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Computational microstructure analyzing technique for quantitative characterization of shrinkage and gas pores in pressure die cast AZ91 magnesium alloys

D.G. Leo Prakash, B. Prasanna, Doris Regener, Leo Prakash Orcid Logo

Computational Materials Science, Volume: 32, Issue: 3-4, Pages: 480 - 488

Swansea University Author: Leo Prakash Orcid Logo

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DOI (Published version): 10.1016/j.commatsci.2004.09.017

Abstract

Pressure die cast AZ91 magnesium alloy contains both shrinkage and gas microporosity. Quantification and characterization of shrinkage and gas microporosity is expected to be useful to understand the processing-properties-microstructure correlations. However, conventional image analysis techniques d...

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Published in: Computational Materials Science
Published: 2005
URI: https://cronfa.swan.ac.uk/Record/cronfa17642
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Abstract: Pressure die cast AZ91 magnesium alloy contains both shrinkage and gas microporosity. Quantification and characterization of shrinkage and gas microporosity is expected to be useful to understand the processing-properties-microstructure correlations. However, conventional image analysis techniques do not permit a separate quantification and characterization of shrinkage and gas microporosity. A computational microstructural (image) analyzing technique has been developed by the use of a programming language to quantify and analyze the microporosity. The shrinkage microporosity and gas microporosity were separated by the above technique and the size distributions of the above micropores were quantified. A microstructural montage from the AZ91 magnesium alloy is created for getting better and consistent results. In addition the image analyzing technique is used to measure the nearest neighbor distance of the shrinkage and the gas microporosity, and to quantify the clustering tendency of the porosity. A new parameter is defined to characterize the affinity of gas pores with the shrinkage pores and vice versa.
Keywords: Gas pores; Shrinkage pores; Quantification; Computational microstructure analysis; Nearest neighbor distribution; Size distribution
College: Faculty of Science and Engineering
Issue: 3-4
Start Page: 480
End Page: 488