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Materials Evaluation (ME) is ASNT's flagship journal with news and features covering all aspects of NDT and the NDT industry. The technical content in ME is peer-reviewed by qualified technicians and researchers, ensuring quality information and discussion.

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Building Pathways for NDT

December 2024 - Volume 82, Issue 12

In this issue we share the latest news on how the ASNT Foundation is working to build pathways in NDT. Readers will also find a guide to conducting thermal and infrared testing for building applications; a sealing detection method for cables; and a study on thermographic assessment of defects in industrial facilities. Also included: Generative AI and Its Potential to Augment NDE: Part 2; ASNT 2024 in review; and frequently asked questions about ASNT’s board of directors elections.  

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Previous Editions

Materials Evaluation

November 2024 – Volume 82, Issue 11

Check out the latest issue of November issue of Materials Evaluation, where we highlight ASNT’s newest initiatives for NDT advocacy and explain the neutron radiography method.
Technical papers focus on line scanning thermography for rail base defects and characterization of cracks based on pulsed eddy current.

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Materials Evaluation

October 2024 – Volume 82, Issue 10

Preview ASNT 2024 events and exhibitors, explore how generative AI enhances NDE, and learn about a ring array probe for inspecting wind turbines. Discover research on magnetostrictive EMAT sensors for stress corrosion cracking and the integration of machine learning with eddy current techniques for metal crack detection.

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Materials Evaluation

September 2024 – Volume 82, Issue 9

This edition covers the impact of digital transformation on NDT, the EU Data Act’s effects on the industry, and ASNT's new ATO certification program. It also highlights research on NDE of weld microstructures in power plants and concrete failure.

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Materials Evaluation

August 2024 – Volume 82, Issue 8

This issue addresses ethics in nondestructive testing, including AI implications and ASNT logo usage. It also explores machine learning for composite aging, PCI for chrome carbide overlays, and enhancing NDE reliability in Grade 91 steel welds.

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