Damage classification in composite structures based on X-ray computed tomography scans using features evaluation and deep neural networks

Recently, due the automation of many industrial processes the intelligent tools for undertaking decisions started to be highly demanded solutions in many industrial branches. This is also the case in non-destructive evaluation, where big amount of data from inspections need to be analyzed, and based on such an analysis a decision must be undertaken. In many cases, the step of analysis is already automated with using a dedicated software, however, the decision-making process still engages a human. In this paper, the authors presented the preliminaries of the damage classification algorithm, which is intended to classify different types of a structural damage in composites based on X-ray computed tomography scans. The proposed approach was based on deep neural networks, which creates a possibility to obtain high values of a classification accuracy. The obtained results within this study clearly show the effectiveness of the proposed approach and create a path for further development of the algorithm. (C) 2022 The Authors. Published by Elsevier B.V.

Automated summary

Due to the automation of industrial processes, intelligent decision-making tools are in high demand in various industries, including non-destructive evaluation. This paper presents a preliminary study on a damage classification algorithm based on deep neural networks, which aims to classify different types of structural damage in composites using X-ray computed tomography scans. The results demonstrate the effectiveness of the proposed approach and provide a pathway for further algorithm development.