Reliable measurements in structural health monitoring mean for the instrumentation to be set in perfect reproducible conditions. The solution described in this study consists of printing the sensors directly on the parts to be controlled. This method solves the reproducibility issue, limits human error, and can be used in confined or hazardous environments. This work was limited to eddy current testing, but the settings and conclusions are transposable to any non-destructive testing methods (ultrasounds, etc.). The first salve of tests was run to establish the best dielectric and conductive ink combination. The Dupont ink combination gave the best performances. Then, the dispenser- and the screen-printing methods were carried out to print flat spiral coils on flexible substrates. The resulting sensors were compared to flex-printed circuit boards (PCB-flex) using copper for the electrical circuit. The conductive ink methods were revealed to be just as efficient. The last stage of this work consisted of printing sensors on solid parts. For this, 20-turn spiral coils were printed on 3 mm thick stainless-steel plates. The permanent sensors showed good sensibility in the same range as the portative ones, demonstrating the method’s feasibility.

This study presents the development of ultrasound (US) sensors for structural health monitoring (SHM) by directly printing the sensors onto structural components. Unlike traditional ultrasound-based SHM systems, this approach minimizes sensor placement and environmental interference challenges, providing a stable, durable, and cost-effective solution for long-term monitoring of critical infrastructure. The core of this technology is a piezoelectric composite made of an OE 132–43/P189 mixture at 80 vol.% ferroelectric particles in a polyimide matrix, selected for its optimal dielectric strength, consistent d₃₃ polarization, high-frequency response (1–20 MHz), and high thermal stability. Experimental validation showed that the printed sensors effectively measured component thickness in steel and aluminum, confirming their suitability for non-destructive evaluation. Additionally, multi-layer sensor designs were explored, producing higher acoustic pressure at lower operating voltages, enabling applications on thicker materials. This work advances SHM by enabling continuous, real-time monitoring, improving structural safety, and reducing maintenance costs across various industries.

Magnetic Particle Inspection (MPI) is one of the most used methods in Non-Destructive Testing (NDT), allowing precise and robust defect detection on industrial-grade manufactured parts. However, human controllers perform this task in full black environments under UV-A lighting only (with safety glasses) and use chemical products in a confined environment. Those constraints tends to lower control performance and increase stress and fatigue. As a solution, we propose an AI-based assistive machine (called “PARADES”) inside the hazardous environment, remotely manipulated by a human operator, outside of the confined area, in cleaner and safer conditions. This paper focuses on the development of a complete industrial-grade AI machine, both in terms of hardware and software. The result is a standalone assistive AI-based vision system, plug-and-play and controller-friendly, which only needs the usual power supply 230 V plug that detects defects and measures defect length. In conclusion, the PARADES machines address for the first time the problem of occupational health in MPI with an industrial standalone machine which can work on several parts and be integrated into current production lines. Providing cleaner and healthier working conditions for operators will invariably lead to increased quality of detection. These results suggest that it would be beneficial to spread this kind of AI-based assistive technology in NDT, in particular MPI, but also in Fluorescent Penetrant Testing (FPT) or in visual inspection.

Dans le cadre du développement des END automatisés et au travers un programme de recherche ANR, CMPhy en collaboration avec le laboratoire ImVia ont développé des équipements permettant la robotisation et la numérisation pour les contrôles par Magn....

Dans le cadre du projet RAPID CMICA financé par l’Agence Innovation Défense, un consortium de trois entités regroupant les sociétés CMPHY et SERPROTRONICS, ainsi que le laboratoire commun LGEF-ELyTMaX de l’INSA de Lyon et Tohoku University (Japon....