Convolutional neural network and level-set spectral element method for ultrasonic imaging of delamination cavities in an anisotropic composite structure

Boyoung Kim; Shashwat Maharjan; Fazle Mahdi Pranto; Bruno Guidio; Christoph Schaal; Chanseok Jeong

doi:10.1016/j.ultras.2024.107254

Convolutional neural network and level-set spectral element method for ultrasonic imaging of delamination cavities in an anisotropic composite structure

Ultrasonics. 2024 Mar:138:107254. doi: 10.1016/j.ultras.2024.107254. Epub 2024 Jan 30.

Authors

Boyoung Kim¹, Shashwat Maharjan¹, Fazle Mahdi Pranto¹, Bruno Guidio¹, Christoph Schaal², Chanseok Jeong³

Affiliations

¹ School of Engineering and Technology, Central Michigan University, Mount Pleasant, MI 48859, USA.
² Department of Mechanical Engineering, California State University, Northridge, CA 91330, USA; Mechanical and Aerospace Engineering Department, University of California, Los Angeles, CA 90095, USA.
³ School of Engineering and Technology, Central Michigan University, Mount Pleasant, MI 48859, USA; Earth and Ecosystem Science Program, Central Michigan University, Mount Pleasant, MI 48859, USA; Institute for Great Lakes Research, Central Michigan University, Mount Pleasant, MI 48859, USA. Electronic address: jeong1c@cmich.edu.

PMID: 38306761
DOI: 10.1016/j.ultras.2024.107254

Abstract

We present a computational approach that incorporates a convolutional neural network (CNN) for detecting internal delamination in a layered 2D plane-strain anisotropic composite structure of transient elastodynamic fields. The two-dimensional spectral element method (SEM) is utilized to simulate the propagation of elastic waves in an orthotropic solid sandwiched by isotropic solids and their interaction with the internal delamination cavity. This work generates training data consisting of input-layer features (i.e., measured wave signals) and output-layer features (i.e., element types, such as void or regular, of all elements in a domain). To accelerate training data generation, we utilize explicit time integration (e.g., the Runge-Kutta scheme) coupled with an SEM wave solver. Applying the level-set method additionally avoids having to perform an expensive re-meshing process for every possible configuration of the delamination cavities during the data-generation phase. The CNN is trained to classify each element as a non-void or void element from the measured wave signals. Clusters of identified void elements reconstruct targeted cavities. Once our neural network is trained using synthetic data, we analyze how effectively the CNN performs on synthetic measurement data. To this end, we use blind test data from a third-party simulator that explicitly models the traction-free boundary of cavities for anisotropic materials without the application of the level-set method. Our numerical examples show that our approach can effectively detect the internal cavities in an anisotropic structure made of aluminum and carbon fiber-reinforced epoxy using the measured elastic waves without any prior information about the cavities' locations, shapes, and numbers. The presented method can be extended into a more realistic 3D setting and utilized for the nondestructive test of various anisotropic composite structures.

Keywords: Convolutional neural network; Element-wise classification; Imaging of delamination cavities; Level-set method; Machine learning; Spectral element method.