2021
DOI: 10.3390/met11010113
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Fatigue Failure Prediction of U-Notched ZK60 Magnesium Samples Using the Strain Energy Density Approach

Abstract: The light weight of magnesium alloys makes them a promising material in different potential industries, such as aerospace and automobile. In addition, magnesium alloys are attractive materials for biomedical applications due to their biocompatibility with the human body. The applications of these alloys in structural parts require an understanding of their fatigue behavior because they are usually subjected to time-varying loading. Furthermore, notches are inevitable in structural parts. Geometrical discontinu… Show more

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Cited by 6 publications
(2 citation statements)
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“…Он основан на критерии плотности энергии деформации (SED), который был разработан Lazzarin и Zambardi [19]. Критерий SED также широко используется для анализа разрушения образцов с надрезом из различных материалов: сталь [20], алюминий [21], титан [22], магний [23][24][25], ПЭЭК [26], ПММА [17,27]. Третий подход основан на использовании коэффициента интенсивности напряжений в вершине надреза (NSIF) [28][29][30].…”
Section: Introductionunclassified
“…Он основан на критерии плотности энергии деформации (SED), который был разработан Lazzarin и Zambardi [19]. Критерий SED также широко используется для анализа разрушения образцов с надрезом из различных материалов: сталь [20], алюминий [21], титан [22], магний [23][24][25], ПЭЭК [26], ПММА [17,27]. Третий подход основан на использовании коэффициента интенсивности напряжений в вершине надреза (NSIF) [28][29][30].…”
Section: Introductionunclassified
“…However, such models require extensive computational effort. Engineering design models such as the strain energy density (SED) [23] and the theory of critical distance (TCD) [24] have been successfully used to analyze the failure of notched members with different notch geometries and materials, and under both static and cyclic loading [25][26][27][28][29]. A key advantage of these models is that they do not require elastic-plastic or complex computational analyses and yet provide accurate predictions.…”
Section: Introductionmentioning
confidence: 99%