Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network

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

“…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,…”

“…Activation function Sigmoid Figueira Pujol and Andrade Pinto, 15 Susmikanti, 17 Rohman et al, 18 Han, 20 Aymerich and Serra, 21 Sohn and Bae, 24 Genel, 25 Junior et al, 26 Vassilopoulos et al, 28 Liao et al, 31 Al-Assadi et al, 32 Kumar et al, 33 Yang et al, 39 Uygur et al, 41 Martinez and Ponce, 47 Barbosa et al, 48 Srinivasan et al, 52 Park and Kang, 54 Vassilopoulos et al, 55 Majidian and Saidi, 56 Xiao et al, 59 Al-Assadi et al, 61 Jin et al, 67 Tapkin, 68 Moghaddam et al, 69 Durodola et al, 70 Durodola et al, 71 Yang et al 73 and Ahmad et al 82…”

“…Al Assaf and El Kadi, 12 Bezazi et al, 13 Rohman et al, 18 Kong et al, 19 Han, 20 Sohn and Bae, 24 Vassilopoulos et al, 28 Cai et al, 30 Kumar et al, 33 Zhaohua, 35 Vadood et al, 43 Liu et al, 46 Barbosa et al, 48 Lotfi and Beiss, 49 Razzaq et al, 50 Vassilopoulos et al, 55 Mathew et al, 57 Mohanty et al, 58 Mohanty et al, 60 Karakas, 63 Karakas and Tomasella, 66 Jin et al, 67 Abdalla and Hawileh, 75 Gao et al, 76 El Kadi and Al-Assaf, 77 Tian, 79 Song et al, 81 Ahmad et al, 82 Zhang et al 93…”

“…Yang et al, 1 Al Assaf and El Kadi, 12 Bezazi et al, 13 Figueira Pujol and Andrade Pinto, 15 Susmikanti, 17 Rohman et al, 18 Sohn and Bae, 24 Genel, 25 Junior et al, 26 Vassilopoulos et al, 28 Mathur et al, 29 Cai et al, 30 Al-Assadi et al, 32 Ma et al, 34 Zhaohua, 35 Mohanty et al, 40 Uygur et al, 41 Xiang et al, 42 Mishra et al, 44 Mohanty, 45 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 Mohanty et al, 58 Mohanty et al, 60 Al-Assadi et al, 61 Karakas, 63 Peng et al, 64 Abdalla and Hawileh, 65 Karakas and Tomasella, 66 Jin et al, 67 Tapkin, 68 Durodola et al, 70 Yan et al, 72 Yang et al, 73 El Kadi and Al-Assaf, 74 Abdalla and Hawileh, 75 Gao et al, 76 El Kadi and Al-Assaf, 77 Li et al, …”

Novel hybridized adaptive neuro‐fuzzy inference system models based particle swarm optimization and genetic algorithms for accurate prediction of stress intensity factor

“…The authors discussed the advantages of the used methodology and emphasized that it was possible, at a low computational cost, to assess the cracks presented in the rotating shafts employing the Paris law taking into account the nonlinear behaviour. Barbosa et al 32 employed a new methodology based on assumptions of Haigh diagram and ANNs, using the probabilistic Stüssi fatigue S ‐ N fields, whereas the proposed algorithm was calibrated using experimental datasets of P355NL1 steel. Mohanty et al 33 proposed the residual fatigue lifetime prediction under mixed‐mode loading conditions based on the ANN.…”