Xu Zhaomei, Liu Yongzhi, Yang Gang, Wang Qing'an. Laser milling quality prediction model of BP neural network by PSO[J]. Infrared and Laser Engineering, 2013, 42(9): 2370-2374.
| Citation:
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Xu Zhaomei, Liu Yongzhi, Yang Gang, Wang Qing'an. Laser milling quality prediction model of BP neural network by PSO[J]. Infrared and Laser Engineering, 2013, 42(9): 2370-2374.
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Laser milling quality prediction model of BP neural network by PSO
- 1.
Faculty of Mechanical Engineering,Huaiyin Institute of Technology,Huai'an 223003,China;
- 2.
Military Representative Office of Second Artillery Corps in Tianjin,Tianjin 300308,China
- Received Date: 2013-01-05
- Rev Recd Date:
2013-02-10
- Publish Date:
2013-09-25
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Abstract
In order to effectively control the quality of laser milling layer, BP neural network model was established between the laser milling quality of layer(the milling layer width, milling depth) and the milling layer parameters(laser power, scanning speed and defocus amount). Using particle swarm optimization (PSO)BP neural network weights and thresholds, quality prediction model based on particle swarm neural network was built. The proposed PSO-BP algorithm solve the problem that the general BP algorithm iteration speed was slow, and prone to local optimum. Al2O3 ceramics laser milling quality prediction model was taben to realize the algorithm.The simulation results show that the number of iterations of proposed PSO-BP algorithm, and the prediction error are greatly reduced. The built quality prediction model has high prediction accuracy and practical value.
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References
|
[1]
|
|
|
[2]
|
Campanelli S L, Ludovico A D, Bonserion C. Experimental analysis of the laser milling process parameters[J]. Journal of Materials Processing Technology, 2007, 191: 220-223. |
|
[3]
|
Huang Shu, Zhou Jianzhong, Sheng Jie, et al. Numerical simulation and experiment on laser milling of Al2O3 ceramic[J]. Transactions of the Chinese Society for Agricultural Machinery, 2011, 42(7): 259-265. (in Chinese) 黄舒, 周建忠, 盛杰, 等. Al2O3陶瓷激光铣削数值模拟与试验[J]. 农业机械学报, 2011, 42(7): 259-265. |
|
[4]
|
|
|
[5]
|
Kenned J, Ebemart R C. Partical swarm optimization[C]//IEEE International Conference on Neural Networks, 1995: 192-194. |
|
[6]
|
|
|
[7]
|
|
|
[8]
|
Zhang Jixian, Mi Xia. BP Neural Networks and its Use in the Engineering[M]. Beijing: China Machinery Industry Press, 1996: 68-71. (in Chinese) 张际先, 宓霞. 神经网络及其在工程中的应用[M]. 北京: 机械工业出版社, 1996: 68-71. |
|
[9]
|
Li Jingxian, Yan Cheng, Wu Jiqiu. Prospect of ANN in materials science and engineering[J]. China Ceramic Industry, 2003, 10(4): 36-38. (in Chinese) 李竟先, 郦程, 吴基球. 材料科学与工程中应用ANN的前景[J]. 中国陶瓷工艺, 2003, 10(4): 36-38. |
|
[10]
|
|
|
[11]
|
Liu Tao, Li Yongfeng, Huang Wei. Application of BP neural network to quantitative identification in thermal wave NDT[J]. Infrared and Laser Engineering, 2012, 41(9): 2304-2306. (in Chinese) 刘涛, 李永峰, 黄威. BP神经网络在红外热波无损检测定量识别中的应用[J]. 红外与激光工程, 2012, 41(9): 2304-2306. |
|
[12]
|
|
|
[13]
|
|
|
[14]
|
Wang Xiaoping, Wang Dacheng. Optimizing control of laser suftace strengthening parameters for processing 20CrMo steel based on BP neural network[J]. Infrared and Laser Engineering, 2004, 33(3): 270-273. (in Chinese) 王小平, 王大承. 基于BP神经网络的20CrMo钢激光强化工艺参数优化控制[J]. 红外与激光工程, 2004, 33(3): 270-273. |
|
[15]
|
|
|
[16]
|
Campanelli S L, Casalino G, Ludovico A D, et al. An artificial neural network approach for the control of the laser milling process[J]. Int J Adv Manuf Technol, 2012(9): 341-346. |
|
[17]
|
Chinmay K Desai Abdulhafiz Shaikh. Prediction of depth of cut for single-pass laser micro-milling process using semi-analytical, ANN and GP approaches[J]. Int J Adv Manuf Technol, 2011 (10): 865-883. |
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