Tool path error prediction of a five-axis machine tool with geometric errors
Bibliographic record
Abstract
Predicting the actual tool path of a machine tool prior to machining a part provides useful data in order to ensure or improve the dimensional accuracy of the part. The actual tool path can be estimated by accounting for the effect of the machine tool geometric error parameters. In computer aided design/computer aided manufacture (CAD/CAM) systems, the nominal tool path [or CL (cutter location) data] is directly generated from the curves and surfaces to be machined and the errors of the machine tool are not considered. In order to take these errors into consideration, they must first be identified and then used in the machine tool forward kinematic model. In this paper a method is presented to identify the geometric errors of machine tools and predict their effect on the tool-tip position. Both the link errors (position-independent geometric error parameters) and the motion errors (position-dependent geometric error parameters) are considered. The nominal and predicted tool paths are compared and an assessment is made of the resulting surfaces with respect to the desired part profile tolerance. A methodology is also suggested to integrate this tool within a CAD/CAPP (computer aided process planning)/CAM environment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".