Application of Artificial Neural Network for Fatigue Life Prediction

Ishak Abdul Azid; Lee Kor Oon; Ong Kang Eu; K.N. Seetharamu; Ghulam Abdul Quadir

doi:10.4028/www.scientific.net/KEM.297-300.96

Paper Titles

Shot Peening and Fatigue Crack Growth in 7075-T7351 Aluminum
p.72

Dynamic Behavior of Underground Structures in Multi-Layered Media
p.78

Development on Stress Monitoring Method for Measurement of Cyclic Stress of Railway Axles
p.84

'Micro-Meso-Process Theory' for Fatigue Source Evolution in Metallic Parts
p.90

Application of Artificial Neural Network for Fatigue Life Prediction
p.96

Prediction of the Fatigue Life of Tires Using CED and VCCT
p.102

Fatigue Life Evaluation of Press-Fitted Specimens by Using Multiaxial Fatigue Theory at Contact Edge
p.108

The Effects of Residual Stress of Contact Fatigue Life for Railway Wheels
p.115

Development of Damage Assessment Method for Steel Structures Using Material-Structure Coupling Analysis
p.122

HomeKey Engineering MaterialsKey Engineering Materials Vols. 297-300Application of Artificial Neural Network for...

Application of Artificial Neural Network for Fatigue Life Prediction

Abstract:

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.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Key Engineering Materials (Volumes 297-300)

Pages:

96-101

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.297-300.96

Citation:

Cite this paper

Online since:

November 2005

Authors:

Ishak Abdul Azid, Lee Kor Oon, Ong Kang Eu, K.N. Seetharamu, Ghulam Abdul Quadir

Keywords:

Artificial Neural Network (ANN), Finite Element (FE) Simulation, Solder Joint Reliability

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] R. Darveaux: Effect of Simulation Methodology on Solder Joint Crack Growth Correlation (Proceedings of the 50th Electronic Components & Technology Conference [ECTC]) (2000), pp.1048-1058.

DOI: 10.1109/ectc.2000.853299

Google Scholar

[2] J.H.L. Pang, D.Y. Chong and T.H. Low. Thermal Cycling Analysis of Flip Chip Solder Joint Reliability (IEEE Transactions on Components and Packaging Technologies) Vol. 24 No. 4 (2001), pp.705-712.

DOI: 10.1109/6144.974964

Google Scholar

[3] B.A. Zahn: Impact of Ball via Configurations on Solder Joint Reliability in Tape Based Chip- Scale Packages (ChipPAC Inc., Arizona 2000).

DOI: 10.1109/ectc.2002.1008301

Google Scholar

[4] B.A. Zahn: Finite Element Based Solder Joint Fatigue Life Predictions for a Same Die Stacked Chip Scale Ball Grid Array Package (ChipPAC Inc., Arizona 2000).

DOI: 10.1109/iemt.2002.1032767

Google Scholar

[5] EIA/JEDEC JESD22-A104B - JEDEC Standard No. 22-A104-B, Temperature Cycling (2000).

Google Scholar