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Record W4386223084 · doi:10.4028/p-s9nhqr

An Application of Injection Molding to Semisolid Processing of Metallic Alloys: A Role of SIMA in Feedstock Transformation

2023· article· en· W4386223084 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

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2023
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsMaterials scienceSimaRaw materialShearing (physics)PlungerMetallurgyMolding (decorative)FormabilityComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Injection molding has proven for over 150 years to be the efficient mass manufacturing technology of net-shape components from plastics, however, its application to metallic alloys, despite four decades of commercialization, still creates challenges. Although early designs assumed a direct replacement of plastic with magnesium, subsequent research revealed essential differences in machinery and processing requirements, imposed by metallic alloys. The key discovery revealed that the dendrite-to-globule transformation during coarse particulate melting is caused by strain induced melt activation (SIMA) due to feedstock deformation imposed at their manufacturing stage, not due to the injection screw shearing during processing. As a result, the process control parameters and the screw and barrel design can be optimized with a focus on other screw functions. That discovery also led to the simplified machinery designs, eliminating the complex injection screw, and replacing it with a simple plunger.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.420
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.001
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 head0.027
GPT teacher head0.289
Teacher spread0.262 · 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