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Record W2090662517 · doi:10.1115/detc2002/dfm-34177

Biomimetic Concept Generation Applied to Design for Remanufacture

2002· article· en· W2090662517 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceBiomimeticsEngineering design processDesign methodsSystems engineeringEngineeringArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

This paper applies a biomimetic design method to generate concepts for design that facilitates remanufacture. Biomimetic design fully or partially imitates or evokes some biological phenomenon. A method for identifying and using biological analogies for engineering problems was introduced in an earlier paper. This initial method was tested on an example in design for remanufacture. Here, the method is further developed and used to find more biomimetic solutions for the same problem in design for remanufacture. While the example problem is in remanufacture, the method can be used to develop biomimetic concepts for engineering design in general. The paper first summarizes previous efforts in developing and testing the biomimetic concept generation technique. Next described are the differences in the method that are used for this paper, including the increased importance of strategies to help identify promising analogies. Results of applying the modified method to design for remanufacture are documented.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.076
GPT teacher head0.259
Teacher spread0.183 · 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

Quick stats

Citations96
Published2002
Admission routes2
Has abstractyes

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