To enhance the efficiency of inspections along with the system digitalization through DDA and CR, we propose three novel solutions with artificial intelligence for NDT.
First, “Workflow AI” assists inspectors by automatically determining whether the required quality of image is realized with appropriately placed IQIs. The AI not only detects IQIs but also analyzes them, annotating the DICOM image with statistical information, which used to be manually measured.
Second, “Wall-Thickness AI” enables automatic calculation of pipe wall thickness based on pixel-wise pipe boundary data derived by a background AI, allowing inspectors to instantly identify the most severely corroded point within arbitrary area of interest. Compared to existing calculation tools, boundary determination accuracy for irregular shapes has improved due to our study on profile analysis of both X-ray images and our partner inspectors’ judgement data.
Third, “Screening AI” automatically highlights defects and possibly unacceptable indications in radiographic images for welding or casting. It demonstrates consistent true positive rates of over 90% for indications like porosity, gas hole and slag inclusion, though it is not limited to these types. Attribute information for the detected indications, including their actual size and their total numbers, can be displayed in a list, supporting inspectors to easily judge the grade of defects.
In developing these three solutions, we placed value on on-site data collected through collaboration with customers, which not only contributes directly as training data to improve AI performance but is also utilized to verify the AI effectiveness. Case studies demonstrated that the proposed applications lead to faster inspections and enhanced stable quality in NDT daily operations.

This paper deals with the implementation of Aritificial Intelligence and supervised Machine Learning to train Large Language Models to read and analyze 8bit monochromatic images and provide basis for autonomous radiographic interpretations.
The setup involved analysis of approximately 200 digital images, interpreted by three independent ASNT Level 3 Professionals and these results are loaded as foundation models which learn about the patterns through this training data. This is made possible by employing a Conditional Probability Distribution that Discriminates between kinds of data instances and classifies defects as trained by the initial data.
These trained models are formed as a database which can either be used as a generative AI or for further ‘Deep Learning’ of these models that uses Neural Networks allowing them to process more complex patterns than traditional machine learning.

The trained model was then allowed to interpret another 50 images, which had a surprising accuracy of 82%. There were misinterpretations with regards to film artefacts, and spurious indications which a regular interpreter would easily identify. Given this success, with more training data and digital images, the model can be trained to create a self-sustainable and robust autonomous interpretation engine.

This presentation provides a roadmap for realistic expectations, ensuring organizations effectively harness AI and ADR for long-term productivity and efficiency gains in NDT operations

Three-dimensional X-ray microscopy (XRM) has emerged as a transformative tool for non-destructive characterization of complex materials, enabling multiscale imaging and quantitative analysis of microstructural features. However, conventional reconstruction algorithms impose trade-offs between resolution, field of view, and acquisition time, limiting throughput and statistical representativity for large-volume studies.
In this work, we present an AI-driven reconstruction framework that integrates deep learning algorithms—such as ZEISS DeepRecon Pro and Advanced Reconstruction Toolbox (ART)—to overcome these limitations. By leveraging convolutional neural networks our approach achieves significant improvements in image quality, artifact suppression, and resolution recovery without increasing acquisition time. Experimental validation on nickel-based metal matrix composites reinforced with TiC demonstrates up to 10× throughput enhancement and sub-micron resolution recovery in dense materials, compared to traditional Feldkamp-Davis-Kress (FDK) methods.
The proposed workflow enables accurate segmentation of secondary phases, porosity quantification, and defect detection across large volumes, facilitating correlative studies and accelerating materials development. These advancements position AI-enabled XRM reconstruction as a critical enabler for high-throughput characterization in energy storage, additive manufacturing, and advanced alloy systems.