Study on Self-Configuration Method of Neural Network Model for Grinding Troubles On-Line Monitoring |
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| Journal | Key Engineering Materials (Volumes 359 - 360) |
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| Volume | Advances in Grinding and Abrasive Technology XIV |
| Edited by | Jiuhua Xu, Xipeng Xu, Guangqi Cai and Renke Kang |
| Pages | 199-203 |
| DOI | 10.4028/www.scientific.net/KEM.359-360.199 |
| Citation | Gui Jie Liu et al., 2007, Key Engineering Materials, 359-360, 199 |
| Online since | November, 2007 |
| Authors | Gui Jie Liu, Qiang Wang, Xiao Long Shi, Ren Ke Kang |
| Keywords | Grinding Trouble, Neural Network (NN), On-Line Monitoring, Self-Configuration Building Mode Method |
| Abstract | A grinding trouble on-line monitoring mode is presented based on the nonlinear building mode principle of neural network. The input units were the peak of the FFT, the peak of RMS, and the standard deviation of AE signals. The outputs were the troubles of the grinding burning, grinding chatter, and grinding wheel dull. The structure of neural network is established by self-configuration method. The network mode is trained and tested by using the experiment data, and the results indicate that the neural network mode obtained by self-configuration method has high recognize rate for grinding troubles, and can be used to monitor grinding troubles on-line. |
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