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单晶硅成型加工刀具损耗建模分析与实验研究

辛彬 李淑娟

辛彬, 李淑娟. 单晶硅成型加工刀具损耗建模分析与实验研究[J]. 机械科学与技术, 2017, 36(4): 567-573. doi: 10.13433/j.cnki.1003-8728.2017.0412
引用本文: 辛彬, 李淑娟. 单晶硅成型加工刀具损耗建模分析与实验研究[J]. 机械科学与技术, 2017, 36(4): 567-573. doi: 10.13433/j.cnki.1003-8728.2017.0412
Xin Bin, Li Shujuan. Modeling and Experiments of Cutting Tool Loss in Monocrystalline Silicon Molding[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(4): 567-573. doi: 10.13433/j.cnki.1003-8728.2017.0412
Citation: Xin Bin, Li Shujuan. Modeling and Experiments of Cutting Tool Loss in Monocrystalline Silicon Molding[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(4): 567-573. doi: 10.13433/j.cnki.1003-8728.2017.0412

单晶硅成型加工刀具损耗建模分析与实验研究

doi: 10.13433/j.cnki.1003-8728.2017.0412
基金项目: 

国家自然科学基金项目(51575442)、陕西省自然基金项目(2016JZ011)及陕西省教育厅基金(2014SZS10-Z01)

详细信息
    作者简介:

    辛彬(1984-),博士研究生,研究方向为硬脆性材料加工与控制,xinbin1227@163.com

    通讯作者:

    李淑娟(联系人),教授,博士,shujuanli@xaut.edu.cn

Modeling and Experiments of Cutting Tool Loss in Monocrystalline Silicon Molding

  • 摘要: 放电成型加工中的刀具损耗直接影响着加工精度,在单晶Si放电加工可行性的基础上,分析了峰值电流、脉冲宽度以及脉冲间隔对刀具损耗的影响。采用RSM中的中心组合设计实验,建立了与峰值电流、脉冲宽度、脉冲间隔等工艺参数相关的刀具损耗预测模型,用Design-Expert 8.0对刀具损耗与工艺参数的2阶响应曲面进行了分析。方差分析结果表明,预测模型具有较好的拟合程度和适应性。采用满意度函数(DFA)获得了单晶Si放电加工刀具损耗的最佳工艺参数组合,最佳工艺参数下的实验结果与模型预测结果平均相对误差为5.1%。验证实验表明,该模型对刀具损耗的预测是准确的,并且能实现相应的半导体材料的放电成型加工过程中的刀具损耗预测。
  • [1] Snoeys R, Dauw D, Kruth J P. Improved adaptive control system for EDM processes[J]. CIRP Annals-Manufacturing Technology, 1980,29(1): 97-101
    [2] Snoeys R, Dauw D, Jennes M. Survey of EDM adaptive control and detection systems[J]. CIRP Annals-Manufacturing Technology, 1982,31(2): 483-489
    [3] Snoeys R, Dauw D F, Kruth J P. Survey of adaptive control in electro discharge machining[J]. Journal of Manufacturing Systems, 1983,2(2): 147-164
    [4] Rajurkar K P, Wang W M, Lindsay R P. A new model reference adaptive control of EDM[J]. CIRP Annals-Manufacturing Technology, 1989,38(1): 183-186
    [5] Yilmaz O, Eyercioglu O, Gindy N N Z. A user-friendly fuzzy-based system for the selection of electro discharge machining process parameters[J]. Journal of Materials Processing Technology, 2006,172(3): 363-371
    [6] 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
    [7] Sengottuvel P, Satishkumar S, Dinakaran D. Optimization of multiple characteristics of EDM parameters based on desirability approach and fuzzy modeling[J]. Procedia Engineering, 2013,64: 1069-1078
    [8] Mandal D, Pal S K, Saha P. Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II[J]. Journal of Materials Processing Technology, 2007,186(1-3): 154-162
    [9] Kumar S, Choudhury S K. Prediction of wear and surface roughness in electro-discharge diamond grinding[J]. Journal of Materials Processing Technology, 2007,191(1-3): 206-209
    [10] Pradhan M K, Biswas C K. Neuro-fuzzy and neural network-based prediction of various responses in electrical discharge machining of AISI D2 steel[J]. The International Journal of Advanced Manufacturing Technology, 2010,50(5-8): 591-610
    [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] Zhang L X, Jia Z Y, Wang F J, et al. A hybrid model using supporting vector machine and multi-objective genetic algorithm for processing parameters optimization in micro-EDM[J]. The International Journal of Advanced Manufacturing Technology, 2010,51(5-8): 575-586
    [13] 任大林,隋修武,杜玉红.基于SVM的电火花加工参数优化研究[J].机械科学与技术,2014,33(8):1167-1171 Ren D L, Sui X W, Du Y H. Study on the optimum processing parameter of EDM based on the SVM[J]. Mechanical Science and Technology for Aerospace Engineering, 2014,33(8): 1167-1171 (in Chinese)
    [14] Chen H R, Liu Z D, Huang S 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
    [15] Clijsters S, Liu K, Reynaerts D, et al. EDM technology and strategy development for the manufacturing of complex parts in SiSiC[J]. Journal of Materials Processing Technology, 2010,210(4): 631-641
    [16] Ojha N, Hösel T, Zeller F, et al. Major parameters affecting the electric discharge machining of non-conductive SiC[C]//Azcárate S, Dimov S, eds. Proceedings of the 10th International Conference on Multi-Material Micro Manufacture. Research Publishing, 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] 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: 889-896
    [19] 余剑武,段文,何利华,等.CuW70铜钨电极损耗试验研究[J].机械科学与技术,2015,34(7):1016-1018 Yu J W, Duan W, He L H, et al. Experimental investigation of CuW70 copper-tungsten electrode wear[J]. Mechanical Science and Technology for Aerospace Engineering, 2015,34(7): 1016-1018 (in Chinese)
    [20] Habib S S. Study of the parameters in electrical discharge machining through response surface methodology approach[J]. Applied Mathematical Modelling, 2009,33(12): 4397-4407
    [21] 李淑娟,万波,胡超.基于RSM的SiC单晶片切割过程多目标优化[J].机械科学与技术,2014,33(3):452-459 Li S J, Wan B, Hu C. Multiple objective optimization for SiC single crystal wafers cutting process based on response surface method[J]. Mechanical Science and Technology for Aerospace Engineering, 2014,33(3): 452-459 (in Chinese)
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出版历程
  • 收稿日期:  2016-01-12
  • 刊出日期:  2017-04-05

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