Digital Twin Enabled Asset Management of Machine Tools
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
Machine tools (MT) are essential equipment in modern manufacturing. They are a large investment which yields great returns to productivity and profitability. MTs enable the high throughput manufacturing of high precision components. Given their great importance, and their large cost, it is beneficial to implement asset management (AM) strategies such as condition monitoring, fault detection and predictive maintenance. Implementing these processes and methods can improve reliability and performance of MTs, while extending their lifetime and reducing operating expenses. Digital twins (DT) are an emerging technology within the Industry 4.0 landscape. They represent a connection between a physical system, object, or process and it’s virtual representation. DTs can be leveraged for AM implementation in MTs. This work examines the potential benefits of applying DTs to AM, examples in the literature of applying AM methods to MTs using DT, and how advanced AM strategies can be deployed using DT. From examining the literature it was clear that DTs are well suited for AM in MTs. DTs enable improved data collection and processing, modeling and model retention, and historical analysis and trend prediction. DTs have been applied to a variety of application scenarios for MTs such as in cutting tools, spindles, and feed drives. DTs can additionally enable more advanced modeling solutions such as physics informed machine learning which can overcome some issues with traditional data-driven and physics-based modeling strategies. These advanced methods can improve overall AM across the MT’s life-cycle and enable effective prognostic health management.
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.
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 | 0.000 |
| 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