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Record W2206581970 · doi:10.1016/j.proeng.2015.12.636

Effect of Cold Forming on the High Cycle Fatigue Behaviour of a 27MnCr5 Steel

2015· article· en· W2206581970 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

VenueProcedia Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMaterials scienceForgingResidual stressFatigue limitForming processesComposite materialElectron backscatter diffractionSurface roughnessMicrostructureMetallurgy

Abstract

fetched live from OpenAlex

Cold extrusion is a process commonly used to manufacture drive train components in the automotive industry. Large plastic strains can be applied during this operation (up to 150%) and greatly changes the mechanical properties of the resulting material. This study, part of the ANR project Defisurf focuses on the impact of cold-forging process parameters on the fatigue behavior of steel components. The goal is to decouple the various effects of cold-working by analyzing the material properties and performing fatigue tests. A specific tool has been developed, in collaboration with the Gevelot company, to get original fatigue specimen able to characterize the effect of the manufacturing process on the fatigue behavior. The specimens are extruded from two different initial diameters, giving two different reductions in cross-section of 18% and 75% respectively. These values represent the range of cross-section reduction found in cold-forging: a minimum reduction is always applied, and above 75% reduction the components can be damaged (e.g. tearing). To understand the influence of cold-forging, the following analyses have been undertaken for each condition: mono- tonic tensile properties, microstructure, EBSD, residual stresses, hardness and surface roughness. Simulation of the forming process and microstructural observations of the two batches show that the plastic strain is homogeneous in the specimen section. For both reduction factors, the forming process has a positive effect on the components properties: induced residual stresses in compression and improve hardness and roughness (Ra decreasing). Push pull and plane bending fatigue tests show that the fatigue strength is about 30% higher for the high wrought batch. Residual stresses are not relaxed by the applied fatigue loads. SEM observations of the fatigue failure surfaces, for both extrusion condi- tions, show that there is no inclusion or surface defect at the initiation site. All investigations show that strain hardening is the principal material parameter responsible for the increase in fatigue strength.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.511

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 head0.012
GPT teacher head0.212
Teacher spread0.199 · 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