燕山大学学报2016，Vol.40Issue(4)：296-300,318,6.DOI:10.3969/j.issn.1007-791X.2016.04.002
基于L-M算法的BP神经网络预测短电弧加工表面质量模型
Analysis of surface quality model of short arc machining based on BP network with L-M algorithm
摘要
Abstract
A short arc machining technique belongs to the special processing industry of EDM technology category. It is especially suitable for super hard, super strength, high toughness of difficult machining in efficient processing.However, the technical charac-teristics of the workpiece surface (surface modification, hardness, residual stress, surface layer defects, etc.) need to be further studied.In order to obtain the good processing results of the short arc milling process, a traditional BP algorithm and a improved Levenberg-Marquardt (L-M) algorithm are introduced to build the model of the surface quality of the short arc milling process.By analyzing the factors of influencing on the surface quality,the discharge voltage,frequency,the pressure,pulse time are selected as the input of the model in this paper.Meanwhile,the surface roughness,metamorphic layer thickness,workpiece material removal rate are selected as output,comparing the prediction accuracy of two models.The results show that using BP algorithm based on the im-proved L-M neural network,the average prediction errors of surface roughness,metamorphic layer thickness,workpiece material re-moval rate are 2.9%,9.4% and 4.6%,respectively,which are lower than that of the traditional neural network.Comparing with the traditional BP neural network,the improved LM-BP neural network model can improve the prediction accuracy which can be used to optimize the process parameters in practical engineering.关键词
短电弧铣削加工技术/BP神经网络/改进L-M算法Key words
short electric arc milling technology/BP neural network/improved LM algorithm分类
机械制造引用本文复制引用
李雪芝,周建平,许燕,王博..基于L-M算法的BP神经网络预测短电弧加工表面质量模型[J].燕山大学学报,2016,40(4):296-300,318,6.基金项目
国家自然科学基金资助项目（51365053）；自治区科技人才培养项目 （）
