Effects of Strain Hardening and Initial Yield Strength on Machining-Induced Residual Stresses
Bibliographic record
Abstract
Finite element analysis was used in the current study to examine the effects of strain hardening and initial yield strength of workpiece material on machining-induced residual stresses (RS). An arbitrary–Lagrangian–Eulerian finite element model was built to simulate orthogonal dry cutting with continuous chip formation, then a pure Lagrangian analysis was used to predict the induced RS. The current work was validated by comparing the predicted RS profiles in four workpiece materials to their corresponding experimental profiles obtained under similar cutting conditions. These materials were AISI H13 tool steel, AISI 316L stainless steel, AISI 52100 hardened steel, and AISI 4340 steel. The Johnson–Cook (J–C) constitutive equation was used to model the plastic behavior of the workpiece material. Different values were assigned to the J-C parameters representing the studied properties. Three values were assigned to each of the initial yield strength (A) and strain hardening coefficient (B), and two values were assigned to the strain hardening exponent (n). Therefore, the full test matrix had 18 different materials, covering a wide range of commercial steels. The yield strength and strain hardening properties had opposite effects on RS, where higher A and lower B or n decreased the tendency for surface tensile RS. Because of the opposite effects of A and (B and n), maximum surface tensile RS was induced in the material with minimum A and maximum B and n values. A physical explanation was provided for the effects of A, B, and n on cutting temperatures, strains, and stresses, which was subsequently used to explain their effects on RS. Finally, the current results were used to predict the type of surface RS in different workpiece materials based on their A, B, and n values.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 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".