Advancements in control systems and integration of artificial intelligence in welding robots: A review
Bibliographic record
Abstract
Welding automation has witnessed significant advancements with the integration of control systems and artificial intelligence (AI) in welding robots. This review paper explores the significance of control systems in various welding methods, including arc, laser, spot, and friction stir welding. It highlights their role in achieving precise, efficient weld quality, reducing human errors, and executing complex tasks with high accuracy and repeatability. Sensor technologies play a crucial role in control systems, enabling real-time monitoring of welding parameters and ensuring optimal process control. Thus, various sensor technologies utilized in welding robots are examined, such as vision systems, force sensors, and temperature sensors, emphasizing their contribution to enhancing weld quality and overall system performance. Additionally, the application of welding robots in challenging environments, such as ocean pipeline welding, is discussed, highlighting the importance of robust control systems and sensor technologies in these contexts. Lastly, the paper delves into applying machine learning methods to welding robots, enabling the development of intelligent systems capable of adapting and optimizing the welding processes. The utilization of machine learning algorithms for weld defect detection, process parameter optimization, and predictive maintenance is discussed, representing the potential of integrating AI in welding robots to enhance their performance and efficiency. • Study of the significance of control systems in various welding methods. • Review and comparison of different control systems applied in Welding Robots. • Review of application and limitations of various Sensors used in welding robots. • Review of the state-of-the-art artificial intelligence use in Welding Robots.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".