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

2Citations
Citations of this article
13Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

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.

Cite

CITATION STYLE

APA

Zhang, W., Luo, C., Ma, Q., Lin, Z., Yang, L., Zheng, J., … Tian, J. (2023). Prediction model of surface roughness of selective laser melting formed parts based on back propagation neural network. Engineering Reports, 5(12). https://doi.org/10.1002/eng2.12570

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free