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Record W4206805010 · doi:10.4018/ijmmme.293225

Accuracy Investigation of Fused Deposition Modelling (FDM) Processed ABS and ULTRAT Parts

2022· article· en· W4206805010 on OpenAlexfundno aff
Insaf Bahnini, Daniel-Constantin Anghel, Alin Daniel Rizea, Uzair Khaleeq uz Zaman, Ali Siadat

Bibliographic record

VenueInternational Journal of Manufacturing Materials and Mechanical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsFused deposition modelingVariance (accounting)Standard deviationMathematicsNoise (video)Deposition (geology)Grey relational analysisStatisticsConcordanceComputer scienceMaterials scienceArtificial intelligenceComposite materialGeology

Abstract

fetched live from OpenAlex

This paper aims to assess the dimensional deviation of Fused Deposition Modeling (FDM) processed ABS and ULTRAT parts using a new geometrical model which can evaluate three types of dimensional deviation: along the z-axis, along external and internal dimensions, and through diameters. The methodology involves a step-by-step procedure wherein after establishing the experimental plan and manufacturing the specimens, the measurements taken are analyzed via Grey Relational Analysis (GRA) to find out the optimal combination of parameters leading to the minimum deviation in all dimensions of parts for both materials. Statistical techniques such as Analysis of Variance (ANOVA) and Signal to Noise (S/N) ratio were also used. Subsequently, a confirmation test was carried out to validate the results obtained. The findings of the ANOVA and the S/N ratio were in good concordance with those of GRA.

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 score0.391
Threshold uncertainty score0.552

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 head0.014
GPT teacher head0.208
Teacher spread0.194 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

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