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Record W4280530001 · doi:10.18280/rcma.320206

Recent Development in Casting Technology: A Pragmatic Review

2022· review· en· W4280530001 on OpenAlexvenueno aff
Adekunle Akanni Adeleke, Makanjuola Oki, Ikechukwu K. Anyim, Peter P. Ikubanni, Adeolu Adesoji Adediran, Ayokunle A. Balogun, Thomas Aghogho Orhadahwe, Peter Omoniyi, Adebayo S. Olabisi, Esther T. Akinlabi

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2022
Typereview
Languageen
FieldEngineering
TopicMaterials Engineering and Processing
Canadian institutionsnot available
Fundersnot available
KeywordsCastingInvestment castingMoldManufacturing engineeringSand castingQuality (philosophy)Mechanical engineeringDurabilityMaterials scienceEngineeringMetallurgyComposite material

Abstract

fetched live from OpenAlex

More reliable and durable parts with high structural integrity are required to meet the increasing advancements in science and technology. This paper reviews five (5) different casting techniques: squeeze casting, sand casting, investment casting, die casting, and continuous casting. Their respective cast products were examined, and their various mechanical properties were discussed. However, these different casting techniques involve a similar fundamental procedure: melting metal, pouring it into a mold, and allowing it to solidify. However, they vary in their physical and mechanical properties, durability, and surface finishing, making one technique more desirable than the other in their application areas. Some techniques were found to be more advantageous and effective than the other, which will aid foundrymen in making the best decision in choosing a technique, considering parameters such as environmental friendliness and cost implications. The appropriate implementation of these will help restructure and refine foundries, improve the quality of cast products, and set the casting technology world on course for the new age.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.005
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.105
GPT teacher head0.319
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations12
Published2022
Admission routes1
Has abstractyes

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