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Record W3100039087 · doi:10.22215/etd/2019-13661

The Role of 3D Printed Objects Facilitating the Process of Mutual Communication and Collective Idea Generation Between Non-designers and Designers

2019· dissertation· en· W3100039087 on OpenAlexaffabout
Pablo Arzate

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsCarleton University
Fundersnot available
KeywordsIdeationCLARITYProcess (computing)Context (archaeology)Design processEngineering design processDomain (mathematical analysis)Human–computer interactionComputer scienceEngineering3d printedWork in processPsychologyManufacturing engineeringCognitive scienceMechanical engineeringOperations management

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.007
Scholarly communication0.0100.006
Open science0.0010.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.015
GPT teacher head0.280
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2019
Admission routes2
Has abstractyes

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