Trends in NDE 4.0: Purpose, Technology, and Application

In this topical collection (10 articles)

1. 

   OriginalPaper

   Application of a Particle Filter to Flaw Identification for Ultrasonic Nondestructive Evaluation: Assimilation of Simulated and Measured Data

   Kazuyuki Nakahata, Taizo Maruyama, Sohichi Hirose Article:34

2. 

   OriginalPaper

   Automated Defect Recognition for Welds Using Simulation Assisted TFM Imaging with Artificial Intelligence

   Thulsiram Gantala, Krishnan Balasubramaniam Article:28

3. OriginalPaper

   The Effect of Different Flaw Data to Machine Learning Powered Ultrasonic Inspection

   Tuomas Koskinen, Iikka Virkkunen, Oskar Siljama… Article:24

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4. OriginalPaper

   Machine Learning Based Preprocessing to Ensure Validity of Cross-Correlated Ultrasound Signals for Time-of-Flight Measurements

   Simon Herter, Sargon Youssef, Michael M. Becker… Article:20

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5. 

   OriginalPaper

   Automated Defect Recognition on X-ray Radiographs of Solid Propellant Using Deep Learning Based on Convolutional Neural Networks

   Dhruv Gamdha, Sreedhar Unnikrishnakurup… Article:18

6. OriginalPaper

   Advancements in Radiographic Evaluation Through the Migration into NDE 4.0

   Hossain Saboonchi, Donald Blanchette… Article:17

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7. OriginalPaper

   Convolutional Neural Networks for Semantic Segmentation as a Tool for Multiclass Face Analysis in Thermal Infrared

   David Müller, Andreas Ehlen, Bernd Valeske Article:9

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8. OriginalPaper

   NDE 4.0—A Design Thinking Perspective

   Johannes Vrana, Ripudaman Singh Article:8

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9. OriginalPaper

   Augmented Ultrasonic Data for Machine Learning

   Iikka Virkkunen, Tuomas Koskinen… Article:4

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10. OriginalPaper

    Exploring Flood Filling Networks for Instance Segmentation of XXL-Volumetric and Bulk Material CT Data

    Roland Gruber, Stefan Gerth, Joelle Claußen… Article:1

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