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Record W2013638824 · doi:10.1002/pen.10119

Optimization of blow molded part performance through process simulation

2003· article· en· W2013638824 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

VenuePolymer Engineering and Science · 2003
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsDeflection (physics)Finite element methodConstraint (computer-aided design)Process (computing)Mathematical optimizationStability (learning theory)Work (physics)Materials scienceOptimization problemComputer scienceStructural engineeringMechanical engineeringMathematicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract In this work, a gradient‐based numerical optimization scheme is proposed to determine the optimal process operating conditions to produce a blow molded part by with a given performance. Finite element simulations are used to relate the part performance to the processing conditions. A performance optimization is first performed to find the minimum part thickness distribution that minimizes the part weight while satisfying mechanical performance constraints such as maximum part deflection or maximum stress for an applied load. Then a process optimization finds the optimal operating conditions, e.g. the die gap opening profile, that minimize the part weight while respecting the minimum thickness distribution dictated by the performance optimization. The results show that the optimization scheme minimizes the part weight with minimal constraint violation. The addition of a constraint associated with process stability is proposed.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.287

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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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