基于BP神经网络的数控加工铣削参数优化

Optimization of CNC milling parameters based on BP neural network

以DMC60H数控机床为试验平台,以壳体类铝合金零件加工为研究对象,提取数控铣削加工试验数据,采用BP神经网络建立数控加工铣削参数优化模型,通过对数控加工铣削参数试验数据的分析与研究,提出了试验数据与样本数据的处理原则,实现了样本数据的优化,提高了BP神经网络模型的收敛精度、收敛速度与预测精度,并分析了验证数据的构成比例。经生产验证:提出的数控加工铣削参数优化方法具有较强的实用性和一定的先进性,能有效提高加工效率,对实现数控机床综合应用效率最优化,实现高效低成本加工具有重要意义。

A study on the processing of an aluminum shell component on a DMC60H NC machine tool was made.The NC milling processing experiment data was extracted and an optimal model for CNC milling parameters was set up.Based on analysis and research on the experiment data,some treatment principles of test data and sample data were proposed,by which the sample data was optimized;the convergence accuracy,convergence speed and prediction precision of BP neural network model were increased.Finally,the proportion of test data was investigated.