The impacts of material uncertainty in electro-migration of SAC solder electronic packaging by Monte Carlo based stochastic finite element model

2021 ◽  
pp. 1-1
Author(s):  
Liu Chu ◽  
Jiajia Shi ◽  
Robin Braun
Keyword(s):  
Finite Element ◽  
Monte Carlo ◽  
Finite Element Model ◽  
Electronic Packaging ◽  
Element Model ◽  
Stochastic Finite Element ◽  
Sac Solder

scholarly journals The correlation between graphene characteristic parameters and resonant frequencies by Monte Carlo based stochastic finite element model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liu Chu ◽  
Jiajia Shi ◽  
Eduardo Souza de Cursi
Keyword(s):  
Finite Element ◽  
Monte Carlo ◽  
Finite Element Model ◽  
Correlation Coefficients ◽  
Element Model ◽  
Characteristic Parameter ◽  
Sampling Procedure ◽  
Stochastic Finite Element ◽  
Characteristic Parameters ◽  
Resonant Frequencies

AbstractThe uncertainty and fluctuations in graphene characteristic parameters are inevitable issues in both of experimental measurements and numerical investigations. In this paper, the correlations between characteristic parameters (Young’s modulus, Poisson’s ratio and thickness of graphene) and resonant frequencies are analyzed by the Monte Carlo based stochastic finite element model. Based on the Monte Carlo stochastic sampling procedure, the uncertainty in the characteristic parameters are properly propagated and quantified. The displacements and rotation modes of graphene under the resonant vibration computed by the finite element method are verified. Furthermore, the result robustness of stochastic samples is discussed based on the statistic records and probability density distributions. In addition, both the Pearson and Spearman correlation coefficients of the corresponding characteristic parameters are calculated and compared. The work in this paper provides a feasible and highly efficient method for the characteristic parameter correlation discussion by taking uncertainty into consideration.

scholarly journals A stochastic finite element model for the dynamics of globular macromolecules

2013 ◽  
Vol 239 ◽  
pp. 147-165 ◽  
Author(s):  
Robin C. Oliver ◽  
Daniel J. Read ◽  
Oliver G. Harlen ◽  
Sarah A. Harris
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Element Model ◽  
Stochastic Finite Element

Statistical Vibration Based Damage Identification Using Artificial Neural Network

2012 ◽  
Author(s):  
Norhisham Bakhary
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Monte Carlo ◽  
Finite Element Model ◽  
Element Model ◽  
Point Estimation ◽  
Structural Vibration ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial Neural Network (ANN) telah digunakan dengan meluas bagi tujuan mengesan kerosakan dalam struktur menggunakan data–data mod dari gegaran. Walau bagaimanapun, ketidakpastian yang wujud dalam model unsur terhingga dan data dari lapangan yang tidak dapat dielakkan boleh menyebabkan kesilapan dalam meramalkan magnitud dan lokasi kerosakan. Dalam kajian ini kaedah statistik digunakan untuk mengambil kira ketidakpastian ini. ANN digunakan untuk meramalkan parameter–parameter kekukuhan dari frekuensi dan mod bentuk bagi sesebuah struktur. Untuk mengambil kira ketidakpastian dalam ramalan, kaedah statistik digunakan di mana kaedah Rossenblueth point estimation diperbandingkan dengan kaedah Monte Carlo diaplikasikan bagi mengambil kira ketidakpastian ini. Keputusan menunjukkan bahawa dengan mengambil kira ketidakpastian dalam membuat ramalan menggunakan ANN, kerosakan boleh diramalkan pada tahap keyakinan yang tinggi. Kata kunci: Artificial neural network; ketidakpastian; kesilapan rawak Artificial Neural Network (ANN) has been widely applied to detect damages in structures based on structural vibration modal parameters. However, uncertainties that inevitably exist in finite element model and measured vibration data might lead to false or unreliable prediction of structural damage. In this study, a statistical approach is proposed to include the effect of uncertainties in the ANN algorithm for damage prediction. ANN is used to predict the stiffness parameters of structures from measured structural vibration frequencies and mode shapes. Uncertainties in the measured data and finite element model of the structure are considered in the prediction. The statistics of the identified parameters are determined using Rossenblueth’s point estimation method and verified by Monte Carlo simulation. The results show that by considering these uncertainties in the ANN model, the damages can be detected with a higher confidence level. Key words: Artificial neural network; uncertainties; random error

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