@Article{JICS-10-154,
author = {Wang Teng and Gao Xiangdong},
title = {Study of different machine learning methods in welded seam width prediction},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {10},
number = {2},
pages = {154--160},
abstract = { As  an  important  new  laser  processing  technique,  the  high-power  disk  laser  welding  has  been 
increasingly  widely used in the  manufacturing  area. Aiming at the strong coupling  multi-variable and real-
time feedback requirements of the welding process, a new method using support vector machine is proposed 
to  predict  the  width  of  the  molten  pools.  The  performance  of  this  model  is  validated  by  the  test  data. 
Meanwhile,  analysis  and  comparison  between  the  support  vector  machines  and  the  BP  neural  network  are 
conducted. Experiment results show that the support vector machine and the BP neural network both have a 
good predictive ability. However, in comparison with the BP neural network, the support vector machine is 
more suitable for high-power disk laser welding process. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22558.html}
}