期刊文献+

车削加工仿真中工艺系统受力变形分析及预测 被引量:1

Analysis and forecast of the machining system deformation in turning machining simulation
下载PDF
导出
摘要 介绍了在CAK5085dj数控车床上,利用KISTLER9257B对切削力进行实测,并与切削力经验公式和MATLAB神经网络预测模型所得的切削力进行比较,得出最符合实际的切削力预测模型,实现对切削力的快速精确预测;实测了数控车床刚度,估算了工艺系统受力变形误差,并将此预测结果应用到车削加工仿真中。 By using KISTLER 9257B for measuring of actual cutting force in CAK 5085dj CNC lathe.A cutting force forecast model in line with the actual is obtained by comparing the measuring results with the calculation of cutting force empirical formula and MATLAB neural network prediction model,and realized fast and accurate prediction of cutting force.Measured the CNC lathe angular rigidity,estimated deformation of machining system by cutting force,and applied this forecasting result in turning machining simulation.
作者 李静 王占礼
机构地区 长春工业大学
出处 《机械设计与制造》 北大核心 2009年第12期181-183,共3页 Machinery Design & Manufacture
基金 吉林省教育厅资助项目(2007第110号)
关键词 切削力 工艺系统 预测 变形 神经网络 Cutting force Machining system Predictions Deformation Neural network
  • 相关文献

参考文献3

  • 1Endres W J. A dynamical model of cutting force system in the turning process. Proc. ASME Symposium on Monitoring and Control for Manufacturing Process[J].PED, 1991(44): 193-212.
  • 2苏高利,邓芳萍.论基于MATLAB语言的BP神经网络的改进算法[J].科技通报,2003,19(2):130-135. 被引量:170
  • 3Wang Zhanli,Yu Junyi,Wang Lingyan An NC machining simulation system development and machining error analysis. Proc. of 7th International Conference on Progress of Machining Technology[ J ]. 2004:645--650.

二级参考文献18

  • 1Rumelhart D E, Hinton G E, Williams R J. Learninginternal repr esentatio ns by error propagation[A].Rumelhart D E James L.McClelland J L. Parallel di stributed processing: explorations in the microstructure of cognition[C], vol ume 1, Cambridge, MA:MIT Press, 1986.318~362.
  • 2Neural Network Toolbox User's Guide .The Mathworks,inc. 1999.
  • 3Fahlman S E. Faster-learning variations on back-propagation: an e mpirical study[A].Touretzky D,Hinton G,Sejnowski T. Proceedings of the 1988 C onnectionist Models Summer School[C].Carnegic Mellon University,1988,38~51.
  • 4Jacobs R A. Increased rates of convergence through learning rate adaptation[J]. Neural Networks,1988,1:295~307.
  • 5Shar S, Palmieri F. MEKA-a fast, local algorithm for training feedforwa rd neural networks[A]. Proceedings of the International Joint Conference on Ne ural Networks[C]. IEEE Press, New York, 1990.41~46.
  • 6Watrous R L. Learning algorithms for connectionist network: appli ed gradie nt methods of nonlinear optimization[A]. Proceedings of IEEE International Con ference on Neural Networks[c]. IEEE Press, New York, 1987.619~627.
  • 7Shar S,Palmieri F,Datum M.Optimal filtering algorithms f or fast l earning in feedforward neural networks[J]. Neural Networks,1992, 5(5):779~7 87.
  • 8Martin R,Heinrich B. A Direct Adaptive Method for F aster Backpropagation Learning: The RPROP Algorithrm[A]. Ruspini H. Proceedi ngs of the IEEE Interna t ional Conference on Neural Networks (ICNN)[C]. IEEE Press, New York. 1993.58 6~591.
  • 9Fletcher R,Reeves C M. Function minimization by conjugate gra dients[J]. Computer Journal ,1964,7:149~154.
  • 10Powell MJD. Restart procedures for the conjugate gradient metho d[J]. Mathematical Programming, 1977, 12: 241~254.

共引文献169

同被引文献7

引证文献1

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部