ReviewFrontiers in artificial intelligence2026
Data driven algorithmic advances in defect detection of additively manufactured materials using non-destructive testing: a review.
Review in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
With the fifth industrial revolution, additive manufacturing (AM) is being extensively employed in industries including manufacturing, automotive, aerospace, medical, and energy. It is now accepted as a state-of-the-art and innovative way of producing complex parts that can exhibit enhanced mechanical properties and fewer design constraints. However, AM creates unique challenges in maintaining the quality and reliability of manufactured components. Addressing the detection and classification of defects induced by AM processes including cracking, porosity, lack of fusion, delamination and surface irregularities, during and after manufacturing is therefore critical. Traditional non-destructive testing (NDT) techniques including ultrasound testing, X-ray computed tomography (XCT), optical or microscopy based methods and thermography, provide reliable defect detection but are often slow, operator dependent, and poorly suited to
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