A sustainable ecofriendly additive manufacturing approach of repairing and coating on the substrate: cold spray
Bibliographic record
Abstract
In this study, surface modification and deformation behaviour of different engineering materials were microscopically examined by an approach called cold spray additive manufacturing (CSAM). The CSAM is a layer-by-layer deposition technique for repairing and coating the deteriorated or dimensionally unstable surface. Furthermore, in this study, both the experimental and numerical investigation have been carried to understand the surface modification approach. In the experimental procedure, the de Laval nozzle accelerates the microsize particle at a high velocity on the substrate to obtain the uniform surface coating. Engineering materials like copper, aluminium, titanium and mild steel were probed with different ranges of velocities. Additionally, an explicit/Abaqus finite element approach was used to investigate the bonding between particle and substrate by using the high velocity of particles. At the interface, refining meshing size reduces the beginning velocity for adiabatic shear instability (ASI). For the different combinations of particles and substrates like copper/copper, aluminium/aluminium, titanium/aluminium, aluminium/ titanium, and copper/mild steel at perpendicular impact, the adiabatic shear instability was numerically estimated. Deformation behaviour for multi-impact has also been seen with different particle diameter sizes to determine coating phenomenon. Furthermore, the characterisation technique called scanning electron microscopy (SEM) also showed the uniform coating on the substrate.
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 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".