Abstract
In this paper an automatic procedure based on a machine learning approach is proposed to classify ductile cast iron specimens according to the American Society for Testing and Materials guidelines. The mechanical properties of a specimen are strongly influenced by the peculiar morphology of their graphite elements and useful characteristics, the features, are extracted from the specimens’ images; these characteristics examine the shape, the distribution and the size of the graphite particle in the specimen, the nodularity and the nodule count. The principal components analysis are used to provide a more efficient representation of these data. Support vector machines are trained to obtain a classification of the data by yielding sequential binary classification steps. Numerical analysis is performed on a significant number of images providing robust results, also in presence of dust, scratches and measurement noise.
Highlights
D iscovered in the years 1943-48, ductile cast irons (DCIs) offer a really interesting combination of cast irons peculiarities and of carbon steels mechanical properties, [1]
In this paper an automatic procedure based on a machine learning approach is proposed to classify ductile cast iron specimens according to the American Society for Testing and Materials guidelines
In this paper the aim is to provide an automatic procedure to classify specimens according to the American Society for Testing and Materials (ASTM) standard with respect to the graphite elements shape, the “Type” parameter
Summary
D iscovered in the years 1943-48, ductile cast irons (DCIs) offer a really interesting combination of cast irons peculiarities (first of all, castability) and of carbon steels mechanical properties (e.g., toughness), [1]. Graphite elements morphology peculiarities (e.g. shape, dimension, distribution) are crucial to define the DCI mechanical properties. Image analysis has been using extensively in the last two decades in order to automatically characterize specimens in material science, [2,3,4]. The aim is to provide quantitative characterization of the materials in order to determine mechanical properties and establish relationship with damaging mechanisms, [5,6]. In this paper the aim is to provide an automatic procedure to classify specimens according to the American Society for Testing and Materials (ASTM) standard with respect to the graphite elements shape, the “Type” parameter. Given the images classified by two experts, useful features are extracted and re-arranged by principal components analysis (PCA) [9] in order to enhance the informative and useful content of the data.
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