Real-Time Adaptive Modeling Approach to Compensate the Thermal Deformation of Nonlinear Machine Tool Structures
Bibliographic record
Abstract
Machine tool structures cannot be fully optimized at the design stage to cover the wide range of operating conditions. Therefore, reliable control systems emerge as the logical solution to compensate for thermal errors. Due to the difficulty of measuring the relative thermal displacement δ between the tool and the workpiece during machining, δ has to be accurately estimated in real-time. A new concept of adaptive modeling is introduced to develop control-based dynamic models to predict and compensate for thermal deformation of nonlinear complex machine tool structures. A key element of this approach is to replace the changes in the contact pressures along the joint by fictitious contact heat sources FCHS. This allows us to track the system nonlinearity through temperature measurements and real-time inverse heat conduction IHCP solution. The proposed approach dealt successfully with a number of challenges; namely, the non-uniqueness of the problem, and the lack of sufficient conditions to identify each of such unusual FCHS separately. The results showed that the models are capable of satisfying the accuracy, stability and computational efficiency requirements, even when the temperature measurement signal is contaminated with random noise. The results also showed that the thermal deformation transfer function behaves as low-pass filters, and as such it attenuates the high frequency noise associated with temperature measurement error.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".