Acoustic Emission Monitoring and Neural Network Fatigue Life Prediction in Steel Members

2010 ◽  
Author(s):  
Jamil Suleman ◽  
Andrej Korcak ◽  
Fady Barsoum ◽  
Eric Hill
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Fatigue Life ◽  
Life Prediction ◽  
Fatigue Life Prediction ◽  
Acoustic Emission Monitoring ◽  
Emission Monitoring ◽  
Steel Members

scholarly journals Probabilistic Fatigue Life Prediction of Dissimilar Material Weld Using Accelerated Life Method and Neural Network Approach

Computation ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 10 ◽  
Author(s):  
Hafiz Waqar Ahmad ◽  
Jeong Ho Hwang ◽  
Kamran Javed ◽  
Umer Masood Chaudry ◽  
Dong Ho Bae
Keyword(s):  
Neural Network ◽  
Fatigue Life ◽  
Life Prediction ◽  
Fatigue Life Prediction ◽  
Dissimilar Material ◽  
Bayesian Regularization ◽  
Network Approach ◽  
Neural Network Approach ◽  
Accelerated Life ◽  
Levenberg Marquardt

Welding alloy 617 with other metals and alloys has been receiving significant attention in the last few years. It is considered to be the benchmark for the development of economical hybrid structures to be used in different engineering applications. The differences in the physical and metallurgical properties of dissimilar materials to be welded usually result in weaker structures. Fatigue failure is one of the most common failure modes of dissimilar material welded structures. In this study, fatigue life prediction of dissimilar material weld was evaluated by the accelerated life method and artificial neural network approach (ANN). The accelerated life testing approach was evaluated for different distributions. Weibull distribution was the most appropriate distribution that fits the fatigue data very well. Acceleration of fatigue life test data was attained with 95% reliability for Weibull distribution. The probability plot verified that accelerating variables at each level were appropriate. Experimental test data and predicted fatigue life were in good agreement with each other. Two training algorithms, Bayesian regularization (BR) and Levenberg–Marquardt (LM), were employed for training ANN. The Bayesian regularization training algorithm exhibited a better performance than the Levenberg–Marquardt algorithm. The results confirmed that the assessment methods are effective for lifetime prediction of dissimilar material welded joints.

Application of Artificial Neural Network for Fatigue Life Prediction

2005 ◽  
Vol 297-300 ◽  
pp. 96-101
Author(s):  
Ishak Abdul Azid ◽  
Lee Kor Oon ◽  
Ong Kang Eu ◽  
K.N. Seetharamu ◽  
Ghulam Abdul Quadir
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Fatigue Life ◽  
Solder Joint ◽  
Finite Element Simulation ◽  
Life Prediction ◽  
Fatigue Life Prediction ◽  
Element Simulation ◽  
Parametric Studies ◽  
Solder Joint Fatigue

An extensively published and correlated solder joint fatigue life prediction methodology is incorporated by which finite element simulation results are translated into estimated cycles to failure. This study discusses the analysis methodologies as implemented in the ANSYSTM finite element simulation software tool. Finite element models are used to study the effect of temperature cycles on the solder joints of a flip chip ball grid array package. Through finite element simulation, the plastic work or the strain-energy density of the solder joints are determined. Using an established methodology, the plastic work obtained through simulation is translated into solder joint fatigue life [1]. The corresponding results for the solder joint fatigue life are used for parametric studies. Artificial Neural Network (ANN) has been used to consolidate the parametric studies.

A neural network approach to fatigue life prediction

2011 ◽  
Vol 33 (3) ◽  
pp. 313-322 ◽  
Author(s):  
João Carlos Figueira Pujol ◽  
João Mário Andrade Pinto
Keyword(s):  
Neural Network ◽  
Fatigue Life ◽  
Life Prediction ◽  
Fatigue Life Prediction ◽  
Network Approach ◽  
Neural Network Approach
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