JOURNAL ARTICLE

Nondestructive Testing, artificial intelligence, NDT Expert, NDT knowledge, digitalisation, education.

  • Published In: CINDE Journal, 2025, v. 46, n. 2. P. 12 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: SICARD, René; CHAHBAZ, Ahmad 3 of 3

Abstract

Non-destructive testing (NDT) is crucial for aeroengine components throughout their lifecycle, from raw material processing to finished product assembly and during maintenance, repair, and overhaul (MRO) operations. Current NDT practices for these components primarily rely on manual methods, including visual inspection, digital X-ray, thermography, ultrasonic testing, and eddy current techniques. However, advancements in engine manufacturing processes, such as laser welding, brazing, and advanced coatings, have resulted in increasingly complex part geometries, posing significant challenges for pre- and post-repair inspections. Furthermore, the emergence of advanced engine designs incorporating novel composite materials and complex 3D-manufactured turbine blades further raise the challenges in quality control and MRO. Consequently, Automated NDT inspection technologies are becoming essential solutions where manual or conventional methods are impractical or infeasible. Automated systems offer the necessary tools for efficient and reliable inspection of complex components. This article presents illustrative examples of Automated NDT applications for specific engine components, including compressor discs, compressor rotor spools, fan blades, and fan cases. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:CINDE Journal. 2025/07, Vol. 46, Issue 2, p12
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:17002729
  • Accession Number:191094729
  • Copyright Statement:Copyright of CINDE Journal is the property of Canadian Institute for Non-Destructive Evaluation and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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