Abstract
To help an automatic welding machine on reasoning dynamic welding process, a Kalman Filter Gaussian Process Regression (KF-GPR) model was proposed, and its theoretical basis was annualized. A prediction model was established later. Compared to conventional statistic method, the KF-GRP method can better estimate the distributed form and parameters for a dynamic welding process, which had higher robustness and fault tolerance. TIG welding experiment of the 304 stainless steel was carried out to verify the method. Totally 8 423 pairs of experiment data were collected and used for the model. The modeling results showed the proposed KF-GPR can suppress noises and provide fast and accurate model, which is essential for future online control experiment.
| Translated title of the contribution | Characteristic performance modeling method for weld pool based on KF-GPR |
|---|---|
| Original language | Chinese (Simplified) |
| Pages (from-to) | 49-52 |
| Number of pages | 4 |
| Journal | Hanjie Xuebao/Transactions of the China Welding Institution |
| Volume | 39 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 25 2018 |
Bibliographical note
Publisher Copyright:© 2018, Editorial Board of Transactions of the China Welding Institution, Magazine Agency Welding. All right reserved.
Keywords
- Critical characteristic performance
- Dynamic welding process
- GPR
- Kalman filter
ASJC Scopus subject areas
- Mechanics of Materials
- Mechanical Engineering