Inverse estimation method for internal defects based on surface stress of carbon-fiber-reinforced plastics using machine learning

Abstract

Carbon-fiber-reinforced plastic (CFRP) is a composite material whose base material is plastic and reinforcement material is carbon fibers. CFRP is widely used in various fields for laminating prepregs. The laminated plate tends to sustain damage, such as delamination, fiber breakage, and base material breakage; hence, we must conduct high-precision and efficient nondestructive testing (NDT). Examples of NDT are ultrasonic examination, X-ray tomography, and infrared stress analysis. With most NDT methods, it is difficult to easily obtain detailed correct information of defects, such as their depth, position, and size. To solve this problem, we develop a machine-learning-aided inverse analysis model that predicts the spatial information of defects from the sum of the principal stresses on the surface calculated from the temperature change measured by infrared analysis, and we propose it as an alternative method to the existing damage analysis. Applying the proposed method to the simulated stress distributions of quasi-isotropic CFRP laminates with defects, the results showed over 99% success to recognize the detail information of defects. Additionally, we examine the properties of the dataset using a forward analysis model and a variational autoencoder. Our method with a convolutional neural network enables us to successfully estimate the information of defects at high speed. Experimental data can be applicable as well as the simulation results to our proposed method, and we believe our method will be a powerful supporting tool for the current NDT for CFRPs.