TY - JOUR
T1 - LSSVM Model for Penetration Depth Detection in Underwater Arc Welding Process
AU - WeiMin Zhang, GuoRong Wang, YongHua Shi and BiLiang Zhong
JO - Journal of Information and Computing Science
VL - 4
SP - 271
EP - 278
PY - 2024
DA - 2024/01
SN - 5
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22701.html
KW - underwater arc welding, penetration depth, least squares support vector machines 

AB -  For  underwater  arc  welding,  it  is  much  more  complexity  and  difficulty  to  detect  penetration 
depth than land arc welding. Based on least squares support vector machines (LSSVM), welding current, arc 
voltage,  travel  speed,  contact-tube-to-work  distance,  and  weld  pool  width  are  extracted  as  input  units. 
Penetration  depth  is  predicted  in  underwater  flux-cored  arc  welding  (FCAW).  For  improvement  prediction 
performance,  the  LSSVM  parameters  are  adaptively  optimized.  The  experimental  results  show  that  this 
model  can  achieve  higher  identification  precision  and  is  more  suitable  to  detect  the  depth  of  underwater 
FCAW penetration than back propagation neural networks (BPNN).