10 Citations (Scopus)

Abstract

Thermal and mechanical processes alter the microstructure of materials, which determines their mechanical properties. This makes reliable microstructural analysis important to the design and manufacture of components. However, the analysis of complex microstructures, such as Ti6Al4V, is difficult and typically requires expert materials scientists to manually identify and measure microstructural features. This process is often slow, labour intensive and suffers from poor repeatability. This paper overcomes these challenges by proposing a new set of automated techniques for 2D microstructural analysis. Digital image processing algorithms are developed to isolate individual microstructural features, such as grains and alpha lath colonies. A segmentation of the image is produced, where regions represent grains and colonies, from which morphological features such as; grain size, volume fraction of globular alpha grains and alpha colony size can be measured. The proposed measurement techniques are shown to obtain similar results to existing manual methods while drastically improving speed and repeatability. The benefits of the proposed approach when measuring complex microstructures are demonstrated by comparing it with existing analysis software. Using a few parameter changes, the proposed techniques are effective on a variety of microstructure types and both SEM and optical microscopy images
LanguageEnglish
Pages395-406
Number of pages12
JournalMaterials & Design
Volume141
Early online date27 Dec 2017
DOIs
Publication statusPublished - 5 Mar 2018

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Image processing
Microstructure
Optical microscopy
Volume fraction
Personnel
Mechanical properties
Scanning electron microscopy

Keywords

  • microstructure analysis
  • segmentation
  • watershed algorithm
  • titanium alloy

Cite this

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title = "New methods for automatic quantification of microstructural features using digital image processing",
abstract = "Thermal and mechanical processes alter the microstructure of materials, which determines their mechanical properties. This makes reliable microstructural analysis important to the design and manufacture of components. However, the analysis of complex microstructures, such as Ti6Al4V, is difficult and typically requires expert materials scientists to manually identify and measure microstructural features. This process is often slow, labour intensive and suffers from poor repeatability. This paper overcomes these challenges by proposing a new set of automated techniques for 2D microstructural analysis. Digital image processing algorithms are developed to isolate individual microstructural features, such as grains and alpha lath colonies. A segmentation of the image is produced, where regions represent grains and colonies, from which morphological features such as; grain size, volume fraction of globular alpha grains and alpha colony size can be measured. The proposed measurement techniques are shown to obtain similar results to existing manual methods while drastically improving speed and repeatability. The benefits of the proposed approach when measuring complex microstructures are demonstrated by comparing it with existing analysis software. Using a few parameter changes, the proposed techniques are effective on a variety of microstructure types and both SEM and optical microscopy images",
keywords = "microstructure analysis, segmentation, watershed algorithm, titanium alloy",
author = "Andrew Campbell and Paul Murray and Evgenia Yakushina and Stephen Marshall and William Ion",
year = "2018",
month = "3",
day = "5",
doi = "10.1016/j.matdes.2017.12.049",
language = "English",
volume = "141",
pages = "395--406",
journal = "Materials & Design",
issn = "0261-3069",

}

TY - JOUR

T1 - New methods for automatic quantification of microstructural features using digital image processing

AU - Campbell, Andrew

AU - Murray, Paul

AU - Yakushina, Evgenia

AU - Marshall, Stephen

AU - Ion, William

PY - 2018/3/5

Y1 - 2018/3/5

N2 - Thermal and mechanical processes alter the microstructure of materials, which determines their mechanical properties. This makes reliable microstructural analysis important to the design and manufacture of components. However, the analysis of complex microstructures, such as Ti6Al4V, is difficult and typically requires expert materials scientists to manually identify and measure microstructural features. This process is often slow, labour intensive and suffers from poor repeatability. This paper overcomes these challenges by proposing a new set of automated techniques for 2D microstructural analysis. Digital image processing algorithms are developed to isolate individual microstructural features, such as grains and alpha lath colonies. A segmentation of the image is produced, where regions represent grains and colonies, from which morphological features such as; grain size, volume fraction of globular alpha grains and alpha colony size can be measured. The proposed measurement techniques are shown to obtain similar results to existing manual methods while drastically improving speed and repeatability. The benefits of the proposed approach when measuring complex microstructures are demonstrated by comparing it with existing analysis software. Using a few parameter changes, the proposed techniques are effective on a variety of microstructure types and both SEM and optical microscopy images

AB - Thermal and mechanical processes alter the microstructure of materials, which determines their mechanical properties. This makes reliable microstructural analysis important to the design and manufacture of components. However, the analysis of complex microstructures, such as Ti6Al4V, is difficult and typically requires expert materials scientists to manually identify and measure microstructural features. This process is often slow, labour intensive and suffers from poor repeatability. This paper overcomes these challenges by proposing a new set of automated techniques for 2D microstructural analysis. Digital image processing algorithms are developed to isolate individual microstructural features, such as grains and alpha lath colonies. A segmentation of the image is produced, where regions represent grains and colonies, from which morphological features such as; grain size, volume fraction of globular alpha grains and alpha colony size can be measured. The proposed measurement techniques are shown to obtain similar results to existing manual methods while drastically improving speed and repeatability. The benefits of the proposed approach when measuring complex microstructures are demonstrated by comparing it with existing analysis software. Using a few parameter changes, the proposed techniques are effective on a variety of microstructure types and both SEM and optical microscopy images

KW - microstructure analysis

KW - segmentation

KW - watershed algorithm

KW - titanium alloy

UR - http://www.sciencedirect.com/science/article/pii/S0264127517311620

U2 - 10.1016/j.matdes.2017.12.049

DO - 10.1016/j.matdes.2017.12.049

M3 - Article

VL - 141

SP - 395

EP - 406

JO - Materials & Design

T2 - Materials & Design

JF - Materials & Design

SN - 0261-3069

ER -