Modeling residual stresses of functionally graded deposits using the PTAAM
Bibliographic record
Abstract
Abstract High maintenance costs due to significant abrasive wear of components is experienced in the energy and mining sectors despite the current use of tough and hard coatings. During the coating process significant tensile residual stresses may build up and result in premature failure of a component. These tensile stresses can be reduced by adopting functionally graded structures of the composite. The main goal of the present study is to design an ideal material gradient in the additively manufactured composite using the plasma transferred arc (PTA) with a WC-Ni alloy graded in WC. To develop a comprehensive analysis of the functionally graded deposit, the thermal history of the WC and Ni alloy powders are first simulated as they travel through the plasma and deposit on the substrate. The initial temperature of each deposited bead depending on the WC content is deduced. These results are used as an input to predict the temperature and stress history of the solidifying deposit. Thermal stresses are computed and trends of residual stresses are obtained as a function of the WC gradient selected. The trends obtained are compared quantitatively before concluding on the most favorable gradient for this wear resistance application.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".