Serge Dos Santos

NDE processes and systems are undergoing unprecedented and exponentially accelerated transformations driven by the impact of a series of Critical and Emerging Technologies (CETs). Advancements in CETs are not only reshaping NDE but also creating profound transformations and, in many cases, disruptions in almost every aspect of human endeavors. The transition from traditional NDE methods to smart, connected systems, evolving towards the integration of both trustworthy autonomous cognitive inspection systems (ACISs) and cognitive NDE ecosystems (CNDEs), necessitates not only technical upskilling but also an enhanced adaptability mindset. The global NDT services market, valued at $16.41 billion USD in 2024, is projected to reach $35.39 billion USD by 2032 (a 115% growth). This growth will translate into significant workforce challenges, including skilled technician shortages, a profound transformation in NDE training, qualification, and certification ecosystems, and an impulse for CETs competencies diversification due to technological disruption, which will bring new and unprecedented ethical dilemmas. Consequently, advancing research that examines the implications of Ethics and Human Factors (E&HF) on AI/ML/DL enhanced NDE Processes and Systems is becoming increasingly relevant to create deployment-maturity assessment tools and guidance elements to catalyze value creation and mitigate negative impacts.

The combination of materials science with advanced tools, including AI, is essential for modern NDT practices and positions the discipline firmly within STEM fields using advanced mathematical theories. As an extension of the classical Pulse Inversion technique for nonlinear ultrasonic testing (UT), we developed the Quaternion Excitation Symmetry Analysis Method (QESAM) as an extension of advanced nonlinear signal processing coding schemes including ESAM, SSM, etc. The 4-dimensional quaternionic extension QESAM is suggested in order to extract nonlinear UT signatures of wire arc additive manufacturing (WAAM) samples. For the proof of concept of this new signal processing method, experiments were conducted using various chirp-coded frequency sweep excitations in the 200kHz-800kHz bandwidth. Several experimental configurations were proposed on each sample: the use of a 1 MHz 16-elements Nondestructive Testing probe where each element was isolated and used as a single receiver. For each excitation on a single element, several symmetrized amplitudes of excitation are proposed in connection with quaternion properties and in order to activate several orders of nonlinear signatures. The basic scheme and some preliminary experimental results are presented and induce the definition of the new quaternion excitation symmetry analysis method.

Non-Destructive Testing (NDT) offers significant opportunities for integrating artificial intelligence (AI) into the training of future specialists at the Master level. As NDT continues to expand to meet industrial and societal demands—particularly in structural safety and energy transition—it plays a key role within the context of Industry 4.0 under the concept of NDE4.0. France has historically been a leader in NDT, supported by high-tech sectors such as nuclear energy and aerospace. Today, the combination of materials science with advanced tools, including AI, is essential for modern NDT practices and positions the discipline firmly within STEM fields. AI, through machine learning and intelligent sensors, enables faster and more accurate defect detection and automated inspections, opening new specialization pathways for master’s students. NDT education is inherently multidisciplinary, closely linked to materials science, structural mechanics and NDT integrity engineering. At Aix-Marseille University and INSA Group, innovative teaching strategies include short targeted training sessions, practical work and professional virtual reality tools. French academic initiatives aim to strengthen interdisciplinary education, expand access to higher education in NDT, and foster innovation through strong research and training networks. An updated syllabus for future NDT education will be presented.

An experimental setup based on stress induced on skin by airflow and captured by fast acquisition camera (300 Hz) is suggested in this paper. This enables the extraction of new multiscale statistical parameters of skin such as relaxation characteristics in the time domain for generating big data needed for the nonlinear analysis of human skin properties using the Preisach-Mayergoyz approach. During current study, the characteristics of the electronic system and biomaterial samples are extracted by applying data and image processing procedures to the experimental outcomes in order to evaluate the nonlinear behaviour of the skin. It allows the development and identification of a model that describes the subject's parameters, validated with a case study of 10 subjects with various multiscale properties.

The electronic calibration process of the nonlin-ear time reversal based ultrasonic (US) imaging device (TR-NEWS) including the memosducer is presented and fabricated experimentally using Knowm Inc. memristors. Due to the wide frequency bandwidth of the instrumentation, an amplitude mod-ulated chirp-coded optimized US excitation is suggested in order to satisfy all constraints induced by the use of memristors, ultrasonic sensors and the biomedical tissues under analysis. The proof of concept is proposed and optimized with a 8-components memosducer prototype providing low and high resistance states values statistically distributed respectively around 200Ω and 5k Ω and associated to the [100kHz-5MHz] TR-NEWS imaging device bandwidth. Experimentally tested on a polymer phantom with analogue properties of the human skin, the demonstrator constitutes a promizing reservoir computing system based on the principle of neuromorphic devices, providing new generation of multiscale data highly compatible within the Health 4.0 community.

Identification of crack positions or anomalies in materials using the time reversal based nonlinear elastic wave spectroscopy (TR-NEWS) is an established method. We propose a system using transducers which emit forward propagating solitonic wave and time-reversed propagating solitonic wave produced by memristers placed on a side of a rectangle and scattered by cracks in the material and received by receivers which are placed on the opposite side of the rectangle. By minimizing the difference of the scattered forward propagating wave and the scattered TR wave, we get information of the position of the crack by using the neural network technique. Route of the solitons are expressed by 2 dimensional projective quaternion functions, and parameters for getting the optimal route from signals are expected to be reduced. We consider the wave is expressed by a soliton which is conformal, and discuss symmetry protected topological impurities and gravitational effects using the Atiyah-Patodi-Singer's index theorem.