An Investigation of Thermal Behaviour of a Machine Tool on the Dimensional Accuracy of Parts
Bibliographic record
Abstract
The thermal behaviour of machine tools can profoundly affect the dimensional accuracy of manufactured parts.Consequently, reducing its influence has become increasingly important to modern manufacturing.One of the most convenient and effective ways to reduce thermal error and enhance working accuracy is volumetric error compensation, especially for CNC machine tools.However, currently available compensation methods are highly complicated and difficult to apply in industry.They require a high-precision laser interferometer to measure 21 geometric error components and numerous thermocouples to monitor temperature variations.These requirements make such techniques costly and time consuming.This paper describes the development of a simplified and economical method of compensating for thermally induced errors.Compensation is implemented on the basis of only three axial positioning errors, which are assumed to be functions of ball screw nut temperature and travel distance.Instead of an expensive laser interferometer and many thermocouples, a simple laser Doppler displacement meter and only three thermocouples are required for the proposed method.Results show good agreement between measured and predicted thermally induced errors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".