A novel approach to determine the translaminar crack resistance curve of composite laminates by means of a machine learning model is presented in this paper. The main objective of the proposed method is to extract hidden information of crack resistance from strength values of center-cracked laminates. Compared to traditional measurements, the notable advantage is that only tensile strength values are required which can be obtained by a rather simpler experimental procedure. This is achieved by the incorporation of the finite fracture mechanics, which links crack resistance with strength values. In order to get training dataset, a semi-analytical method using both finite element method and finite fracture mechanics is employed to generate strength values of center-cracked specimens with different random R-curves, which serve as inputs for our artificial neural network. Regarding the outputs, principal component analysis is performed to reduce dimensionality and find suitable descriptors for crack resistance curves. After successfully training machine learning model, experimental studies on basalt fiber reinforced laminates are conducted as validation. Results have proven the effectiveness of the proposed strategy for predicting crack resistance curves, as well as the feasibility of using machine learning-based framework to find out more information about composites from simple experimental data.
Experimental Characterization of Mode I Crack Resistance Curves of CFRP Laminates under Impact Loading