工程陶瓷WEDM加工工艺参数优化及表面粗糙度预测研究
|
摘要
在了解国内外电火花线切割(wire electrical discharge machining，简称WEDM)加工工程陶瓷材料现状的基础上，以常用的导电工程陶瓷材料Al_2O_3／TiC为研究对象，分析了电加工理论及工程陶瓷材料电加工表面粗糙度、表面残余应力和表面变质层的产生机理，探讨脉冲宽度、脉冲间隔、工作电压、功率管数等工艺因素对工程陶瓷材料电加工的影响。
研究了模糊推理知识和人工神经网络的理论基础，考虑到人工神经网络存在收敛速度慢，容易陷入局部极小值及全局搜索能力弱等问题，提出了模糊Modular算法来构建神经网络结构。本文采用模糊Modular神经网络，Taguchi(田口式)试验规划方法和标准变量分析技术(ANOVA)，对工程陶瓷材料电加工理论及试验进行研究，实现了工艺参数的优化，建立了工程陶瓷材料电加工表面粗糙度随工艺参数变化的预测模型，为实现WEDM高效精密加工工程陶瓷材料提供理论与技术支持。利用Taguchi(田口式)试验规划方法，对脉冲宽度、脉冲间隔、工作电压、功率管数等工艺因素进行正交试验排序，设计了L16正交试验矩阵。在不同的工况下，按照正交试验矩阵来进行工程陶瓷材料放电加工试验。试验过程中，为了排除噪声等干扰，引入信噪比S／N来对试验数据进行处理，以提高数据的准确性。运用标准变量分析技术(ANOVA)，分析了四个电参数对工程陶瓷材料电加工表面质量和加工速度的影响，并优化了工艺参数。
研究结果表明，在所选择的工艺因素中，对表面粗糙度的影响程度依次为工作电压(F=37.16％)、功率管数(F=27.7％)、脉冲间隔(F=19.0％)、脉冲宽度(F=16.14％)，而对加工速度的影响程度依次为脉冲间隔(F=44.42％)、功率管数(F=25.45％)、工作电压(F=20.71％)、脉冲宽度(F=9.42％)。在优化电加工工艺参数组合下，分别得到了优化的最小表面粗糙度(1.215μm)和最大加工速度(24.78 mm~2／min)，优化值与试验结果吻合得较好。电加工后工件表面残余应力均为拉应力，表面变质层组织疏松，强度较差，厚度一般为50～100μm。表面残余应力和表面变质层，都随着脉冲能量(脉冲宽度、功率管数、工作电压)的增加而增加，随着脉冲间隔的增加而减小。通过Weibull函数分析表明，经电加工后的陶瓷材料，其抗弯强度降低，威布尔系数低于11，表面质量和表面完整性变差，需要进行表面改性或强化处理。以正交试验数据为输入学习样本，试验和仿真结果表明，基于模糊Modulalr神经网络不仅可以克服单纯使用人工神经网络容易陷入局部极小值等问题，而且预测精度比较高，误差大约在7％之内，对工程陶瓷材料电加工获得较佳的表面粗糙度有着重要的指导意义，具有一定的实用价值。
Based on the realizing of national inside and outside engineering ceramics material of WEDM (Wire Electrical Discharge Machining), taking electric Al_2O_3/TiC as example, analyzing the theory of WEDM and the theory of surface roughness、surface remnant stress、surface decaying layer of engineering ceramics of WEDM,it studied on the effect of impulse width、impulse separation、work voltage、the number of power transistors on the engineering ceramics of WEDM.
studying faintness system and artifical neural networks, focusing on some disadvantages in neural networks algorithm, such as low convergence rate、easily falling into local minimum point and weak global search capability,the author used a new learning presented algorithm that used the faintness and Modular arithmetic to train neural networks, taguchi method and a standard analysis of variance (ANOVA) to study on theory of WEDM and the experiment. During the experiment, it introduced in S/N in order to deal with data and eliminate disturb.The prediction model of surface roughness in engineering ceramics of WEDM based on faintness Modular neural networks was proposed in detail.An L16 experimental matrix design based on Taguchi method was conducted to process Al_2O_3/TiC ceramics of electrical discharge machining.
Based on a standard analysis of variance (ANOVA),it has been found that the relative significance of each factor on surface roughness was arranged in decreasing order of work voltage(F=37．16%), the number of power transistors (F=27．7%), impulse separation(F=19．0%), impulse width (F=16．14%),while on process rate was arranged in decreasing order of impulse separation(F=44．42%),the number of power transistors (F=25．45%),work voltage(F=20．71%),impulse width(F=9．42%).Under the optimum factors levels,it had least surface roughness (1．215μm)and most process rate(24．78 mm~2/min),the prediction value in good agreement with experimental result.And it also analysed the remnant surface stress and the decaying layer after ceramics is machined with electrical charges.The result indicates that the remnant surface stress of engineering ceramic after electrical discharge machining is tension stress,and the decaying layer of the surface averaging 50～100μm becomes loose and weakened.The remnant surface stress and the decaying layer of the surface will increase with the incresase of pulse energy (impulse width,the number of power transistors,work voltage)whereas decrease with the increase of interval between two pulses. The result shows that the electro-discharge machining has a damaging effect on the surface of ceramics,giving low flexural strength and low Weibull modulus by Weibull statistical method.Taking engineering ceramics of WEDM for L16 orthogon-al array and experimental results as the stylebook,the experimental and simulating results showed that the improved faintness Modular neural networks can not only effectively overcome the problems of easily falling into local minimum point,but also the model can obtain higher accuracy of prediction,error 7%,certainly having practical value.
研究了模糊推理知识和人工神经网络的理论基础，考虑到人工神经网络存在收敛速度慢，容易陷入局部极小值及全局搜索能力弱等问题，提出了模糊Modular算法来构建神经网络结构。本文采用模糊Modular神经网络，Taguchi(田口式)试验规划方法和标准变量分析技术(ANOVA)，对工程陶瓷材料电加工理论及试验进行研究，实现了工艺参数的优化，建立了工程陶瓷材料电加工表面粗糙度随工艺参数变化的预测模型，为实现WEDM高效精密加工工程陶瓷材料提供理论与技术支持。利用Taguchi(田口式)试验规划方法，对脉冲宽度、脉冲间隔、工作电压、功率管数等工艺因素进行正交试验排序，设计了L16正交试验矩阵。在不同的工况下，按照正交试验矩阵来进行工程陶瓷材料放电加工试验。试验过程中，为了排除噪声等干扰，引入信噪比S／N来对试验数据进行处理，以提高数据的准确性。运用标准变量分析技术(ANOVA)，分析了四个电参数对工程陶瓷材料电加工表面质量和加工速度的影响，并优化了工艺参数。
研究结果表明，在所选择的工艺因素中，对表面粗糙度的影响程度依次为工作电压(F=37.16％)、功率管数(F=27.7％)、脉冲间隔(F=19.0％)、脉冲宽度(F=16.14％)，而对加工速度的影响程度依次为脉冲间隔(F=44.42％)、功率管数(F=25.45％)、工作电压(F=20.71％)、脉冲宽度(F=9.42％)。在优化电加工工艺参数组合下，分别得到了优化的最小表面粗糙度(1.215μm)和最大加工速度(24.78 mm~2／min)，优化值与试验结果吻合得较好。电加工后工件表面残余应力均为拉应力，表面变质层组织疏松，强度较差，厚度一般为50～100μm。表面残余应力和表面变质层，都随着脉冲能量(脉冲宽度、功率管数、工作电压)的增加而增加，随着脉冲间隔的增加而减小。通过Weibull函数分析表明，经电加工后的陶瓷材料，其抗弯强度降低，威布尔系数低于11，表面质量和表面完整性变差，需要进行表面改性或强化处理。以正交试验数据为输入学习样本，试验和仿真结果表明，基于模糊Modulalr神经网络不仅可以克服单纯使用人工神经网络容易陷入局部极小值等问题，而且预测精度比较高，误差大约在7％之内，对工程陶瓷材料电加工获得较佳的表面粗糙度有着重要的指导意义，具有一定的实用价值。
Based on the realizing of national inside and outside engineering ceramics material of WEDM (Wire Electrical Discharge Machining), taking electric Al_2O_3/TiC as example, analyzing the theory of WEDM and the theory of surface roughness、surface remnant stress、surface decaying layer of engineering ceramics of WEDM,it studied on the effect of impulse width、impulse separation、work voltage、the number of power transistors on the engineering ceramics of WEDM.
studying faintness system and artifical neural networks, focusing on some disadvantages in neural networks algorithm, such as low convergence rate、easily falling into local minimum point and weak global search capability,the author used a new learning presented algorithm that used the faintness and Modular arithmetic to train neural networks, taguchi method and a standard analysis of variance (ANOVA) to study on theory of WEDM and the experiment. During the experiment, it introduced in S/N in order to deal with data and eliminate disturb.The prediction model of surface roughness in engineering ceramics of WEDM based on faintness Modular neural networks was proposed in detail.An L16 experimental matrix design based on Taguchi method was conducted to process Al_2O_3/TiC ceramics of electrical discharge machining.
Based on a standard analysis of variance (ANOVA),it has been found that the relative significance of each factor on surface roughness was arranged in decreasing order of work voltage(F=37．16%), the number of power transistors (F=27．7%), impulse separation(F=19．0%), impulse width (F=16．14%),while on process rate was arranged in decreasing order of impulse separation(F=44．42%),the number of power transistors (F=25．45%),work voltage(F=20．71%),impulse width(F=9．42%).Under the optimum factors levels,it had least surface roughness (1．215μm)and most process rate(24．78 mm~2/min),the prediction value in good agreement with experimental result.And it also analysed the remnant surface stress and the decaying layer after ceramics is machined with electrical charges.The result indicates that the remnant surface stress of engineering ceramic after electrical discharge machining is tension stress,and the decaying layer of the surface averaging 50～100μm becomes loose and weakened.The remnant surface stress and the decaying layer of the surface will increase with the incresase of pulse energy (impulse width,the number of power transistors,work voltage)whereas decrease with the increase of interval between two pulses. The result shows that the electro-discharge machining has a damaging effect on the surface of ceramics,giving low flexural strength and low Weibull modulus by Weibull statistical method.Taking engineering ceramics of WEDM for L16 orthogon-al array and experimental results as the stylebook,the experimental and simulating results showed that the improved faintness Modular neural networks can not only effectively overcome the problems of easily falling into local minimum point,but also the model can obtain higher accuracy of prediction,error 7%,certainly having practical value.
引文
[1] 李明辉．电火花线切割技术的研究现状及发展趋势．模具技术，2002(6)：49-52。
[2] CIMT’99特种加工机床评述组．第六届中国国际机床展览会特种加工机床展品水平分析．电加工与模具，2000(1)．
[3] 邢世凯，屈玲玲，王静．工程陶瓷材料及其与金属连接工艺的研究发展，陶瓷，2003(6)：9-12。
[4] 李淑华，王建江，李树堂．陶瓷与金属的连接．特种铸造及有色合金，2000(2)：51-53。
[5] 李淑玉．工程陶瓷电火花加工工艺．机械工程师，2000．4。
[6] 华子乐，等．结构陶瓷放电加工过程的蚀除机理．陶瓷工程，1998(5)．
[7] Chien-Cheng Liu, Jow-Lay Huang, Effect of the electrical discharge machining on strength and reliability of TiN/Si3N4 composites, Ceramics International 29(2003): 679-687.
[8] 王斌修．结构陶瓷电火花加工蚀除机理分析．电加工与模具，2000(3)．
[9] 骆志高．陶瓷材料电加工表面质量的研究．机械工程学报，2000，11，36(11)：75-79．
[10] 林滨，林彬．新型陶瓷材料超声磨削加工的研究[J]．金刚石与磨料磨具工程，2002，(1)：23-26．
[11] I. Cabanes, J.A. Sénchez, L.N. López de Lacalle, A. Lamikiz, Mecanizado por electroerosión de componentes cerámicos de precisión,IMHE, May 2001, pp. 63-72.
[12] J.A. Sánchez, I. Cabanes, L.N. López de Lacalle, A. Lamikiz, Development of optimum electrodischarge machining technology for advanced ceramics, Int. J. Adv. Manuf. Technol. 18(2001) 897-905.
[13] C.F. Noble, A.J. Ajmal, L.F. Green, Electro-discharge machining of silicon carbide, in: Proceedings of the International Symposium of Electro Machining-ISEM 7, Birmingham, 1983, pp. 305-312.
[14] I. Puertas, C.J. Luis, A revision of the applications of the electrical discharge machining process to the manufacture of conductive ceramics, Rev. Met. Madrid 38(5)(2002)358-372.
[15] M.T.Yan,Y.S.Liao,C.C.Chang.On-line Estimation of Workpiece Height by using Neural Networks and Hierarchical Adaptive Control of WEDM.Int J Adv Manuf technol(2001)18:884-891.
[16] Y.S.liao,J.T.Huang,H.C.Su.A study on the machining-parameters optimization of wire electrical discharge machining. Journal of Materials Processing Technology 71(1997):487-493.
[17] Takayuki Tani, Yasushi Fukuzawa, Naotake Mohri, Nagao Saito, Masaaki Okada, Machining phenomena in WEDM of insulating ceramics, Journal of Materials Processing Technology 149(2004) 124-128.
[18] 朱燕堂，赵选明，徐伟．应用概率统计方法．西安：西北工业大学出版社，2002(2)。
[19] Fengguo Cao.,Qinjian Zhang.Nerual network modeling and parameters optimization of increased explosive electrical discharge grinding(IEEDG) process for large area polycrystalline diamond. Journal of Materials Processing Technology 149(2004): 106-111.
[20] J.Y.Kao,Y.S.Tarng.A neural-network approach for the on-line monitoring of the electrical discharge machining process. Journal of Materials Processing Technology 69(1997): 112-119
[21] D.Scott,S.Boyina,K.P.Rajurkar.Analysis and optimization of parameters combination in wire electrical discharage machining,Int.J.Prod.Res. 29(11)(1991): 2189-2207.
[22] Peace G S Taguchi Methods:A Hands-On Approach[M].New York:: Addison -Wesley, 1993.114-137.
[23] Phadke M S Quality Engineering Using RobustDesign[M].New Jersey: Prentice-hall, 1989.41-69.
[24] A. Bellosi, S. Guicciardi, A. Tampieri, Development and characterization of electroconductive Si_3N_4-TiN composites, J. Eur.Ceram. Soc. 9(1992): 83-93.
[25] 王士同．神经模糊系统及其应用．北京：北京航空航天大学出版社，1998(2)．
[26] Z.L.Wang, Y.Fang, P.N.Wu, W.S.Zhao, K.Cheng, Surface modification process by electrical discharge machining with a Ti power green compact electrode, Journal of Materials Processing Technology 129(2002) 139-142
[27] Josko Valentincic,Mihael Junkar.On-line selection of rough machining parameters.Journal of Materials Processing Technology 149(2004):256-262.
[28] J.L.Lin,K.S.Wang,B.H.Yan,Y.S.Tamg.Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics. Journal of Materials Processing Technology 102(2000):48-55.
[29] J.R. Davis, ASM Materials Engineering Dictionary, ASM International,USA, 1992.
[30] X. De Maidagan, J.A. Sánchez, J.I. Llorente, R. Lenzen, T. Nothe,Electroe rosión: Llave para un crecimiento real de la aplicación de las cerámicas, IMHE, June/July 2000, pp. 43-48.
[31] 熊光耀，李明辉．改善WEDM HS加工表面粗糙度值的工艺探讨．模具制造，2002(6)．
[32] S.H. Lee, X.P. Li, Study of the effect of machining parameters on the machining characteristics in electrical discharge machining of tungsten carbide, J. Mater. Process. Technol. 115(2001) 344-358.
[33] K. Hirao, K. Watari, M.E. Brito, M. Toriyama, S. Kanzaki,High thermal conductivity in silicon nitride with anisotropic microstructure, J. Am. Ceram. Soc. 79(9)(1996):2485-2488.
[34] David J．Kruglinski著，潘爱民，王国印译．Visual C++技术内幕(第四版)．北京：清华大学出版社，2001．11．
[2] CIMT’99特种加工机床评述组．第六届中国国际机床展览会特种加工机床展品水平分析．电加工与模具，2000(1)．
[3] 邢世凯，屈玲玲，王静．工程陶瓷材料及其与金属连接工艺的研究发展，陶瓷，2003(6)：9-12。
[4] 李淑华，王建江，李树堂．陶瓷与金属的连接．特种铸造及有色合金，2000(2)：51-53。
[5] 李淑玉．工程陶瓷电火花加工工艺．机械工程师，2000．4。
[6] 华子乐，等．结构陶瓷放电加工过程的蚀除机理．陶瓷工程，1998(5)．
[7] Chien-Cheng Liu, Jow-Lay Huang, Effect of the electrical discharge machining on strength and reliability of TiN/Si3N4 composites, Ceramics International 29(2003): 679-687.
[8] 王斌修．结构陶瓷电火花加工蚀除机理分析．电加工与模具，2000(3)．
[9] 骆志高．陶瓷材料电加工表面质量的研究．机械工程学报，2000，11，36(11)：75-79．
[10] 林滨，林彬．新型陶瓷材料超声磨削加工的研究[J]．金刚石与磨料磨具工程，2002，(1)：23-26．
[11] I. Cabanes, J.A. Sénchez, L.N. López de Lacalle, A. Lamikiz, Mecanizado por electroerosión de componentes cerámicos de precisión,IMHE, May 2001, pp. 63-72.
[12] J.A. Sánchez, I. Cabanes, L.N. López de Lacalle, A. Lamikiz, Development of optimum electrodischarge machining technology for advanced ceramics, Int. J. Adv. Manuf. Technol. 18(2001) 897-905.
[13] C.F. Noble, A.J. Ajmal, L.F. Green, Electro-discharge machining of silicon carbide, in: Proceedings of the International Symposium of Electro Machining-ISEM 7, Birmingham, 1983, pp. 305-312.
[14] I. Puertas, C.J. Luis, A revision of the applications of the electrical discharge machining process to the manufacture of conductive ceramics, Rev. Met. Madrid 38(5)(2002)358-372.
[15] M.T.Yan,Y.S.Liao,C.C.Chang.On-line Estimation of Workpiece Height by using Neural Networks and Hierarchical Adaptive Control of WEDM.Int J Adv Manuf technol(2001)18:884-891.
[16] Y.S.liao,J.T.Huang,H.C.Su.A study on the machining-parameters optimization of wire electrical discharge machining. Journal of Materials Processing Technology 71(1997):487-493.
[17] Takayuki Tani, Yasushi Fukuzawa, Naotake Mohri, Nagao Saito, Masaaki Okada, Machining phenomena in WEDM of insulating ceramics, Journal of Materials Processing Technology 149(2004) 124-128.
[18] 朱燕堂，赵选明，徐伟．应用概率统计方法．西安：西北工业大学出版社，2002(2)。
[19] Fengguo Cao.,Qinjian Zhang.Nerual network modeling and parameters optimization of increased explosive electrical discharge grinding(IEEDG) process for large area polycrystalline diamond. Journal of Materials Processing Technology 149(2004): 106-111.
[20] J.Y.Kao,Y.S.Tarng.A neural-network approach for the on-line monitoring of the electrical discharge machining process. Journal of Materials Processing Technology 69(1997): 112-119
[21] D.Scott,S.Boyina,K.P.Rajurkar.Analysis and optimization of parameters combination in wire electrical discharage machining,Int.J.Prod.Res. 29(11)(1991): 2189-2207.
[22] Peace G S Taguchi Methods:A Hands-On Approach[M].New York:: Addison -Wesley, 1993.114-137.
[23] Phadke M S Quality Engineering Using RobustDesign[M].New Jersey: Prentice-hall, 1989.41-69.
[24] A. Bellosi, S. Guicciardi, A. Tampieri, Development and characterization of electroconductive Si_3N_4-TiN composites, J. Eur.Ceram. Soc. 9(1992): 83-93.
[25] 王士同．神经模糊系统及其应用．北京：北京航空航天大学出版社，1998(2)．
[26] Z.L.Wang, Y.Fang, P.N.Wu, W.S.Zhao, K.Cheng, Surface modification process by electrical discharge machining with a Ti power green compact electrode, Journal of Materials Processing Technology 129(2002) 139-142
[27] Josko Valentincic,Mihael Junkar.On-line selection of rough machining parameters.Journal of Materials Processing Technology 149(2004):256-262.
[28] J.L.Lin,K.S.Wang,B.H.Yan,Y.S.Tamg.Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics. Journal of Materials Processing Technology 102(2000):48-55.
[29] J.R. Davis, ASM Materials Engineering Dictionary, ASM International,USA, 1992.
[30] X. De Maidagan, J.A. Sánchez, J.I. Llorente, R. Lenzen, T. Nothe,Electroe rosión: Llave para un crecimiento real de la aplicación de las cerámicas, IMHE, June/July 2000, pp. 43-48.
[31] 熊光耀，李明辉．改善WEDM HS加工表面粗糙度值的工艺探讨．模具制造，2002(6)．
[32] S.H. Lee, X.P. Li, Study of the effect of machining parameters on the machining characteristics in electrical discharge machining of tungsten carbide, J. Mater. Process. Technol. 115(2001) 344-358.
[33] K. Hirao, K. Watari, M.E. Brito, M. Toriyama, S. Kanzaki,High thermal conductivity in silicon nitride with anisotropic microstructure, J. Am. Ceram. Soc. 79(9)(1996):2485-2488.
[34] David J．Kruglinski著，潘爱民，王国印译．Visual C++技术内幕(第四版)．北京：清华大学出版社，2001．11．