Technical support for Hardware &Software Designed to Empower your Automated Systems 1131 Marie-Victorin Saint-Bruno, Quebec Canada J3V 0M7 !(1)450.233.4973 %info@tecscan.ca www.tecscan.ca Automated NDT SCANNERS Online Support TecViewTM Software Email us! info@tecscan.ca N 8 University discounts Yes, we offer up to 15% discount on our systems to universities! 2307 ME July dup.indd 6 6/19/23 3:41 PM

JULY 2023 Volume 81 • Number 7 JOURNAL STAFF PUBLISHER: Neal J. Couture, CAE DIRECTOR OF PUBLICATIONS/ EDITOR: Jill Ross ASSOCIATE EDITOR: Cara Markland PRODUCTION MANAGER: Joy Grimm DIGITAL PUBLISHING MANAGER: Synthia Jester ASNT MEDIA &EVENT SALES Holly Klarman, MCI Group holly.klarman@wearemci.com 1-410-584-8576 Christina Kardon, MCI Group christina.kardon@wearemci.com 1-410-584-8646 TECHNICAL EDITOR John Z. Chen, KBR ASSOCIATE TECHNICAL EDITORS John C. Aldrin, Computational Tools Sreenivas Alampalli, Stantec Ali Abdul-Aziz, Kent State University Narendra K. Batra, Naval Research Laboratory (retired) Yiming Deng, Michigan State University Dave Farson, Ohio State University Jin-Yeon Kim, Georgia Institute of Technology Cara A.C. Leckey, NASA Langley Research Center Mani Mina, Iowa State University Ehsan Dehghan-Niri, Arizona State University Yi-Cheng (Peter) Pan, Emerson Inc. Anish Poudel, MxV Rail Donald J. Roth, Roth Technical Consulting LLC Ram P. Samy, Consultant Steven M. Shepard, Thermal Wave Imaging Ripi Singh, Inspiring Next Surendra Singh, Honeywell Roderic K. Stanley, NDE Information Consultants Lianxiang Yang, Oakland University Reza Zoughi, Iowa State University CONTRIBUTING EDITORS Toni Bailey, TB3NDT Consulting Bruce G. Crouse, Inspection Services Huidong Gao, PEMEX Deer Park Saptarshi Mukherjee, Lawrence Livermore National Laboratory Hossein Taheri, Georgia Southern University UPFRONT |SCANNER ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN NDT It is my pleasure to share with you this technical focus issue on the subject of artificial intelligence and machine learning (AI/ML) in nondestructive testing (NDT). I have had the great opportunity in my career to work actively on this subject over the past 25 years, going all the way back to my graduate work at Northwestern University. That work, performed in collaboration with so many important mentors of mine and others in the NDT community (Professor Jan Achenbach, Glenn Andrew, Charlie P’an, Bob Grills, Tommy Mullis, Floyd Spencer, and Matt Golis) resulted in the successful demonstration of making calls on complex ultrasonic data through a probability of detection study of a neural network–based approach. There exists great potential with the application of AI/ML in NDT. Such tools can excel at repetitive tasks, performing complex data review faster than inspectors. The vision of AI/ML has been to reduce the burden of laborious data review and ideally elimi- nate missed calls, ensuring greater reliability. However, the wide- spread application of AI/ML in NDT has not yet been adopted for a number of reasons. Training deep learning neural networks requires very large, well-understood datasets, which are not typi- cally available for many NDT applications. Also, many promising research demonstrations have run into issues with overtraining or robustness to variability found outside of the laboratory. In addi- tion, while human factors are frequently cited as being sources for error in NDT applications, humans are inherently more flexible in handling unexpected inspection scenarios and are better at making judgement calls. AI/ML clearly has provided us with so many advances to our daily lives in recent years. For example, Google Translate can trans- late text well between English and more than 100 other languages, enabling broader communication throughout the world. Apps like Shazam can detect a song being played in seconds. Computers using deep learning routines can beat the best human chess players. How can the NDT community make better use of AI/ML, while ensuring we are doing what is best for our customers and members of ASNT? My goal with this technical focus issue is to highlight progress and success stories and share best practices for AI/ML use, but also discuss concerns and the value of having humans in the loop. In the first feature article, I write about emerging AI chatbots and explore the benefits and concerns with using them as part of our work in NDT. As well, in the NDE Outlook column, Singh and Garg highlight the importance of balancing innovation and responsibility with these generative AI tools, ensuring a safer world. JOHN ALDRIN TECHNICAL FOCUS ISSUE EDITOR ALDRIN@COMPUTA TIONALTOOLS.COM The vision of AI/ML has been to reduce the burden of laborious data review and ideally eliminate missed calls, ensuring greater reliability. J U L Y 2 0 2 3 • M A T E R I A L S E V A L U A T I O N 7 2307 ME July dup.indd 7 6/19/23 3:41 PM