Research on the thermal characteristics of motorized spindle under magnetic–thermal coupling based on digital twin
Bibliographic record
Abstract
In operation, traditional mechanistic models fail to iteratively update with the motorized spindle, leading to inaccuracies in dynamic data like the motorized spindle's internal magnetic and thermal fields. To address the issue, we propose a data-driven, magnetic–thermal bidirectional coupling twin model based on digital twin technology. This model, considering temperature effects on the motorized spindle motor's materials, enables dynamic updates through a twin database and service platform. Comparative analysis shows minor temperature calculation differences between this twin model and traditional mechanistic models at speeds below 9000 r/min. As the speed reaches 9000 r/min and the motorized spindle's efficiency peaks, the disparity between the two models grows. Experimental data show that the twin model outperforms the traditional model in temperature accuracy at the motorized spindle's test points, achieving up to a 4.57% improvement in front bearing temperature calculations. The proposed motorized spindle twin model demonstrates higher accuracy, providing significant assistance in the dynamic process simulation and operational monitoring of the motorized spindle.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".