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Record W3036659280 · doi:10.3390/ma13122795

On the Post-Printing Heat Treatment of a Wire Arc Additively Manufactured ER70S Part

2020· article· en· W3036659280 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMaterials · 2020
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of Newfoundland
KeywordsMaterials scienceMicrostructureIndentation hardnessLamellar structurePearliteFerrite (magnet)Hardening (computing)Ultimate tensile strengthComposite materialMetallurgyAcicularGrain boundaryAcicular ferriteBainiteAustenite

Abstract

fetched live from OpenAlex

Wire arc additive manufacturing (WAAM) is known to induce a considerable microstructural inhomogeneity and anisotropy in mechanical properties, which can potentially be minimized by adopting appropriate post-printing heat treatment. In this paper, the effects of two heat treatment cycles, including hardening and normalizing on the microstructure and mechanical properties of a WAAM-fabricated low-carbon low-alloy steel (ER70S-6) are studied. The microstructure in the melt pools of the as-printed sample was found to contain a low volume fraction of lamellar pearlite formed along the grain boundaries of polygonal ferrite as the predominant micro-constituents. The grain coarsening in the heat affected zone (HAZ) was also detected at the periphery of each melt pool boundary, leading to a noticeable microstructural inhomogeneity in the as-fabricated sample. In order to modify the nonuniformity of the microstructure, a normalizing treatment was employed to promote a homogenous microstructure with uniform grain size throughout the melt pools and HAZs. Differently, the hardening treatment contributed to the formation of two non-equilibrium micro-constituents, i.e., acicular ferrite and bainite, primarily adjacent to the lamellar pearlite phase. The results of microhardness testing revealed that the normalizing treatment slightly decreases the microhardness of the sample; however, the formation of non-equilibrium phases during hardening process significantly increased the microhardness of the component. Tensile testing of the as-printed part in the building and deposition directions revealed an anisotropic ductility. Although normalizing treatment did not contribute to the tensile strength improvement of the component, it suppressed the observed anisotropy in ductility. On the contrary, the hardening treatment raised the tensile strength, but further intensified the anisotropic behavior of the component.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.013
Threshold uncertainty score0.996

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)0.0050.000

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 head0.022
GPT teacher head0.205
Teacher spread0.183 · 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