Fatigue failure probability estimation of the 7075-T651 aluminum alloy under multiaxial loading based on the life-dependent material parameters concept

2021 ◽  
Vol 147 ◽  
pp. 106174
Author(s):  
Aleksander Karolczuk ◽  
Krzysztof Kluger ◽  
Thierry Palin-Luc
Keyword(s):  
Aluminum Alloy ◽  
Fatigue Failure ◽  
Failure Probability ◽  
Probability Estimation ◽  
Multiaxial Loading ◽  
Material Parameters

Prognostic Health Monitoring Method for Fatigue Failure of Solder Joints on Printed Circuit Boards Based on a Canary Circuit

2013 ◽  
Author(s):  
Kenji Hirohata ◽  
Yousuke Hisakuni ◽  
Takahiro Omori ◽  
Tomoko Monda ◽  
Minoru Mukai
Keyword(s):  
Fatigue Failure ◽  
Health Monitoring ◽  
Failure Probability ◽  
Printed Circuit Boards ◽  
Solder Joints ◽  
High Density ◽  
Probability Estimation ◽  
Circuit Boards ◽  
Printed Circuit ◽  
High Density Packaging

Continuing improvements in both capacity and miniaturization of electronic equipment such as solid state drives (SSDs) are spurring demand for high-density packaging of NAND-type flash memory mounted on SSD printed circuit boards. High-density packaging leads to increased fatigue failure risk of solder joints due to the decreased reliability margin for stress. We have developed a failure precursor detection technology based on fatigue failure probability estimation during use. This method estimates the cycles to fatigue failure of an actual circuit by detecting broken connections in a canary circuit (a dummy circuit of daisy-chained solder joints). The canary circuit is designed to fail earlier than the actual circuit under the same failure mode by using accelerated reliability testing and inelastic stress simulation. The statistical distribution of the strain range of solder joints can be provided by Monte Carlo simulations based on the finite element method and random load modeling. A feasibility study of the failure probability estimation method is conducted by applying the method to a printed circuit board on which a ball grid array (BGA) package is mounted using BGA solder joints. The proposed method is found to be useful for prognostic health monitoring of solder joint’s fatigue failure.

Failure probability estimation of steam turbine blades by enhanced Monte Carlo Method

2015 ◽  
Vol 56 ◽  
pp. 80-88 ◽  
Author(s):  
J.A. Rodríguez ◽  
J.C. Garcia ◽  
E. Alonso ◽  
Y. El Hamzaoui ◽  
J.M. Rodríguez ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Steam Turbine ◽  
Failure Probability ◽  
Turbine Blades ◽  
Probability Estimation ◽  
Steam Turbine Blades

Fuzzy failure probability estimation applying intervening variables

2020 ◽  
Vol 83 ◽  
pp. 101909 ◽  
Author(s):  
Marcos A. Valdebenito ◽  
Michael Beer ◽  
Héctor A. Jensen ◽  
Jianbing Chen ◽  
Pengfei Wei
Keyword(s):  
Failure Probability ◽  
Probability Estimation ◽  
Intervening Variables

Experimental Research on Fatigue Failure for 2219-T6 Aluminum Alloy Friction Stir-Welded Joints

2017 ◽  
Vol 26 (8) ◽  
pp. 3767-3774 ◽  
Author(s):  
Guo-Qin Sun ◽  
Jiang-Pei Niu ◽  
Ya-Jing Chen ◽  
Feng-Yang Sun ◽  
De-Guang Shang ◽  
...  
Keyword(s):  
Aluminum Alloy ◽  
Fatigue Failure ◽  
Experimental Research ◽  
Welded Joints ◽  
Friction Stir

scholarly journals Failure Probability Estimation Using Asymptotic Sampling and Its Dependence upon the Selected Sampling Scheme

2017 ◽  
Vol 17 (2) ◽  
pp. 65-72
Author(s):  
Magdalena Martinásková ◽  
Miroslav Vořechovský
Keyword(s):  
Monte Carlo ◽  
Failure Probability ◽  
Sampling Scheme ◽  
Probability Estimation ◽  
Test Functions ◽  
Random Vectors ◽  
Sampling Schemes ◽  
Alternative Means ◽  
Asymptotic Sampling

Abstract The article examines the use of Asymptotic Sampling (AS) for the estimation of failure probability. The AS algorithm requires samples of multidimensional Gaussian random vectors, which may be obtained by many alternative means that influence the performance of the AS method. Several reliability problems (test functions) have been selected in order to test AS with various sampling schemes: (i) Monte Carlo designs; (ii) LHS designs optimized using the Periodic Audze-Eglājs (PAE) criterion; (iii) designs prepared using Sobol’ sequences. All results are compared with the exact failure probability value.

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