2020
DOI: 10.1016/j.matdes.2019.108438
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An in situ and ex situ study of χ phase formation in a hypoeutectic Fe-based hardfacing alloy

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Cited by 22 publications
(12 citation statements)
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“…This involves fitting several Gaussian distributions to approximate a cumulative distribution function extracted from the property to be deconvoluted. This has been used with good success for a variety of materials where the distribution of the phases is somewhat random: cemented carbides [13,18,19], steels [20], and cements [22,23]. Another technique is to use multivariate K-means cluster analysis [26], which incorporates several properties and performs an error minimization to split the data into several clusters which can be individually evaluated.…”
Section: Discussionmentioning
confidence: 99%
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“…This involves fitting several Gaussian distributions to approximate a cumulative distribution function extracted from the property to be deconvoluted. This has been used with good success for a variety of materials where the distribution of the phases is somewhat random: cemented carbides [13,18,19], steels [20], and cements [22,23]. Another technique is to use multivariate K-means cluster analysis [26], which incorporates several properties and performs an error minimization to split the data into several clusters which can be individually evaluated.…”
Section: Discussionmentioning
confidence: 99%
“…Using statistical analysis [15] or machine learning [16,17], the properties of each of these phases can be extracted. This has been applied on a range of materials from cemented carbides [13,18,19], steels [20], and thermal barrier coatings [21] to actual cements [22,23]. By combining this technique with elevated temperature capable systems, the activation parameters of individual phases can also be extracted [24], or using a combinatorial approach on a diffusion couple, entire slices of phase diagrams can be interrogated at once [25].…”
Section: Introductionmentioning
confidence: 99%
“…This involves fitting several Gaussian distributions to approximate a cumulative distribution function extracted from the property to be deconvoluted. This has been used with good success for a variety of materials where the distribution of the phases is somewhat random: cemented carbides [13,18,19], steels [20], and cements [22,23]. Another technique is to use multivariate K-means cluster analysis [26], which incorporates several properties and performs an error minimization to split the data into several clusters which can be individually evaluated.…”
Section: Discussionmentioning
confidence: 99%
“…Using statistical analysis [15] or machine learning [16,17], the properties of each of these phases can be extracted. This has been applied on a range of materials from cemented carbides [13,18,19], steels [20], and thermal barrier coatings [21] to actual cements [22,23]. By combining this technique with elevated temperature capable systems, the activation parameters of individual phases can also be extracted [24], or using a combinatorial approach on a diffusion couple, entire slices of phase diagrams can be interrogated at once [25].…”
Section: Introductionmentioning
confidence: 99%
“…However, advances in instrumentation and electronics have now enabled the speed of indentation to be increased approximately 100 times, such that many thousands of indentations can now easily be performed within an hour [5][6][7]. This makes both techniques simultaneously more accessible and thus more popular with numerous applications emerging in the recent literature on a variety of materials: cements [8,9], hardmetals [6,10,11], Ni-Fe meteorites [12], titanium [13], duplex [14] and tool steels [15], and thermal barrier coatings [16].…”
Section: Introductionmentioning
confidence: 99%