Prediction of weld quality using intelligent decision making tools
Bibliographic record
Abstract
Decision-making process in manufacturing environment is increasingly difficult due to the rapid changes in design anddemand of quality products. To make decision making process online, effective and efficient artificial intelligent tools likeneural networks are being attempted. This paper proposes the development of neural network models for prediction ofweld quality in Submerged Arc Welding (SAW). Experiments are designed according to Taguchi’s principles andmathematical equations are developed using multiple regression model. Proposed neural network models are developedusing experimental data, supported with the data generated by regression model. The performances of the developedmodels are compared in terms of computational speed and prediction accuracy. It is found that Neural Network trainedwith Particle Swarm Optimization (NNPSO) performs better than Neural Network trained with Back Propagation (BPNN)algorithm, Radial Basis Functional Neural Network (RBFNN) and Neural Network trained with Genetic Algorithm(NNGA). The developed scheme for weld quality prediction is flexible, competent, and accurate than existing models andit scopes better online monitoring system. Finally the developed models are validated. The proposed and developedtechnique finds a good scope and a better future in the relevant field where human can avoid unwanted risks duringoperations with the deployment of robots.
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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 | 0.000 | 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".