Development of methods for the quantification of microstructural features in α+β-processed α/β titanium alloys

Collins, Peter C.; Welk, Brian; Searles, T; Tiley, J.; Russ, John C.; Fraser, Hamish L.

doi:10.1016/j.msea.2008.12.038

Cited by 98 publications

(59 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The backscattered electron micrographs were collected in a random and unbiased fashion, and quantified using stereological techniques developed for these types of a/b-processed Ti-6Al-4V microstructures. [23] The features that were quantified are the size and volume fraction of the equiaxed alpha particles (equiaxed-a size (lm) and F V equiaxed-alpha ), the volume fraction of total alpha (F V total-alpha ), and the thickness of the alpha laths in the transformed b regions (a-lath width, lm). [23] These microstructural features, and the corresponding compositions, were first used as inputs to predict their yield strengths, based on the well-developed and validated model developed in previous work.…”

Section: Neural Network Developmentmentioning

confidence: 99%

“…[23] The features that were quantified are the size and volume fraction of the equiaxed alpha particles (equiaxed-a size (lm) and F V equiaxed-alpha ), the volume fraction of total alpha (F V total-alpha ), and the thickness of the alpha laths in the transformed b regions (a-lath width, lm). [23] These microstructural features, and the corresponding compositions, were first used as inputs to predict their yield strengths, based on the well-developed and validated model developed in previous work. [20] The results were then compared to tensile specimens that had nominally the same thermomechanical processing history, and therefore, nominally, the same microstructure and properties.…”

Section: Neural Network Developmentmentioning

confidence: 99%

See 1 more Smart Citation

Production of a Powder Metallurgical Hot Work Tool Steel with Harmonic Structure by Mechanical Milling and Spark Plasma Sintering

Deirmina

Pellizzari

Federici

2017

Metall Mater Trans A

View full text Add to dashboard Cite

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 a/b-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 1C 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 1C 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 a/b-processed Ti-6Al-4V.

show abstract

Section: Neural Network Developmentmentioning

confidence: 99%

Section: Neural Network Developmentmentioning

confidence: 99%

Production of a Powder Metallurgical Hot Work Tool Steel with Harmonic Structure by Mechanical Milling and Spark Plasma Sintering

Deirmina

Pellizzari

Federici

2017

Metall Mater Trans A

View full text Add to dashboard Cite

show abstract

“…The initial dislocation configuration consists of random distributed segments that are pinned at their ends, leading to the operation of the multiplication mechanism based on Frank-Read source activation as in real crystals. The single grain is oriented so that a tensile load is applied with a constant strain rate in a specific crystallographic direction ([0001], [11][12][13][14][15][16][17][18][19][20], and ). The time step is dt = 5 9 10 12 , and the strain rate is constant at 10 3 in the simulation setup.…”

Section: Methodologiesmentioning

confidence: 99%

“…These approaches have been based on neural-network models incorporating Bayesian statistics developed by MacKay 7-10 and include accurate descriptions of the microstructural features based on rigorously developed stereological methods, as described elsewhere. 11 In addition to their ability to make blind predictions, such models may be used to perform virtual experiments, where a single input parameter, such as a microstructural feature or solute content, is changed while all other inputs are kept fixed at some value (e.g., their average). These virtual experiments, so called because they might otherwise be impossible to achieve in the laboratory, given that the microstructural features are often very complex and interrelated, can be used to probe the functional dependencies of certain microstructural features on the mechanical properties.…”

Section: Motivationmentioning

confidence: 99%

Discovery via Integration of Experimentation and Modeling: Three Examples for Titanium Alloys

Liu

Samimi

Ghamarian

et al. 2014

JOM

View full text Add to dashboard Cite

One key aspect of any integrated computational materials engineering approach is the integration of experiments that provide critical information for the modeling activities. This article describes, using case studies, three examples of critical experiments that have been conducted in an integrated fashion with modeling activities for titanium alloys, providing valuable information in an accelerated manner. The first has been used to identify key microstructural features associated with fracture toughness in Ti-6Al-4V and integrates artificial neural networks and various experimental techniques. The second is associated with defect accumulation in highly constrained titanium structures and integrates a highly innovative characterization technique (precession electron diffraction) and dislocation dynamics. The third is a high-throughput combinatorial technique to understand the oxidation behavior of titanium alloys and couples the experimental effort with the CALPHAD approach. INTRODUCTIONThe composition, microstructure, and defect structures of advanced nonferrous structural alloys are the predominant factors that govern the response of the material to an externally applied stimulus. For example, there will be an elasticplastic response of the material to an externally applied load. Similarly, there will be a compositional and structural evolution in response to thermal excursions. Although these responses are expected to occur, the precise details of the responses are less well understood. integrated computational materials engineering (ICME), or integrated computational materials science and engineering (ICMSE), is a strategy to integrate knowledge, represented by both computation/simulation and high-fidelity experimental databases, from across the composition-microstructure-processing-property paradigm so as to engineer a solution to a design problem. ICME strategies are increasingly being put into service and adding value. 1-4 However, to be effective the individual components must accurately describe materials phenomena.For many structural materials, the precise details of materials response to external stimuli are not well understood. Often, an integrated experimental and computational approach can help elucidate these missing details. While not representing a complete ICME program, each of these integrated activities has provided knowledge that was otherwise lacking. Each of these programs is related to a different problem facing a particular class of nonferrous structural alloys-titanium-based alloys. These include efforts to understand the following: the influence of microstructure on the fracture toughness of a + b-processed Ti-6Al-4V, the influence of defect populations on performance of single-phase and two-phase titanium alloys, and the role of composition on the oxidation behavior of several titanium alloys. The motivation and details of each problem are described separately, as are the conclusions. It is seen that the integration of critical and careful experiments with various modeling schemes ca...

show abstract

“…The size of an a-ferrite region was determined 30 to be roughly equivalent (OR = 21 lm, NR = 25 lm), indicating that the grain size would exhibit a negligible difference in the properties of these two rivets. 22 The bulk of this microstructural analysis focuses on the observed inclusions, which are markedly different when comparing the original and modern rivets.…”

Section: Microstructurementioning

confidence: 98%

Revisiting (Some of) the Lasting Impacts of the Liberty Ships via a Metallurgical Analysis of Rivets from the SS “John W. Brown”

Harris

Grogg

Akoma

et al. 2015

JOM

View full text Add to dashboard Cite

Development of methods for the quantification of microstructural features in α+β-processed α/β titanium alloys

Cited by 98 publications

References 7 publications

Production of a Powder Metallurgical Hot Work Tool Steel with Harmonic Structure by Mechanical Milling and Spark Plasma Sintering

Production of a Powder Metallurgical Hot Work Tool Steel with Harmonic Structure by Mechanical Milling and Spark Plasma Sintering

Discovery via Integration of Experimentation and Modeling: Three Examples for Titanium Alloys

Revisiting (Some of) the Lasting Impacts of the Liberty Ships via a Metallurgical Analysis of Rivets from the SS “John W. Brown”

Contact Info

Product

Resources

About