Artificial Intelligence (AI) is a branch of computer science that aims to create systems capable of intelligent behavior, simulating human cognitive functions like learning, problem-solving, and pattern recognition. In the realm of Nondestructive Testing (NDT), AI's potential is vast. AI algorithms, especially machine learning and deep learning models have the potential to analyze certain complex datasets much faster and with greater accuracy than human operators. This advancement is pivotal in fields where swift and precise flaw detection is vital for safety and reliability, such as in aerospace and energy sectors. AI's integration in Non-Destructive Testing (NDT) methods like ultrasonic testing, radiography, and thermography not only enhances the efficiency and accuracy of inspections but also provides comprehensive insights into material conditions. Its predictive analytics capabilities foresee potential failures, enabling preemptive maintenance and reducing costly repairs and downtime. This evolution in NDT practices is significant, as AI opens the door to advanced diagnostic capabilities and the development of smarter, automated systems. The ongoing advancement of AI promises a future in NDT where safety and operational efficiency are substantially improved. Learn about the basics of ML/AI and how it can be used within the world of NDE. Participants will get to know practical examples and how the technology works on a high level. This is the perfect introduction for anybody that wants to learn more about AI, its impact and how to qualify this new technology in NDT processes.

08:00 Orientation and welcome

08:15 Why: Introduction and relevance

08:45 How: Digitalization, Automation and Digital Transformation in NDE

9:30 Coffee Break – Networking and discussions

10:00 Fundamentals of AI and its Use in NDE

11:00 AI Enhanced Digital Radiography

12:00 Summary, Live Demo and questions

Lennart Schulenburg is an author and expert in Non Destructive Testing (NDT) and quality control. As CEO of the leader in industrial X-ray and CT systems VisiConsult X-ray Systems & Solutions from Northern Germany, he has a extensive experience in industries ranging from automotive to aerospace. With a degree in Computational Informatics and a MBA, he successfully initiates insightful technology development and is a regular speaker at industry events to share the latest trends and innovations in quality control and impact on the broader organization.

When: Monday June 9th, 2025, Registration starts at 8:30am, course starts at 9:00am.

Where: Fallsview Casino, sign in at the PANNDT Registration Desk in the Grand Hall Foyer.

Cost: $299 CAD

Super-Resolution (SR) is a groundbreaking computational approach that enhances image resolution beyond conventional system limitations. Originally explored for military and surveillance applications, SR has evolved into a crucial tool across multiple disciplines, including nondestructive testing (NDT), medical imaging, industrial diagnostics, and art restoration. This short course provides a deep dive into the historical evolution, principles, methodologies, and applications of SR, with a particular focus on its future potential in NDT.

    Through two engaging sessions, we will cover fundamental concepts, key algorithms, and real-world applications, distinguishing between SR-enabled applications and actual SR-driven products. By drawing insights from historical projects to modern discussions on whether SR imaging could enhance the exploration of other universes, we will build a strong foundation for understanding SR's transformative role.

The course is designed for a broad audience, including experts, researchers, industry professionals, students, and technical personnel, ensuring that both theoretical and practical aspects are effectively conveyed.

Part 1: Fundamentals and Evolution of Super-Resolution

• Introduction & Historical Background

• Defining Super-Resolution

• Super-Resolution Techniques & Algorithms

Part 2: Applications and Future Directions

• Applications of Super-Resolution in Different Fields

• Products vs. Applications

• Future Perspectives & Open Challenges

Q&A

Aryaz Baradarani received his PhD in electrical engineering from the University of Windsor, Ontario, Canada, in 2012. He is currently a Principal Research and Development Scientist at Tessonics Inc, Windsor. He has served as a peer reviewer for various esteemed journals, conferences, and symposia. His primary research interests include feature extraction and diagnostic imaging, where he has made significant contributions to the design, analysis, and implementation of advanced medical and industrial diagnostic ultrasound imaging systems. In addition to his professional roles, he actively contributes to Technical Program Committees, and international advisory boards. Dr. Baradarani has received several prestigious awards, including the Endowment Scholar Award, the Queen Elizabeth II Graduate Scholarship in Science and Technology, the University of Windsor President’s Excellence Scholarship, the University of Windsor International Graduate Excellence Scholarship (IGES), and the 3M Company Bursary Award.

When: Monday June 9th, 2025, Registration starts at 12:00pm, course starts at 12:30pm.

Duration: 2 hours

Where: Fallsview Casino, sign in at the PANNDT Registration Desk in the Grand Hall Foyer.

Cost: $249 CAD

Deep learning has brought ground-breaking advancement in NDE data interpretation and downstream decision-making – and promises to continue to do so. On the other hand, widespread use of deep learning artificial intelligence in NDE systems is impeded due to some major challenges and concerns regarding its role, development, standardization, consistency, validation, generality, trustworthiness, coexistence with human experts, and ethical use – among others.

In this short course, an introduction to artificial intelligence, machine learning, and deep learning will first be provided. This introduction will highlight the types of machine learning, the typical machine learning workflow, the current limitations of machine learning, and the state of the art.

After this introduction, we will dive into various topics surrounding the use of deep learning in NDE. We will discuss some of the major challenges and concerns regarding the use of deep learning in NDE and how some of them may be alleviated. We will discuss what is still needed from the NDE community to further facilitate widespread use of modern-day artificial intelligence in NDE. We will then review some of the many applications of deep learning in NDE, and highlight a few case studies from ongoing work.

Finally, we will discuss what the future of NDE may hold, and consider potential ways in which the growing use of artificial intelligence in NDE may elicit changes in the field of NDE itself.

1.       Introduction to Artificial Intelligence, Machine Learning, and Deep Learning

• Rationale for Machine Learning

• Types of Machine Learning

• Typical Workflow

• Current General Limitations

• State of the Art

2.       Deep Learning In NDE

• Industry 4.0, NDE 4.0, and Deep Learning

• Deep Learning Limitations, Challenges, and Concerns in NDE

• Applications Of Deep Learning In NDE

• Case Studies:

• Nuclear Dry Storage Weld Inspection

• Ultrasonic Inspect of Laser Brazed Joints

• Ultrasonic Monitoring of the Resistance Spot Welding Process

3.       Outlook

Dr. Ryan Scott is a research associate at the Institute for Diagnostic Imaging Research at the University of Windsor and head of Artificial Intelligence at Tessonics in Windsor, Canada. Ryan’s current work involves the research and development of artificial intelligence solutions for the interpretation of data from ultrasonic inspections for a variety of applications.

When: Monday June 9th, 2025, Registration starts at 2:30pm, course starts at 3:00pm.

Duration: 2 hours

Where: Fallsview Casino, sign in at the PANNDT Registration Desk in the Grand Hall Foyer.

Cost: $249CAD