Mode III critical distance determination with optimized V-notched specimen under torsional fatigue and size effects on the inverse search probability distribution

Santus, Ciro; Berto, Filippo; Pedranz, M.; Benedetti, M.

doi:10.1016/j.ijfatigue.2021.106351

Cited by 11 publications

(2 citation statements)

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“…However, due to the fatigue size effect, 2 the fatigue life of notches is limited by notch opening angle, notch radius, and notch depth. The influence of size effect on fatigue has been evaluated through some experimental and numerical studies; for instance, critical distance theories and stress gradient were applied to predict fatigue life of samples with different depths and root radii 3–5 . The relationship between fatigue life and fatigue region can be well described by correlating with the notch depth based on stress field intensity approach 6 .…”

Section: Introductionmentioning

confidence: 99%

“…The influence of size effect on fatigue has been evaluated through some experimental and numerical studies; for instance, critical distance theories and stress gradient were applied to predict fatigue life of samples with different depths and root radii. [3][4][5] The relationship between fatigue life and fatigue region can be well described by correlating with the notch depth based on stress field intensity approach. 6 With the aim to study the effects of notch size on the fracture behavior of the specimens, different hole radii were analyzed through finite fracture mechanics theory.…”

Section: Introductionmentioning

confidence: 99%

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Data‐driven approach to design fatigue‐resistant notched structures

Wang

Gao

Berto

2022

Fatigue Fract Eng Mat Struct

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Fatigue is the most common failure mode for engineering materials in various industrial applications. Generally, designing new products and components based on typical deterministic fatigue design approaches is slow and expensive. Although numerous investigations have been carried on this topic over decades, there still lacks a robust and efficient method to design a super‐fatigue‐resistant and testless structure. In this investigation, a novel data‐driven approach based on the deep learning algorithm is applied on designing a fatigue‐resistant notched structure under different loading via numerical simulations and deep learning. The notches with various shapes are designed by employing the same material. Then finite element (FE) simulations are performed to obtain the mechanical behaviors of notched structures under different loading conditions. The deep learning algorithm is applied to predict mechanical behaviors of unknown notched structures based on input training set of simple notched geometries and relative lower computational cost. It is demonstrated that deep learning on the fatigue‐resistant structure is a promising approach of fatigue design. This work offers an alternative design strategy of fatigue‐resistant structure and cost‐effective solution to accelerate the design of engineering components under different loading conditions.

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Section: Introductionmentioning

confidence: 99%