Artificial neural network-based resistance spot welding quality assessment system
Bibliographic record
Abstract
On-line quality assessment has become one of the most critical requirements for improving the efficiency and the autonomy of automatic resistance spot welding (RSW) processes. An accurate and efficient model to perform non-destructive quality estimation is an essential part of the assessment process. This paper presents a structured and systematic approach developed to design an effective ANN-based model for on-line quality assessment in RSW. The proposed approach examines 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 examining, through a structured experimental design, the effect of welding parameters (welding time, welding current, electrode force and sheet metal thickness) and welding conditions represented by typical characteristics of the dynamic resistance curves on multiple welding quality indicators (indentation depth, nugget diameter and nugget penetration) and by analyzing their interactions and their sensitivity to the variation of the dynamic process conditions. Using these results and by combining an efficient modeling planning method, neural network paradigm, multi-criteria optimization and various statistical tools, the identification of the model form and the variables to be included in the model is achieved by executing a systematic model optimization procedure. The results demonstrate that the proposed approach can lead to a general ANN-based model able to accurately and reliably provide an appropriate assessment of the weld quality under diverse and variable 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".