Surface Finish and Residual Stresses Induced by Orthogonal Dry Machining of AA7075-T651
Bibliographic record
Abstract
The surface finish was extensively studied in usual machining processes (turning, milling, and drilling). For these processes, the surface finish is strongly influenced by the cutting feed and the tool nose radius. However, a basic understanding of tool/surface finish interaction and residual stress generation has been lacking. This paper aims to investigate the surface finish and residual stresses under the orthogonal cutting since it can provide this information by avoiding the effect of the tool nose radius. The orthogonal machining of AA7075-T651 alloy through a series of cutting experiments was performed under dry conditions. Surface finish was studied using height and amplitude distribution roughness parameters. SEM and EDS were used to analyze surface damage and built-up edge (BUE) formation. An analysis of the surface topography showed that the surface roughness was sensitive to changes in cutting parameters. It was found that the formation of BUE and the interaction between the tool edge and the iron-rich intermetallic particles play a determinant role in controlling the surface finish during dry orthogonal machining of the AA7075-T651 alloy. Hoop stress was predominantly compressive on the surface and tended to be tensile with increased cutting speed. The reverse occurred for the surface axial stress. The smaller the cutting feed, the greater is the effect of cutting speed on both axial and hoop stresses. By controlling the cutting speed and feed, it is possible to generate a benchmark residual stress state and good surface finish using dry machining.
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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 | 0.000 |
| 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 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".