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The Path to NDE 4.0: A Review of Algorithms, Techniques, and Strategies for Autonomous Inspection

Summary

A new arXiv review surveys non-destructive inspection across four lenses—computational algorithms, physical techniques, industrial applications, and research objectives—charting the shift from manual, analog interpretation to a data-centric NDE 4.0 driven by deep learning and generative models, while flagging scarce standardized open datasets and the AI "black box" problem as key bottlenecks.

SourcearXiv Machine LearningAuthor: Ashiqur Rahmana, Venkata Devesh Reddy Seethi, Zachary Kral, Austin Yunker, Rajkumar Kettimuthu, Hamed Alhoori
The Path to NDE 4.0: A Review of Algorithms, Techniques, and Strategies for Autonomous Inspection
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[Submitted on 21 Oct 2026]

Title:The Path to NDE 4.0: A Review of Algorithms, Techniques, and Strategies for Autonomous Inspection

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Abstract:Non-Destructive Inspection (NDI) is a cornerstone of modern quality assurance, critical for ensuring the safety and reliability of infrastructure and manufactured components without causing damage. This paper presents a comprehensive review of the current NDI landscape, analyzing the field through four distinct lenses, namely, computational algorithms, physical techniques, industrial applications, and research objectives. We synthesize a broad range of academic literature to illustrate the rapid evolution from manual, analog interpretation to a digitized, data-centric paradigm known as NDE 4.0. Our analysis highlights the dominance of ultrasound and visual inspection as primary physical techniques, while revealing a significant shift toward deep learning and generative models for automated defect recognition. Despite these technological strides, the industry faces critical bottlenecks, notably the scarcity of standardized open-access datasets and the "black box" nature of AI models, which hinders widespread adoption. By charting these developments and identifying key research gaps, such as the need for explainable AI (XAI) and standardized digital twin protocols, this review offers a strategic roadmap for future research. It concludes that the future of NDI research should focus on the successful integration of intelligent, autonomous fusion of inspection data with advanced sensor technologies into asset lifecycle management.

Subjects:

Machine Learning (cs.LG); Emerging Technologies (cs.ET)

ACM classes: J.6

Cite as: arXiv:2610.31704 [cs.LG]

(or arXiv:2610.31704v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.31704

arXiv-issued DOI via DataCite

Submission history

From: Ashiqur Rahman [view email] [v1] Wed, 21 Oct 2026 20:41:35 UTC (791 KB)

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Key points and analysis

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Key points

  • The review analyzes NDI through four lenses: algorithms, physical techniques, industrial applications, and research objectives
  • Ultrasound and visual inspection remain dominant physical methods, while deep learning and generative models are reshaping automated defect recognition
  • A lack of standardized open-access datasets and the opaque nature of AI models hinder adoption
  • The authors call for explainable AI, standardized digital twin protocols, and integration of inspection data into asset lifecycle management

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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