Investigation of residual stresses in laser clad coatings
Bibliographic record
Abstract
Laser cladding process employs a controlled heat input to create a layer of clad on the surface of a substrate, producing a coating with refined microstructure and a metallurgical bond with the substrate. However, because of the nature of the laser cladding, this process introduces a certain amount of residual stresses into the clad and substrate, affecting the mechanical properties and dimensional stability of the workpiece being clad. Moreover, the quantitative determination of the residual stresses thus produced is always technically challenging since their distribution across the thickness of the coating, in nature, is non-uniform. Integrated Manufacturing Technologies Institute (IMTI) and Neutron Program for Materials Research (NPMR) of the Steacie Institute for Molecular Sciences (SIMS) collaboratively performed a research on the determination of residual stresses in laser clad coatings through neutron diffraction and hole-drilling methods. In this investigation, nickel-based IN-625 alloy and AISI P20 tool steel powders were selected as cladding materials. A blown powder laser cladding technique was used to produce clad specimens. During the cladding, the IN-625 clad did not undergo a solid-state phase transformation after re-solidification while the P20 clad exhibited a martensitic transformation through the period of the cool-down. The residual stresses thus developed in both coatings represented two different but typical cases. Both neutron diffraction and hole-drilling methods were used to determine the nature of these induced stresses. Moreover, x-ray diffraction (XRD) and scanning electron microscope (SEM) were used to study the microstructures in the both laser clad specimens in order to obtain a better understanding on the development of the residual stresses in the laser clad IN-625 and P20 coatings. This report includes a summary of the experimental results and the corresponding discussion.
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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".