Experimental investigation and simulation of laser surface heating and its effects on residual stresses and microstructure for AISI 52100 and H13
Bibliographic record
Abstract
Laser surface heat treatment (LSHT) is a viable solution to control surface hardness, and consequently to increase its abrasion and corrosion resistance, without causing thermal distortion of the component. In laser-assisted machining (LAM), the use of laser heating ahead of the cutting tool raises the surface temperature and reduces the strength of the workpiece materials, thus improves its machinability. However, both processes produce tensile residual stresses. Additionally, rapid heating and cooling can induce microstructural changes near the material surface. This paper consists of an experimental investigation and numerical simulation of LSHT to study the effect of laser beam parameters on the surface integrity of tool steel H13 and bearing steel AISI 52100. This knowledge provides basic information for optimization of LSHT and LAM processes. In the present LSHT experiments, the effect of the process parameters, namely, laser spot size, inclination angle, laser power and feed on the temperature profile, residual stresses, and microstructure evolution was investigated. A 3D FE model of moving heat source was implemented in DEFORM-3D to simulate the LSHT process. The FE model was calibrated and validated using the experimental data. For H13, the predicted thickness of the hardened layer agreed with the measurements with an error < 10%. For AISI 52100, the predicted thicknesses of white and dark layers agreed well with the experimental measurements. The results showed that predicted tensile residual stresses that appeared at LSHT surfaces for H13 and AISI 52100 were similar to those obtained experimentally, with maximum error < 30%.
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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) | 0.002 | 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".