Investigating the Impact of PVD Process Parameters on Residual Stress Distribution: A Multiscale Numerical Analysis Approach
Bibliographic record
Abstract
Abstract Residual stress in thin PVD coatings arises from a combination of growth stress and thermal stress. Despite the lower processing temperature, thermal mismatch stresses can be significant due to variations in physio-thermal properties between the coating and substrate materials. Understanding the underlying physics of the deposition process and resulting stresses, particularly thermal stresses within the coating-substrate system, is paramount. Key physical parameters of both the coating and the substrate, such as coefficient of thermal expansion and elastic modulus, significantly influence thermal stresses. There is a demand for a comprehensive methodology in thermo-mechanical design, specifically for layered coatings, that should complement experimental procedures used to evaluate coating performance. Although analytical models can handle linear-elastic or simple elastic-plastic materials for thermal stress prediction, numerical techniques such as finite element analysis (FEA) can address more general 2D or 3D problems in simulating thermal stresses within coating-substrate systems. This study employs a multi-scale numerical approach to estimate the residual stress distribution within the coating-substrate system. Validation of the numerical model is conducted against experimental findings concerning a TiB2 - Tungsten Carbide coating-substrate configuration. The validated model is then utilized to explore the influence of process parameters on residual stress distribution across various coating-substrate systems.
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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 | 0.001 |
| 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".