Nondestructive Evaluation 4.0: Ultrasonic Intelligent Nondestructive Testing and Evaluation for Composites

Liu, Songping; Liu, Feifei; Yang, Yusen; Li, Legang; Li, Zhiying

doi:10.1080/09349847.2020.1826613

Cited by 12 publications

(4 citation statements)

References 47 publications

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“…Another way is to look at their parallel paths and overlapping areas. The relationship between the industrial revolutions and the evolution of non-destructive testing is successfully described in the study by Liu et al [11] as a parallel course from manually applied NDT during Industry 1.0, to the use of instruments (Industry 2.0), to a digital, automated NDT (Industry 3.0) up until the current version of NDT which is not only digital and automated but also visualized. During this evolution the dependent degree of operator appears to be declining giving its place to the degree of intelligence.…”

Section: The Impact Of Ir4 On Ndtmentioning

confidence: 99%

THE IMPACT OF 4 th INDUSTRIAL REVOLUTION (4IR) ON NON-DESTRUCTIVE TESTING METHODS

Misokefalou¹,

Papoutsidakis²,

Priniotakis³

2022

IJEAST

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The 4th Industrial Revolution (4IR) is developing, leading the transition to the post-industrial era. Both the industry and the business world as well as the civil society, have already realized that the world as it exists is about to change due to the 4th Industrial Revolution. Many countries and stakeholders, realizing (4IR)’s importance, have already incorporated it into official policies while the companies at the forefront are called upon to integrate a variety of new technologies. Among other technologies, in this context, the evolution of non-destructive testing is of significant importance, having a positive impact not only on productivity but also on numerous technical, managerial, and economical aspects of the final product. The present work attempts to highlight this issue and form the basis for relevant future research.

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Section: The Impact Of Ir4 On Ndtmentioning

confidence: 99%

THE IMPACT OF 4 th INDUSTRIAL REVOLUTION (4IR) ON NON-DESTRUCTIVE TESTING METHODS

Misokefalou¹,

Papoutsidakis²,

Priniotakis³

2022

IJEAST

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“…Non-destructive techniques such as ultrasonic scanning, 3 thermography, 4 transmittance, 5 and radiography 6,7 have been frequently utilized for delamination detection in composite laminates. However, such techniques are usually time-consuming and labor-intensive.…”

Section: Introductionmentioning

confidence: 99%

“…Structural Health Monitoring 21 (3) National Natural Science Foundation of China (51808147) and the Innovation Research for the Postgraduates of Guangzhou University under Grant (2020GDJC-D16).…”

mentioning

confidence: 99%

A two-step method for delamination detection in composite laminates using experience-based learning algorithm

Zheng

Luo

Tong

et al. 2021

Structural Health Monitoring

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Delamination in composite laminates reduces the structural stiffness and thus causes changes in the vibration responses of the laminates. Therefore, it is feasible to employ dynamic characteristics (such as natural frequencies and mode shapes) for delamination detection by using an optimization method. In the present study, a two-step method is proposed for the delamination detection in composite laminates using an experience-based learning algorithm. In the first step, one-dimensional equivalent through-thickness beam elements are employed to model the composite laminated beam and potential delamination locations are identified. In the second step, a typical three-dimensional finite mesh is utilized for the beam’s modeling and the detailed delamination information (including the delamination location, size, and interface layer) is detected. This two-step method combines the advantages of the two different modeling techniques and is able to significantly reduce the computational cost without reducing detection accuracy. The proposed method is applied for an eight-layer quasi-isotropic symmetric (0/-45/45/90)s composited beam with different delamination situations to verify its effectiveness and robustness. The performance of the two-step method is demonstrated by comparing with the one-step method and other three state-of-the-art algorithms (CMFOA, PSO, and SSA). Moreover, the influence of artificial noise on the accuracy of the detection performance is also investigated. Both numerical and experimental results confirm the superiority of the proposed method for delamination detection in composite laminates especially for the prediction of delamination interface.

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“…The move to increasingly digital working practices, in what is widely referred to as Industry 4.0, will produce major growth in the numbers of large datasets over the next few years. These data will be key for the development of new ML based inspection processes that match the new opportunities that Industry 4.0 provides, including big data and autonomous systems [4,5]. Note that ML technologies are nowadays relatively inexpensive to develop and easy to implement.…”

Section: Introductionmentioning

confidence: 99%

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

Cantero-Chinchilla,

Wilcox,

Croxford

2021

Preprint

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The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations of deep learning (DL) techniques that address individual NDE tasks (data acquisition, data pre-processing, defect detection, and defect characterisation) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data; moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve full artificial intelligence driven automation of ultrasonic NDE so that the research community, industry, and regulatory bodies embrace them. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.

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Nondestructive Evaluation 4.0: Ultrasonic Intelligent Nondestructive Testing and Evaluation for Composites

Cited by 12 publications

References 47 publications

THE IMPACT OF 4 th INDUSTRIAL REVOLUTION (4IR) ON NON-DESTRUCTIVE TESTING METHODS

THE IMPACT OF 4 th INDUSTRIAL REVOLUTION (4IR) ON NON-DESTRUCTIVE TESTING METHODS

A two-step method for delamination detection in composite laminates using experience-based learning algorithm

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

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