Early detection of failure in composite materials remains unexplored... until now
Our Solution
The following methodology has been researched and tested, showing great promise for its real-world application. The combined use of virtual technologies and machine learning are used to help enhance the computer-to-human interaction. An enhanced computer-assisted intuition is therefore achieved.
Innovative cloud-based machine learning system, developed with the help of leading experts in the field. Generates predictive data and early damage-detection through intricate IIoT.
State-of-the-art sensor array imbedded into composite structures. Dense-formation network ensures reliable damage location diagnosis with pin-point accuracy.
Combination of latest AR and XR technologies to produce comprehensive data visualization. Significantly improves analysis and data interpretation by user.
LIDAR camera array which allows for an exact 3D reconstruction of composite structures.
Combining LIDAR, 3D reconstruction & AR
The use of LIDAR for 3D reconstruction is not a novel concept. The use of this technology is slowly replacing the conventional use of photogrammetry for 3D reconstruction. The following is the proof-of-concept conducted by one of our team members, involving the measurement and analysis of a bent composite panel. By overlaying a “bent” and “unbent” model created through LIDAR, the deformation of the composite panel is measured, through which the stresses and strain can be computed through a simple digital application of a mathematical model.
Damage assessment using AR interface.
Machine Learning, Data Management & Multiple Scenario Simulation
The task of conducting a Finite Element Analysis can be done automatically using a machine learning model. In a study conducted by one of our team members, this ML model generates various scenarios and stores the result in a database. These datasets can be therefore used to predict and interpret any data being fed into it.
AR & XR, the future of data visualisation
Augmented Reality and Mixed Reality brings computer-generated data into the real world. By making digital information more tangible through this medium, a more reliable interaction between the computer and the user is created. Training novice technicians becomes easier, quality control during assembly can be made more effective, and data interpretation can be made more intuitive.
Possible Applications
UAV
UAV and other autonomous vehicles research, development and deployment can be greatly benefited by the innovations brought by AIR-Labs. Systems can be rendered more autonomous and independent through regular self-health check-ups our technology provides. The health diagnostics can be easily visualized by operators of any experience and any distance.
Civil Aviation
The implementation of our technology in this field would aid technician and engineering crews to reliably keep up with the trend of increased use of composite materials in commercial aircraft. Similarly for other employees such as flight stewards and pilots for on-board structural health checks.
Aerospace
Vehicles controlled from distance such as rovers, satellites, micro-satellites etc… need to be able to carry out automated health diagnostics, with pin-point accuracy. This is a crucial aspect which can be addressed using our services
Decreasing Our Carbon Footprint
Vehicles controlled from distance such as rovers, satellites, micro-satellites etc… need to be able to carry out automated health diagnostics, with pin-point accuracy. This is a crucial aspect which can be addressed using our services
Roadmap
In progress
About Us
What We Offer
Non-destructive testing method which allows for in situ and real time monitoring.
This uses a combination of insutrial IoT and machine learning to create a reliable diagnostics model, and mixed reality (XR) and augmented reality (AR) for an efficient human to computer interaction.
Our Mission
Vision and Value Proposition
- Provide and intelligent approach to aircraft maintenace through the usse of intelligent and predictive Machine learning models.
- Develop a robust and flexible famework tailored to composite structures, encouraging efficient and tailored maintenance.
Meet the team who make us today!
Two members of this team are researching the mathematical models as part of their undergrad and postgrad thesis. Whereas one concentrates on the mixed reality and visual data interpretation aspect, the other focuses on machine learning and in-depth data analysis.
Idriss Ben Jamaa
Co-Founder
Arnaud Delille
Co-Founder
