Quantitative analysis of variant selection in ausformed lath martensite

2012 ◽  
Vol 60 (3) ◽  
pp. 1139-1148 ◽  
Author(s):  
G. Miyamoto ◽  
N. Iwata ◽  
N. Takayama ◽  
T. Furuhara
Keyword(s):  
Quantitative Analysis ◽  
Lath Martensite ◽  
Variant Selection

scholarly journals Comparison of Variant Selection between Lenticular and Lath Martensite Transformed from Deformed Austenite

2013 ◽  
Vol 53 (5) ◽  
pp. 915-919 ◽  
Author(s):  
Tadachika Chiba ◽  
Goro Miyamoto ◽  
Tadashi Furuhara
Keyword(s):  
Lath Martensite ◽  
Variant Selection

Key Factors in Grain Refinement of Martensite and Bainite

2010 ◽  
Vol 638-642 ◽  
pp. 3044-3049 ◽  
Author(s):  
Tadashi Furuhara ◽  
Naoki Takayama ◽  
Goro Miyamoto
Keyword(s):  
Grain Refinement ◽  
Lath Martensite ◽  
Bainitic Ferrite ◽  
Transformation Strain ◽  
Variant Selection ◽  
Key Factors ◽  
Austenite Grain ◽  
Austenite Grain Boundary ◽  
Block Sizes ◽  
Selection Of

Grain refinement in lath martensite and bainite structures, which is important for strengthening and toughening, are discussed in various aspects. Strain accommodation plays important roles to determine final crystal sizes of bainitic ferrite (BF) and martensite. There is strong variant selection of BF by natures of the austenite grain boundary where it nucleates. For small undercooling, such variant selection leads to coarse bainite block and packet sizes. More BF variants are formed by increasing undercooling, which leads to nucleation of BF variants of less potency, and by increasing strength of materials, which results in more self-accommodation of transformation strain due to suppression of plastic accommodation. In lath martensite, there seems to be similar variant selection at austenite grain boundaries. However, packet/block sizes in lath martensite decreases with an increase in carbon content because of more extensive self-accommodation due to lower transformation temperatures than bainite.

scholarly journals Effect of Stress on Variant Selection of Lath Martensite in Low-carbon Steel

2012 ◽  
Vol 98 (8) ◽  
pp. 425-433 ◽  
Author(s):  
Yamato Mishiro ◽  
Shoichi Nambu ◽  
Junya Inoue ◽  
Toshihiko Koseki
Keyword(s):  
Carbon Steel ◽  
Lath Martensite ◽  
Low Carbon Steel ◽  
Variant Selection ◽  
Low Carbon ◽  
Selection Of

scholarly journals Variant Selection of Reversed Austenite in Lath Martensite

2007 ◽  
Vol 47 (10) ◽  
pp. 1527-1532 ◽  
Author(s):  
Nobuo Nakada ◽  
Toshihiro Tsuchiyama ◽  
Setsuo Takaki ◽  
Shuji Hashizume
Keyword(s):  
Lath Martensite ◽  
Variant Selection ◽  
Reversed Austenite ◽  
Selection Of

scholarly journals Effect of Stress on Variant Selection in Lath Martensite in Low-carbon Steel

2013 ◽  
Vol 53 (8) ◽  
pp. 1453-1461 ◽  
Author(s):  
Yamato Mishiro ◽  
Shoichi Nambu ◽  
Junya Inoue ◽  
Toshihiko Koseki
Keyword(s):  
Carbon Steel ◽  
Lath Martensite ◽  
Low Carbon Steel ◽  
Variant Selection ◽  
Low Carbon

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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