New Input Factors for Machine Learning Approaches to Predict the Weld Quality of Ultrasonically Welded Thermoplastic Composite Materials
Bibliographic record
Abstract
Thermoplastic composites (TCs) enjoy high popularity in the field of engineering. Due to this popularity, there is a growing need to assemble this material with the help of fast and efficient joining processes. One joining process, which has seen increased use, is the process of ultrasonic welding. To make reliable statements about the quality of the joined material, some kind of quality assurance has to be made. In terms of ultrasonic spot welding, there are already some documented approaches for observing or predicting the joining quality, but some of these most promising parameters for quality assurance are difficult to measure in the process of continuous ultrasonic welding. This is why new parameters are investigated for their potential to improve the prediction of ultrasonic-welded TCs’ quality. Thermography and sound emission data have been found to have a correlation with the produced weld quality and are fed into different machine learning algorithms. Despite the relatively small dataset, trained algorithms reach binary classification rates of over 90%, indicating that the newly discovered parameters show the potential to improve the quality assurance of ultrasonic-welded TCs in the future. This improvement may enable the establishment of the ultrasonic welding of TCs in manufacturing.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".