Classification of fatigue crack damage in polycrystalline alloy structures using convolutional neural networks. (January 2021)

• Record Type: Journal Article
• Authors: Alqahtani, Hassan; Bharadwaj, Skanda; Ray, Asok
• Is Part Of: Engineering failure analysis. Volume 119(2021)
• Publication Date: 2021-01
• Subjects: Fatigue damage -- Crack tip opening displacement -- Convolutional neural networks -- Image augmentation and classification; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2020.104908
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: Low risk damaged component needs inspection, while high risk damaged component requires replacement. The convolutional neural networks have to be performed well on a big data. The convolutional neural network classifies the images based on the features of the image. Abstract: This paper proposes an autonomous method for detection and classification of fatigue crack damage and risk assessment in polycrystalline alloys. In this paper, the analytical and computational tools are developed based on convolutional neural networks (CNNs), where the execution time is much less than that for visual inspection, and the detection and classification process is expected to be significantly less error-prone. The underlying concept has been experimentally validated on a computer-instrumented and computer-controlled MTS fatigue testing apparatus, which is equipped with optical microscopes for generation of image data sets. The proposed CNN classifier is trained by using a combination of the original images and augmented images. The results of experimentation demonstrate that the proposed CNN classifier is able to identify the images into their respective classes with an accuracy greater than 90%.