THERMAL ERROR MODELING OF A MACHINE TOOL USING DATA MINING SCHEME

勤益科大機構典藏 > 工程學院 > 機械工程系(所) > 【機械工程系】研討會論文 > Item 987654321/3845

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题名:	THERMAL ERROR MODELING OF A MACHINE TOOL USING DATA MINING SCHEME
作者:	Kun-Chieh Wang Pai-Chang Tseng Zue-Chin Chang
贡献者:	國立勤益科技大學機械工程系
关键词:	No more than five items.
日期:	2009-10-14
上传时间:	2012-08-28 15:25:42 (UTC+8)
出版者:	台中市：國立勤益科技大學工程學院
摘要:	This paper uses the knowledge discovery technique to build an effective and transparent mathematic thermal error model for tool machinery. Our proposed thermal error modeling methodology (called KRL) integrates the schemes of K-means theory (KM), rough-set theory (RS), and linear regression model (LR). Firstly, to explore the machine tool’s thermal behavior, an integrated measurement system is designed to measure the temperature ascents at selected characteristic points and the thermal deformations at spindle nose at the same time under suitable real machining conditions. Secondly, the obtained data are classified by the KM method and further reduced by the RS scheme, and eventually a linear thermal error model is then established by the LR technique. To evaluate the performance of our proposed model, an adaptive neural fuzzy inference system (ANFIS) thermal error model is introduced for comparison. At last, a verification experiment is carried out and results reveal that the proposed KRL model has good predictive ability of thermal behavior in tool machinery. Our proposed KRL model takes the advantages of transparency and easy-understanding to users, and can be easily programmed as well as modified to adapt to any different machining conditions.
關聯:	2009綠色科技工程與應用研討會論文集, 362-371
显示于类别:	[機械工程系(所)] 【機械工程系】研討會論文

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