Issue 42

A. De Santis et alii, Frattura ed Integrità Strutturale, 42 (2017) 231-238; DOI: 10.3221/IGF-ESIS.42.25 232 dangerous) to spheroids (with a decrease of the stress intensification near the graphite elements): these grades are widely used to produce pressure pipes and fittings, in the automotive industry (e.g., crankshafts), in road and construction application. 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, [2-4]. The aim is to provide quantitative characterization of the materials in order to determine mechanical properties and establish relationship with damaging mechanisms, [5-6]. Nevertheless up to now the official guide of the International Standard [7] is applied almost manually for visual inspection. Only few attempts have been made to automatize the classification of microstructural image data [8]. 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. As will be recalled in Section II, the shape is the first characteristic to be evaluated in order to determine whether a graphite has a desirable shape or not. In case the shape is not nodular, different levels are possible and it could be determined if the graphite has vermicular aspect or if it contains exploded nodules and so on. 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. The classification is performed by support vector machine (SVM) suitably trained, [10]; it is a versatile tool useful to classify signals of different nature [11-12]. The classes identified with respect to the Type in the ASTM 2016 are seven; nevertheless binary classifiers are trained in order to simplify the classification step and guarantee the modularity of the procedure. The paper is organized as follows: in Section II, after the description of the data, the image analysis and features extraction is described. Then the training and classification procedure by the SVM is outlined. In Section III numerical results are proposed and discussed, whereas in Section IV conclusions and future work are presented. M ATERIALS AND METHODS n this section the procedure for the image acquisition and classification is outlined. Different DCIs have been considered, focusing the attention only on the graphite elements morphological peculiarities and not on the metal matrix microstructure. Specimens have been obtained by means of a metallographic preparation according to the following procedure: - specimen sectioning operation by abrasive cutting; - specimen mounting; - specimen grinding (decreasing grit sizes for abrasive papers up to P1200) and polishing (6 micron diamond followed by 1 micron diamond on low napped polishing cloths); - observation of the metallographically prepared specimen by means of a Light Optical microscope (LOM); Graphite elements characterization is usually performed by means of a visual inspection and a qualitatively evaluation according to the standards [7, 13]. The standardized procedure is based on the visual comparison between the observed images and the charts that are available in the standards. To classify automatically images of ductile cast iron specimens the idea is to extract features useful to describe the specimens and, once the classes of interest are defined, train a classifier able to assign each image to the specific class. This implies the identification of a sort of signature of the images, so that once a new unknown image is proposed, it could be classified by evaluating its signature. On the basis of the International Standard ASTM [7] the information to be retrieved from the images are: - the shape, in particular a measure of its nodularity in shape; the classes with respect to the shape are indicated by: Type I-II-III-IV-V-VI-VII; - the distribution of the graphite in the specimen: it is particularly important in rating the flake graphite and the distribution is described by the letters A-B-C-D-E; - the size of the graphite particles, and the classes are indicated by 1-2-3-4-5-6-7-8 depending on the actual dimension ; - the nodularity, measured as the percentage of the nodular particles present in the microstructure; - the nodule count evaluated as the number of nodules per mm 2 at a magnification of 100x. In Fig. 1 examples of specimens belonging to the Type I, IV-and VII (whose differences between them are more evident) are proposed. I