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Volume 5, Issue 4
LSSVM Model for Penetration Depth Detection in Underwater Arc Welding Process

WeiMin Zhang, GuoRong Wang, YongHua Shi and BiLiang Zhong

J. Info. Comput. Sci. , 5 (2010), pp. 271-278.

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  • Abstract
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).
  • Keywords

underwater arc welding, penetration depth, least squares support vector machines

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COPYRIGHT: © Global Science Press

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@Article{JICS-5-271, author = {WeiMin Zhang, GuoRong Wang, YongHua Shi and BiLiang Zhong}, title = {LSSVM Model for Penetration Depth Detection in Underwater Arc Welding Process}, journal = {Journal of Information and Computing Science}, year = {2024}, volume = {5}, number = {4}, pages = {271--278}, abstract = { 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). }, issn = {1746-7659}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22701.html} }
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).
WeiMin Zhang, GuoRong Wang, YongHua Shi and BiLiang Zhong. (2024). LSSVM Model for Penetration Depth Detection in Underwater Arc Welding Process. Journal of Information and Computing Science. 5 (4). 271-278. doi:
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