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《Manufacturing Technology & Machine Tool》 2014-08
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Optimized analysis of precision grinding machine bed based on neural network

KE Xiaolong;LIN Xiaohui;WANG Chunjin;School of Mechanical and Automotive Engineering,Xiamen University of Technology;School of Physics and Mechanical Engineering,Xiamen University;  
Deformation and inherent frequency are key parameters to evaluate structure rigidity for precision machine tool. In order to improve rigidity for grinding machine,this study proposes a method that using neural network to optimize the structure of precision grinding machine bed. Take precision grinding machine tool( 2MK1760) which is self developed for an example,the static structure and dynamic modal analysis is carried out by using finite element method( ANSYS). In order to alleviate the influence of deformation and vibration on precision grinding machine bed,some parameters are served as input of BP neural work model,which estimate the relationship between deformation and inherent frequency.According to analysis results,an optimization is obtained to determine the dimension of precision grinding machine bed. The results indicate that the deformation after neural network optimization compared with initial structure has decreased by 36. 51%,meanwhile,the natural frequency has increased by 11. 96%.
【Fund】: 国家自然科学基金项目(51075343);; 福建省自然科学基金项目(2012J05102)
【CateGory Index】: TG580
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