Segmentation and Grain Size of Ceramics

Arnould, Xavier; Coster, Michel; Chermant, Jean‐Louis; Chermant, Liliane; Chartier, Thierry; Elmoataz, Abder

doi:10.5566/ias.v20.p131-135

Cited by 14 publications

(8 citation statements)

References 10 publications

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“…Several investigators have shown a correlation between nodularity and mechanical properties but, again, the correlations are based on visual estimates of nodularity and ferrite quantity [1,4,8,11,2,13]. With the advent of modern computational facilities and analytical tools, it seems appropriate to reconsider this analytical issue.…”

Section: Fig1: Microstructure Image Of Ductile Cast Ironmentioning

confidence: 99%

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Microstructure Image Analysis for Estimating Mechanical Properties of Ductile Cast Iron

Hiremath¹,

Sadashivappa²,

Pattan³

2014

IJCA

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The digital image processing and computer vision technologies have key role to play in the field of material manufacturing and quality control. The microstructure images of materials provide vital information about materials properties. The microstructure visual and mechanical properties are strongly related. The mechanical properties, namely, yield strength, tensile strength and elongation, of ductile iron are directly dependent on ferrite composition and nodularity value of the material. Castings with poor nodularity will exhibit lower tensile elongation and often do not meet minimum tensile strength and finally impact strength requirements. Hence, it is established by experimental results that the composition and nodularity value of the material have paramount importance in material manufacturing.In this paper, a novel automatic method of digital image analysis for estimating important mechanical properties with the help of microstructure visual properties has been proposed. Microstructure image analysis is performed for deriving microstructure properties, namely, nodularity value and percentage of ferrite phase present in material sample. A fuzzy rule based inference system is built using known authentic relationship data published in the research literature [3] to estimate important mechanical properties of the sample material using nodularity value and percentage of ferrite phase. With the inputs, namely, percentage of ferrite phase and nodularity values, to fuzzy inference system, the mechanical properties, namely, yield strength, tensile strength and elongation are predicted. The nodularity of the samples were determined by using image analysis techniques based on ASTM A 247-67(1968) standard. The automatic image analysis minimized the variability of the measurement due to operator bias. The results of the proposed method are compared with results obtained by manual method. The results of proposed method are accurate and close to practical limits. The proposed method is easily repeatable, fast and economical and is expected to be useful in manufacturing of ductile cast iron and quality control practices.

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Section: Fig1: Microstructure Image Of Ductile Cast Ironmentioning

confidence: 99%

“…Many visual based methods provide key inputs for material manufacturing and quality control, which are automated with the help of DIP and CV technologies [1,2,4, 8,10,12]. The microstructure images of materials ( Fig.1) are processed using digital image processing techniques and the microstructure properties are assessed.…”

Section: Introductionmentioning

confidence: 99%

Microstructure Image Analysis for Estimating Mechanical Properties of Ductile Cast Iron

Hiremath¹,

Sadashivappa²,

Pattan³

2014

IJCA

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“…They have used roundness, form factor, sphericity, circularity length-width ratio, Tortousity as grain features for recognition system. Xaviour Arnould et al [15] have proposed methods to automatically extract the grain boundaries of the materials to determine grain morphological parameters of ceramic materials. Wanda B., Alfred R and Gerhalrd [14] have worked with morphological methods and contour analysis method for grain boundary segmentation of microstructure image.…”

Section: Background Literaturementioning

confidence: 99%

An improved algorithm for classification of graphite grains in cast iron microstructure images using geometric shape features

Prakash

Mytri²,

Hiremath

2011

Thinkquest~2010

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“…To estimate Eq. 10 it is necessary to provide a segmentation of pixellated grain boundaries (Arnould et al, 2001;Jeulin and Moreaud, 2008). During this smoothing procedure, the grain boundaries are identified as lines or curves separating different phases in the image.…”

Section: Quantitative Analysis Of Grain Boundariesmentioning

confidence: 99%

Quantitative Characterization of Microstructure of Pure Copper Processed by Ecap

et al. 2013

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Orientation imaging microscopy (OIM) allows to measure crystallic orientations at the surface of a material. Digitalized data representing the orientations are processed to recognize the grain structure and they are visualized in crystal orientation maps. Analysis of the data firstly consists in recognition of grain boundaries followed by identification of grains themselves. Knowing the grain morphology, it is possible to characterize the grain size homogeneity and estimate structural parameters related to the physical properties of the material. The paper describes methods of imaging and quantitative characterization of the grain boundary structure in metals based on data from electron backscatter diffraction (EBSD). These methods are applied to samples of copper processed by equal-channel angular pressing (ECAP).

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Segmentation and Grain Size of Ceramics

Abstract: This paper presents some methods to automatically extract the grain boundaries of materials in order to develop an automatic method to determine the grain size and morphological parameters of ceramic materials. Results are presented in the case of sintered cerine (CeO 2 ) materials.

Cited by 14 publications

References 10 publications

Microstructure Image Analysis for Estimating Mechanical Properties of Ductile Cast Iron

Microstructure Image Analysis for Estimating Mechanical Properties of Ductile Cast Iron

An improved algorithm for classification of graphite grains in cast iron microstructure images using geometric shape features

Quantitative Characterization of Microstructure of Pure Copper Processed by Ecap

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