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Record W4321368186 · doi:10.1016/j.heliyon.2023.e13882

Supersolidus liquid phase sintering of water-atomized low-alloy steel in binder jetting additive manufacturing

2023· article· en· W4321368186 on OpenAlexaff
Mingzhang Yang, Mohsen K. Keshavarz, Mihaela Vlasea, Amin Molavi-Kakhki, Martin Laher

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsRio Tinto (Canada)University of Waterloo
Fundersnot available
KeywordsSinteringMaterials sciencePowder metallurgyAlloyShrinkagePorosityIsothermal processMetallurgyLiquid phasePhase (matter)Composite material

Abstract

fetched live from OpenAlex

This work investigates the feasibility of net shape manufacturing of parts using water-atomized (WA) low-alloy steel with comparable densities to conventional powder metallurgy parts via binder jetting additive manufacturing (BJAM) and supersolidus liquid phase sintering (SLPS). In this study, a modified water-atomized powder grade with similar composition as MPIF FL-4405 was printed and pressure-less sintered under a 95% N2–5% H2 atmosphere. Combinations of two different sintering schedules (direct-sintering and step-sintering) and three different heating rates (1, 3, and 5 °C/min) were applied to study the densification, shrinkage, and microstructural evolution of BJAM parts. This study demonstrated that, although the green density of the BJAM samples was ∼42% of the theoretical density, the sintered parts experienced large linear shrinkage up to ∼25% and reached ∼97% density without compromising shape fidelity. This was ascribed to a more homogeneous pore distribution throughout the part before ramping up to the SLPS region. The synergistic effects of carbon residue, the slow heating rate, and the additional isothermal holding stage at the solid-phase sintering region were determined to be the key factors for sintering BJAM WA low-alloy steel powders with resulting minimal entrapped porosity and good shape fidelity.

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 categoriesMeta-epidemiology (narrow)
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 score0.084
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.016
GPT teacher head0.244
Teacher spread0.229 · 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.

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

Citations10
Published2023
Admission routes1
Has abstractyes

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