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Record W1974362856 · doi:10.1504/ijpd.2013.052156

Exploring the parametric design space to manage computational weld mechanics analyses using design of experiment

2013· article· en· W1974362856 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

VenueInternational Journal of Product Development · 2013
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsCarleton University
Fundersnot available
KeywordsCwmParametric statisticsWeldingParametric designDesign of experimentsSet (abstract data type)Computer scienceDistortion (music)Industrial engineeringSpace (punctuation)EngineeringMechanical engineeringMathematicsArtificial intelligenceRDF

Abstract

fetched live from OpenAlex

Development of a computational weld mechanics (CWM) framework that automates multiple set-ups and evaluations is required to practically explore a design space by given design of experiment (DOE) matrices. Saving an expert-user’s time to prepare several analyses and allocating CPUs to be utilised efficiently make this framework cost effective and time effective to manage designer-driven optimisation and control application of CWM. A validation analysis is conducted in this framework to identify the CWM control vector that minimises the difference between the computed and experimental data. Actual CWM problems with continuous and/or discontinuous parametric design spaces are solved in this framework to minimise weld distortion using derivative-free optimisation algorithms and DOE matrices that become attractive in this framework.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.383

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)0.0000.000

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 head0.227
GPT teacher head0.334
Teacher spread0.107 · 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