Research Article
Year 2019,
Volume: 5 Issue: 1, 137 - 142, 30.06.2019
Abstract
References
- Oluwole OO, Olorunniwo OE, Ogundare OO, Atanda PO and Oridota OO “Effect of Magnesium and Calcium as Spheroidizers on the Graphite Morphology in Ductile Cast Iron“, Journal of Minerals & Materials Characterization & Engineering 6, 1, 25-37, 2007.
- Ductile Iron Quality Assurance Guide by Ductile Iron Society; https://www.ductile.org/
- Szczotok A, “Guidance and advice to image analysis applied in materials science“, Technical Transactions, Mechanics Issue 3-M, 10, 15-21, 2016.
- Imasogie BI, Wendt U., “Characterization of Graphite Particle Shape in Spheroidal Graphite Iron using a Computer-Based Image Analyser“, Journal of Minerals and Materials Characterization and Engineering 3, 1 1-12, 2004.
- Albuquerque V, Tavares JMRS, Cortez P., “Quantification of the Microstructures of Hypoeutectic White Cast Iron using Mathematical Morphology and an Artificial Neuronal Network“, International Journal of Microstructure and Materials Properties 5, 1, 52-64, 2010.
- Sarojadevi H, Shetty AB, Murthy AK, Shetty PB, Mukunda PG., “Digital Image Processing Technique for Microstructure Analysis of Spheroidal Graphite Iron“, International Journal of Combined Research & Development 1, 4 2321-2241, 2013.
- Godbole S, Jayashree V., “Microstructure analysis of spheroidal graphite iron (SGI) using hybrid image processing approach“, International Journal of Advanced Research in Computer Engineering & Technology 3, 7, 2268-2273, 2014.
- Mirashi A, Jayashree V., “Microstructure analysis of cast iron using image processing“, International journal of innovations in egineering technology 6, 2 409- 417, 2015.
- Davut K, Çetin B, Arslan E, Meço H, Yazganarıkan C., “Nodularity and Nodule Count Analysis of Austempered Ductile Iron Castings by Digital Image Processing“, 18th International Metallurgy & Materials Congress 497-500, 2016.
- https://www.olympus-ims.com/en/applications/cast-iron-analysis/
- http://www.nis-elements.cz/en/solutions/industrial/26
- http://www.clemex.com/clemex/media/assets/pdf/Applications/Image-Analysis-Reports/331_CastIron_Porosity.pdf?ext=.pdf
- Schneider CA, Rasband, WS, Eliceiri KW., “NIH Image to ImageJ: 25 years of image analysis“, Nature Methods 9, 7, 671-675, 2012.
- Rueden CT, Schindelin J, Hiner MC. et al., “ImageJ2: ImageJ for the next generation of scientific image data“, BMC Bioinformatics 18, 529, 2012.
- ASTM E2567-14, Standard Test Method for Determining Nodularity and Nodule Count in ductile Uron Using Image Analysis. ASTM International, West Conshohocken, PA, 2014, www.astm.org
- ASTM A247 – 17, Standard Test Method for Evaluating the Microstructure of Graphite in Iron Castings. ASTM International, West Conshohocken, PA, 2014, www.astm.org
- Santis A. De, Bartolomeo O. Di, Iacoviello D., Iacoviello F., “Quantitative shape evaluation of graphite particles in ductile iron“, Journal of Materials Processing Technology 196, 292–302, 2008.
- Vasko A., “Evaluation of shape of graphite particles in cast irons by a shape factor“, Materials Today: Proceedings 3, 1199–1204, 2016.
ASSESSING THE VOLUME FRACTIONS OF THE PHASES, NODULARITY AND NODULE COUNT OF SPHEROIDAL GRAPHITE CAST IRON USING IMAGEJ SOFTWARE
Abstract
In this study, a
simple method for assessing the microstructure of ductile cast iron by using a
freeware digital image processing software is described. The method is applied
to three different ductile iron grades to assess their ferrite, pearlite and
graphite volume fractions as well as graphite nodularity, nodule count, nodule
size. All specimens were found to show good nodularity (~ 88 % by area) with
different graphite average size and perimeter morphology. The amount of
graphite was also found to be similar (11±2 % by area) in all specimens. On the
other hand, ferrite percentages were found as 85.8, 57.1, and 52.5 %
respectively for ASTM A536 grades 60-40-18, 65-45-12, and 80-55-06. It was also
found that among these three grades, the higher the pearlite content, the
higher the hardness. The agreement with the standards confirms that
quantitative metallography through image processing is a powerful tool in order
to estimate the mechanical properties of cast irons.
References
- Oluwole OO, Olorunniwo OE, Ogundare OO, Atanda PO and Oridota OO “Effect of Magnesium and Calcium as Spheroidizers on the Graphite Morphology in Ductile Cast Iron“, Journal of Minerals & Materials Characterization & Engineering 6, 1, 25-37, 2007.
- Ductile Iron Quality Assurance Guide by Ductile Iron Society; https://www.ductile.org/
- Szczotok A, “Guidance and advice to image analysis applied in materials science“, Technical Transactions, Mechanics Issue 3-M, 10, 15-21, 2016.
- Imasogie BI, Wendt U., “Characterization of Graphite Particle Shape in Spheroidal Graphite Iron using a Computer-Based Image Analyser“, Journal of Minerals and Materials Characterization and Engineering 3, 1 1-12, 2004.
- Albuquerque V, Tavares JMRS, Cortez P., “Quantification of the Microstructures of Hypoeutectic White Cast Iron using Mathematical Morphology and an Artificial Neuronal Network“, International Journal of Microstructure and Materials Properties 5, 1, 52-64, 2010.
- Sarojadevi H, Shetty AB, Murthy AK, Shetty PB, Mukunda PG., “Digital Image Processing Technique for Microstructure Analysis of Spheroidal Graphite Iron“, International Journal of Combined Research & Development 1, 4 2321-2241, 2013.
- Godbole S, Jayashree V., “Microstructure analysis of spheroidal graphite iron (SGI) using hybrid image processing approach“, International Journal of Advanced Research in Computer Engineering & Technology 3, 7, 2268-2273, 2014.
- Mirashi A, Jayashree V., “Microstructure analysis of cast iron using image processing“, International journal of innovations in egineering technology 6, 2 409- 417, 2015.
- Davut K, Çetin B, Arslan E, Meço H, Yazganarıkan C., “Nodularity and Nodule Count Analysis of Austempered Ductile Iron Castings by Digital Image Processing“, 18th International Metallurgy & Materials Congress 497-500, 2016.
- https://www.olympus-ims.com/en/applications/cast-iron-analysis/
- http://www.nis-elements.cz/en/solutions/industrial/26
- http://www.clemex.com/clemex/media/assets/pdf/Applications/Image-Analysis-Reports/331_CastIron_Porosity.pdf?ext=.pdf
- Schneider CA, Rasband, WS, Eliceiri KW., “NIH Image to ImageJ: 25 years of image analysis“, Nature Methods 9, 7, 671-675, 2012.
- Rueden CT, Schindelin J, Hiner MC. et al., “ImageJ2: ImageJ for the next generation of scientific image data“, BMC Bioinformatics 18, 529, 2012.
- ASTM E2567-14, Standard Test Method for Determining Nodularity and Nodule Count in ductile Uron Using Image Analysis. ASTM International, West Conshohocken, PA, 2014, www.astm.org
- ASTM A247 – 17, Standard Test Method for Evaluating the Microstructure of Graphite in Iron Castings. ASTM International, West Conshohocken, PA, 2014, www.astm.org
- Santis A. De, Bartolomeo O. Di, Iacoviello D., Iacoviello F., “Quantitative shape evaluation of graphite particles in ductile iron“, Journal of Materials Processing Technology 196, 292–302, 2008.
- Vasko A., “Evaluation of shape of graphite particles in cast irons by a shape factor“, Materials Today: Proceedings 3, 1199–1204, 2016.
There are 18 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Journals |
| Authors | |
| Publication Date | June 30, 2019 |
| Published in Issue | Year 2019 Volume: 5 Issue: 1 |
Cite
Cited By
Effect of regular thicknesses on the microstructural and quantitative analysis for a hypo-eutectic ductile iron alloyed with Ni and V
Journal of Mining and Metallurgy, Section B: Metallurgy
https://doi.org/10.2298/JMMB231114002C
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