Predictive Modeling and Optimization of Layer-Cladded Ti-Al-Nb-Zr High-Entropy Alloys Using Machine Learning
Bibliographic record
Abstract
In this work, the influence of laser power (LP), scanning speed (SS), and powder feeding speed (PF) on the porosity, dilution, and microhardness of lightweight refractory high-entropy alloy (RHEA) coatings produced via laser cladding (LC) was investigated. Variance analysis (ANOVA) was deployed to ascertain the effect of LP, SS, and PF on performance metrics such as porosity, dilution, and microhardness. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) was then applied to optimize these processing parameters to minimize porosity, achieve suitable dilution, and maximize microhardness, enhancing the mechanical properties of RHEA coatings. Finally, machine learning models—Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Genetic Algorithm-enhanced GBDT (GA-GBDT)—were developed using orthogonal experimental data, with GA-GBDT demonstrating superior predictive accuracy. The proposed approach integrates statistical analysis and advanced ML techniques, providing a better understanding into optimizing LP, SS, and PF for improved RHEA coatings performance in industrial applications, thereby advancing laser cladding technology.
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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 | 0.000 |
| 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".