Measuring in-depth Residual Stress Gradients: The Challenge of Induction Hardened Parts
Bibliographic record
Abstract
Highly stressed machine parts such as gears and shafts are often surface treated to increase wear and fatigue resistance at critical locations. For example, induction surface hardening (ISH) is increasingly used in the automotive and aerospace industries thanks to the availability of modern multiple frequencies generators and complex shaped coils that provide a great flexibility in process control. With similar end-results in terms of hardened depths, very different residual stress profiles may be obtained, and optimized by modifying both heating and quenching kinetics. If hardness and microstructures variations are routinely verified, some challenges raise for the measurement of the residual stress gradients within complex geometry parts, in particular for the case of deep hardened layers. The most commonly used technique is X-ray diffraction (XRD). It requires using successive layer removal to get access to in-depth stresses. The measurements must therefore be corrected for the stress redistribution occurring during layer removal. However, industrial geometries are often not covered by traditional correction methods. The present work aims at applying XRD to precisely measure in-depth residual stress profiles in induction hardened thin discs made of martensitic steel. Both issues of microstructural variations and redistribution of stresses during layer removal are tackled. First, X-ray elastic constants were determined experimentally using a miniature custom-made tensile machine with specimens heat treated to simulate different microstructures found in ISH parts. Second, a recently introduced finite elements based layer removal correction method was applied. The proposed methodology is used to show the impact of preheating and core hardness on the residual stresses obtained after induction hardening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".