Traffic signs are a key element in driver safety. Governments invest a great amount of resources in maintaining the traffic signs in good condition, for which a correct inventory is necessary. This work presents a novel method for mapping traffic signs based on data acquired with MMS (Mobile Mapping System): images and point clouds. On the one hand, images are faster to process and artificial intelligence techniques, specifically Convolutional Neural Networks, are more optimized than in point clouds. On the other hand, point clouds allow a more exact positioning than the exclusive use of images. The false positive rate per image is only 0.004. First, traffic signs are detected in the images obtained by the 360° camera of the MMS through RetinaNet and they are classified by their corresponding InceptionV3 network. The signs are then positioned in the georeferenced point cloud by means of a projection according to the pinhole model from the images. Finally, duplicate geolocalized signs detected in multiple images are filtered. The method has been tested in two real case studies with 214 images, where 89.7% of the signals have been correctly detected, of which 92.5% have been correctly classified and 97.5% have been located with an error of less than 0.5 m. This sequence, which combines images to detection–classification, and point clouds to geo-referencing, in this order, optimizes processing time and allows this method to be included in a company’s production process. The method is conducted automatically and takes advantage of the strengths of each data type.

While the monitoring techniques employed are well established in other fields, their application to the novel processes of ultrasonic and resistance welding of composites is innovative. This study details the adaptation of these techniques to monitor the essential parameters that influence the quality of welds in composite materials within the context of the MFFD (Multi-Functional Fuselage Demonstrator) during the WELDER project. This article presents findings from the application of a sophisticated monitoring system to new welding processes for composites, demonstrating significant enhancements in process control, quality assurance, and operational safety. In ultrasonic welding, temperature, speed, power, amplitude, and displacement were monitored, whereas in resistance welding, voltage, load, and temperature were tracked. This comprehensive data collection is crucial due to the sensitivity of composite materials to welding parameters, which directly affect the integrity and performance of the final product. The data transmission utilized the OPC UA protocol, ensuring secure, reliable real-time data flow to visualization interfaces in safe environments. The integration of real-time monitoring and advanced data analysis helps in identifying potential defects during the welding process, thereby enhancing both the reliability and efficiency of composite welding. The results underscore the potential of these technologies to advance the manufacturing practices for high-performance composite materials in the aeronautics field.

The increase in composite material waste from the aviation and wind energy sectors will become a significant environmental challenge in the near future. This escalation is attributed to the enhanced use of new, advanced composite materials, such as Glass Fiber Reinforced Polymer (GFRP). Despite their benefits, the disposal of these materials at their end-of-life poses considerable environmental and logistical challenges. Assessing the condition of these materials is thus pivotal to develop sustainable strategies for their recycling, reusing, or repurposing. This study investigates the use of Non-Destructive Testing (NDT) techniques, with a focus on Active Thermography, to evaluate GFRP components’ suitability for sustainable management without compromising the material integrity. This research highlights the use of Active Thermography for extensive, non-invasive inspections, due to its capability to inspect a large area quickly using external energy heating. It delves into Pulse Phase Thermography (PPT) and Principal Component Thermography (PCT), two advanced signal post-processing techniques, tested on GFRP materials with purposefully induced defects. Finally, an automated method based on the Signal-to-Noise Ratio (SNR) value is implemented for defect detection, with which defects of a 5 mm diameter and a 3 mm depth can be detected. The document elaborates on the theoretical principle of NDT, PPT, and PCT, details the experimental methodology and specimens, and analyzes the outcomes of employing these techniques, drawing comparisons between them.

Research on sustainable business models (BMs) rooted in the circular economy is expanding in industry and academia, encouraging companies to enhance their impact on profits, people, and the planet. However, developing sustainable BMs is complex due to the conflicting objectives of sustainability dimensions and competition with traditional models. This work addresses this challenge by proposing a Sustainable Business Guide (SBG) supported by artificial intelligence (AI) to assist in decision-making. The SBG supports the design and assessment of business models by integrating inspection; business opportunity exploration; technical, economic, and environmental analyses; an AI-based decision support system; and Porter’s Five Forces, focusing on the aeronautical and wind energy sectors.