AI and robotics give aviation inspection new wings
A collaboration between Sentigrate and ACRATS enables smart aviation inspection.
Dries Lorent
Data scientist
Aircrafts are becoming lighter and more efficient thanks to the use of composite materials. Safety is an absolute priority, but the sector faces complex challenges: how do you detect damage that is difficult to see, such as hairline cracks? And how do you address the shortage of technical personnel? With support from CrossRoads, ACRATS Europe and Sentigrate are developing an autonomous inspection crawler that makes aircraft maintenance safer, faster, and smarter.
Composites require a new approach
More than half of modern aircraft are made of composite materials. “Composites are strong, light, and highly resistant to corrosion, but damage—such as hairline cracks or delamination—is often invisible to the naked eye,” says Rick van Opdorp of ACRATS Europe. This is a challenge, because inspections are often done manually today. It is error-prone and labor-intensive. “By automating inspection processes, we are not only making the work more efficient, but also more attractive to a new generation of maintenance personnel,” says Rick.
An autonomous crawler
ACRATS Europe develops, assembles, and sells inspection and repair kits for composite maintenance and is internationally active as a knowledge partner and trainer in the field of composite manufacturing, inspection, and damage repair. Sentigrate specializes in data science and artificial intelligence (AI), with a focus on Industry 4.0, mobility, and industrial production.
In this CrossRoads project, the two companies are combining their knowledge to develop an autonomous inspection crawler. This robot literally crawls over the fuselage of an aircraft and performs inspections independently, without manual calibration or positioning. Using ultrasonic sensors, which have already proven their usefulness in aviation, the crawler detects damage to the composite material.
“By robotizing these sensors, we are replacing traditional NDT (Non-Destructive Testing) inspections with a standardized, reproducible, and digital process,” explains Rick. “This increases the speed and reliability of inspections and reduces the risk of human error.”
Predictive maintenance
All inspection data is collected centrally and processed in a scalable data pipeline. This is where Sentigrate’s AI algorithm comes into play. Gert Trekels of Sentigrate: “We analyze the signals from the sensors and, using machine learning techniques, we can recognize and quantify changes in structural properties over time. This enables predictive maintenance, whereby not only current damage is identified, but trends can also be tracked that indicate future degradation or risks.”
The prototype developed will be tested on an airplane and helicopter. These are two use cases that are representative of both civil and military applications.
Impact
Rick: “The inspection robot increases safety in aviation by detecting damage more quickly and accurately. It also reduces the physical strain on technicians, who now have to operate in less risky conditions.”
“The project also contributes to making transport more sustainable. Composites reduce the weight of vehicles and thus energy consumption. But without robust inspection methods, wider application is stagnating. Our innovation offers opportunities for companies that want to use sustainable materials but are currently held back by uncertainty about safety or maintenance costs.”
“We are also developing the system in a modular way, so that it can also be used in other sectors where composites play a role, such as wind energy or maritime applications.”
