Minimum Time Trajectory Planning Along Specified Tool Path With Consideration of Cutting Force Constraint and Feed Drive Dynamics
Bibliographic record
Abstract
This paper proposes an Optimized Feed Scheduling Strategy (OFSS). This strategy integrates the feed drive dynamics with the minimum-time trajectory planing to achieve the desired feed rate at the appropriate tool position along specified tool path. It optimizes the use of the feed drive capabilities and provides good tracking of the cutting geometry variations. The feed scheduling is applied to maintain near-constant cutting force magnitude. An integrated geometric and mechanistic force model is used to estimate the in-cut geometry and the cutting force. A methodology based on time series modeling and analysis is proposed to identify the low frequency feed drive dynamics. The resulting model is applied as an acceleration/deceleration processor (Acc/Dec) to relate the actual feed rate to the commanded feed rate specified in the G-Code file. The effectiveness of the OFSS is analyzed using ball end milling operations. Its performance in terms of productivity and machining safety is assessed based on comparison with other feed scheduling techniques where the trajectory planing does not consider the feed drive dynamics.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".