and optimization of die-sinking electric discharge machining[J]. Applied Soft Computing, 2011, 11(2): 2743-2755.
[12］ Chen H R， Liu Z D， Huang R J，et al. Study of the mechanism of multi-channel discharge in semiconductor processing by WEDM[J]. Materials Science in Semiconductor Processing, 2015, 32: 125-130.
[13］ Uno Y, Okada A, Okamoto Y，et al. Wire EDM slicing of monocrystalline silicon ingot[C]∥Proceedings of ASPE 2000 Annual Conference. Scottsdale, AZ, US: ASPE, 2000:172-175.
[14］ Puertas I， Luis C J， Villa G. Spacing roughness parameters study on the EDM of silicon carbide[J]. Journal of Materials Processing Technology, 2005, 164/165(10): 1590-1596.
[15］ Luis C J， Puertas I， Villa G. Material removal rate and electrode wear study on the EDM of silicon carbide[J]. Journal of Materials Processing Technology, 2005, 164/165(10): 889-896.
[16］ Ojha N, Hoesel T, Zeller F，et al. Major parameters affecting the electric discharge machining of non-conductive SiC[C]∥Proceedings of the 10th International Conference on Multi-Material Micro Manufacture. Singapore: Reasearch Publishing Services, 2013:978-981.
[17］ Ji R J，Liu Y H，Zhang Y Z，et al. High-speed end electric discharge milling of silicon carbide ceramics[J]. Materials and Manufacturing Processes, 2011, 26(8): 1050-1058.
[18］ Yu B H， Lee H K， Lin Y X，et al. Study of wire electrical discharge machining for poly-silicon[C］∥Asian Symposium for Precision Engineering and Nanotechnology. Kokura, Japan: Asian Society for Precision Engineering and Nanotechnology, 2009: 170-174.
[19］ Habib R S. Study of the parameters in electrical discharge machining through response surface methodology approach[J]. Applied Mathematical Modelling, 2009, 33(12): 4397-4407.
[20］巩亚东，孙瑶奚，刘寅.低速单向走丝电火花线切割钛合金TC4表面粗糙度试验研究与建模[J].兵工学报,2016,37(6): 1058-1065.
GONG Ya-dong,SUN Yao-xi,LIU Yin. Experimental investigation and modeling of three-dimensional surface roughness in LS-WEDM of TC4[J]. Acta Armamentarii, 2016,37(6):1058-1065.(in Chinese)
[21］ Deb K，Pratap A，Agarwal S，et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.

第38卷
第9期2017 年9月兵工学报ACTA
ARMAMENTARIIVol.38No.9Sep.2017

[1]	SU Sheng, GU Sen, SONG Zhiqiang, LIU Ping. Military Cockpit Color Design Method Based on Deep Representation Learning and Genetic Algorithm [J]. Acta Armamentarii, 2024, 45(4): 1060-1069.
[2]	MA Ye， FAN Wenhui， CHANG Tianqing. Optimization Method of Unmanned Swarm Defensive Combat Scheme Based on Intelligent Algorithm [J]. Acta Armamentarii, 2022, 43(6): 1415-1425.
[3]	WANG Hexiang， MAO Xiaodong， PANG Liping， FENG Xiaohan. Design on Layout Optimization of Air Supply System for Special Vehicle Cabin Based on Multi-objective Genetic Algorithm [J]. Acta Armamentarii, 2022, 43(12): 2981-2988.
[4]	ZHAO Pengcheng, SONG Baowei, MAO Zhaoyong, DING Wenjun. Path Planning for UUV Underwater Recovery based on Improved Composite Adaptive Genetic Algorithm [J]. Acta Armamentarii, 2022, 43(10): 2598-2608.
[5]	TANG Runzhi, BAN Liming, QIAN Le, LEI Zhengjun, SONG Weixing. Optimization of Vehicle Maintenance Process Scheduling under Limited Resources [J]. Acta Armamentarii, 2021, 42(9): 2032-2039.
[6]	SI Jiyue， PANG Zhaojun， YOU Meng， FENG Guangbin， DU Zhonghua. Structural Design and Optimization of Space Tether-net [J]. Acta Armamentarii, 2021, 42(5): 1065-1073.
[7]	FENG Kuikui， ZHANG Faping， WANG Wuhong， ZHANG Wenjie， ZHANG Tianhui. Assembly Process Parameters Optimization of Turbine Engine Rotor System Based on Non-dominated Sorting Genetic Algorithm [J]. Acta Armamentarii, 2021, 42(5): 1092-1100.
[8]	GAO Ziyin， DU Minggang， LI Shenlong， LI Jin. Design of Shifting Rules of Automatic Transmission Based on Genetic Algorithm Optimization and Fuzzy Control DynamicOptimization [J]. Acta Armamentarii, 2021, 42(4): 684-696.
[9]	WANG Shaohua， L Huiqiang， DONG Yuansheng， ZHANG Yuan. Optimal Assignment of Accompanying Rush-repair Tasks of Equipment [J]. Acta Armamentarii, 2021, 42(1): 192-198.
[10]	LIU Qiu, SUN Jinwei, ZHANG Hua, HU Xu, GU Liang. Road Identification and Semi-active Suspension Control Based on Convolutional Neural Network [J]. Acta Armamentarii, 2020, 41(8): 1483-1493.
[11]	YE Haichao, FAN Wulin, QIN Guohua, LIN Feng, ZHENG Xu, YANG Yang. Optimization and Experimental Validation of Aluminum Alloy Plate Hot-rolling Based on Constraint of Warpage Threshold [J]. Acta Armamentarii, 2020, 41(8): 1623-1632.
[12]	YANG Guanjinzi， LI Jianchen， HUANG Hai， GUO Linna. Non-turntable Field-calibration Method for Gyroscope Based on Optimal Analysis [J]. Acta Armamentarii, 2020, 41(3): 577-584.
[13]	ZHAO Xingyu, BAI Chunhua, YAO Jian, SUN Binfeng. Parameters Calculation of JWL EOS of FAE Detonation Products [J]. Acta Armamentarii, 2020, 41(10): 1921-1929.
[14]	ZHANG Fuyi, WU Qin, ZHAO Xiaoyang, LIU Ying, WANG Guoyu. Multi-objective Optimization of Inlet Duct of Water-jet Propulsion Based on Response Surface Method [J]. Acta Armamentarii, 2020, 41(10): 2071-2080.
[15]	GONG Zhihua， DUAN Pengwei， LIU Yang， CHEN Chunjiang， L Haidong. Application of Multi-sample Genetic Algorithm in Weapon Exterior Ballistics Networking Test [J]. Acta Armamentarii, 2019, 40(7): 1503-1510.