Dynamic Lifecycle Cost Modeling for Adaptable Design Optimization of Additively Remanufactured Aeroengine 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
Additive manufacturing (AM) is being used increasingly for repair and remanufacturing of aeroengine components. This enables the consideration of a design margin approach to satisfy changing requirements, in which component lifespan can be optimized for different lifecycle scenarios. This paradigm requires lifecycle cost (LCC) modeling; however, the LCC models available in the literature consider mostly the manufacturing of a component, not its repair or remanufacturing. There is thus a need for an LCC model that can consider AM for repair/remanufacturing to quantify corresponding costs and benefits. This paper presents a dynamic LCC model that estimates cumulative costs over the in-service phase and a nested design optimization problem formulation that determines the optimal component lifespan range to minimize overall cost while maximizing performance. The developed methodology is demonstrated by means of an aeroengine turbine rear structure.
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