Designing the geometry of compact tension specimens for easy fracture toughness measurement using reinforcement learning

For composite laminates, a rising R-curve is observed for their fracture toughness under Mode I stress, which is important for a comprehensive failure analysis of the materials. Since it is laborious to measure the R-curve due to its dependence on both the load and the crack extension, we put forward a novel compact tension specimen by modifying its geometry to eliminate the relation between fracture toughness and crack extension, so as to simplify the experimental process of the R-curve measurement by only recording the load history. Two machine learning models were developed for the optimum sample design based on the finite element analysis of the effect of sample geometries on the R-curve. A simple neural network model was built for designing tapered specimen and a reinforcement learning model was created for further finding the best design from a broader design space. The results showed that, in contrast to the specimens with a tapered shape, which only ensure the independence between the R-curve and crack extension in the case of a small extension, the design provided by the reinforcement learning provides such independence across a wider range of crack length and an improved accuracy.