Building of constant life diagrams of fatigue using artificialneural networks

Júnior, Raimundo Carlos Silvério Freire; Neto, Adrião Duarte Dória; Aquino, Eve Maria Freire de

doi:10.1016/j.ijfatigue.2005.02.003

Cited by 49 publications

(22 citation statements)

References 9 publications

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“…Composite Al Assaf and El Kadi, 12 Bezazi et al, 13 Salmalian et al, 14 Salmalian et al, 16 Rohman et al, 18 Aymerich and Serra, 21 Junior et al, 26 Vassilopoulos et al, 28 Mathur et al, 29 Cai et al, 30 Liao et al, 31 Al-Assadi et al, 32 Kumar et al, 33 Uygur et al, 41 Xiang et al, 42 Vadood et al, 43 Al-Assaf, and El Kadi, 53 Vassilopoulos et al, 55 Xiao et al, 59 Al-Assadi et al, 61 El Kadi, 62 Tapkin, 68 Moghaddam et al, 69 Yan et al, 72 El Kadi and Al-Assaf, 74 El Kadi and Al-Assaf, 77 Lee et al, 78 Deveci and Artem, 95 Vassilopoulos et al, 100 Azarhoosh et al, 102 Ertas, 111 Ertas and Sonmez, 117 Ertas and Sonmez, 118 El Kadi et al, 125 Deveci and Artem 130 and Sai et al 131 Alloys Figueira Pujol and Andrade Pinto, 15 Susmikanti, 17 Venkatesh and Rack, 22 Pleune and Chopra, 23 Sohn and Bae, 24 Genel, 25 Marquardt and Zenner, 27 Zhaohua, 35 Xu et al,…”

Section: Materials Publicationsmentioning

confidence: 99%

“…Benchmarking Marquardt and Zenner, 27 Mathur et al, 29 Liao et al, 31 Artymiak et al 51 and Kim et al 133 Case study Yang et al, 1 Al Assaf and El Kadi, 12 Bezazi et al, 13 Salmalian et al, 14 Figueira Pujol and Andrade Pinto, 15 Salmalian et al, 16 Rohman et al, 18 Kong et al, 19 Han, 20 Aymerich and Serra, 21 Venkatesh and Rack, 22 Pleune and Chopra, 23 Sohn and Bae, 24 Genel, 25 Junior et al, 26 Vassilopoulos et al, 28 Cai et al, 30 Al-Assadi et al, 32 Kumar et al, 33 Ma et al, 34 Zhaohua, 35 Xu et al, 36 Barsoum et al, 37 Zhang and Lin, 38 Mohanty et al, 40 Uygur et al, 41 Xiang et al, 42 Vadood et al, 43 Mishra et al, 44 Mohanty, 45 Liu et al, 46 Martinez and Ponce, 47 Barbosa et al, 48 Lotfi and Beiss, 49 Razzaq et al, 50 Srinivasan et al, 52 Al-Assaf and El Kadi, 53 Park and Kang, 54 Vassilopoulos et al, 55 Majidian and Saidi,…”

Section: Datasets Publicationsmentioning

confidence: 99%

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Soft computing methods for fatigue life estimation: A review of the current state and future trends

Kalaycı

Karagöz

Karakaş

2020

Fatigue Fract Eng Mat Struct

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Fatigue causes cracking or breakage in a material due to repeated loads; it causes the material to become unusable. Therefore, knowing the fatigue life of materials is crucial for the implementation of designs, economy and human life. Soft computing methodologies, a subset of artificial intelligence emerging to simulate human intelligence, deal with approximate models and seek solutions to complex real-life problems relying on both computational power of machines and the high accuracy of the algorithms. In this study, soft computing methods adapted for estimating/predicting the fatigue life of engineering structures and materials are investigated. For this purpose, 95 articles published between 1995 and 2020 have been examined in detail. With this review, it is aimed to reveal the efficiency of soft computing methods and contribute to their development. Recommendations have been made to draw attention to these methodologies which are expected to be used in many areas in the future.

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Section: Materials Publicationsmentioning

confidence: 99%

Section: Datasets Publicationsmentioning

confidence: 99%

Soft computing methods for fatigue life estimation: A review of the current state and future trends

Kalaycı

Karagöz

Karakaş

2020

Fatigue Fract Eng Mat Struct

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“…For example, the information reported by Júnior etal, 50 Vassilopoulos et al, 51 and Flore and Wegener 52 may be used through a proper interpolation function or neural network to construct the Ω function of some types of the glass/epoxy composites. Some typical CLDs (of the glass/epoxy material) are illustrated in Figure 1.…”

Section: Proposing the Damage-based Modelmentioning

confidence: 99%

Generalized 3D high cycle fatigue criteria for multiscale bridging‐based progressive damage analysis of multilayer composite parts under random loads and material deterioration

Shariyat

Rahimi‐Ghozat

2019

Fatigue Fract Eng Mat Struct

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Two frameworks are employed to develop two distinct categories of multiaxial high cycle fatigue life assessment models for composite components experiencing general and random loading conditions. In this regard, the decay in the material properties with cycles is also taken into account. It is obvious that in multilayer components, the fatigue failure is a progressive process that may be accompanied by gradual or sudden changes in the material properties and, consequently, the resulting stresses. In addition to using the traditional progressive damage analyses, a new concept is proposed for tracing of the localized fatigue failures more accurately. It is postulated that generally, the stress components have distinct frequencies, phase shifts, and mean values that all vary with time in a random manner. The proposed fatigue criteria, especially, the equivalent-stress-based ones, are capable of predicting various fatigue failure modes, such as the fibre breakage, matrix cracking, and interfacial debonding. A special and comprehensive fatigue failure tracking and cycle counting algorithms that are capable of handling the mentioned general peculiarities are proposed. The proposed HCF criteria and the relevant fatigue life assessment algorithm are then implemented on a composite multilayer monoleaf spring of a realistic vehicle under a random field-measured loading condition, as a typical component, and the results are compared and the experimental results conducted by the authors, for accuracy investigations. The considered stochastic road inputs have been chosen on the basis of the consumption times and field measurements. K E Y W O R D Scomposite mono-leaf spring, deterioration of the material, experimental results, new 3D HCF criteria, progressive fatigue-damage, realistic random load inputs

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“…In the current decade, ANNs have been widely used in the investigations of material strength, in particular, in the field of fatigue, creep rupture strength, and fracture mechanics [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: Back-propagation Artificial Neural Networkmentioning

confidence: 99%

“…The prediction accuracy achieved in the ANN-assisted calculations was shown to be better than that in calculations by conventional analytical methods. A study and prediction of fatigue characteristics using ANN were also discussed in [13][14][15][16][17].…”

Section: Back-propagation Artificial Neural Networkmentioning

confidence: 99%

Strain–life curves of steels and methods for determining the curve parameters. Part 2. Methods based on the use of artificial neural networks

et al. 2011

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The authors look into the possibility of using artificial neural networks for predicting the deformation characteristics of steels (the parameters of the Basquin-Manson-Coffin strain-life curve equation) based on static strength and plasticity characteristics, by constructing four independent neural networks with different configurations of input and output data. The prediction of parameters of the Basquin-Manson-Coffin equation and the fatigue life calculations by means of artificial neural networks are demonstrated to provide a better accuracy in comparison to the available conventional methods. Keywords: artificial neural networks, parameters of the Basquin-Manson-Coffin strain-life curve equation, fatigue life, static strength and plasticity characteristics.Introduction. In Part 1 [1] we reviewed the currently available methods for assessing the strain-life curves (the parameters of the Basquin-Manson-Coffin equation) and revealed some drawbacks of their definition. Here we will study the possibility of predicting the parameters of the strain-life curves by means of artificial neural networks (ANN).ANN Concept Description. The neurocomputing methodology is a relatively new area of artificial intelligence, where attempts are made to simulate the structure and operation of biological neural systems such as the human brain by constructing ANN in a computer. The use of ANN is especially helpful where one encounters the problems for which the solution is not clearly formulated or where one has to reproduce some mechanism which is otherwise difficult to describe using the available physical models.The methodology of this approach with a comprehensive description of neural networks, considering the multidiscipline nature of this subject, was detailed in [2]. Here we will just outline the ANN background and look into the possibility of using ANN in solving the applied engineering problems.Artificial neural networks consist of simple interconnected elements referred to as the processing elements or artificial neurons, which act as microprocessors. Thus, a neuron represents an information processing unit in a neural network. Figure 1 shows a model of a neuron used as a basis of ANN [2], where three main elements can be identified:1. A set of synapses (or connections), each characterized by its peculiar weight or connection strength. In particular, a signal x j at the input to synapse j connected to neuron k is multiplied by weight w kj . As opposed to brain synapses, the synaptic weight of an artificial neuron can have either positive or negative values.2. The summator adds the input signals weighted relative to the corresponding neuron synapses. This operation can be described as a linear combination.

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Building of constant life diagrams of fatigue using artificialneural networks

Cited by 49 publications

References 9 publications

Soft computing methods for fatigue life estimation: A review of the current state and future trends

Soft computing methods for fatigue life estimation: A review of the current state and future trends

Generalized 3D high cycle fatigue criteria for multiscale bridging‐based progressive damage analysis of multilayer composite parts under random loads and material deterioration

Strain–life curves of steels and methods for determining the curve parameters. Part 2. Methods based on the use of artificial neural networks

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