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Record W2066228674 · doi:10.1115/1.4025489

Product Design Retrieval by Matching Bills of Materials

2013· article· en· W2066228674 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.

Bibliographic record

VenueJournal of Mechanical Design · 2013
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputer scienceMatching (statistics)Product (mathematics)Tree (set theory)Process (computing)Product designData miningEngineering drawingIndustrial engineeringEngineeringMathematicsProgramming language

Abstract

fetched live from OpenAlex

A new automatic design retrieval method that identifies the legacy product design most similar to a new one is proposed. Matching phylogenetic trees has been utilized in biological science for decades and is referred to as “tree reconciliation.” A new application of this approach in manufacturing is presented where legacy designs are retrieved based on reconciliation of trees representing products bill of materials (BOM). A product BOM is a structured tree, which represents its components and their hierarchal relationships; hence, it captures the contents and structure of assembled products. Making use of data associated with the retrieved designs also helps speed-up other downstream planning activities such as process planning, hence improving planning efficiency. A chemical processing centrifugal pump is used as a case study for illustration. The results obtained using the proposed method is compared with those recently published on BOM trees matching for further analysis and verification. This novel method is less computationally complex than available state-of-the-art algorithms.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.019
GPT teacher head0.212
Teacher spread0.193 · 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