AI and Robotics: A Symbiotic Relationship in Digital Manufacturing
Bibliographic record
Abstract
This study illustrates the relationship between robotics and machine learning, two disciplines that are revolutionising our surroundings. The synergy that makes it possible to create intelligent, flexible, and self-sufficient machines that are capable of carrying out a greater variety of activities than previously possible is demonstrated by this research. Machine learning (ML) has gained popularity as a computational tool in manufacturing in recent years. The current examination analyzed various ML calculations to foresee the geometry of the bead in WAAM (wire-arc additive manufacturing). A WAAM method in view of mechanical controlled GMAW (gas metal arc welding) was utilized to store the beads. Five machine learning models were used in the study: CNN, DT, SVM, XGBoost algorithms, and LR. Statistical measures like the coefficient of determination (R2 value), mean square error (MSE), mean absolute error (MAE), and index of merit (IM) were used to assess the performance of ML models. The comparison of ML algorithms showed that machine learning-based computational labour produces high-performance outcomes with less time and resource consumption. Therefore, in contemporary manufacturing businesses, ML may be the most efficient computational instrument.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".