Start your education journey at QNDE with a deep dive into specific NDE topics that will take place before conference begins.
Sunday, July 23, 2023
NDE for Metal Additive Manufacturing
Instructor: Sohn Hoon
Description: This short course first introduces the concept of metal additive manufacturing and challenges associated with additive manufacturing. Then, this course showcases a suite of NDE techniques that can potentially be used for online quality control during metal additive manufacturing.
Instructor: Henrique Reis
Description: In this short course the general principles of Acoustic Emission (AE) technology will be introduced first. Then, AE applications including (1) assessment of components structural integrity and (2) materials characterization will be presented and discussed.
Ultrasonic Imaging: From Phased Array Ultrasonic Testing to Phase Coherence Imaging
Instructor: Pierre Belanger
Description: This short course introduces the basic principles of ultrasonic phased array imaging. The conventional imaging techniques as well as the total focusing method are introduced. Phased coherence imaging is then presented as an amplitude-free extension.
Nonlinear Ultrasonic Techniques for NDE
Instructor: Tribikram Kundu
Description: In this short course the general principle of Non-Linear Ultrasonic (NLU) technique will be introduced first. When NLU techniques are preferred over linear ultrasonic techniques will be discussed. Then, traditional NLU techniques such as (1) Higher Harmonic, (2) Frequency Modulation, (3) Nonlinear Impact Resonance Acoustic Spectroscopy as well as newer techniques like (4) Sideband Peak Count – Index will be discussed along with their limitations and strengths. Application areas will include metals, polymer composites, mortar and concrete.
Artificial Intelligence and Deep Learning for NDE
Instructor: Joel B. Harley
Description: In this short course the general principles of artificial intelligence, machine learning, and deep learning will be discussed. The course will describe common modern machine learning architectures such as (1) feedforward neural networks, (2) convolutional neural networks, and (3) recurrent neural networks. The course will discuss how and when these architectures can be applied to NDE problems. The course will also explore cutting-edge topics in artificial intelligence that have great relevance to NDE, including uncertainty quantification and physics-informed learning. Practical examples of these architectures in Python will be shown.
Guided Waves for NDE
Instructor: Paul Fromme
Description: The short course will introduce the concept and general principles for guided wave NDE. Considerations for the choice of guided wave mode and excitation frequency for different applications, taking into account measurement constraints, will be discussed. Examples for damage detection in metallic and composite structures will be presented, including numerical simulation techniques (Finite Element Analysis).
Electromagnetic Techniques for NDE
Instructor: Saptarshi Mukherjee
Description: This short course will provide a summary of different EM systems spanning from the static to quasi-static to dynamic wave propagation frequencies, for a wide range of diagnostic applications. The EM NDE problem is multi-disciplinary in nature and engages development of forward and inverse solutions for new sensors, instrumentation, and computational methods for understanding the underlying physics. Specifically, I will be discussing: (1) real time imaging algorithms, (2) compact EM sensor systems, (3) numerical methods for accurate modeling of complicated environments and (4) signal processing techniques for improving the detection capability.