Application of machine learning for seam profile identification in robotic welding
Bibliographic record
Abstract
This paper addresses critical challenges in automated robotic welding, emphasizing precise weld groove profiling for pipe welding applications. By integrating advanced laser scanning technology with the Local Outlier Factor (LOF) algorithm, the research effectively mitigates outliers and compensates for incomplete data—persistent issues in dynamic manufacturing environments. To further enhance accuracy, a robust neural network model is employed to predict weld groove alignment, a crucial factor in maintaining weld structural integrity. The LOF algorithm was chosen for its ability to detect spatial anomalies, ensuring the exclusion of erroneous data that could compromise welding precision. Experimental results demonstrate that the combined use of LOF and neural networks significantly improves the operational efficiency of robotic welding, delivering consistently strong and precise welds across diverse manufacturing scenarios. The model achieved an average mean square error of 0.078 and an R² value of 0.995, accurately predicting 99.5 % of data. Therefore, neural network modeling enables accurate interpolation of missing data and real-time adjustments to varying operational conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".