Residual strength prediction of artificially damaged composite laminates based on neural networks

Authors:
D. D'Addona;R. Teti;G. Caprino
Affiliations:
Department of Materials and Production Engineering, University of Naples, Federico II, Naples, Italy;Department of Materials and Production Engineering, University of Naples, Federico II, Naples, Italy;Department of Materials and Production Engineering, University of Naples, Federico II, Naples, Italy
Venue:
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Year:
2012

Citing 1
Cited 0

Technical efficiency-based selection of learning cases to improve forecasting accuracy of neural networks under monotonicity assumption

Decision Support Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper deals with the evaluation of residual tensile strength of composite laminates containing artificial defects, consisting of impact damages of different severity and implanted holes of various diameters. Sensor fusion of acoustic emission and load data was carried out through artificial neural networks, to obtain a reliable prediction of residual tensile strength as early as possible in the loading history. The results show that neural network processing offers an effective method for the monitoring of composite specimens based on acoustic emission detection and analysis.