Defect detection of 3D braided composites based on semantic segmentation

Guo, Yangming; Xiao, Zhitao; Geng, Lei

doi:10.1080/00405000.2022.2054103

The Journal of The Textile Institute

2022

DOI: 10.1080/00405000.2022.2054103

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Defect detection of 3D braided composites based on semantic segmentation

Yangming Guo

Zhitao Xiao

Lei Geng

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2023

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Cited by 2 publications

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Application of Artificial Neural Networks for Solving Problems of Acoustic Nondestructive Testing (Review)

Dolmatov

2023

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Nowadays, artificial neural networks are finding more and more applications in various fields of human activity. This paper reviews the current state of research and development in the field of application of artificial neural networks for solving problems in acoustic non-destructive testing. The areas of application of neural networks include processing of inspection data, flaw detection, determination of flaw parameters, and determination of material properties. Test data processing includes increasing the signal-to-noise ratio of results, data compression, restoration and processing of flaw images. Within flaw parameter determination, flaw sizing and flaw type identification tasks have been solved using artificial neural networks. One of the most challenging problems related to the introduction of artificial neural networks in acoustic nondestructive testing is the small amount of inspection data that complicates the training of artificial networks. The ways to solve this problem are data exchange between organizations and standardization of inspection results, data augmentation and application of computer modeling.

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Application of Artificial Neural Networks for Solving Problems of Acoustic Nondestructive Testing (Review)

Dolmatov

2023

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Real-Time AI driven Interpretation of Ultrasonic Data from Resistance Spot Weld Process Monitoring For Adaptive Welding

Scott¹,

Stocco²,

Chertov³

et al. 2023

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Adaptive resistance spot welding systems typically rely on real-time analysis of dynamic resistance curves and other indirect measurements to estimate weld progress and guide adaptive weld control algorithms. Though efficient, these approaches are not always reliable, and consequently there is a need for improved feedback systems to drive adaptive welding algorithms. As an alternative, an advanced in-line integrated ultrasonic monitoring system is proposed, with real-time weld process characterization driven by artificial intelligence (AI) to create actionable feedback for the weld controller. Such a system would require real-time ultrasonic data interpretation, and for this a solution using deep learning was investigated. The proposed solution monitors the ultrasonic data for key process events and estimates the vertical size of the weld nugget proportional to the stack size throughout the welding process. This study shows that adaptive welding using ultrasonic process monitoring backed by AI-based data interpretation has immense potential. This research highlights the importance of nondestructive evaluation (NDE) in the zero-defect manufacturing paradigm.

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Defect detection of 3D braided composites based on semantic segmentation

Cited by 2 publications

References 20 publications

Application of Artificial Neural Networks for Solving Problems of Acoustic Nondestructive Testing (Review)

Application of Artificial Neural Networks for Solving Problems of Acoustic Nondestructive Testing (Review)

Real-Time AI driven Interpretation of Ultrasonic Data from Resistance Spot Weld Process Monitoring For Adaptive Welding

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