Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks

Abstract

This paper presents a numerical study of the feasibility of using vibration mode shapes to identify material degradation in composite structures. The considered structure is a multilayer composite cylinder, while the material degradation zone is, for simplicity, considered a square section of the lateral surface of the cylinder. The material degradation zone size and location along the cylinder axis are identified using a deep learning approach (convolutional neural networks, CNNs, are applied) on the basis of previously identified vibration mode shapes. The different numbers and combinations of identified mode shapes used to assess the damaged zone size and location were analyzed in detail. The final selection of mode shapes considered in the identification procedure yielded high accuracy in the identification of the degradation zone.

Multi-Objective Optimization of Thin-Walled Composite Axisymmetric Structures Using Neural Surrogate Models and Genetic Algorithms

“…The results of previous studies conducted by Miller and Ziemia ński on single-and multi-objective optimization, including aspects such as maximizing the fundamental natural frequency, broadening frequency-free bands, and maximizing critical buckling load, have been presented in [22][23][24][25][26]. The present work significantly extends the scope of the previous findings.…”

“…The points where the first derivative is non-continuous due to mode shapes crossing are nearly impossible to be precisely assessed using any surrogate model built based on the ascending order of natural frequencies. The advantages of using mode shape identification were presented in both single-and multi-objective optimization issues in [25,26], where selected dynamic parameters of the analyzed structure were optimized, along with optimizing its buckling behavior.…”