Artificial Neural Network Based Prediction of Impact Loads and Thickness in CFRP and GFRP Composite Laminates - Gourav Vivek Kulkarni; Ramesh S Sharma; M.N. Vijaya Kumar; Sunith Babu L. (International Journal of Fracture Mechanics and Damage Science, 2024)
Recent technological advancements, particularly the integration of neural networks, have facilitated a predictive approach to complex engineering problems, especially those involving composite materials with directional properties. The scarcity of literature on predicting impact damage using experimental and ultrasonic flaw detection data motivated this study. Experimental assessment of impact damage on carbon fiber/epoxy (CFRP) and glass fiber/epoxy (GFRP) composites was conducted using low-velocity drop weight impact testing. Damage assessment employed an ultrasonic flaw detector with a single crystal wedge delay probe, capturing damaged data in A-scan format. Load vs. time data from impact testing and point-wise damage detection data from ultrasonic testing were used to develop a Feedforward artificial neural network (ANN) with two hidden layers (64 neurons each) and a one-neuron output layer. Training epochs ranged from 100 to 1000, resulting in a decreasing trend of mean absolute error (MAE) over time. The ANN model demonstrated consistent accuracy in predicting impact damage thickness and loads for both CFRP and GFRP. This predictive capability holds promise for anticipating potential failures in composite structures under impact loads, enabling preventive design measures and enhancing structural reliability. The model's versatility extends to incorporating C-Scan ultrasonic data, enhancing its utility in damage forecasting and mitigation strategies.
Keywords: ANN; Impact; damage; prediction; ultrasonic