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Record W2093418119 · doi:10.1016/j.msea.2015.04.063

Numerical study of surface roughening in blow-formed aluminum bottle with crystal plasticity

2015· article· en· W2093418119 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.

Bibliographic record

VenueMaterials Science and Engineering A · 2015
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCrystal plasticityPlasticityBottleAluminiumMaterials scienceCrystal (programming language)Surface (topology)Composite materialGeometryMathematicsComputer science

Abstract

fetched live from OpenAlex

Surface roughening due to the plastic straining in the blow-formed bottle is an important issue because of the cosmetically related surface appearance on the final product. In this paper, a finite-element-based crystal plasticity model is used to simulate surface roughening in aluminum tubes under blow-forming. The measured electron backscatter diffraction (EBSD) data is directly incorporated into the finite element model and the constitutive response at an integration point is described by the single crystal plasticity theory . Besides the influence of the texture and its spatial distribution, rate sensitivity and work hardening on surface roughening, the study shows that the better surface finishing in the final product can be achieved either through cladding a layer with more randomized texture or through improving the initial surface roughness of the tube before expansion, while the latter is more feasible and practical from industrial application point of view.

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)0.001
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 score0.158
Threshold uncertainty score0.387

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 head0.017
GPT teacher head0.231
Teacher spread0.215 · 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