Texture evolution and variations of α-phase volume fraction in cold-rolled AISI 301 steel strip

2006 ◽  
Vol 177 (1-3) ◽  
pp. 555-560 ◽  
Author(s):  
J. Łuksza ◽  
M. Rumiński ◽  
W. Ratuszek ◽  
M. Blicharski
Keyword(s):  
Volume Fraction ◽  
Texture Evolution ◽  
Steel Strip ◽  
Phase Volume ◽  
Phase Volume Fraction ◽  
Α Phase ◽  
Cold Rolled

Effects of the Variation in α-Phase Volume Fraction on the Thermal Stability of TiAl Alloys with Lamellar Microstructures

2002 ◽  
Vol 753 ◽  
Author(s):  
S. W. Kim ◽  
H. N. Lee ◽  
M. H. Oh ◽  
M. Yamaguchi ◽  
D. M. Wee
Keyword(s):  
Thermal Stability ◽  
Volume Fraction ◽  
Lamellar Microstructure ◽  
Phase Volume ◽  
Heating Rates ◽  
Tial Alloys ◽  
Phase Volume Fraction ◽  
Volume Fractions ◽  
Α Phase ◽  
Thermal Stability Of

ABSTRACTThe thermal stability of lamellar microstructure in Ti-Al-Mo PST crystals containing C or Si, was investigated. In addition, the variation of α-phase volume fraction in Ti-Al-Mo-(C,Si) systems was investigated at several temperatures. Ti-46Al-1.5Mo-0.2C and Ti-46Al-1.5Mo-1.0Si alloys were found to be very stable during heat treatments at various heating rates and temperatures. Moreover, the α-phase volume fractions of Ti-46Al-1.5Mo-0.2C and Ti-46Al-1.5Mo-1.0Si alloys, which were stable compositions, changed less than those of Ti-47Al and Ti-46–1.5Mo alloys, which were unstable compositions. From these results, it was determined that the instability of the latter alloys was caused by their relatively higher variation of α-phase volume fraction during heating. Therefore, it is suggested that the variation of α-phase volume fraction is an important factor in controlling the thermal stability of lamellar microstructure.

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.

Microstructure and Texture Evolution in a Cold-Rolled High Mn Steel with Microadditions of Ti and Nb

2013 ◽  
Vol 203-204 ◽  
pp. 71-76
Author(s):  
Sławomir Kołodziej ◽  
Joanna Kowalska ◽  
Wiktoria Ratuszek ◽  
Wojciech Ozgowicz ◽  
Krzysztof Chruściel
Keyword(s):  
Plastic Deformation ◽  
Texture Evolution ◽  
Hexagonal Structure ◽  
Cold Plastic Deformation ◽  
Α Phase ◽  
Ε Phase ◽  
Cold Rolled ◽  
Goss Orientation ◽  
Mn Steel ◽  
Microscopic Investigations

The aim of this work was the microstructure and texture analysis of a deformed via cold-rolling 24.5Mn-3.5Si-1.5Al-Ti-Nb TWIP/TRIP type steel. It was found, that during cold plastic deformation a phase transformation of austenite into martensite takes place. The transformation progress was confirmed by the microscopic investigations. The texture of austenite is characterized by a limited α1=||RD fibre and the γ=||ND fibre. The texture of austenite changed with increasing deformation rate. In the texture of deformed austenite the strongest orientation is the {110} Goss orientation, which belongs to the α=||ND orientation fibre. During cold plastic deformation γ→ε and γ→ε→α’ phase transformations as well as the deformation of γ, ε and α’ phases are taking place in the steel. The formed ε phase (hexagonal structure) also possesses a distinct texture.

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

Effect of Hot-Rolled Microstructure on the Recrystallization Texture of Cold-Rolled Non-Oriented Electrical Steel

2011 ◽  
Vol 298 ◽  
pp. 203-208 ◽  
Author(s):  
Zi Li Jin ◽  
Wei Li ◽  
Yi Ming Li
Keyword(s):  
Cold Rolling ◽  
Electrical Steel ◽  
Texture Evolution ◽  
Steel Strip ◽  
Silicon Steel ◽  
Grain Structure ◽  
Recrystallization Annealing ◽  
Rolled Sheet ◽  
Cold Rolled ◽  
Hot Rolled

With the help of orientation distribution function (ODF) analysis, experiments of different hot band grain microstructure 0.33% silicon steel were cold-rolled and annealed in the laboratory，to study the effect of the microstructure hot-rolled steel strip for cold rolled non-oriented silicon steel microstructure and texture of recrystallization annealing. The results show that hot rolled microstructure on cold rolled Non-Oriented Electrical Steel cold-rolled sheet evolution of texture and recrystallization have important influence, the quiaxed grain structure of steel by cold rolling and recrystallization annealing, the recrystallization speed than the fiber grain-based mixed crystals recrystallization fast , With the equiaxed grains made of cold rolled silicon steel after annealing the {110}<UVW> texture components was enhanced and {100}<uwv> texture components weakened. Different microstructure condition prior to cold rolling in the recrystallization annealing process the texture evolution has the obvious difference, the equiaxial grain steel belt cold rolling and annealing, has the strong crystal orientation. This shows that the equiaxed grain when hot microstructure is detrimental to the magnetic properties of cold-rolled non-oriented silicon steel to improve and increase.

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