ARTIFICAL INTELLIGENCE SUPPORTED APPLICATION FOR EXPLOSIVE CLADDING PROCESS SPECIFICATION
Bibliographic record
Abstract
<p>Explosive cladding is becoming increasingly widespread in the field of metalworking<br />technologies. The advantage of this technology is that it cannot be combined with other<br />welding technologies, and dissimilar metals can be joined by cohesion joint. The wide range<br />of materials and the different properties of metallic materials (modulus of elasticity, tensile<br />strength, hardness, ductility, etc.) are the reasons for the difficulty of determining the welding<br />process specification. In addition, many explosives (with blast velocities below the speed of<br />sound) are suitable for creating the appropriate bond strength. AI is a good tool for several<br />applications and process parameter calculations. The innovative application supported by AI<br />can help the welding engineer in the explosive welding process parameter determinations.<br />For the welding process, the engineer chooses suitable metal and explosive materials. AI,<br />based on the explosive material parameters and the metallic materials' mechanical properties,<br />calculate the explosive welding setup parameters. In this article, the algorithm of the<br />application and the theoretical and practical elements of the technological design are<br />presented in detail. The developed application facilitates the technological design of the<br />otherwise complex blast welding process.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".