Quantitative Assessment of the Impact of Alternative Manufacturing Methods on Aeroengine Component Lifing Decisions
Bibliographic record
Abstract
Static structural aeroengine components are typically designed for full lifetime operation. Under this assumption, efforts to reduce weight in order to improve the performance result in structural designs that necessitate proven yet expensive manufacturing solutions to ensure high reliability. However, rapid developments in fabrication technologies such as additive manufacturing may offer viable alternatives for manufacturing and/or repair, in which case different component lifing decisions may be preferable. The research presented in this paper proposes a value-maximizing design framework that models and optimizes component lifing decisions in an aeroengine product–service system context by considering manufacturing and maintenance alternatives. To that end, a lifecycle cost model is developed as a proxy of value creation. Component lifing decisions are made to minimize net present value of lifecycle costs. The impact of manufacturing (represented by associated intial defects) and maintenance strategies (repair and/or replace) on lifing design decisions is quantified by means of failure models whose output is an input to the lifecycle cost model. It is shown that, under different conditions, it may not be prudent to design for full life but rather accept shorter life and then repair or replace the component. This is especially evident if volumetric effects on low cycle fatigue life are taken into account. It is possible that failure rates based on legacy engines do not translate necessarily to weight-optimized components. Such an analysis can play a significant supporting role in engine component design in a product–service system context.
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.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".