Effect of the porosity on the fatigue strength of metals

Abstract

In the present paper, a procedure for fatigue strength assessment of metals containing solidification defects is employed to analyze the fatigue behavior of a ductile cast iron (DCI) characterized by a relevant micro‐shrinkage porosity. The procedure implements (i) a statistical method deriving from extreme value theory, (ii) the ‐parameter model, and (iii) the multiaxial critical plane‐based criterion by Carpinteri et al. According to the above statistical method, both the distribution of defects and the return period are determined. More precisely, the return period is computed by also exploiting a relationship here proposed to optimize the accuracy of the procedure in terms of fatigue strength estimation. The great potential of the present procedure is that the defect content analysis (performed by means of a statistical method deriving from extreme value theory) can be easily performed using machine learning techniques.

Fatigue crack growth simulation by extended finite element method: A review of case studies

“…Since all of them are based on experimental or analytical data, they are not presented here, but some of them are referenced here for the benefit of readers. [136][137][138][139][140][141][142] In addition, many other recent papers presented also this important topic, some of them described briefly in the following text.…”

“…Excellent source for such an overview is provided by the special issue published couple of months ago in Fatigue & Fracture of Engineering Materials & Structures under the title “Data science and machine learning for fatigue and fracture assessment.” 135 As presented and explained in Editorial, 11 papers described the use of data sciences and ML in structural durability investigations with a particular emphasis on material cyclic behavior and fractures. Since all of them are based on experimental or analytical data, they are not presented here, but some of them are referenced here for the benefit of readers 136–142 …”

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“…The use of machine learning methods to predict material properties is a very interesting topic. In [36,37], ML methods were used to link defects with mechanical properties. In many works, the use of ML tools was used to improve the level of control over the production process [38].…”

Fatigue & Fracture of Engineering Materials & Structures Virtual Special Issue: Data science and machine learning for fatigue and fracture assessment

“…Vantadori et al 3 investigated a procedure for fatigue strength assessment of metals containing solidification defects. The fatigue behavior of a ductile cast iron (DCI) characterized by a relevant micro‐shrinkage porosity was investigated.…”