The surface quality control of curve grinding process based on wavelet neural network

Yonghong Zhang; Huiqiang Tang; Kai Zhang; Dejin Hu

doi:10.1117/12.664646

2 May 2006 The surface quality control of curve grinding process based on wavelet neural network

Yonghong Zhang, Huiqiang Tang, Kai Zhang, Dejin Hu

Proceedings Volume 6042, ICMIT 2005: Control Systems and Robotics; 604226 (2006) https://doi.org/10.1117/12.664646
Event: ICMIT 2005: Merchatronics, MEMS, and Smart Materials, 2005, Chongqing, China

Abstract

Wavelet neural network was used in the area of curve grinding. The prediction model of surface machining quality in curve grinding based on wavelet neural network was founded. The work piece feed amount, rotation speed of grinding wheel and vibration frequencies were chosen as input variables of wavelet neural network. The roughness was used to assess grinding surface quality. Prediction results were feedback to adjust machining parameters. In order to solve disadvantages of "dimension disaster", slow rate of convergence and easily falling into local minimum point caused by multi-input and output. A new local evolutionary algorithm was used to train wavelet neural network. From some experiments, it can be seen that this method increase rate of convergence effectively. The surface quality of curve grinding process can be obtained.

Citation Download Citation

Yonghong Zhang, Huiqiang Tang, Kai Zhang, and Dejin Hu "The surface quality control of curve grinding process based on wavelet neural network", Proc. SPIE 6042, ICMIT 2005: Control Systems and Robotics, 604226 (2 May 2006); https://doi.org/10.1117/12.664646

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