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Record W4387056511 · doi:10.3390/polym15193877

Shrinkage Compensation and Effect of Building Orientation on Mechanical Properties of Ceramic Stereolithography Parts

2023· article· en· W4387056511 on OpenAlexaff
Piyush Arora, Khaled G. Mostafa, Emmanuel Russell, Shirin Dehgahi, Sajid Ullah Butt, Didier Talamona, Ahmed Jawad Qureshi

Bibliographic record

VenuePolymers · 2023
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStereolithographyShrinkageMaterials scienceCeramicComposite materialCompensation (psychology)Orientation (vector space)Mechanical engineeringEngineeringMathematicsGeometryPsychology

Abstract

fetched live from OpenAlex

Stereolithography additive manufacturing (SLA-AM) can be used to produce ceramic structures by selectively curing a photosensitive resin that has ceramic powder in it. The photosensitive resin acts as a ceramic powder binder, which is burned, and the remaining ceramic part is sintered during post-processing using a temperature-time-controlled furnace. Due to this process, the ceramic part shrinks and becomes porous. Moreover, additive manufacturing leads to the orthotropic behavior of the manufactured parts. This article studies the effect of the manufacturing orientation of ceramic parts produced via SLA-AM on dimensional accuracy. Scaled CAD models were created by including the calculated shrinkage factor. The dimensions of the final sintered specimens were very close to the desired dimensions. As sintering induces porosity and reduces the mechanical strength, in this study, the effect of orientation on strength was investigated, and it was concluded that the on-edge specimen possessed by far the highest strength in terms of both compression and tension.

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 score0.169
Threshold uncertainty score0.262

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 head0.015
GPT teacher head0.230
Teacher spread0.215 · 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

Citations19
Published2023
Admission routes1
Has abstractyes

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