Capacitance sensors for measurement of phase volume fraction in two-phase pipelines

1993 ◽  
Vol 42 (3) ◽  
pp. 726-729 ◽  
Author(s):  
C.N. Strizzolo ◽  
J. Converti
Keyword(s):  
Volume Fraction ◽  
Phase Volume ◽  
Two Phase ◽  
Phase Volume Fraction

Tensile and Creep Rupture Properties of Co + Co3AIC0.5 Two-Phase Alloys

1998 ◽  
Vol 552 ◽  
Author(s):  
R. Ishibashi ◽  
S. Nakamura ◽  
Y. Aono ◽  
S. Miura ◽  
Y. Mishima
Keyword(s):  
Tensile Strength ◽  
Volume Fraction ◽  
Tensile Axis ◽  
Rupture Life ◽  
Creep Rupture ◽  
Average Particle ◽  
Phase Volume ◽  
Two Phase ◽  
Phase Volume Fraction ◽  
Rupture Properties

ABSTRACTIt is expected that the κ-phase of the intermetallic compound Co3A1C0.5 would strengthen Cobase alloys used at high temperatures like the γ' -phase of Ni-base superalloys. Tensile and creep rupture properties of Co+κ two-phase alloys with κ-phase volume fractions up to 0.75 were investigated. Alloy samples made by directional solidification casting were annealed at 1573 K for 3.6 ks and at 1373 K for 28.8 ks in vacuum, followed by Ar gas cooling. Tensile tests at RT and 1073 K and creep rupture tests at 1089 K under a stress of 172 MPa were conducted with the tensile axis parallel to the solidification direction. In alloys with low κ-phase volume fraction, cuboidal K-precipitates with average particle diameters of 0.4 to 1.0 μm were observed. They were coherent with the Co(fcc) matrix with misfits of about 3%. As the κ-phase volume fraction increased, tensile strength also increased. The alloy with κ-phase volume fraction of 0.4 had a 0.2% proof stress of 817MPa, tensile strength of 1047 MPa at RT, creep rupture life of 1.43 Ms, and tensile strain higher than 10%. These strengths are better than those of the conventional Co-base alloys. However, ductility of alloys with κ-phase volume fraction larger than 0.4 decreased due to large eutectic and primary κ-phase particles.

scholarly journals Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Wook Kim ◽  
Seong-Hoon Kang ◽  
Se-Jong Kim ◽  
Seungchul Lee
Keyword(s):  
High Performance ◽  
Materials Science ◽  
Volume Fraction ◽  
Training Data ◽  
Phase Fraction ◽  
Phase Volume ◽  
Generative Adversarial Network ◽  
Phase Volume Fraction ◽  
Original Dataset ◽  
Multi Phase

AbstractAdvanced high strength steel (AHSS) is a steel of multi-phase microstructure that is processed under several conditions to meet the current high-performance requirements from the industry. Deep neural network (DNN) has emerged as a promising tool in materials science for the task of estimating the phase volume fraction of these steels. Despite its advantages, one of its major drawbacks is its requirement of a sufficient amount of training data with correct labels to the network. This often comes as a challenge in many areas where obtaining data and labeling it is extremely labor-intensive. To overcome this challenge, an unsupervised way of learning DNN, which does not require any manual labeling, is proposed. Information maximizing generative adversarial network (InfoGAN) is used to learn the underlying probability distribution of each phase and generate realistic sample points with class labels. Then, the generated data is used for training an MLP classifier, which in turn predicts the labels for the original dataset. The result shows a mean relative error of 4.53% at most, while it can be as low as 0.73%, which implies the estimated phase fraction closely matches the true phase fraction. This presents the high feasibility of using the proposed methodology for fast and precise estimation of phase volume fraction in both industry and academia.

The Influence of the Ferrite and Pearlite Grain Size on the S-N Fatigue Characteristics of Steel

2014 ◽  
Vol 224 ◽  
pp. 3-8 ◽  
Author(s):  
Sebastian Kamiński ◽  
Marcel Szymaniec ◽  
Tadeusz Łagoda
Keyword(s):  
Mechanical Properties ◽  
Grain Size ◽  
Volume Fraction ◽  
Fatigue Properties ◽  
Phase Volume ◽  
Bending Tests ◽  
Phase Volume Fraction ◽  
Similar Chemical ◽  
Fatigue Characteristics ◽  
Ferrite Grain Size

In this work an investigation of internal structure influence on mechanical and fatigue properties of ferritic-pearlitic steels is shown. Ferrite grain size and phase volume fraction of three grades of structural steel with similar chemical composition, but different mechanical properties, were examined. Afterwards, samples of the materials were subjected to cyclic bending tests. The results and conclusions are presented in this paper

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