Design for manufacture: Cost-estimating model for randomly oriented strand advanced composite aerospace parts
Bibliographic record
Abstract
This work aimed at developing a parametric cost-estimating model based on physical laws for making three categories of thermoplastic composites structural aerospace parts from discontinuous prepreg randomly oriented strands. The proposed cost model will use Microsoft Excel spreadsheet developed in house which imputes all industrial and academic data for calculating costs elements such as material, labour, energy, machinery, building costs and costs of working capital, overheads and then the total cost per part. This research study focused, on one hand, at estimating the heating costs for experimental and virtual parts by changing the volume and keeping the same process cycle times. The heating power was determined by simulating the process thermal diagram numerically using finite elements COMSOL software and validated by experimental data. On the other hand, the tooling costs were estimated by DFMA software for experimental and virtual moulds by changing the projected area. Then, the heating energy and tooling costs sizing scaling laws were established under linear equation forms limited to the size of platens areas. These linear equations were inputted in an Excel spreadsheet to calculate the cost of new parts, which have not been made yet. The variation of the total cost with the size and the complexity of the part were investigated. The results showed that the calculated heating energy costs of the three experimental randomly oriented strand parts were different due to different geometries of the heating platens and the moulds. For the mould cost, the more complex the form was the higher the cost. For total cost, it was also demonstrated that the manufacturing cost of L-bracket part was higher than that of flat plate and T-shape part due to higher process cycle time.
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 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".