Control-Oriented Modeling of Thermal Deformation of Machine Tools Based on Inverse Solution of Time-Variant Thermal Loads with Delayed Response
Bibliographic record
Abstract
With the new emerging technologies of high performance machining and the increasing demand for improved machining accuracy in recent years, the problem of thermal deformation of machine tool structures is becoming more critical than ever. The major problem in implementing real-time control systems is the difficulty of measuring the relative thermal displacement between the tool and the workpiece during machining. Therefore, the design of a generic multi-axis control system requires the development of control-based models to estimate the transient thermal load and the thermal deformation of the structure in real-time. To satisfy the stringent accuracy and stability requirements of the control system, a new inverse heat conduction problem IHCP solver is developed. This solution is capable of including the inertia effect and the delay in the thermal response, in order to accommodate situations where the measured points cannot be located near the heat source, which may be buried into the structure. Experimental validation of these models showed their inherent stability even when the temperature measurements are contaminated with random errors. The excellent computational efficiency of the integrated system, which is well suited for real-time control applications involving multi-dimensional structures, was achieved by incorporating an inverse numerical Laplace transformation procedure. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".