Application of Laser Assisted Cold Spraying Process for Materials Deposition
Bibliographic record
Abstract
The Laser-Assisted Cold Spraying (LACS) process is a hybrid technique that uses laser and cold spray mechanism to deposit solid powders on metal substrates. For bonding to occur, the particle velocities must be supersonic. The supersonic effects can be achieved by passing a highly compressed Nitrogen gas (˜30 bars) through de Laval supersonic nozzle. LACS is a surface coating technique that is desirable in rapid prototyping and manufacturing, particularly for biomedical applications. Current world research reveals that the capability of the LACS regarding the enhancement of surface properties of coating titanium alloys with hydroxyapatite will be essential for fabricating scaffolds for bone implants using Laser Engineered Net Shaping (LENS) technique. In this chapter, coatings of composite powders made of titanium and hydroxyapatite deposited on Ti-6Al-4V substrate by LACS technology are presented. These coatings were successfully characterised by means of X-Ray Diffraction (XRD) and optical microscopy for their phases, composition, and microstructure, respectively. The results of the produced LACS coatings compare well with those obtained with traditional thermal spray and cold spray techniques, respectively. In addition, the XRD results were found to be similar to the precursor powders, which indicated that no phase transformation occurred to HAP. Coatings comprising of other crystalline phases of HAP are less bio-integrable and fail quicker within the human body fluids environments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".