Numerical Simulation of Solid Ti6AI4V Particles Impinging on a Stainless-Steel Substrate at High Speed: Influence of the Particle Temperature
Bibliographic record
Abstract
Abstract Additive manufacturing processes have been used to produce or repair components in different industry sectors like aerospace, automotive, and biomedical. In these processes, a part can be built by either melted particles as in selective laser melting (SLM) or solid-state particles as in the cold spray process. The cold spray has gained significant attention due to its potential for high deposition rate and nearly zero oxidation. However, the main concern associated with using the cold spray is the level of porosity in as-fabricated samples, altering their mechanical properties. These pores are primarily found in the regions where adiabatic shear instability does not occur. It is worth noting that the deformation of the impacted solid particle plays a vital role in reaching the shear instability. Therefore, for investigating the adiabatic shear instability region, an elastic-plastic simulation approach has been used. For this purpose, it is assumed that an elevated temperature solid Ti6Al4V particle impacts on a stainless-steel substrate surface at high velocity. The results show that increasing particle temperature will significantly enhance particle deformation because of thermal softening. Additionally, they illustrate that a material jet responsible for producing a bonding between particle and substrate by ejecting the broken oxide layer will be formed when the particle has a temperature above 1073 K and substrate remains at room temperature. In the end, it should be noted that increasing particle temperature up to 723 K will not have a significant effect on substrate deformation and final substrate temperature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".