Design Innovation Incorporating Additive Manufacturing: Creation and Assessment of a Design Tool
Bibliographic record
Abstract
Abstract Additive Manufacturing (AM) offers design engineers new and advanced manufacturing processes to consider when developing new products or redesigning and evolving current products. AM includes 3D printing processes to quickly produce complex parts and prototypes, that were previously uneconomical or impossible to fabricate. Engineers and organizations have an increasing need to incorporate AM as part of product development; however, design heuristics, design methodologies, and design tools to support AM are nascent and only recently emerging. To enhance Design for Additive Manufacturing (DfAM), this research seeks to develop an accessible, computer-based design assistant that will aid designers in incorporating AM into their design processes. The design assistant implements a distinctive and user-centered Design Innovation (DI) process, set of methods, and set of principles based on a 4D design framework. This 4D framework encompasses the UK Design Council’s double diamond model and includes the phases of Discover, Define, Develop, and Deliver. The Discover phase entails user studies and a deep understanding and empathy for the user. The Define phase considers the reframing of design opportunities based on derived insights from the modeling users’ interactions. The Develop phase uses a variety of methods to create a large quantity of innovative ideas and concepts, and the Deliver phase implements a set of methods to prototype, test, pitch, and ultimately produce deliverables for a market or community. We demonstrate the design assistant tool for AM through the development of high-end bracket design for space applications. The design considers the Selective Laser Melting (SLM) process for productions and incorporated topology optimization approaches. This demonstrative case study shows how the tool includes design heuristics and approaches for each of the 4-Ds that assist designers in implementing AM capabilities as part of repeatable design processes. Assessment of the tool is carried out through systematic assessments performed by practicing design engineers that have knowledge of AM. Initial results show that the design assessment tool is very helpful when designers consider using AM and also in helping them use AM in effective and efficient manners.
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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 | 0.045 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".