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Record W1686988970 · doi:10.1108/rpj-09-2013-0093

Evaluation of dimensional accuracy and material properties of the MakerBot 3D desktop printer

2015· article· en· W1686988970 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

VenueRapid Prototyping Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsGlenrose Rehabilitation HospitalUniversity of Alberta
Fundersnot available
Keywords3D printingRapid prototypingInfill3d printerEngineering drawingDesign of experimentsFused deposition modelingOrientation (vector space)Mechanical engineeringComputer scienceMaterials science3d printedComposite materialStructural engineeringManufacturing engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

Purpose – This paper aims to evaluate the material properties and dimensional accuracy of a MakerBot Replicator 2 desktop 3D printer. Design/methodology/approach – A design of experiments (DOE) test protocol was applied to determine the effect of the following variables on the material properties of 3D printed part: layer height, per cent infill and print orientation using a MakerBot Replicator 2 printer. Classical laminate plate theory was used to compare results from the DOE experiments with theoretically predicted elastic moduli for the tensile samples. Dimensional accuracy of test samples was also investigated. Findings – DOE results suggest that per cent infill has a significant effect on the longitudinal elastic modulus and ultimate strength of the test specimens, whereas print orientation and layer thickness fail to achieve significance. Dimensional analysis of test specimens shows that the test specimen varied significantly ( p < 0.05) from the nominal print dimensions. Practical implications – Although desktop 3D printers are an attractive manufacturing option to quickly produce functional components, this study suggests that users must be aware of this manufacturing process’ inherent limitations, especially for components requiring high geometric tolerance or specific material properties. Therefore, higher quality 3D printers and more detailed investigation into the MakerBot MakerWare printing settings are recommended if consistent material properties or geometries are required. Originality/value – Three-dimensional (3D) printing is a rapidly expanding manufacturing method. Initially, 3D printing was used for prototyping, but now this method is being used to create functional final products. In recent years, desktop 3D printers have become commercially available to academics and hobbyists as a means of rapid component manufacturing. Although these desktop printers are able to facilitate reduced manufacturing times, material costs and labor costs, relatively little literature exists to quantify the physical properties of the printed material as well as the dimensional consistency of the printing processes.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.835
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.064
GPT teacher head0.255
Teacher spread0.191 · 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