Fatigue behaviors prediction method of welded joints based on soft computing methods

Yang, Xinhua; Zou, Li; Hong-xia, Zhao; Liu, Jingjing

doi:10.1016/j.msea.2012.08.144

Cited by 17 publications

(17 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…114 However, it has been the least utilized solution technic among soft computing algorithms for fatigue life prediction. Yang et al 39 used an ant colony algorithm in their hybrid study to estimate the fatigue life of aluminium alloy-welded joints and obtained satisfactory results from this study.…”

Section: Swarm-based Algorithmsmentioning

confidence: 62%

“…With feature reduction, a minimum feature subset is obtained by deleting the noisy or irrelevant features while maintaining classification accuracy. 127 Yang et al 1 and Yang et al 39 applied data preprocessing using RST to compensate for missing data between data sets, delete invalid data and protect data effectively. Data preprocessing is the preprocessing of missing, inconsistent and dirty data between fatigue data to estimate fatigue life.…”

Section: Rough Set Theorymentioning

confidence: 99%

“…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%

“…Publications MATLAB Yang et al, 1 Bezazi et al, 13 Susmikanti, 17 Rohman et al, 18 Junior et al, 26 Zhaohua, 35 Xu et al, 36 Yang et al, 39 Mohanty et al, 40 Uygur et al, 41 Xiang et al, 42 Vadood et al, 43 Mishra et al, 44 Mohanty, 45 Razzaq et al, 50 Al-Assaf, and El Kadi, 53 Mohanty et al, 60 Al-Assadi et al, 61 Karakas, 63 Karakas and Tomasella, 66 Tapkin, 68 Moghaddam et al, 69 Durodola et al, 70 Durodola et al, 71 Yan et al, 72 El Kadi and Al-Assaf, 77 Deveci and Artem, 95 Azarhoosh et al, 102 El Kadi et al 125 and Deveci and Artem 130 C ++ Genel, 25 Mathur et al, 29 Kumar et al, 33 Mohanty et al, 40 Mohanty et al 58 and Kim et al 133 Neurosolution Al Assaf and El Kadi, 12 Abdalla and Hawileh, 65 El Kadi and Al-Assaf 74 and El Kadi and Al-Assaf 77…”

Section: Softwarementioning

confidence: 99%

See 3 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

View full text Add to dashboard Cite

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.

show abstract

Section: Swarm-based Algorithmsmentioning

confidence: 62%

Section: Rough Set Theorymentioning

confidence: 99%

Section: Functional Publicationsmentioning

confidence: 99%

Section: Softwarementioning

confidence: 99%

See 2 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

View full text Add to dashboard Cite

show abstract

“…The linking of information across multiple scales can of course be done in many ways, but no single tool can account for the interaction of the myriad of parameters that govern materials development and assess the complexity of interactions of these parameters in defining engineering performance. Current approaches that utilize informatics tools such as data mining, evolutionary algorithms, and other statistical methods do so in conjunction with physically based and/or heuristically driven models, in which the primary focus is to search for information from large data sets generated by computations and/or experiments (5)(6)(7)(8).…”

Section: Big Data and The Materials Genementioning

confidence: 99%

Materials Informatics: The Materials “Gene” and Big Data

Rajan

2015

Annu. Rev. Mater. Res.

256

150

View full text Add to dashboard Cite

Materials informatics provides the foundations for a new paradigm of materials discovery. It shifts our emphasis from one of solely searching among large volumes of data that may be generated by experiment or computation to one of targeted materials discovery via high-throughput identification of the key factors (i.e., "genes") and via showing how these factors can be quantitatively integrated by statistical learning methods into design rules (i.e., "gene sequencing") governing targeted materials functionality. However, a critical challenge in discovering these materials genes is the difficulty in unraveling the complexity of the data associated with numerous factors including noise, uncertainty, and the complex diversity of data that one needs to consider (i.e., Big Data). In this article, we explore one aspect of materials informatics, namely how one can efficiently explore for new knowledge in regimes of structure-property space, especially when no reasonable selection pathways based on theory or clear trends in observations exist among an almost infinite set of possibilities. 153 Annu. Rev. Mater. Res. 2015.45:153-169. Downloaded from www.annualreviews.org Access provided by Yale University -Law Library on 07/04/15. For personal use only.

show abstract

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng

Zhao

et al. 2023

Adv Eng Mater

View full text Add to dashboard Cite

Fatigue fracture of welded joints is an important cause for engineering accidents. Due to the coexistence of so many influencing factors, the current prediction model of fatigue behavior cannot be used for all service conditions. The progress of each module (database establishment, data processing, data augmentation, dimension reduction, and comprehensive consideration of influencing factors) during the process of constructing a prediction model is summarized herein. Specifically, the state‐of‐art prediction models of fatigue strength, fatigue crack growth rate, fatigue life, and fatigue reliability are introduced. Moreover, suggestions for future work are given at the end of each section. Finally, according to the influential prediction methods, the establishment of fatigue performance prediction methods of welded joints via data‐driven method is summarized.

show abstract

Fatigue behaviors prediction method of welded joints based on soft computing methods

Cited by 17 publications

References 24 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

Materials Informatics: The Materials “Gene” and Big Data

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Contact Info

Product

Resources

About