Low and High Speed Orthogonal Cutting of AA6061-T6 under Dry and Flood-Coolant Modes: Tool Wear and Residual Stress Measurements and Predictions
Bibliographic record
Abstract
The present research work aimed to study the effects of cutting environments and conditions on tool wear and residual stresses induced by orthogonal cutting of AA6061-T6. Cutting environments included dry- and flood-coolant modes and cutting conditions consisted of cutting speed and feed rate. A 2D finite element (FE) model was developed to predict tool wear and residual stresses and was validated by experimental measurements including machining forces, tool wear, and residual stresses. This was obtained by exploring various magnitudes of the shear friction factor and heat transfer coefficient and choosing proper coefficients using the calibration of the predicted results with the measured ones. The experimental results showed that the effect of cutting environment including dry and flood-coolant modes was negligible on machining forces. The experimental investigation also demonstrated that increasing feed rate raised machining forces, tool wear and residual stresses in both cutting environments. Low Speed Cutting (LSC) led to the highest value of tool wear and High Speed Cutting (HSC) provided the lowest values of resultant machining forces and residual stresses in both modes. Flood-coolant mode reduced tool wear and slightly decreased tensile residual stresses in comparison with dry mode. As a result, low feed rate and high-speed cutting under flood-coolant mode were proposed in order to improve tool wear and residual stress in orthogonal cutting of AA6061-T6.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".