A digital twin-driven part spatio-temporal quality prediction framework integrated with equipment degradation state analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Key parts in high-value equipment have critical requirements of high precision and performance. The machining processes of such parts normally involve multiple stations. Therefore, the machining quality of finished parts is an accumulated result of process chains (multi-stations, i.e. spatiodimension) and machine state conditions over different parts in batches (i.e. temporal dimension), which makes quality prediction difficult. Current quality prediction methods have no consideration of equipment state degradation (ESD) or simply investigate a single machine. To improve the prediction accuracy of machining quality, a digital twin-driven part spatio-temporal quality prediction (DT-PSTQP) framework for multi-stage machining processes (MMP) is proposed with full considerations of multi-machine processes and multi-state machine degradation. The relationship graph analysis (RGA) is used to classify continuous ESD into limited discrete states to construct MMP reconstruction module. The DT-QPL module is a collection of quality prediction models that are trained with the refined sub-datasets obtained by MMP reconstruction. The proposed framework and the three models are validated through a thin-walled part production line. The results show that the proposed framework can help to improve the quality prediction average accuracy by 18.8% compared to the traditional framework without DT-PSTQP.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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