Prediction model of surface roughness of selective laser melting formed parts based on back propagation neural network

In this article, selective laser melting (SLM) equipment is used to print 316L stainless steel parts under different process parameters, and the surface roughness of the parts is measured. Based on back propagation neural networks (BP neural networks, BPNN), the upper surface roughness prediction model is established. The laser power, scanning speed, and scanning interval are used as model input, and the surface roughness of the workpiece is output. This model can easily and quickly predict the surface roughness of SLM metal printing, with high prediction accuracy, and can provide a basis for the optimization of SLM process parameters. In this article, selective laser melting equipment is used to print 316L stainless steel parts under different process parameters, and the surface roughness of the parts is measured and predicted by a roughness measuring instrument and a BP neural network.