Numerical study on the impacts of tool edge geometry and cutting conditions in orthogonal machining of AISI 1045 steel
Bibliographic record
Abstract
The present article presents a numerical study of the effects of tool geometries and cutting parameters on temperature, effective stress, chip thickness and tool wear depth during orthogonal cutting of AISI 1045 steel. This study consists to perform different numerical simulation tests using finite elements method on cutting chamfer tool. The important parameters that significantly influence the different studied machining characteristics were identified using analysis of variance (ANOVA). The obtained numerical results showed that the high values of temperature, stress, chip thickness and wear depth were almost obtained for the chamfer widths of (0.35, 0.45) mm. With increasing chamfer angle, there were different fluctuations of all the studied characteristics. In term of combinate influences, for different cutting speeds, the optimum chamfer width of (0.25, 0.35) mm produces respectively minimum and maximum cutting stress, chip thickness and wear depth. For different chamfer angles, cutting temperature, stress, and wear depth increase with increasing feed rate, whereas, maximum chip thickness was found for chamfer angle of 25°. The ANOVA showed that the feed rate, the cutting speed and their interactions influence almost significantly cutting temperature, stress, chip thickness and wear depth.
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 | 0.001 |
| 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".