Prediction of constant amplitude fatigue crack growth life of 2024 T3 Al alloy with R-ratio effect by GP

Mohanty, Jyoti Ranjan; Mahanta, Tapan K.; Mohanty, A.R.; Thatoi, Dhirendra Nath

doi:10.1016/j.asoc.2014.10.024

Cited by 27 publications

(18 citation statements)

References 13 publications

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“…Article/conference paper Publications using genetic algorithms Article Ma et al, 34 Zhang and Lin, 38 Vadood et al, 43 Liu et al, 46 Lotfi and Beiss, 49 Majidian and Saidi, 56 Gao et al, 76 Canyurt, 89 Karakas et al, 90 Chen et al, 91 Kamal et al, 92 Zhang et al, 93 Agius et al 94 and Deveci and Artem 95 Conference paper Susmikanti, 17 Rohman et al, 18 Cai et al, 30 Zhaohua, 35 Tian 79 and Xu and Cui 96 prediction method for different purposes, such as the creation of constant life diagrams. Mohanty et al 101 estimated the fatigue crack growth rate of aluminium alloys (2024-T3) using GP and ANN, whereas GP provided a higher precision than ANN. In the study, it has been proposed to conduct research based on intelligent approaches to estimate fatigue life under different conditions.…”

Section: T a B L E 4 Genetic Algorithms Adapted For Estimating Fatigumentioning

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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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: T a B L E 4 Genetic Algorithms Adapted For Estimating Fatigumentioning

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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“…This phenomenon is caused by the loading interaction effect, and its existence obviously stimulates nonlinear damage accumulation. Wheeler [ 5 ], De Koning [ 6 ] and many other researchers [ 21 , 22 , 23 , 24 , 25 , 26 , 27 ] have introduced additional parameters to describe the influence of the historical loading sequence. Wheeler [ 5 ] models the retardation by correlating the plastic zone size ahead of the crack tip with the crack growth rate.…”

Section: Methodsmentioning

confidence: 99%

An Artificial Neural Network-Based Algorithm for Evaluation of Fatigue Crack Propagation Considering Nonlinear Damage Accumulation

et al. 2016

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In the aerospace and aviation sectors, the damage tolerance concept has been applied widely so that the modeling analysis of fatigue crack growth has become more and more significant. Since the process of crack propagation is highly nonlinear and determined by many factors, such as applied stress, plastic zone in the crack tip, length of the crack, etc., it is difficult to build up a general and flexible explicit function to accurately quantify this complicated relationship. Fortunately, the artificial neural network (ANN) is considered a powerful tool for establishing the nonlinear multivariate projection which shows potential in handling the fatigue crack problem. In this paper, a novel fatigue crack calculation algorithm based on a radial basis function (RBF)-ANN is proposed to study this relationship from the experimental data. In addition, a parameter called the equivalent stress intensity factor is also employed as training data to account for loading interaction effects. The testing data is then placed under constant amplitude loading with different stress ratios or overloads used for model validation. Moreover, the Forman and Wheeler equations are also adopted to compare with our proposed algorithm. The current investigation shows that the ANN-based approach can deliver a better agreement with the experimental data than the other two models, which supports that the RBF-ANN has nontrivial advantages in handling the fatigue crack growth problem. Furthermore, it implies that the proposed algorithm is possibly a sophisticated and promising method to compute fatigue crack growth in terms of loading interaction effects.

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“…Up to date, various soft-computing methods, e.g. artificial neural network [21][22][23], and genetic programming (GP) [24,25] have been used in the field of thermal fatigue.…”

Section: Introductionmentioning

confidence: 99%

Back Propagation Neural Network in Predicting the Thermal Fatigue Life of Microelectronic Chips

2020

Informacije MIDEM

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The present study gives an efficient approach to predict the thermal fatigue lives of microelectronic chips under cyclic thermal load using back propagation (BP) artificial neural network method. Strain based and stress-strain based thermal fatigue life models are established, respectively, according to the experimental results of thermal fatigue tests and the singularity parameters at the failure interface calculated by finite element method (FEM). According to the existing FEM results, the BP approach is configured to predict the singularity parameters at the failure interface in the new chips once the dimensions and thermal-mechanical properties of solders are obtained. By comparison, the predicted thermal fatigue lives according to the established thermal fatigue life models are in good agreement with the experimental results. The thermal fatigue life prediction of microelectronic chips based on the BP network approach is feasible.

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Prediction of constant amplitude fatigue crack growth life of 2024 T3 Al alloy with R-ratio effect by GP

Cited by 27 publications

References 13 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

An Artificial Neural Network-Based Algorithm for Evaluation of Fatigue Crack Propagation Considering Nonlinear Damage Accumulation

Back Propagation Neural Network in Predicting the Thermal Fatigue Life of Microelectronic Chips

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