MétaCan
Menu
Back to cohort

Strain hardening characteristics of deep drawing quality steels with geometric constraints

2011· article· en· W2062512466 on OpenAlexaff
Ushasi Roy, Abhishek Kumar, Ladislav Pešek

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2011
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsThinkpath Engineering Services (Canada)Carleton University
Fundersnot available
KeywordsHardening (computing)Deep drawingStrain hardening exponentMaterials scienceStrain (injury)Quality (philosophy)MetallurgyComposite materialPhysics

Abstract

fetched live from OpenAlex

Strain hardening characteristics of two rolled deep drawing quality (DDQ) steels, namely, interstitial free (IF) and aluminium killed (AK), have been studied with and without the presence of geometric constraints at room temperature. Strain hardening exponent and rate are investigated for plain, centre holed and double notched sheet specimens for both longitudinal and transversal orientations. Four empirical relations based on total and incremental stress and strain components are considered to get a deeper insight into the deformation and workhardening behaviour of annealed sheets. Strain hardening exponents and yield strengths for steel with ultralow carbon contents (IF) are found in the range of 0·22–0·30 and 145–170 MPa respectively. The steel grade (AK) with higher carbon content (0·028%) and yield strength (174–197 MPa) exhibits strain hardening exponent around 0·18–0·27. Single and two-stage workhardening is observed for the IF and AK grades. Workhardening as a function of plastic strain follows the power law relation of the form workhardening rate (WHR) = α(ϵT)β . A transition from high to low hardening rate tends to occur for strains ranging from 0·05 to 0·10. An analytical model is formulated for theoretical estimation of strain hardening exponent from true strain data. The experimentally determined and the theoretically computed strain hardening exponent data match fairly well.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.231
Teacher spread0.205 · 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 designObservational
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

Citations0
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Metallurgical QuarterlySame topicMetal Forming Simulation TechniquesFrench-language works237,207