摘要
电解加工中通过适当地控制工艺参数可获得较好的表面质量。运用正交试验方法研究了工作电压、电解液压力、初始间隙、阴极转速及阴极进给速度对电解加工表面质量的影响,得到了优化的工艺组合。在此基础上运用BP神经网络对试验数据进行分析处理,预测了较正交试验分析结果更为优化的工艺组合,并用试验验证了其正确性。结果表明,经正交试验数据训练过的BP神经网络,较好地映射了工艺参数与优化指标之间的复杂非线性关系,应用BP神经网络对工艺参数进行优选研究能得到更全面、准确的结果。
In the electrochemical machining,better surface roughness is gained by selecting appropriate process parameters.Using the orthogonal array technique,the influence of the process parameters of working voltage,the electrolyte pressure,the initial gap size,the cathode feed rate and the cathode rotating rate on the surface roughness of electrochemical machining were studied in this paper.The process parameters were optimized,on the basis of which the experimental data were analyzed by using BP neural network.The process parameters with better resolution than the orthogonal array technique were predicted by the BP neural network and their correctness was verified by experiment.The experimental results proved that after being trained by the data of orthogonal tests,the BP neural network had a good capability of mapping the complex nonlinear relationship between the process parameters and the optimization targets,and more comprehensive and accurate optimization of process parameters could be got.
出处
《电加工与模具》
2010年第6期58-61,共4页
Electromachining & Mould
关键词
电解加工
正交试验
BP神经网络
electrochemical machining
orthogonal array technique
BP neural network
