An On-Line ANN-Based Approach for Quality Estimation in Resistance Spot Welding
Bibliographic record
Abstract
The aim of this study is to develop an effective on-line ANN-based approach for quality estimation in resistance spot welding. The proposed approach examines the welding parameters and conditions known to have an influence on weld quality, and builds a quality estimation model step by step. The modeling procedure begins by establishing relationships between welding parameters (welding time, welding current, electrode force and sheet metal thickness), welding conditions represented by typical characteristics of the dynamic resistance curve and welding quality indices (nugget diameter, nugget penetration, and indentation depth), and the sensitivity of these elements to the variation of the process conditions. Using these results and various statistical tools, three estimation models are developed. The first one is based exclusively on welding parameters. The second model is based on characteristics of the dynamic resistance curve. The third estimation model combines welding parameters and characteristics of dynamic resistance curves. In order to carry out the models building procedure, an extensive number of welding experiments are required. For this purpose, Taguchi’s efficient method of experimental planning is adopted. The results demonstrate that the developed models can provide an accurate on-line estimate of the weld quality, under different welding 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".