Deep Learning to Directly Predict Compensation Values of Thermally Induced Volumetric Errors
Why this work is in the frame
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Bibliographic record
Abstract
The activities of the rotary axes of a five-axis machine tool generate heat causing temperature changes within the machine that contribute to tool center point (TCP) deviations. Real time prediction of these thermally induced volumetric errors (TVEs) at different positions within the workspace may be used for their compensation. A Stacked Long Short Term Memories (SLSTMs) model is proposed to find the relationship between the TVEs for different axis command positions and power consumptions of the rotary axes, machine’s linear and rotary axis positions. In addition, a Stacked Gated Recurrent Units (SGRUs) model is also used to predict some cases, which are the best and the worst predictions of SLSTMs to know the abilities of their predictions. Training data come from a long motion activity experiment lasting 132 h (528 measuring cycles). Adaptive moment with decoupled weight decay (AdamW) optimizer is used to strengthen the models and increase the quality of prediction. Multistep ahead prediction in the testing phase is applied to seven positions not used for training in the long activity sequence and 31 positions in a different short activity sequence of the rotary axes lasting a total of 40 h (160 cycles) to test the ability of the trained model. The testing phase with SLSTMs yields fittings between the predicted values and measured data (without using the measured values as targets) from 69.2% to 98.8%. SGRUs show performance similar to SLSTMs with no clear winner.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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