基于神经网络的电火花线切割工艺目标建模

利用BP神经网络技术建立了电火花线切割加工工况参数与工艺目标间的预测模型.以脉冲宽度、脉冲间隙、峰值电流、间隙电压及工件厚度等工况参数为网络输入,加工效率和表面粗糙度等工艺目标为网络输出,通过用样本数据对网络的训练,实现了对工艺目标的预测.试验结果表明:所建预测模型能较好地反映线切割机床的工艺规律,实现对指定切割条件下加工效率和表面粗糙度的预测,最大预测误差小于10%.

Modeling of wire electrical discharge machining process object based on neural network

This paper establishes a prediction model which reveals the relationship between machining parameters and the process object of wire electrical discharge machining using BP neural network method. Input of the BP network includes pulse width, pulse interval, peak current, servo voltage, workpiece thickness, and output includes machining efficiency, surface roughness. The network is trained by the samples to realize the prediction of process object. The results show that the model reflects the principle of machining process of the machine tool, realizes the prediction of the machining efficiency and surface roughness under specific conditions, and the maximum predictive error is less than 10%.