Prediction of Contact Fatigue Life of Alloy Cast Steel Rolls Using Back-Propagation Neural Network

Jin, Hui-Jin; Wu, Sujuan; Peng, Yuncheng

doi:10.1007/s11665-013-0695-8

Cited by 15 publications

(13 citation statements)

References 26 publications

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“…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…”

Section: Functional Publicationsmentioning

confidence: 99%

“…Yang et al, 1 Bezazi et al, 13 Al-Assadi et al, 32 Zhaohua, 35 Barsoum et al, 37 Mohanty et al, 40 Xiang et al, 42 Vadood et al, 43 Mishra et al, 44 Razzaq et al, 50 Srinivasan et al, 52 Al-Assaf, and El Kadi, 53 Majidian and Saidi, 56 Mohanty et al, 58 Mohanty et al, 60 Karakas, 63 Peng et al, 64 Karakas and Tomasella, 66 Jin et al 67 and Abdalla and Hawileh 75…”

Section: Tansigmentioning

confidence: 99%

“…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…”

Section: Msementioning

confidence: 99%

“…Junior et al, 26 Al-Assadi et al, 32 Mohanty et al, 40 Uygur et al, 41 Mohanty, 45 Lotfi and Beiss, 49 Razzaq et al, 50 Srinivasan et al, 52 Mathew et al, 57 Al-Assadi et al, 61 El Kadi, 62 Abdalla and Hawileh, 65 Jin et al, 67 Tapkin, 68 Moghaddam et al, 69 Durodola et al, 71 Yan et al, 72 El Kadi and Al-Assaf, 77 Lee et al, 78 Song et al, 81 Chen et al, 91 Azarhoosh et al, 102 El Kadi et al, 125 Gulgec et al 134…”

Section: Rmsementioning

confidence: 99%

“…Vassilopoulos et al, 28 Lotfi and Beiss, 49 Al-Assaf and El Kadi, 53 Abdalla and Hawileh, 65 Jin et al, 67 Abdalla and Hawileh, 75 El Kadi and Al-Assaf, 77 Azarhoosh et al 102…”

Section: Maementioning

confidence: 99%

See 4 more Smart Citations

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: Functional Publicationsmentioning

confidence: 99%

Section: Tansigmentioning

confidence: 99%

Section: Msementioning

confidence: 99%

Section: Rmsementioning

confidence: 99%

“…Vassilopoulos et al, 28 Lotfi and Beiss, 49 Al-Assaf and El Kadi, 53 Abdalla and Hawileh, 65 Jin et al, 67 Abdalla and Hawileh, 75 El Kadi and Al-Assaf, 77 Azarhoosh et al 102…”

Section: Maementioning

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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Material matters: predicting the core hardness variance in industrialized case hardening of 18CrNi8

Lingelbach

Waldenmaier

Hagymási

et al. 2022

Materialwissenschaft Werkst

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To explain the variance in core hardness of 18CrNi8 nozzle bodies after industrial heat treatment, several data sources, including steel melt composition, sensor process data, and measurement errors, of five years are aggregated. In order to predict hardness variations caused by alloy composition, traditional physical models by Maynier are compared with data-driven machine learning models, which show no advantage due to low data variability. Neither method can fully explain the visible drifts, which are better tracked by an alternative (i. e., filter model) that uses past measurements. Machine learning on features from heat treatment is not successful in predicting hardness change, presumably because the process is too stable. Finally, a large part of the variance is caused by the HV 1 measurement error.

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Improved Clonal Selection Algorithm Optimizing Neural Network for Solving Terminal Anti-missile Collaborative Intercepting Assistant Decision-Making Model

Xiao

Liu

Xiao

et al. 2016

Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems

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Prediction of Contact Fatigue Life of Alloy Cast Steel Rolls Using Back-Propagation Neural Network

Cited by 15 publications

References 26 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

Material matters: predicting the core hardness variance in industrialized case hardening of 18CrNi8

Improved Clonal Selection Algorithm Optimizing Neural Network for Solving Terminal Anti-missile Collaborative Intercepting Assistant Decision-Making Model

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