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| AACMM Length Error Compensation Based on PSO-BP Neural Network |
| LU Yi,ZHANG Pei-pei,WANG Xue-ying,WANG Hua,ZHAO Chen-xin |
| College of Metrology Technology and Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China |
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Abstract In order to resolve the problem of the length error compensation for articulated arm coordinate measuring machine (AACMM), the error sources were analyzed and the parameters of affecting the measurement length error for AACMM were determined by experiments.The AACMM length error compensation model was built up by BP neural network, and the particle swarm optimization algorithm was introduced to overcome the drawbacks of slow convergence and easily trapping in the local minimum values of BP neural network. For getting the training data of neural network, the standard ruler was measured on different error parameters, and the measurement compensation verification was also carried out. After compensation, the mean error reduced by 0.014mm and the measurement precision of AACMM increased by 31.8%.
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Received: 14 July 2016
Published: 19 April 2017
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Corresponding Authors:
Yi LU
E-mail: luyi9798paper@163.com
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2017, Vol. 38 
