Effect of Shear Localization on Surface Residual Stress Distribution in Machining of Waspaloy
Bibliographic record
Abstract
Abstract In the machining of high-strength materials, shear localization in serrated chip formation leads to time-varying thermo-mechanical loads exerted by the cutting tool on the machined surface. This results in periodic changes to surface integrity. This article explains the formation mechanism of machined surface microfeatures and residual stress fluctuations associated with serrated chip formation, based on a finite element model of machining Waspaloy using the coupled Eulerian–Lagrangian method. The model is validated by comparing the simulation results with experimentally measured chip morphologies and machined surface profiles. During machining with a constant chip thickness, the machined surface exhibits a uniformly distributed residual stress pattern along the cutting velocity direction. However, increased cutting velocity and serrated chip formation cause periodic shear bands, leading to time-varying location of the stagnation point on the cutting tool. This results in variations in the workpiece material volume and the thermo-mechanical loads in the plowing region. After machining, the periodical variation in the elastic recovery of the plowed material at the bottom of the cutting tool creates waveforms on the finished surface, accompanied by fluctuations in residual stress at the same frequency as chip serration. The simulations quantitatively determine the normal/shear contact force at the tool-workpiece interfaces to reveal the effect of the time-varying stagnation point location on surface topographies and residual stress distributions.
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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 | 0.001 | 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 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".