Phase Field Modelling of Microstructure Evolution of Cold-Sprayed Ni-Ti Composite upon Post-Spray Heat Treatment
Bibliographic record
Abstract
Abstract The Cold spray (CS) is a promising solid-state additive manufacturing method. The interesting physics involved in the CS process including cold, high strain rate, adiabatic and severe plastic deformation results in a unique and complex structure of CS deposits at different length scales that directly determines the properties of the deposits. Therefore process- structure properties (performance) (PSP) linkages explorations are pivotal. Integrated computational materials engineering (ICME) methods in complement with experimental analyses are required to evaluate materials properties and behaviour in PSP links exploration. Finite element modelling is used to simulate the thermomechanical response of materials and evolution of field variables in CS, i.e stress, strain, strain rate, and temperature, at structural scales. Molecular dynamics modellings of nano-particle impact have provided useful insights into atomic-scale phenomena of individual particle impact while the modelling of microstructure evolution in micro and mesoscale has yet to be investigated. In this study, we developed and implemented a thermodynamic phase field simulation method to capture the structure evolution of CS composite Ni-Ti deposit upon post-spray heat treatment (PSHT) in microstructure scale. The external or internal stimuli such as heat and strain either generated in the system because of phase transformation or stored as internal energy upon CS process are accounted for. The interface mobility and microstructure development are calculated by minimization of Gibbs free energy of the system. The comparison of the simulated microstructure with experimental results confirms that the phase field modelling precisely predicts the microstructure evolution of the CS deposits upon PSHT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".