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Record W2044133977 · doi:10.1016/j.procir.2014.01.124

Grouping Product Variants based on Alternate Machines for Each Operation

2014· article· en· W2044133977 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

VenueProcedia CIRP · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicProduct Development and Customization
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsProduct (mathematics)Computer scienceComputational biologyEngineeringManufacturing engineeringBiologyMathematics

Abstract

fetched live from OpenAlex

A new method for calculating the commonality between product variants in terms of machine usage is proposed. Grouping products has been studied by various authors under different titles such as product family formation, cell formation, part-machine groups formation. However, having more than one alternative machine that can be selected for performing an operation has not been considered in the area of grouping product variants. In this paper, it is assumed that each product variant requires some operations and for each operation there exist some alternative machines. The focus is on grouping the products and consequently, building a dendogram based on commonality. A new method for grouping the products is proposed and a dendogram is depicted by using Average Linkage Clustering (ALC). The resulting dendogram is very helpful as it shows the levels of similarity between product variants and can be further used by product designers, process planners and production planners.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.899
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.214
Teacher spread0.202 · 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