Adaptive metal deposition and data management for automated overhaul of complex turbine components
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
The manufacture and repair of turbine components consist of a chain of different processes. Many of these processes are carried out manually today, especially in the case of repair. Since maintenance, repair and overhaul (MRO) are of vital importance for aircraft engines and industrial turbines from the point of view of process technology, safety and economics, special attention should be devoted to automating the repair of turbine components. For example: At present the repair of blisks (blade-integrated disks) is a central issue whenever consideration is given to replacing bladed stages with blisks; however, the feasibility of such a step hinges on the available capabilities for automated repair. A generic data management system has been developed which will constitute the core of automated repair systems for turbine components. The web-based data management system handles the logistics of the components and the accompanying data sets. As a result, different repair processes can be carried out at different facilities without any loss of information (“virtual workshop”). Furthermore, the approach described supports efficient part flow control as well as life cycle monitoring. In addition to the data management developments, existing repair methods have been improved by employing adaptive machining technology that makes use of the geometrical information provided by dedicated scanning systems or in-process measuring devices and compensates for the part-to-part variation and inaccurate clamping position of the turbine components to be machined. Different types of damage and repair methods have to be taken into account for compressor components as well as for turbine components. Here, a special focus is on the automated repair of complex components (such as blisks) and recent advances in this area.
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