The potential impact of the project results is relevant: safety in the aerospace sector is of utmost importance not only in air transportation but also in the total supply chain. Hence, NDT plays a crucial role in the achievement of the aerospace industry design targets. The EU aviation and space industry competes for the sector leadership and innovation in NDT and SHM is a key ingredient for have success in this competition.
Further, the 21st-century industries continuously adopt new materials and design methods to face ever more challenges. Research in NDE is thus a key ingredient for the safe and sustainable future of many sectors of the EU industry.
During the project new SHM protocols were developed to face relevant issues in aircraft maintenance —acoustic emission procedures combined with the use of percolation sensors were developed to detect ice and water in fuel tanks as well in composites parts. The early detection of such unwanted events increases safety and it reduces repair costs and avoidable downtimes. The achieved results were promising so that their commercial exploitation is under evaluation.
Besides this, many other results were achieved.
The mode-selective generation of guided waves for SHM and NDT was made possible through new sensors’ design or optimization of existing ones. Such devices enhanced the performance of guided waves NDE techniques which are the key ones to rapidly inspect large structures such as aircraft, pipelines, bridges, rails, etc. Progresses in these sectors may have a huge socio-economic impact.
Several methods for the evaluation of the quality of composites and metallic materials were implemented and optimized. Bonding quality, delamination, impact damages, open and closed cracks, fatigue, and residual stress; all these problems were faced by comparing, optimizing, and fusing ultrasonic, optical, electromagnetic (eddy current & capacitive imaging), and thermography techniques. Innovative imaging procedures, signal and image processing strategies, feature extraction protocols, and machine learning approaches were developed and used for this aim.
Semi-analytic and numerical models for the simulation of the electromagnetic and thermal behaviour of carbon-fibre based composites were also realized for improving simulation-assisted NDT, which is in many cases the only way to quantitatively evaluate defects.
