Temperature Prediction Based on Neural Network for Selective Laser Sintering

被引:2
作者
Xie Ruidong [1 ]
Zhu Jinwei [1 ]
Zhong Qi [2 ]
Gao Feng [1 ]
机构
[1] Xian Univ Technol, Key Lab Mfg Equipment Shaanxi Prov, Xian 710048, Shaanxi, Peoples R China
[2] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Shaanxi, Peoples R China
关键词
materials; selective laser sintering; neural network; sintering point; temperature; prediction; MODEL;
D O I
10.3788/LOP202259.1916005
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A selective laser sintering (SLS) technique uses a method called finite element simulation to forecast and analyze temperature fields. However, the temperature field simulation computation takes a long time. An SLS sintering points temperature prediction approach, based on a genetic algorithm (GA) optimized hack propagation (BP) neural network, is proposed to enhance the computation efficiency. A large number of simulation experiments of sintering point temperatures of coated sand multitrack-multilayer parts were conducted. A sintering point temperature prediction model based on GA-BP neural network was created and trained based on the above experiments. A piece of software for predicting SLS sintering point temperatures was developed. The software can quickly calculate and visually display the sintering point's temperatures based on the dimension and process parameters. The accuracy of temperature prediction was confirmed when the predicted and detected sintering point temperatures of the parts were compared experimentally.
引用
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页数:8
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