Image enhancement methods for inspection of planar and non-planar FRP structures using a noise-based microwave NDT inspection system

Abstract

Microwave nondestructive testing (MNDT) includes inspection techniques that assess a particular material’s health status using low-power and contactless inspection systems. In near-field microwave inspections, imaging results are heavily influenced by the standoff distance parameter, i.e., the physical separation between the microwave sensor and the sample under test (SUT). Variations in the standoff distance during an inspection tend to cause defect masking of disbonds and delaminations in fiber-reinforced polymer (FRP) materials, causing defects to go undetected frequently. An MNDT near-field inspection system using noise waveforms is used to identify engineered internal defects within carbon fiber-reinforced polymer (CFRP) samples. Tactics utilizing Principal Component Analysis (PCA), Stacked Sparse Autoencoders (SSAEs), and an autoencoder network trained in a manner for anomaly detection are used to minimize the effects of standoff distance, reduce defect masking, and increase the ability to identify hidden defects. The samples tested are constructed to possess planar and non-planar geometries, such that the viability of the data-driven image enhancement and standoff distance correction methods are demonstrated with respect to a wide variety of in situ inspection applications.