Thermal deformation prediction in machine tools by using neural network

  • Authors:

  • Chuan-Wei Chang;Yuan Kang;Yi-Wei Chen;Ming-Hui Chu;Yea-Ping Wang

  • Affiliations:

  • Department of Mechanical Engineering, Chung Yuan Christian University, Chung Li, Taiwan, R.O.C.;Department of Mechanical Engineering, Chung Yuan Christian University, Chung Li, Taiwan, R.O.C.;Department of Mechanical Engineering, Chung Yuan Christian University, Chung Li, Taiwan, R.O.C.;Department of Automation Engineering, Tung Nan Institute of Technology, Taipei, Taiwan, R.O.C.;Department of Automation Engineering, Tung Nan Institute of Technology, Taipei, Taiwan, R.O.C.

  • Venue:
  • ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
  • Year:
  • 2006

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Abstract

Thermal deformation is a nonlinear dynamic phenomenon and is one of the significant factors for the accuracy of machine tools. In this study, a dynamic feed-forward neural network model is built to predict the thermal deformation of machine tool. The temperatures and thermal deformations data at present and past sampling time interval are used train the proposed neural model. Thus, it can model dynamic and the nonlinear relationship between input and output data pairs. According to the comparison results, the proposed neural model can obtain better predictive accuracy than that of some other neural model.