Parametric design methodology for manufacturing of metallic and composite structures
Bibliographic record
Abstract
A parametric, concurrent design methodology for manufacturing of metallic and composite structures is established. Often, during a new product development, designs prepared using the “Sequential” or “Waterfall” approach are rejected or require significant rework during manufacturing, as designers are not always versed with manufacturing principles. Similarly, manufacturers are not always versed in design principles resulting in designs that do not cater to the functional requirements. The goal of this study is to establish a methodology right from the scope to the detailed design for developing manufacturable structures using the “Concurrent Engineering” approach. Existing literature on “Design Optimization for Manufacturing” predominantly focus on single variable optimization problems geared towards conceptual designs. The designs developed through such optimization cater towards functional performance within a “Fixed Design Space” while not accounting for manufacturing or operational challenges. The methodology developed in this study enables “Design for Manufacturing” for “Detailed Designs” through selection of a conceptual design and subsequently optimizing the selected conceptual design for a set of functional parameters. An “Integrated Product Development” approach is used, whereby, the functional requirements are linked to both design and manufacturing variables and optimization is conducted in an “Augmented Design Space” which is not available when only considering design or manufacturing variables. Three case studies involving both “Conceptual” and “Detailed” designs have been used to illustrate the methodology presented. Case I documents the design of a Flight Control System Bracket. Case II illustrates the use of “2D” composite structures to fabricate a roll frame. Case III involves the development of a “3D” composite door for a light unpressurized aircraft. For each of the three case studies a separate development approach has been employed. Case I uses an analytical approach, Case II uses FEM while CASE III employs a hybrid approach comprising of both FEM and analytical techniques.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".