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Record W4401419187 · doi:10.1016/j.ohx.2024.e00566

Open-source cold and hot scientific sheet press for investigating polymer-based material properties

2024· article· en· W4401419187 on OpenAlexafffund
M. Woods, Cameron K. Brooks, Joshua M. Pearce

Bibliographic record

VenueHardwareX · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolymer scienceMaterials science

Abstract

fetched live from OpenAlex

To produce samples for both material testing and molded sheets/parts, this article details an open-source scientific cold and hot press design. It consists of two independent and modular upper and lower plate (929 cm 2 ) assemblies each containing four 125 W insulated steel strip heaters. The steel housing for these heaters is entirely modular and designed for ease of manufacture, assembly, and customization. This system allows a researcher with access to a hydraulic press to repurpose existing equipment into a multipurpose hot and cold press, or if an independent machine is warranted, an additional welded support frame and commercially available bottle jack offer standalone operation. By utilizing this small-scale hot press either in conjunction with a hydraulic press or on its own, samples can be produced to determine the critical material properties of any polymer, composite, or polymer blend. A series of modular molds allow for the rapid production of flat sheet stock and solid testing samples adhering to the ASTM D695 standard for rigid plastics tested in compression and ASTM D638 standard for testing plastics in tension. The sheet mold offers the user the ability to produce stock sheets that can be cut and assembled into 2.5-D applications with post processing.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.999
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.009

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 head0.044
GPT teacher head0.239
Teacher spread0.195 · 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.

Study designBench or experimental
Domainnot available
GenreMethods

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

Citations7
Published2024
Admission routes2
Has abstractyes

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