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A Review of Synthetic and Augmented Training Data for Machine Learning in Ultrasonic Non-destructive Evaluation by Sebastian Uhlig; Ilkin Alkhasli; Frank Schubert; Constanze Tschöpe; Matthias Wolff is a Engineering article available to read on EtoBox.

What is A Review of Synthetic and Augmented Training Data for Machine Learning in Ultrasonic Non-destructive Evaluation about?

Ultrasonic Testing (UT) has seen increasing application of machine learning (ML) in recent years, promoting higher-level automation and decision-making in flaw detection and classification. Building a generalized training dataset to apply ML in non-destructive evaluation (NDE), and thus UT, is exceptionally difficult since data on pristine and representative flawed specimens are needed. Yet, in most UT test cases flawed specimen data is inherently rare making data coverage the leading problem when applying ML. Common data augmentation (DA) strategies offer limited solutions as they don’t increase the dataset variance, which can lead to overfitting of the training data. The virtual defect method and the recent application of generative adversarial neural networks (GANs) in UT are sophisticated DA methods targeting to solve this problem. On the other hand, well-established research in modeling ultrasonic wave propagations allows for the generation of synthetic UT training data. In this context, we present a first thematic review to summarize the progress of the last decades on synthetic and augmented UT training data in NDE. Additionally, an overview of methods for synthetic UT data

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Author

Sebastian Uhlig; Ilkin Alkhasli; Frank Schubert; Constanze Tschöpe; Matthias Wolff

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)