Modelling Fatigue Life of Multidirectional GFRP Laminates under Constant Amplitude Loading with Artificial Neural Networks

Advanced Composites Letters

Article

Efstratios F. Georgopoulos¹  Vasileios Dionysopoulos²  Anastasios P. Vassilopoulos³ 

[1] Pattern Recognition Laboratory, Department of Computer Engineering & Informatics, University of Patras, 265 04 Patras, Greece;Technological Educational Institute of Kalamata, 241 00 Kalamata, Greece;Technological Educational Institute of Kalamata, 241 00 Kalamata, Greece;Technological Educational Institute of Patras, Faculty of Technological Applications, Dept. Mechanical Engineering, Section of Manufacturing Eng, 1 M. Alexandrou Str, Koukouli, 263 34 Patras, Greece;

关键词: Fatigue;    Composites;    Artificial neural network;    Life prediction;    S-N curves;    Constant amplitude;   

DOI  :  10.1177/096369350601500201

来源: Sage Journals

【 摘 要 】

An artificial neural network has proven to be a sufficient tool for modelling fatigue life of multidirectional composite laminates made of Glass Fibre Reinforced Plastic (GFRP) composite materials and tested under constant amplitude loading patterns. Modelling efficiency of the network was satisfactory for both on- and off-axis coupons life, irrespective of test conditions, i.e., R-ratio that defines the developed stress state on the coupon. Tension-Tension, Compression-Compression and even Tension-Compression loading patterns were investigated and modelling accuracy of the proposed ANN model was validated. The main benefit that this new modelling tool brings is that only a small portion, in the order of 40%-50%, of the experimental data is needed for the whole analysis and thus, expensive and time consuming tests needed for the establishment of S-N curves could be eliminated without any significant loss of accuracy.

【 授权许可】

Unknown   
© 2006 SAGE Publications Ltd

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