Explainable AI-infused ultrasonic inspection for internal defect detection

Adithyaa Karthikeyan, Akash Tiwari, Yuhao Zhong, Satish T.S. Bukkapatnam

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

While AI and imaging technologies are dramatically transforming the process and machine condition monitoring, product inspection remains confined to probing the geometry and surface morphology. Subsurface and bulk inspection remain prohibitively slow and imprecise. This paper presents an explainable AI (XAI)-infused ultrasound imaging approach for rapid detection of artifacts including product defects. The approach led to the discovery of correlated spatial patterns in the images located away from the artifacts. This discovery enabled accurate (> 80%) detection of artifacts that are not discernible with the current image segmentation methods, and it could profoundly impact product quality and (cyber)security assurance technologies.

Original languageEnglish
Pages (from-to)449-452
Number of pages4
JournalCIRP Annals - Manufacturing Technology
Volume71
Issue number1
DOIs
StatePublished - Jan 2022
Externally publishedYes

Keywords

  • Artificial intelligence
  • Inspection
  • Quality assurance
  • Ultrasonic

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