基于神经网络的并联机床表面粗糙度预测

Prediction of Surface Roughness of Parallel Machine Tools Based on the Neural Network

将人工神经网络改进后应用到并联机床粗糙度的预测模型中,有效预测了机床进给速度、主轴转速、加工角度、加工作用力以及加工次数等工艺参数变化下对粗糙度的影响。结果表明:当网络的训练步数控制在200到400次时,整个网络模型的训练样本均方误差是平稳且收敛的,并且训练中加入的检验样本的预测误差可以控制在5%以下,满足预测模型的训练要求,证明经过改进的神经网络预测模型用于实际加工预测过程中是可行的,且精度较高。

Improved artificial neural network was applied to the prediction model of parallel machine tool roughness, which effec- tively predicted the influence on roughness by change process parameters such as machine tool feed rate, spindle speed, processing an- gle, machining force and number of times of machining, and etc. Results show that the steps when the training of the network controlled from 200 to 400, the training sample mean square error of the entire network model is stable and convergent, the prediction error of added inspection training samples can be controlled under 5%, which satisfies the requirement of the prediction model in training. The improved neural network prediction model is proved to be feasible with higher precision when used to forecast the actual machining process.