Application of machine learning for seam profile identification in robotic welding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it