Microstructure and process induced residual stresses of laser clad CPM-9V and CPM-10V tool steels
Bibliographic record
Abstract
Laser cladding uses a focused laser beam to melt injected or pre-placed powder (or wire) to deposit a layer of desired material onto the surface of a substrate to form a dense and metallurgically sound coating with improved wear, corrosion and/or oxidation resistance. Compared to the conventional weld deposition, laser cladding induces much less heat input to the substrate and also produces a refined microstructure in the coating due to a relatively fast cooling inherent in the process. However, there is still certain amount of process induced residual stresses in the clad, which may adversely affect the mechanical properties and dimensional stability of the parts being clad. In this paper, a blown powder laser cladding technique was used to deposit high-vanadium CPM-9V and CPM-10V tool steel powders on AISI 1070 carbon steel substrate in order to improve its wear resistance. After the cladding, a series of heat treatments were performed on the clad specimens to alleviate the process induced residual stresses. The residual stresses were evaluated using a hole-drilling method. The evolution of the microstructure in the laser clad CPM-9V and CPM-10V coatings during the treatments was also examined using scanning electron microscope and X-ray diffraction. This study was performed to obtain a better understanding of the nature of the residual stresses in the laser clad CPM-9V and CPM-10V tool steel coatings.
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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) | 0.000 | 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".