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Erschienen in: Metallurgical and Materials Transactions A 3/2018

04.01.2018

Understanding the Interdependencies Between Composition, Microstructure, and Continuum Variables and Their Influence on the Fracture Toughness of α/β-Processed Ti-6Al-4V

verfasst von: P. C. Collins, S. Koduri, V. Dixit, H. L. Fraser

Erschienen in: Metallurgical and Materials Transactions A | Ausgabe 3/2018

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Abstract

The fracture toughness of a material depends upon the material’s composition and microstructure, as well as other material properties operating at the continuum level. The interrelationships between these variables are complex, and thus difficult to interpret, especially in multi-component, multi-phase ductile engineering alloys such as α/β-processed Ti-6Al-4V (nominal composition, wt pct). Neural networks have been used to elucidate how variables such as composition and microstructure influence the fracture toughness directly (i.e., via a crack initiation or propagation mechanism)—and independent of the influence of the same variables influence on the yield strength and plasticity of the material. The variables included in the models and analysis include (i) alloy composition, specifically, Al, V, O, and Fe; (ii) materials microstructure, including phase fractions and average sizes of key microstructural features; (iii) the yield strength and reduction in area obtained from uniaxial tensile tests; and (iv) an assessment of the degree to which plane strain conditions were satisfied by including a factor related to the plane strain thickness. Once trained, virtual experiments have been conducted which permit the determination of each variable’s functional dependency on the resulting fracture toughness. Given that the database includes both K 1 C and K Q values, as well as the in-plane component of the stress state of the crack tip, it is possible to quantitatively assess the effect of sample thickness on K Q and the degree to which the K Q and K 1 C values may vary. These interpretations drawn by comparing multiple neural networks have a significant impact on the general understanding of how the microstructure influences the fracture toughness in ductile materials, as well as an ability to predict the fracture toughness of α/β-processed Ti-6Al-4V.

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Fußnoten
1
For reference, AMS specification #4920 is 5.5 < wt pct Al < 6.75; 3.5 < wt pct V < 4.5; wt pct O(max) = 0.20; and wt pct Fe(max) = 0.30. The Al content ranged below that of the nominal composition, while the vanadium exhibited a slightly extended range. The maximum Al level was designed to avoid short-range ordering of the hcp phase. Oxygen was designed to exhibit a slightly extended range to cover both extra-low interstitial (ELI) and non-ELI grades. Iron, a known strengthener, was also designed to have an extended range.
 
2
The numerical component of the model designations in the figure captions for Figures 8, 9, 10, 11, 12, 13 corresponds to the neural network architecture itself. The format is value of the seed-number of hidden nodes-initial Gaussian width. For example, “500-3-1” corresponds to a value of the seed number (500) for an architecture with 3 hidden nodes, and an initial Gaussian width of 1, as described in detail elsewhere.[26]
 
3
The full extent of this research cannot be adequately presented in a single publication. The characterization activities (though published first[24]) were extensively informed by this modeling effort. Indeed, without the identification of the importance of the size of the equiaxed alpha particle size, the characterization efforts would not have been pursued as carefully.
 
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Metadaten
Titel
Understanding the Interdependencies Between Composition, Microstructure, and Continuum Variables and Their Influence on the Fracture Toughness of α/β-Processed Ti-6Al-4V
verfasst von
P. C. Collins
S. Koduri
V. Dixit
H. L. Fraser
Publikationsdatum
04.01.2018
Verlag
Springer US
Erschienen in
Metallurgical and Materials Transactions A / Ausgabe 3/2018
Print ISSN: 1073-5623
Elektronische ISSN: 1543-1940
DOI
https://doi.org/10.1007/s11661-017-4443-9

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