Development of a Precise Manufacturing Cost Model for the Optimisation of Aircraft Structures
Bibliographic record
Abstract
A methodology for the development of a precise manufacturing cost model to be used for trade-offs at the early design stage is presented. The main objective of the research is the development of techniques to estimate the cost implications of early design decisions, which have a major influence on the product life-cycle cost, by integrating cost as a design parameter. The cost modelling relies on the genetic-causal method, where each cost element is computed in relation to its main cost drivers, these being linked to particular genetic identifiers relating to materials, processes and forms. In this paper, results of a preliminary study, which shows that the design optimisation process can be achieved by linking manufacturing costs models with structural analysis models through shared design parameters, are summarized. Then, the emphasis is put on a deeper understanding of the hidden costs, which are highly influential on cost but not easily observed. A classification of the different cost elements is proposed and the impact of the design decisions on these elements is highlighted. In this context, the need to improve current manufacturing cost models is based on two considerations: on the one hand, the current practice in the industry is often to use a single overhead rate for the whole factory, which does not necessarily reflect the true overhead cost to be assigned to a particular part or product and can bias the results of the optimisation. On the other hand, part commonality and standardization of the processes lead to reductions in the manufacturing cost due to learning and this has to be quantifies at the early design stage. The approach is illustrated through the analysis of the cost elements involved at the different stages of the material conversion route during the manufacturing of aircraft fuselage panels. Although the presented results concern aircraft structures, the methodology is generic and can be applied to any assembled structure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".