- 中文核心期刊
- CSCD中国科学引文数据库来源期刊
- 中国科技核心期刊
- 中国机械工程学会材料分会会刊
| Citation: |
LIU Haomin, YANG Hongcai, LIU Zhan, LI Ziwei, SUN Junhua, ZHANG Yuanbin. Backpropagation Neural Network Prediction Model of Arc Additive Manufacturing Weld Size Base on Particle Swarm Optimization Algorithm[J]. Materials and Mechanical Engineering, 2024, 48(2): 97-102. DOI: 10.11973/jxgccl202402015
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With welding current, wire feed speed, welding speed and substrate temperature as input variables, weld width and residual height as output variables, and the 4-12-2 structure particle swarm optimization backpropagation（PSO-BP）neural network model of melt intert-gas welding arc additive manufacturing 316L stainess steel was built with optimal particle inertia weight and learning factor in PSO algorithm. The results show that the root-mean-square error, maximum relative error and average relative error of predicted weld width obtained by PSO-BP neural network model and expected values were 0.386, 13.477% and 2.580%, and those of weld reinforcement were 0.152, 10.372% and 2.810%, respectively. Compared with BP neural network model, PSO-BP neural network model had higher prediction accuracy and stronger stability for the prediction of weld size.
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