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Record W3158460789 · doi:10.1177/14644207211013434

Modelling the asymmetric tension–compression properties of additively manufactured alloys using continuum damage mechanics

2021· article· en· W3158460789 on OpenAlexaff
A. Nayebi, H. Rokhgireh, Mehran Araghi, Mohsen Mohammadi

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications · 2021
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMaterials scienceCompression (physics)Continuum mechanicsIsotropyUltimate tensile strengthTension (geology)Composite materialFinite element methodStructural engineeringMechanicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Additively manufactured parts often comprise internal porosities due to the manufacturing process, which needs to be considered in modelling their mechanical behaviour. It was experimentally shown that additively manufactured parts’ tensile and compressive mechanical properties are different for various metallic alloys. In this study, isotropic continuum damage mechanics is used to model additively manufactured alloys’ tension and compression behaviours. Compressive stress components can shrink discontinuities present in additively manufactured alloys. Therefore, the crack closure effect was employed to describe different behaviours during uniaxial tension and compression tests. A finite element model embedded in an ABAQUS’s UMAT format was developed to account for the isotropic continuum damage mechanics model. The numerical results of tension and compression tests were compared with experimental observations for additively manufactured maraging steel, AlSi10Mg and Ti-6Al-4V. Stress–strain curves in tension and compression of these alloys were obtained using the continuum damage mechanics model and compared well with the experimental results.

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)0.001
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 score0.452
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.034
GPT teacher head0.213
Teacher spread0.179 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and ApplicationsSame topicAdditive Manufacturing Materials and ProcessesFrench-language works237,207