Deep Learning to Directly Predict Compensation Values of Thermally Induced Volumetric Errors
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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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) | 0.001 | 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 | 0.001 |
| 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".