A Numerical Model for Predicting the Effect of Tool Nose Radius on Machining Process Performance during Orthogonal Cutting of AISI 1045 Steel
Bibliographic record
Abstract
This paper presents the development of a numerical model for predicting and studying the effects of tool nose geometries and its interactions with cutting parameters during orthogonal cutting of AISI 1045 steel. The process performance characteristics studied were cutting temperature, effective stress, cutting forces and tool wear. The cutting simulations were done using the commercial DEFORM-2D R V 11.3 software, based on the finite element method (FEM). The cutting tool used had a round nose with various nose radii (0.01–0.9 mm), while the machining parameters tested were the feed rate (0.1–0.3 mm/rev), the cutting speed (100–500 m/min) and the rake angle (–5° to +10°). The interactions between the tool nose radius and the cutting parameters (speed, feed) were found to affect mostly the cutting stress and, slightly, the tool wear rate. These interactions did not much influence the cutting temperature, that was found to be high when the tool nose radius and/or the cutting speed were high. The maximum temperature was found to occur at the middle of the tool-chip contact length and at the interaction of nose radius and flank face of the tool. Except for some fluctuations, there was no significant difference in tool wear rate between small and large nose radius scales.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".