Advanced methods in NDE using machine learning approaches

Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

doi:10.1063/1.5031519

Cited by 21 publications

(12 citation statements)

References 7 publications

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“…Within the various presented concepts and frameworks [ 34 , 35 , 36 , 37 , 38 , 39 ], automated production systems, including mixed reality assistance systems [ 40 , 41 ], could be rapidly modularized [ 42 ] and reconfigured [ 43 , 44 , 45 ], enhanced with AI [ 46 , 47 ] and sensors [ 48 , 49 ] and, in combination with cloud and edge computing [ 50 ], transformed into distributed control systems, while detailed production environments can be generated and updated in the form of 3D point clouds [ 51 , 52 , 53 , 54 , 55 , 56 ]. Based on these infrastructures, DT demonstrates the capability of handling increasingly complex operational problems, such as production planning and scheduling [ 57 , 58 , 59 , 60 ], production monitoring and control [ 61 , 62 , 63 , 64 , 65 , 66 ], quality control and management [ 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ], as well as logistics [ 76 , 77 , 78 ], supply chain management (SCM) [ ...…”

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

194

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

194

View full text Add to dashboard Cite

show abstract

“…An approach based on an artificial neural network (ANN) has the potential for depth profiling, and have been employed by many researchers for NDT applications, particularly for material defect identification, classification and characterization of conducting and composite materials [25] [26], [27], [28], [29]. A well-trained ANN is capable of mapping non-linear relationships between the measured responses and defects within the material of interest, whilst improving the solution speed by a significant amount.…”

Section: Electrical Resistivity Reconstruction Of Graphitementioning

confidence: 99%

Electrical Resistivity Reconstruction of Graphite Moderator Bricks From Multi-Frequency Measurements and Artificial Neural Networks

et al. 2021

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“…Beside for crack detection for surface, there are several other works involving CNN in NDT, such as for phase detection in shearography proposed by Sawaf and Groves [27], welding detection using X-Ray images by Hou et al [28], and damaged steel and CFRP using infrared (IR) images by Yousefi et al [29]. As pointed out by Wunderlich et al [30], we believe that advances in machine learning will have a huge impact in several key areas of NDI.…”

Section: State Of the Art: Advances Of Machine Learning And Its Applimentioning

confidence: 99%

DeepSHM: a deep learning approach for structural health monitoring based on guided Lamb wave technique

Ewald

Groves

Benedictus

2019

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019

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A deep learning approach for structural health monitoring based on guided Lamb wave technique Ewald, Vincentius; Groves, Roger M.; Benedictus, Rinze ABSTRACTIn our previous work, we demonstrated how to use inductive bias to infuse a convolutional neural network (CNN) with domain knowledge from fatigue analysis for aircraft visual NDE. We extend this concept to SHM and therefore in this paper, we present a novel framework called DeepSHM which involves data augmentation of captured sensor signals and formalizes a generic method for end-to-end deep learning for SHM. The study case is limited to ultrasonic guided waves SHM. The sensor signal response from a Finite-Element-Model (FEM) is pre-processed through wavelet transform to obtain the wavelet coefficient matrix (WCM), which is then fed into the CNN to be trained to obtain the neural weights. In this paper, we present the results of our investigation on CNN complexities that is needed to model the sensor signals based on simulation and experimental testing within the framework of DeepSHM concept.

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Advanced methods in NDE using machine learning approaches

Cited by 21 publications

References 7 publications

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Electrical Resistivity Reconstruction of Graphite Moderator Bricks From Multi-Frequency Measurements and Artificial Neural Networks

DeepSHM: a deep learning approach for structural health monitoring based on guided Lamb wave technique

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