Effect of Cold Forming on the High Cycle Fatigue Behaviour of a 27MnCr5 Steel
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".