数控机床热误差检测及建模技术研究
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摘要
论文在分析国内外数控机床热误差补偿技术研究现状及发展趋势的基础上,以数控机床热误差补偿中温度与热误差检测系统的开发、热关键点辨识技术、热误差建模方法为主要研究内容。
开发了温度与热误差检测系统。系统硬件以温度传感器、位移传感器和PCI数据采集卡为核心;数据连续采集模块采用局部循环缓存技术,提高了整个采集程序的运行速率。为实现较高的测量精度,采取了包括信号调理电路在内的一系列防干扰措施。经试验,此采集系统满足测量精度和自动采集的要求。
在机床热关键点的辨识研究中,针对聚类分组法当温度变量组之间出现交叉现象时分组难以进行的问题,提出了改进方法,即典型温度变量选择方法。该方法对温度变量与热误差观测值之间相关性高的变量优先分析,选出典型温度变量;用多元线性回归模型对各变量组合的模型拟合精度进行分析,从而优选出温度变量组合。
研究了基于最小二乘的多元线性回归、BP神经网络和RBF神经网络三种热误差建模方法。采用这三种建模方法对HG-410立式数控铣床和MCH63卧式加工中心分别建立其热误差模型,通过试验验证了各模型的热误差预测能力。分析比较各模型的热误差预测精度,综合考虑模型的复杂程度,得出多元线性回归法适用于HG-410立式数控铣床进行热误差建模,BP神经网络适用于MCH63卧式加工中心进行热误差建模的结论。
Based on fully understanding and deeply analyzing the current status of the research and trend of thermal error compensation technique for CNC machine tools, the development of temperature and thermal error data acquisition system, identification technique of thermal key points and the modeling methods of thermal errors for CNC machine tools were the main research contents of this paper.
A temperature and thermal errors measurement system was developed. It’s hardware was based on temperature sensors, displacement sensors and PCI data acquisition card; the whole operation rate of data acquisition program was enhanced by using local circulation cache technology for dada continuous acquisition module. In order to acquire high measuring accuracy, a series of measures including signal processing circuit were adopted. Test results indicated that the system completely met the requirements of precision and automatic measurement.
In the research of thermal key points identification, in order to solve the problem of when the crossover phenomenon appeared among temperature variable groups grouping was hard to implement, a improved method ,selection method of typical temperature variables was proposed; in order to choose the reasonable temperature variable combination multiple linear regression (MLR) model was also used.
The modeling methods of multiple linear regression based on least square method,BP neural network and RBF neural network were studied. Taking HG-410 vertical CNC machine tools and MCH63 horizontal machining center as the test objects, the models of these machine tools were established respectively by the three modeling methods, and the prediction ability of these models were proved by experiments. By comparison of the prediction accuracy and comprehensive consideration of the complex degree of these models , a conclusion is: MLR modeling methods is suitable for HG-410 vertical CNC machine tools and BP neural network is suitable for MCH63 horizontal machining center.
开发了温度与热误差检测系统。系统硬件以温度传感器、位移传感器和PCI数据采集卡为核心;数据连续采集模块采用局部循环缓存技术,提高了整个采集程序的运行速率。为实现较高的测量精度,采取了包括信号调理电路在内的一系列防干扰措施。经试验,此采集系统满足测量精度和自动采集的要求。
在机床热关键点的辨识研究中,针对聚类分组法当温度变量组之间出现交叉现象时分组难以进行的问题,提出了改进方法,即典型温度变量选择方法。该方法对温度变量与热误差观测值之间相关性高的变量优先分析,选出典型温度变量;用多元线性回归模型对各变量组合的模型拟合精度进行分析,从而优选出温度变量组合。
研究了基于最小二乘的多元线性回归、BP神经网络和RBF神经网络三种热误差建模方法。采用这三种建模方法对HG-410立式数控铣床和MCH63卧式加工中心分别建立其热误差模型,通过试验验证了各模型的热误差预测能力。分析比较各模型的热误差预测精度,综合考虑模型的复杂程度,得出多元线性回归法适用于HG-410立式数控铣床进行热误差建模,BP神经网络适用于MCH63卧式加工中心进行热误差建模的结论。
Based on fully understanding and deeply analyzing the current status of the research and trend of thermal error compensation technique for CNC machine tools, the development of temperature and thermal error data acquisition system, identification technique of thermal key points and the modeling methods of thermal errors for CNC machine tools were the main research contents of this paper.
A temperature and thermal errors measurement system was developed. It’s hardware was based on temperature sensors, displacement sensors and PCI data acquisition card; the whole operation rate of data acquisition program was enhanced by using local circulation cache technology for dada continuous acquisition module. In order to acquire high measuring accuracy, a series of measures including signal processing circuit were adopted. Test results indicated that the system completely met the requirements of precision and automatic measurement.
In the research of thermal key points identification, in order to solve the problem of when the crossover phenomenon appeared among temperature variable groups grouping was hard to implement, a improved method ,selection method of typical temperature variables was proposed; in order to choose the reasonable temperature variable combination multiple linear regression (MLR) model was also used.
The modeling methods of multiple linear regression based on least square method,BP neural network and RBF neural network were studied. Taking HG-410 vertical CNC machine tools and MCH63 horizontal machining center as the test objects, the models of these machine tools were established respectively by the three modeling methods, and the prediction ability of these models were proved by experiments. By comparison of the prediction accuracy and comprehensive consideration of the complex degree of these models , a conclusion is: MLR modeling methods is suitable for HG-410 vertical CNC machine tools and BP neural network is suitable for MCH63 horizontal machining center.
引文
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[5]潘淑微,傅建中.数控车床嵌入式智能温度检测与试验[J].制造技术与机床,2007,(9):44-47.
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[12] D.S.Lee, J.Y.Choi,D.H.Choi. ICA based thermal source extraction and thermal distortion compensation method for a machine tool[J]. Machine tools & manufacture, 2003, (3):589-597.
[13] J.S.Chen, Computer-aided accuracy enhancement for multi-axis CNC machine tools[J], International Journal of Machine Tools and Manufacture, 1995,35 (4):593–605.
[14] J.C. Liang, H.F. Li,J.X. Yuan, J. Ni. A comprehensive error compensation system for correctinggeometric thermal and cutting force induced errors[J]. The International Journal of Advanced Manufacturing Technology, 1997,(13):708-712.
[15]曹永洁,傅建中.基于高精度位移传感器的机床主轴热变形实时测量[J].组合机床与自动化加工技术, 2006,(12):42-45.
[16]潘淑微,傅建中.数控机床嵌入式智能温度检测与实验[J].制造技术与机床, 2007,(9):44-47.
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[18]张志飞.多轴数控机床热误差与几何误差建模及补偿技术的研究[D].天津,天津大学, 2001.
[19]张志飞.加工中心热误差补偿研究[J].制造技术与机床, 2001,(1):27-29.
[20] D. A.Krulewic. Temperature integration model and measurement selection for thermally induced machine tool errors[J]. Mechatronics, 1998,(8):395-412.
[21] M.H.Attia, S.Fraser. A generalized modeling methodology for optimized real-time compensation of thermal deformation of machine tools and CMM structures[J]. Internal Journal of Machine Tools& Manufature, 1999,(39):1383-1396.
[22] J.H. Lee, S.H.Yang. Statistical optimization and assessment of a thermal error model for CNC machine tools[J]. International Journal of Machine Tool &Manufacture, 2002,(42):147-155.
[23] C.H. Lo, jingxia Yuan, Jun Ni. Optimal temperature Variable selection by grouping approach for thermal error modeling and compensation[J]. International Journal of Machine Tool & Manufacture, 1999,(39):1383-1396.
[24]于金.数控机床热误差的模型预报补偿[J].组合机床与自动化加工技术, 2002,(4):7-8.
[25]于金,赵树国,于治明.数控机床热变形关键点的辨识与补偿方法的研究[J].机械设计与制造, 2000,(6):73-74.
[26]李小力,周云飞,李作清,等.数控机床热敏感点识别研究[J].机械与电子, 1998,(6):30-32.
[27]闫守红,马术文,闫开印,等.数控机床热变形模型中测温点的优化选择研究[J].机械, 2006,33(5):37-39.
[28]沈金华,赵海涛,张宏韬,等.数控机床热补偿中温度变量的选择与建模[J].上海交通大学学报, 2006,40(2):181-184.
[29]邓卫国,杨建国,任永强,等.精密车削中心热误差测试和优化建模[J].机械制造, 2004,42(479):22-25.
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