Intelligent evaluation of ultimate strength prediction of composites and fabrics’ extensibility

An approach to intelligent evaluation of residual flexural strength of glass fiber reinforced plastic composites and to tensile test for the estimation of the fabrics extensibility is proposed. The glass fiber reinforced plastic composites were subjected to hydrothermal aging cycles in water at 60 °C. Sensor fusion of acoustic emission and load data is carried out through neural networks to obtain a prediction of ultimate flexural strength as early as possible in the loading history of glass fiber reinforced plastic composites. A robotized system is used to perform the fabrics tensile test and the extensibility of the samples is estimated using a feed-forward neural network while trying to imitate the human expert valuation. The results show that neural network processing provides an effective method for the monitoring of composite material strength behavior based on acoustic emission detection and analysis and for the estimation of the extensibility of new fabrics.