Intelligent tool condition monitoring system for turning operations

  • Authors:

  • Hongli Gao;Mingheng Xu

  • Affiliations:

  • School Of Mechanical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China;School Of Mechanical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2005

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Abstract

In order to predict tool wear accurately and reliably under different cutting conditions, a new monitoring methodology for turning operations is proposed. Firstly, the correlation coefficients approaches were used to indicate the dependencies between the different sensed information features and tool wear amount, and the most appropriate features were selected. Secondly, B-spline neural networks were introduced to model the non-linear relationship between extracted features and tool wear amount, and multi-sensor information were fused by an integrated neural network. Lastly, the final result of tool wear was given through fuzzy modeling. Experimental results have proved that the monitoring system based on the methodology is reliable and practical.