An Integrated Modeling Approach for ANN-Based Real-Time Thermal Error Compensation on a CNC Turning Center
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Thermally induced errors play a critical role in controlling the level of machining accuracy. They can represent a significant proportion of dimensional errors in produced parts. Since thermal errors cannot totally be eliminated at the design stage, active errors compensation appears to be the most economical and realistic solution. Accurate and efficient modeling of the thermally induced errors is an indispensable part of the error compensation process. This paper presents an integrated and comprehensive modeling approach for real-time thermal error compensation. The modeling process is based on multiple temperature measurements, Taguchi’s orthogonal arrays, artificial neural networks and various statistical tools to provide cost effective selection of appropriate temperature variables and modeling conditions as well as to achieve robust and accurate thermal error models. The experimental results on a CNC turning center confirm the feasibility and efficiency of the proposed approach and show that the resultant model can accurately predict the time-variant spindle thermal drift errors under various operating conditions. After compensation, the thermally induced spindle errors were reduced from 19m to less than 1 m. The proposed modeling optimization strategy can be effectively and advantageously used for real-time error compensation since it presents the benefit of straightforward application, reduced modeling time and uncertainty.
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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 | 0.001 | 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 it