Machine learning models for predicting volumetric errors based on scale and master balls artefact probing data
Bibliographic record
Abstract
The volumetric accuracy of machine tools is important to both machine tool manufacturers and users. Predicting volumetric errors (VEs) is a pre-requisite for their compensation yielding increased dimensional quality of machined parts. However, predicting VEs in five-axis machine tools is challenging due to the complexity of error sources and their associated physics-based model. Machine learning (ML) is used to predict VEs under no load and stable thermal conditions. Data is acquired using a scale and master ball artefact (SAMBA) and on-machine touch probing. A general process for determining the minimum number of balls required to generate data to satisfactorily train an ML model is proposed. The VEs prediction is verified using synthetic data for inter-axis and some intra-axis geometric errors, and then validated using only experimental data. Different datasets based on decreasing number of balls are tested to train either a Neural Networks (NN) or an eXtreme Gradient Boosting (XGBoost) algorithm to compare their performances. The results show that, both NN and XGBoost are effective to predict VEs of a five-axis machine tool with wCBXfZY(S)t topology regardless of the geometric error parameter values. By using only experimental data of twenty balls to train the models, XGBoost outperforms NN in all four error metrics and processing time. A time efficient scheme was tested whereby only two master balls plus one scale bar dataset and an additional master ball (when only the spindle rotates) were used for training NN .
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".