Classification of ductile cast iron specimens: A machine learning approach

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

Cast Irons

The demand for cast iron components, with weights ranging from a few kilograms to several tons, has increased significantly in recent years, both for technical and economic reasons. In fact, the lower cost compared to other alloys, and the good castability, which allow one to obtain near-net shape components in as-cast conditions, and the mechanical properties that can be obtained, are just some of the motivations that attract mechanical designers. However, correct design requires a good knowledge of the intrinsic correlation among alloy chemical composition, process parameters, microstructure (with casting defects) and mechanical properties. This book is aimed at collecting excellent and recent research experimental and theoretical works in this filed. Technological (say, wear resistance and weldability) and mechanical properties (say, Young modulus, static and fatigue strength) of different grades of cast irons, ranging from solution strengthened ferritic ductile iron to compacted graphite iron as well as white and nodular cast irons, are correlated with the alloy chemical composition, process parameters and casting dimension

Effect of in-mould inoculant composition on microstructure and fatigue behaviour of heavy section ductile iron castings

In this paper, the influence of the in-mould inoculant composition on microstructure and fatigue behaviour of heavy section ductile iron (EN GJS 700-2) castings has been investigated. Axial fatigue tests under nominal load ratio R=0 have been performed on specimens taken from the core of large casting components. Metallographic analyses have been carried out by means of optical microscopy and important microstructural parameters that affect the mechanical properties of the alloy, such as nodule count, nodularity and graphite shape, were measured. Furthermore, Scanning Electron Microscopy was used to investigate the fracture surfaces of the samples in order to identify crack initiation and propagation zones. Cracks initiation sites have been found to be microshrinkages close to specimens\u2019 surface in most cases. It was found that in-mould inoculant composition strongly influences the alloy microstructure, such as nodule count and shrinkage porosities size, as well as the fatigue resistance of heavy section ductile iron castings

Assessment of fatigue damage in a fully pearlitic ductile cast iron by evaluation of Acoustic Emission Entropy

Abstract The paper presents the preliminary results of Acoustic Emission (AE) tests on a peralitic ductile cast iron (DCI) subjected to fatigue tensile loading. The focus is on the evaluation of the information Entropy of the AE data, as an innovative tool for a reliable assessment of fatigue damage in DCIs. Two damage indexes are proposed for the identification of the damage evolution and for the prediction of the fracture failure

Push-pull fatigue tests on ductile and vermicular cast irons

This article aims at measuring and comparing the fatigue strength with fully reversed push-pull tests in the case of two different cast irons: ductile and vermicular. Spheroidal Graphite Iron (SGI), also known as ductile cast iron, is nowadays used in a very large variety of applications. It represents a valid option when strength and stiffness are required, namely, when high values of tensile strength and Young’s modulus are coupled with appreciable deformation before failure. By contrast, a different cast iron, known as Compacted Graphite Iron (CGI) or vermicular cast iron, presents its benefits in replacing SGI with respect to specific applications. In particular, with better castability, machinability and thermal resistance, SGI is ideal when components suffer simultaneous mechanical and thermal loadings, such as cylinder blocks and heads. While SGI benefits of a wide scientific literature, CGI is a relatively unknown material, especially referring to its response under fatigue loads

Fatigue analysis-based numerical design of stamping tools made of cast iron

This work concerns stress and fatigue analysis of stamping tools made of cast iron with an essentially pearlitic matrix and containing foundry defects. Our approach consists at first, in coupling the stamping numerical processing simulations and structure analysis in order to improve the tool stiffness geometry for minimizing the stress state and optimizing their fatigue lifetime. The method consists in simulating the stamping process by considering the tool as a perfect rigid body. The estimated contact pressure is then used as boundary condition for FEM structure loading analysis of the tool. The result of this analysis is compared with the critical stress limit depending on the automotive model. The acceptance of this test allows calculating the fatigue lifetime of the critical zone by using the S–N curve of corresponding load ratio. If the prescribed tool life requirements are not satisfied, then the critical region of the tool is redesigned and the whole simulation procedures are reactivated. This method is applied for a cast iron EN-GJS-600-3. The stress-failure (S–N) curves for this material is determined at room temperature under push pull loading with different load ratios R0σmin/σmax0−2, R0−1 and R00.1. The effects of the foundry defects are determined by SEM observations of crack initiation sites. Their presence in tested specimens is associated with a reduction of fatigue lifetime by a factor of 2. However, the effect of the load ratio is more important