Adaptive metal deposition and data management for automated overhaul of complex turbine components
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".