Machine learning for crack detection in an anisotropic electrically conductive nano-engineered composite interleave with realistic geometry

Abstract

Engineering interleaves of composite laminates with carbon nanotubes (CNTs) improves interlaminar fracture toughness, creating also conductivity, which can be employed for damage identification. The paper explores machine learning (ML) solution of the inverse problem of the defect identification for interleaves with anisotropic conductivity (aligned CNTs). The electrical and geometrical properties of the interleave are assigned based on the synchrotron X-ray computer tomography of glass fibre / epoxy laminates with nanostitch. Several machine learning (ML) models are applied (XGBoost, fully connected (FCNN) and convolution neural (CNN) networks). XGBoost and FCNN algorithms performed poorly, failing to detect smaller defects and giving significant errors for larger ones. CNN algorithm detects defects well: It predicts the geometric characteristics of the defect with error below 16 %.