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Record W2762342641 · doi:10.1089/3dp.2017.0032

3D Printed Sandstone Strength: Curing of Furfuryl Alcohol Resin-Based Sandstones

2017· article· en· W2762342641 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venue3D Printing and Additive Manufacturing · 2017
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsUniversity of Alberta
FundersCMG Reservoir Simulation Foundation
KeywordsFurfuryl alcoholCompressive strengthCuring (chemistry)Materials scienceComposite materialBedClassification of discontinuitiesFlexural strengthFabricationMechanical strengthGeotechnical engineeringGeologyMathematicsChemistryAnisotropy

Abstract

fetched live from OpenAlex

Natural sedimentary rocks can be widely heterogeneous and often include discontinuities on many scales—no two samples are truly identical. This poses a major roadblock for geomechanical experiments since most of them are destructive in nature. Recent advances in additive manufacturing technology allow fabrication of identical sandstone analogs. The technology allows control over grain size, packing, mineralogy, cementing type and content, bedding orientation, and discontinuities. This article explored how curing temperature affects the strength of sand and furfuryl alcohol resin-based specimens. When cured at optimal oven temperature of 80°C, specimens reach unconfined compressive strength (UCS) of 19.0 MPa with only 1.1 MPa in standard deviation. Additionally, this article determines a minimal number of UCS test repetitions required to reach a desired degree of confidence in the strength results. Outcomes of this study can be used as a guide in preparing and strength testing of furfuryl alcohol resin and sand powder-based 3D printed rocks.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.981

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 head0.017
GPT teacher head0.251
Teacher spread0.235 · 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