Process planning for corner machining based on a looping tool path strategy
Bibliographic record
Abstract
A corner is an elemental machining feature for internal pockets that is difficult to plan and execute. During machining of a corner, there is continuous variation in radial depth of cut and frequent changes in magnitude as well as direction of the feed rate. These result in inconsistent machining leading to machine tool jerk, excessive cutting force, and poor surface finish. In this paper, an integrated process planning approach for optimal corner machining has been proposed that combines the tool path generation and machining parameter selection tasks. As a first step a looping tool path strategy was implemented to progressively remove material in multiple loops in order to keep the radial depth under a permissible limit. The tool path consists of G 1 continuous biarc and arc spline segments which allow a constant feed rate to be held over the entire tool path. The geometries of the corner and cutting tool and the kinematics of the machine tool structure were considered in the calculation of the allowable constant feed rate. In the next step, the machining time was minimized by iteratively adjusting the feed per tooth value under cutting force constraints. The constraint ensured that the tool deflection was always under a tolerance limit. The resulting tool paths for different test cases indicated the ability of the tool path generation strategy to minimize the number of loops. A comparison of the results on machining times based on initial and optimal feed values and their corresponding tool path lengths indicated the potential for the improvement in productivity of corner machining. The proposed integrated approach that combines both geometric and machining parameters can generate more optimal process plans than approaches that consider these parameters separately.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".