文献类型：期刊文献

英文题名：Prediction of weld morphology in laser-welded 316L stainless steel using a multilayer feedforward neural network

作者：Diao, Yalong[1];Shi, Wenqing[1,2];Zhang, Bingqing[1];Jiang, Longwei[1];Lin, Yiming[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Guangdong, Peoples R China;[2]Guangdong Ocean Univ, Sch Mat Sci & Engn, Yangjiang 529500, Guangdong, Peoples R China

年份：2024

外文期刊名：WELDING IN THE WORLD

收录：SCI-EXPANDED(收录号：WOS:001358527400001)、、EI(收录号：20244717409265)、Scopus(收录号：2-s2.0-85209636696)、WOS

基金：This study was supported by the National Natural Science Foundation of China (Grant No. 62073089), the Special Project for Research and Development in Key areas of Guangdong Province (Grant No. 2020ZDZX2061), and the Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province (Grant No. SDZX2023004).

语种：英文

外文关键词：Laser welding; 316L stainless steel; Neural network; Optimization algorithm; Melt pool prediction

外文摘要：This study aims to predict the melt pool depth and width of 316L stainless steel welds during laser welding using a multilayer feed-forward neural network (MLFNN). The Taguchi method was employed to design the laser welding parameters and generate experimental data on melt depth and width. This allowed for an in-depth investigation of the effects of these parameters on the melt pool characteristics of 316L stainless steel. The results demonstrate that the MLFNN model, with a 3-10-10-10-2 structure, exhibits high accuracy and stability across training, validation, and testing phases. The correlation coefficient R-value between predicted and experimental results is 0.99995, indicating an excellent fit to the experimental data. The model's predictions can effectively reduce defects in 316L stainless steel during laser welding, significantly enhancing weld quality.