Neural network (NN) models have significantly impacted fatigue-related engineering communities and are expected to increase rapidly due to the recent advancements in machine learning and artificial intelligence. A comprehensive review of fatigue modeling methods using NNs is lacking and will help to recognize past achievements and suggest future research directions. Thus, this paper presents a survey of 251 publications between 1990 and July 2021. The NN modeling in fatigue is classified into five applications: fatigue life prediction, fatigue crack, fatigue damage diagnosis, fatigue strength, and fatigue load. A wide range of NN architectures are employed in the literature and are summarized in this review. An overview of important considerations and current limitations for the application of NNs in fatigue is provided. Statistical analysis for the past and the current trend is provided with representative examples. Existing gaps and future research directions are also presented based on the reviewed articles. 

Fatigue modeling using neural networks: A comprehensive review

Neural network (NN) models have made a significant impact on
fatigue-related engineering communities and are expected to increase
rapidly soon due to the recent advancements in machine learning and
artificial intelligence. A comprehensive review of fatigue modeling
methods using NNs is lacking and will help to recognize past
achievements and suggest future research directions. Thus, this paper
presents a survey of 251 publications between 1990 and July 2021. The NN
modeling in fatigue is classified into five applications: fatigue life
prediction, fatigue crack, fatigue damage diagnosis, fatigue strength,
and fatigue load. A wide range of NN architectures are employed in the
literature and are summarized in this review. An overview of important
considerations and current limitations for the application of NNs in
fatigue is provided. Statistical analysis for the past and the current
trend is provided with representative examples. Existing gaps and future
research directions are also presented based on the reviewed articles.

Fatigue modeling using neural networks: a comprehensive review

Flaw detection in non-destructive testing, especially for complex signals like ultrasonic data, has thus far relied heavily on the expertise and judgement of trained human inspectors. While automated systems have been used for a long time, these have mostly been limited to using simple decision automation, such as signal amplitude threshold. The recent advances in various machine learning algorithms have solved many similarly difficult classification problems, that have previously been considered intractable. For non-destructive testing, encouraging results have already been reported in the open literature, but the use of machine learning is still very limited in NDT applications in the field. Key issue hindering their use, is the limited availability of representative flawed data-sets to be used for training. In the present paper, we develop modern, deep convolutional network to detect flaws from phased-array ultrasonic data. We make extensive use of data augmentation to enhance the initially limited raw data and to aid learning. The data augmentation utilizes virtual flaws—a technique, that has successfully been used in training human inspectors and is soon to be used in nuclear inspection qualification. The results from the machine learning classifier are compared to human performance. We show, that using sophisticated data augmentation, modern deep learning networks can be trained to achieve human-level performance.

/pdf/augmented-ultrasonic-data-for-machine-learning-1dw4sqf7r8.pdf

Augmented Ultrasonic Data for Machine Learning

Modeling for quantitative non-destructive evaluation

Reliable detection of both surface and subsurface cracks around fastener sites continues to be a need for maintaining ageing aircraft structures. In this work, a comprehensive overview of both experimental and modelling work addressing eddy current crack detection around fastener holes in multi-layer structures is presented. This overview consists of a strategy for the application of computational methods along with case studies that demonstrate the role of modelling for this particular class of problems. The state-of-the-art on eddy current modelling applicable to low-frequency eddy current problems is reviewed. The role of modelling in feature extraction algorithm development, crack characterisation, and probability of detection evaluation is highlighted.

Computational methods in eddy current crack detection at fastener sites in multi-layer structures

Weep holes in C-141 aircraft have been found to be locations for the initiation of fatigue crack growth. Eddy current testing of weep holes in C-141 aircraft requires fuel tank entry by a technician and is a costly and time consuming process. This paper describes the implementation of a neural network assisted, automated ultrasonic testing technique from the outside of the wing. Toward achieving this goal of field implementation of an automated testing technique, this work demonstrates the value of numerical simulation, laboratory studies and algorithm training with samples representing infield variation, infield demonstration, parametric sensitivity studies and probability of detection (POD) validation. The testing capability for the automated procedure was found to exceed both the defined testing requirements and the ability of testing through viewing C-scan images. Field implementation would eliminate the fuel tank entry requirement, drastically reduce weep hole testing costs for the US Air Force and reduce detection variability between technicians in making classification calls.

Case Study for the Implementation of an Automated Ultrasonic Technique to Detect Fatigue Cracks in Aircraft Weep Holes

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Case Study for the Implementation of an Automated Ultrasonic Technique to Detect Fatigue Cracks in Aircraft Weep Holes