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合成灰色关联理论的热点优化机床热误差补偿中的应用
为大家介绍一篇关于机床制造的外文，翻译摘要，说明出处及参考文献，希望对业内同行有所帮助，另如果需要更多资讯,请登录成海万能铣床http://www.chjc.cn.
国际先进制造技术杂志
2009八月，43卷，问题11，PP 1124-1132
摘要
本文提出了两种新的方法来优化最小热传感器数目的选择机床热误差补偿。这两种方法，即，直接标准法和间接的分组方法，基于灰色关联理论的合成。他们是用来分析空气切削实验在数控车削中心的数据。优化后，热点的数量从16减少到四。因此，机床热误差建模，温度变量的数目而温度变量之间的耦合问题可大大降低。一个真正的切割实验进行了实际生产条件下，验证所提出的优化方法的效率。优化后的模型结果之间的所有16个热传感器和四最佳热传感器的模型表明，比较，模型精度大大提高。
Ramesh R, Mannan MA, Poo AN (2000) Error compensation in machine tools—a review. Part II: thermal errors. Int J Mach Tools Manuf 40:1257–1284 doi:10.1016/S0890-6955(00)00010-9 CrossRef立式铣床http://www.x5032.com
Bryan J (1990) International status of thermal error research. Ann CIRP 39:645–656 CrossRef
Ni J (1997) A perspective review of CNC machine accuracy enhancement through real-time error compensation. China Mech Eng 8:29–32
Chen JS, Yuan JX, Ni J (1996) Thermal error modeling for real-time compensation. Int J Adv Manuf Technol 12:266–275 doi:10.1007/BF01239613 CrossRef
Wang Y, Zhang G, Moon KS, Sutherland JW (1998) Compensation for the thermal error of a multi-axis machining center. J Mater Process Technol 75:45–53 doi:10.1016/S0924-0136(97)00291-4 CrossRef
Jedrzejewski J, Modrzyci W (1992) A new approach to modeling thermal behavior of a machine tool under service conditions. Ann CIRP 37:401–405
Zhang B, Zhuang Y, Cui C (1992) Improving the thermal center in corporation acting active compensation for thermal distortion. Ann CIRP 141:455–458
Hatamura Y, Nagao T, Mitsuishi M, Kato K, Taguchi S, Okumura T, Nakagawa G, Sugishita H (1993) Development of an intelligent machining center incorporating active compensation for thermal distortion. Ann CIRP 42:549–552 CrossRef
Postlethwaite SR, Allen JP, Ford DG (1999) Machine tool thermal error reduction—an appraisal. Proc Inst Mech Eng Part B J Eng Manuf 213:1–9 doi:10.1243/0954405991516598 CrossRef立式铣床http://www.tzchenghai.com
Yang H, Ni J (2005) Dynamic neural network modeling for nonlinear nonstationary machine tool thermally induced error. Int J Mach Tools Manuf 45:455–465 doi:10.1016/j.ijmachtools.2004.09.004 CrossRef
Kang Y, Chang CW, Huang YR, Hsu CL, Nieh F (2007) Modification of a neural network utilizing hybrid filters for the compensation of thermal deformation in machine tools. Int J Mach Tools Manuf 47:376–387 doi:10.1016/j.ijmachtools.2006.03.007 CrossRef
Yang JG, Yuan JX, Ni J (1999) Thermal error mode analysis and robust modeling for error compensation on a CNC turning center. Int J Mach Tools Manuf 39:1367–1381 doi:10.1016/S0890-6955(99)00008-5 CrossRef
Yang JG, Ren YQ, Liu GL, Zhao HT, Dou XL, Chen WZ, He SW (2005) Testing, variable selecting and modeling of thermal errors on an INDEX-G200 turning center. Int J Adv Manuf Technol 26:814–818 doi:10.1007/s00170-003-1908-3 CrossRef万能铣床http://www.x52k.com
Li YX, Yang JG, Gelvis T, Li YY (2008) Optimization of measuring points for machine tool thermal error based on grey system theory. Int J Adv Manuf Technol 35:745–750 doi:10.1007/s00170-006-0751-8 CrossRef
为大家介绍一篇关于机床制造的外文，翻译摘要，说明出处及参考文献，希望对业内同行有所帮助，另如果需要更多资讯,请登录成海万能铣床http://www.chjc.cn.
国际先进制造技术杂志
2009八月，43卷，问题11，PP 1124-1132
摘要
本文提出了两种新的方法来优化最小热传感器数目的选择机床热误差补偿。这两种方法，即，直接标准法和间接的分组方法，基于灰色关联理论的合成。他们是用来分析空气切削实验在数控车削中心的数据。优化后，热点的数量从16减少到四。因此，机床热误差建模，温度变量的数目而温度变量之间的耦合问题可大大降低。一个真正的切割实验进行了实际生产条件下，验证所提出的优化方法的效率。优化后的模型结果之间的所有16个热传感器和四最佳热传感器的模型表明，比较，模型精度大大提高。
Ramesh R, Mannan MA, Poo AN (2000) Error compensation in machine tools—a review. Part II: thermal errors. Int J Mach Tools Manuf 40:1257–1284 doi:10.1016/S0890-6955(00)00010-9 CrossRef立式铣床http://www.x5032.com
Bryan J (1990) International status of thermal error research. Ann CIRP 39:645–656 CrossRef
Ni J (1997) A perspective review of CNC machine accuracy enhancement through real-time error compensation. China Mech Eng 8:29–32
Chen JS, Yuan JX, Ni J (1996) Thermal error modeling for real-time compensation. Int J Adv Manuf Technol 12:266–275 doi:10.1007/BF01239613 CrossRef
Wang Y, Zhang G, Moon KS, Sutherland JW (1998) Compensation for the thermal error of a multi-axis machining center. J Mater Process Technol 75:45–53 doi:10.1016/S0924-0136(97)00291-4 CrossRef
Jedrzejewski J, Modrzyci W (1992) A new approach to modeling thermal behavior of a machine tool under service conditions. Ann CIRP 37:401–405
Zhang B, Zhuang Y, Cui C (1992) Improving the thermal center in corporation acting active compensation for thermal distortion. Ann CIRP 141:455–458
Hatamura Y, Nagao T, Mitsuishi M, Kato K, Taguchi S, Okumura T, Nakagawa G, Sugishita H (1993) Development of an intelligent machining center incorporating active compensation for thermal distortion. Ann CIRP 42:549–552 CrossRef
Postlethwaite SR, Allen JP, Ford DG (1999) Machine tool thermal error reduction—an appraisal. Proc Inst Mech Eng Part B J Eng Manuf 213:1–9 doi:10.1243/0954405991516598 CrossRef立式铣床http://www.tzchenghai.com
Yang H, Ni J (2005) Dynamic neural network modeling for nonlinear nonstationary machine tool thermally induced error. Int J Mach Tools Manuf 45:455–465 doi:10.1016/j.ijmachtools.2004.09.004 CrossRef
Kang Y, Chang CW, Huang YR, Hsu CL, Nieh F (2007) Modification of a neural network utilizing hybrid filters for the compensation of thermal deformation in machine tools. Int J Mach Tools Manuf 47:376–387 doi:10.1016/j.ijmachtools.2006.03.007 CrossRef
Yang JG, Yuan JX, Ni J (1999) Thermal error mode analysis and robust modeling for error compensation on a CNC turning center. Int J Mach Tools Manuf 39:1367–1381 doi:10.1016/S0890-6955(99)00008-5 CrossRef
Yang JG, Ren YQ, Liu GL, Zhao HT, Dou XL, Chen WZ, He SW (2005) Testing, variable selecting and modeling of thermal errors on an INDEX-G200 turning center. Int J Adv Manuf Technol 26:814–818 doi:10.1007/s00170-003-1908-3 CrossRef万能铣床http://www.x52k.com
Li YX, Yang JG, Gelvis T, Li YY (2008) Optimization of measuring points for machine tool thermal error based on grey system theory. Int J Adv Manuf Technol 35:745–750 doi:10.1007/s00170-006-0751-8 CrossRef
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