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Record W4404850097 · doi:10.1007/s43939-024-00154-x

Experimental and computational studies on hot cracking in single laser tracks of aluminium alloy AA2024 and related implications for laser powder bed fusion

2024· article· en· W4404850097 on OpenAlexaff
Giuseppe Del Guercio, Sébastien Faron, D.G. McCartney, Stuart Robertson, Nesma T. Aboulkhair, Christopher Tuck, Adam T. Clare, Marco Simonelli

Bibliographic record

VenueDiscover Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of British Columbia
FundersEngineering and Physical Sciences Research Council
KeywordsMaterials scienceAluminiumFusionMetallurgyLaserAlloyCrackingAluminium alloyComposite materialOptics

Abstract

fetched live from OpenAlex

Laser powder bed fusion of high-strength aluminium alloys remains challenging due to the formation of hot cracks during the printing process. Hot cracking is a complex phenomenon involving a complex thermo-mechanical and metallurgical interplay. Such relationship needs to be fully understood to reap the benefits of advanced laser modulation (temporal and spatial) capabilities, now becoming available in PBF-LB. To this end, we explore the formation of hot cracks in single tracks produced using a simple spatial–temporal laser modulation characterised by laser pulses of various distance. The formation of cracks is then rationalised by physical and numerical modelling using multi-physics CFD simulation. We demonstrate that the area of the mushy zone at the back of the moving melt pool, dynamically contracts and expands according to the laser temporal regimes and find maxima in correspondence to the largest laser pulse distance and the time steps between exposures. By then analysing in detail in the crack observed in the printed parts fabricated with the same laser modulation, it is possible to conclude that the mechanisms leading to hot cracks in these specimens is analogous. In turn, the insights on single tracks can be extended, for the most part, to the case of bulk specimens. Nevertheless, printed specimens appear to be more sensitive to the erratic movement of the melt pool, due to the presence of a larger number of highly energetical grain boundaries and unintended microstructural defects (voids and inclusions), from which cracks can nucleate.

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 score0.004
Threshold uncertainty score0.467

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 head0.025
GPT teacher head0.290
Teacher spread0.265 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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