Vol. 21 No. 1 (2024): Journal of Non Destructive Testing and Evaluation (JNDE), March 2024
Research Papers

Future of Non-Destructive Testing in the Era of Additive Manufacturing and Machine Learning

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  • Nikhil Gupta

Published 10-03-2024

Keywords

  • Non-destructive testing,
  • Additive manufacturing,
  • Machine learning,
  • Thermal imaging,
  • Artificial intelligence

How to Cite

Nikhil Gupta. (2024). Future of Non-Destructive Testing in the Era of Additive Manufacturing and Machine Learning. Journal of Non Destructive Testing and Evaluation (JNDE), 21(1), 47–54. Retrieved from https://jnde.isnt.in/index.php/JNDE/article/view/78

Copyright (c) 2024 Journal of Non-Destructive Testing and Evaluation (JNDE)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Additive manufacturing (AM) methods have become mainstream in many industry sectors, especially aeronautics and space structures, where production volume for components is low and designs are highly customized. The frequency of launching space missions is increasing around the world. Some of these missions are sending landers and rovers to moon, mars, and other planets. Such space structures require numerous parts that are unique in design or are produced in just one or a very small production run. Such parts produced for high stake and very expensive missions require complete confidence in the quality of each part. Characterization of parts manufactured by AM is a significant challenge for many existing methods due to the geometric complexity, feature size in the structure, and size of the part. This paper discusses various challenges in applying current characterization methods to the AM sector. Machine learning (ML) methods are considered promising in materials and manufacturing fields. However, generating the training dataset by creating a large number of parts is expensive and impractical. New methods are required to train the ML algorithms on small datasets, especially for parts of unique geometry that are produced in limited production run such as space structures.

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