Comparison study of H13 tool steel microstructure produced by laser cladding and laser consolidation
Bibliographic record
Abstract
Laser cladding is used to deposit desired materials at specified locations to enhance the surface properties or to repair damaged regions, while laser consolidation is used to build functional components or features on existing components. Although both processes are based on the melting of substrate surface along with injected powder (or wire) to deposit material, their cooling patterns and cooling rates could be significantly different. As a result, the microstructures of the same material deposited by laser cladding and laser consolidation may show some differences, which could ultimately affect mechanical properties of the deposited material. In this paper, x-ray diffraction, optical microscopy, scanning electron microscopy and other techniques were used to compare the microstructure of AISI H13 tool steel deposited by laser cladding and laser consolidation processes. Their microhardness and residual stresses were also measured. It was found that although both laser clad and laser consolidated H13 material exhibit "as-deposited" and "re-heated" regions in their microstructures, the morphology of these respective regions shows substantial differences. These factors may have been influenced microhardness and residual stresses.
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 | 0.000 | 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".