The Influence of Tool Geometry Parameters on Thermo-Mechanical Loads and Residual Stresses Induced by Orthogonal Cutting of AA6061-T6: A Numerical Investigation
Bibliographic record
Abstract
The residual stresses state that a mechanical part obtained after machining is a crucial factor that impacts its in-service performance. This stress state is influenced by the thermomechanical loads exerted on the parts during the machining process, which are, in turn, determined by the tool parameters, process, and machining conditions. The aim of the present research was to anticipate how the cutting tool’s edge radius, rake angle, and clearance angle would affect the forces, temperature, and residual stresses induced while orthogonally cutting aluminum AA6061-T6. To achieve this, two-dimensional DEFORM™ software was utilized to develop a finite element model. The residual stresses trend results obtained demonstrated that rake angles of 0° and 17.5–20° values with a small edge radius (5 to 10 µm) and clearance angles of 7 and 17.5° values gave higher compressive stresses. The obtained simulated results were in good agreement with the experiments. The cutting forces, the temperature, and the maximum and minimum machining-induced residual stresses were found to be influenced more by the tool edge radius and the tool rake angle. The influence of the clearance angles on the above-mentioned machining responses was the least. Residual stresses can have a significant impact on the in-service performance of machined parts. The obtained results will help engineers select or design tools that promote a desired surface integrity during machining. This task is not obvious in practice because of difficulties in measuring residual stresses and also because the machining parameters and the tool geometry parameters have different and opposite impacts on thermo-mechanical loads, productivity, and on machining induced residual stresses.
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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) | 0.000 | 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 | 0.001 | 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".