Characterization of inclusions in clean steels: a review including the statistics of extremes methods
By H. Atkinson, G. Shi. 2003
286 citations recorded by the source corpus.
Subfields: Mechanical Engineering, Mechanics of Materials
View Paper: https://www.semanticscholar.org/paper/62bd8de8bbd5dc738da4e916e77486608268c095
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Mechanical Engineering
- Craig P. Przybyla
- David L. McDowell
- A. C. E. Silva
- H. Bhadeshia
- Michael D. Sangid
- Damiano Pasini
- Surya R. Kalidindi
- Dean Deng
In Mechanics of Materials
- Craig P. Przybyla
- David L. McDowell
- A. C. E. Silva
- H. Bhadeshia
- Michael D. Sangid
- Surya R. Kalidindi
- Damiano Pasini
- F. Dunne
Citations in the corpus
- Non-metallic inclusions in steels – origin and control (2018, methodology): “It must be mentioned that, in this method, there would be no limit to maximum inclusion size, since it increases with the volume of steel used in the calculations [188].”
- Influence of inclusion size on fatigue behavior of high strength steels in the gigacycle fatigue regime (2007, unknown): “between inclusions and matrix [10].”
- Microstructure-sensitive extreme value probabilities for high cycle fatigue of Ni-base superalloy IN100 (2010, background): “The problem of the largest inclusion size in clean steels has been considered by several different workers and is summarized in a review by Atkinson [26].”
- Steels for bearings (2012, background): “The subject has been reviewed recently [754] where the seminal work of Murakami and co–workers where estimates of the maximum size of inclusions in a large volume of strong steel are related to their fatigue behaviour.”
- The effects of non-metallic inclusions on properties relevant to the performance of steel in structural and mechanical applications (2019, methodology): “An important challenge in characterization and quantification of NMIs in current clean steel [4] is being able to detect and quantify the “cloud” (or background) of small inclusions and the few large ones.”