Neural network prediction of buckling load of steel arch-shells

Hasanzadehshooiili, Hadi; Lakirouhani, Ali; Šapalas, Antanas

doi:10.1016/j.acme.2012.07.005

Cited by 19 publications

(11 citation statements)

References 32 publications

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“…[35][36][37][38][39][40] Artificial neural networks are computational systems inspired by biological neural networks formed by different layers: an input layer, one or more hidden layers, and an output layer. [35][36][37][38][39][40] Artificial neural networks are computational systems inspired by biological neural networks formed by different layers: an input layer, one or more hidden layers, and an output layer.…”

Section: Introductionmentioning

confidence: 99%

“…In the last 2 decades, many researchers have used artificial neural networks (ANNs) to solve a great variety of complex engineering problems, as pattern recognition, optimization, or prediction. [35][36][37][38][39][40] Artificial neural networks are computational systems inspired by biological neural networks formed by different layers: an input layer, one or more hidden layers, and an output layer. The layers involve different nodes that are connected by weights.…”

Section: Introductionmentioning

confidence: 99%

“…[39][40][41] Similarly, some prediction models (linear or nonlinear) have been recently developed. 35,36,38,42 Moreover, ANNs have been used to determine the SIF in cracked shafts. Muñoz-Abella et al 37 used a neural network to predict the SIF in the case of a cracked unbalanced rotating shaft subjected to bending loads.…”

Section: Introductionmentioning

confidence: 99%

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Neural approach to estimate the stress intensity factor of semi‐elliptical cracks in rotating cracked shafts in bending

Rubio

Muñoz-Abella

Rubio

2017

Fatigue Fract Eng Mat Struct

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In the last decades, neural network approach has often been used to study various and complex engineering problems, such as optimization or prediction. In this paper, a methodology founded on artificial neural networks (ANNs) was used to calculate the stress intensity factor (SIF) in different points of the front of a semi‐elliptical crack present in a rotating shaft, taking into account the shape and depth of the crack, the angle of rotation, and the location of the point in the front. In the event of rotating machines, such as shafts, it is crucial to know the SIF along the crack front because this parameter, according to the Paris Law, is related to the performance of the crack during its propagation. Previously, it was necessary to achieve the data for the ANN training, for this a quasi‐static numerical model was made, which simulates a rotating cracked shaft with a semi‐elliptical crack. The numerical solutions cover a wide range of crack depths and shapes, and rotation angles. The values of the SIF estimated by the ANNs were contrasted with other solutions available in the literature finding a good agreement between them. The proposed neural network methodology is an alternative that offers a very good option for the SIF estimation, because it is efficient and easy to use, does not require high computational costs, and can be used to analyse the propagation of cracks contained in rotating shafts by means of the Paris Law taking into account the nonlinear behaviour of the shaft.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural approach to estimate the stress intensity factor of semi‐elliptical cracks in rotating cracked shafts in bending

Rubio

Muñoz-Abella

Rubio

2017

Fatigue Fract Eng Mat Struct

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“…12,13,16,[25][26][27][28] In the case of cracked balanced shafts, FE computations of SIF values along the crack during the breathing mechanism are available in literature. [34][35][36][37][38] In this paper, the commercial finite element code ABAQUS 39 has been used to develop a quasi-static shaft model in order to obtain the SIF values in an unbalanced rotating cracked shaft. They have some advantages that make them very attractive: the ability to treat damage mechanisms implicitly and the capacity to generalize their responses and robustness in the presence of noise.…”

Section: Introductionmentioning

confidence: 99%

“…31 Furthermore, ANNs have successfully been applied in damage detection problems such as crack identification inverse problems [32][33][34] or development of prediction models (linear or nonlinear) depending on many variables. [34][35][36][37][38] In this paper, the commercial finite element code ABAQUS 39 has been used to develop a quasi-static shaft model in order to obtain the SIF values in an unbalanced rotating cracked shaft. We have analysed the SIF along the crack front, for several angles of rotation, different crack sizes and positions of the eccentricity.…”

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confidence: 99%

Stress intensity factor estimation for unbalanced rotating cracked shafts by artificial neural networks

Muñoz-Abella

Rubio

2014

Fatigue Fract Eng Mat Struct

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The knowledge of the stress intensity factor (SIF) values along a crack front is essential to calculate the crack growth rate and the remaining life of a mechanical component. In the case of a rotating shaft, usually it presents disalignments, which modify the SIF data with regard to a balanced one. This paper presents the use of an artificial neural network (ANN) for estimating the SIF at the crack front in an unbalanced shaft under rotating bending, previously, a quasi‐static numerical (finite element) model, which simulates a rotating shaft, has been developed to create the training cases for the ANN. The obtained results allow to study the influence of the unbalance of rotating shafts in the crack breathing mechanism and will allow to predict the influence of this behaviour on the values of the SIF and in the propagation of cracks.

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Inverse Determination of B340LA Material Parameters in Bending Springback Process by Dynamic Optimization Approach

Guo

Zhou

et al. 2021

Intelligent Robotics and Applications

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Neural network prediction of buckling load of steel arch-shells

Cited by 19 publications

References 32 publications

Neural approach to estimate the stress intensity factor of semi‐elliptical cracks in rotating cracked shafts in bending

Neural approach to estimate the stress intensity factor of semi‐elliptical cracks in rotating cracked shafts in bending

Stress intensity factor estimation for unbalanced rotating cracked shafts by artificial neural networks

Inverse Determination of B340LA Material Parameters in Bending Springback Process by Dynamic Optimization Approach

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