Aerospace Product Cost Management at the early Concept Operations Phase
Bibliographic record
Abstract
The key aim of the presented work is to provide a product cost management system methodology that can be used as a basis for product cost estimation for new product development fr om the very outset of the design activity, when design intent first begins to be captured. The solution was shown to deliver a truly concurrent engineering process through the capture and utilization of knowledge from the design, manufacturing and procure ment functional areas, while also being developed into an automated tool that was deployed when little specialist manufactur ing knowledge was available; thus enabling a C ompany to quickly estimate their designs based on actual company manufacturing capabil ity. As part of the functional development , a part cost estimating function and an assembly time estimating function have been developed . In particular, the Ge netic -Causal costing philosophy wa s utilized in conjunction with the Cost CENTRE -ing methodology for the actual generation of the estimating relati onships. Ultimately, it is illustrated that the minimal input of basic part attributes, such as geometry, can be used in conjunction with process characterization in or der to facilitate cost estimating . Det ailed validation case studies are presented and the system is applied to show the potential economic benefit of such a cost management capability to be deployed already at the concept stage. It will be shown that this ability can have a profound affect on product development, when cost is integrated as an analytical performance metric.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".