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Record W2587222394 · doi:10.1115/imece2016-65927

Manufacturability of Overhang Structures Fabricated by Binder Jetting Process

2016· article· en· W2587222394 on OpenAlexaff
Fan Yang, Yunlong Tang, Yaoyao Fiona Zhao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsDesign for manufacturabilityProcess (computing)Mechanical engineeringMaterials scienceComputer scienceStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Due to the superior mechanical properties of metals and the inherent capability of additive manufacturing (AM) to fabricate complex structures, metal AM reveals a promising future in the industrial fields. Compared with other metal AM processes, Binder Jetting (BJ) process has potential of producing overhang structures without additional supports. This advantage of BJ process significantly enlarges the design freedom of complex metal parts with intricate overhang structures. However, it should be noted that there is still a certain manufacturing limitation of overhang structures for BJ process. Without the support of loose powder after the depowdering process, the green part is vulnerable to the inevitable external loads, such as self-weight. In this paper, a theoretical model has been proposed to evaluate the self-support capability of printed green parts after the depowdering process. A set of experiments has been designed to find the maximum normal stress that printed green parts can withstand. This proposed theoretical model can be used to predict manufacturability of overhang structure of any arbitrary shape. Based on this model, some design guidelines and future work are summarized at the end of this paper.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.365

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 head0.009
GPT teacher head0.217
Teacher spread0.209 · 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
Published2016
Admission routes1
Has abstractyes

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