The Role of 3D Printed Objects Facilitating the Process of Mutual Communication and Collective Idea Generation Between Non-designers and Designers
Bibliographic record
Abstract
The arrival of 3D printing technology, otherwise known as additive manufacture, has had a profound influence on the design and manufacturing fields.It has significantly reduced the cost of design and increased its accessibility to diverse industries, such as science and medicine.Despite the remarkable technological improvements in 3D printing technology, there are still diverse challenges that scientific laboratories encounter when using such equipment to develop customized lab equipment.A major challenge is successfully transferring and incorporating the scientists' ideas during the design process using the 3D printer due to their limited experience in design practices.The majority of the literature on 3D printers and the design field focus on its manufacture benefits.This paper fills a gap in the existing literature and explores the role of 3D printers in the ideation process of design, specifically in the context of Co-design environments with scientists.It is argued that 3D printed objects can support ideation by allowing enhanced engagement between participants, helping uncover important insights and increasing the team's idea generation process, as well as enhancing the clarity of communicating these design ideas.A workshop was conducted to test this hypothesis at the University of Ottawa Laboratory of Cellular and Molecular Medicine with neurobiologists and engineers.The results of the workshop demonstrated that the 3D printer could support scientists in the ideation process by enabling different domainspecific types of interactions.In the case of the scientists, realistic simulations brought by 3D printed objects displayed an opportunity to explore a design iii challenge through different perspectives and domain knowledge lenses.In addition, two examples of collaboration with the group scientist using the 3D printer and co-design practices will be presented.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".