Optimisation of Laser Cladding Process Using Taguchi and EM Methods for MMC Coating Production
Bibliographic record
Abstract
Abstract This study investigates the influence of laser cladding parameters on the geometry and composition of metal-matrix composite (MMC) coatings. Composite coatings are made of a Ni-Cr-B-Si metallic matrix and of WC reinforcement with a volume fraction of 50 %. Optical microscopy is used to characterize the coating geometry (height, width and penetration depth) and to determine the real volumetric content of WC. Laser cladding on low carbon steel substrate is carried out using a cw Nd:YAG laser, a coaxial powder injection system and a combination of Taguchi and EM methods to design the experiments. This combination explores efficiently the multidimensional volume of laser cladding parameters. The results, which express the interrelationship between laser cladding parameters and the characteristics of the clad produced, can be used to find optimum laser parameters, to predict the responses and to improve the understanding of laser cladding process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".