CHARACTERISATION OF STEEL BLADE MICROSTRUCTURE PRODUCED USING LASER ENGINEERING NET SHAPE PROCESS
Bibliographic record
Abstract
Laser rapid forming is a recently developed method for the production of metal parts. Several laser based method such as selective laser sintering (SLS), direct metal laser sintering (DMLS), and laser engineering net shaping (LENS) are available. Of the laser based techniques, the LENS process the LENS process was employed to produce steel blades. The advantages of the technique are the fast processing speed, the elimination of tooling requirements, the ability to fabricate intricate shapes at lower cost and the retention of the metastable microstructure. The LENS process has been concentrated on producing titanium metal parts. Suitability and possible defects of components produced by the LENS process on steel material has not yet been fully studied according to the author’s knowledge. The microstructure homogeneity plays an important role in producing uniform mechanical properties of 316L stainless steel during production. It has been found that the microstructure is very dependent on the heating profile during the laser heating process. 410L stainless steel powder was used to produce blades using the LENS process. The microstructure characterisation was performed using optical and scanning electron microscopes. Fully martensitic microstructure was observed. The hardness profile of the blade is high on the middle section of the blade than it is on the round and sharp edges of the blade. The aim of the study was to evaluate the microstructure and hardness of the steel blade produced by the LENS process.
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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 | 0.000 | 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) | 0.001 | 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".