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Record W1198502733 · doi:10.1088/1757-899x/84/1/012036

Application of Numerical Optimization to Aluminum Alloy Wheel Casting

2015· article· en· W1198502733 on OpenAlexaff
Jianglan Duan, Carl Reilly, Daan M. Maijer, Steve Cockcroft, A.B. Phillion

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsCastingProcess (computing)Heat transferMechanical engineeringConstraint (computer-aided design)Production cycleDie castingAlloyAluminiumComputer scienceMaterials scienceEngineeringMetallurgyMechanicsManufacturing engineering

Abstract

fetched live from OpenAlex

A method of numerically optimizing the cooling conditions in a low- pressure die casting process from the standpoint of maintaining good directional solidification, high cooling rates and reduced cycle times has been developed for the production of aluminumalloy wheels. The method focuses on the optimization of cooling channel timing and utilizes an open source numerical optimization algorithm coupled with an experimentally validated, ABAQUS-based, heat transfer model of the casting process. Key features of the method include: 1) carefully designed constraint functions to ensure directional solidification along the centerlineof the wheel; and 2) carefully formulated objective functions to maximize cooling rate. The method has been implemented on a prototype production die and the results have been tested with plant trial test.

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.

How this classification was reachedexpand

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.664

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 communication0.0000.001
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 head0.015
GPT teacher head0.206
Teacher spread0.191 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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