Optimization of Welding Speed Using Mahalanobis Distance Method on a Vertical-Position Welding Process
Bibliographic record
Abstract
For the automation of complex manufacturing systems, a great deal of progress came up precision and on-line quality control.However, the welding process for the vertical-position is not only much more difficult, but also has tendency that the welding quality is lower compared to a horizontal-position welding because the effect of gravity force on metal transfer during welding process could cause to the welding fault.The common method in detecting of the welding fault has still been based on off-line technique whereas the weld fault could be detected after the welding process finished, and hence, it leads to inefficient process.In order to deal with that challenge, a new algorithm based on Mahalanobis Distance (MD) method for an on-line monitoring system for the vertical-position welding process is proposed in this study.From the results, it was found that the optimal welding speed setting at 53 mm/min has obtained the highest welding quality whereby the welding quality 98.01% of the start position and 99.36% of the middle position.The verified results confirmed that the developed algorithm could be defined the welding quality so that it is useful method to be applied for welding control system to achieve the desired welding quality.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".