Safety in aeronautics could be improved if continuous checks were guaranteed during the in-service inspection of aircraft. However, until now, the maintenance costs for in-service inspection  have proved to be prohibitive for the aircraft companies. For this reason there is a great interest for the development of low cost nondestructive inspection techniques that can be applied during normal routine tests. The analysis of the internal defects (not detectable by a visual inspection) of the aircraft composite materials is a difficult task unless invasive techniques are applied.  The ISSIA computer vision research group has been involved for a number of years to address the problem of inspecting composite materials by using automatic analysis of thermographic techniques or ultrasonic signals.

The computer vision research group in collaboration with Alenia Aeronautica has been involved through a number of research contracts on Non Destructive Techniques  with the aims of developing:

  1. Automatic tools for the detection of internal defect in composite materials by means of thermographic techniques and ultrasonic  techniques.
  2. Mobile Robotic Platform for MultiSpar Box Inspection in Horizontal Stabilizers.



   Private Contracts
   ENDING DATE – 2007

COST € 256.000,00
FUNDING € 256.000,00


Setup of Thermocamera system for image acquisition.

Automatic tools for the detection of internal defect in composite materials by means of thermographic techniques.

The analysis of the time/space variations in a sequence of thermographic images allows the identification of internal defects in composite materials that otherwise could not be detected.  Neural network approaches have been used to extract the information that characterizes a range of internal defects in different types of composite materials. After the training phase the same neural network was applied to all the points of a sequence of thermographic images.

A thermograhic image of the composite material test panel.

Detected defects.

Automatic tools for the detection and recognition  of internal defect in composite materials by means of ultrasonic  techniques.

A comparison between the reactions of different materials to ultrasonic signals can be used to highlight the difference in the internal structures and also to detect the depth position of these anomalies. However, ultrasonic data are difficult to interpret since they require the analysis of a continuous signal for each point of the material under consideration. Automatic procedure are necessary to manage large data sets and to extract significant differences between them.  We have addressed the problem of developing an automatic system for the analysis of ultrasonic data in order to detect and classify internal defects in composite materials. We considered two main steps for interpreting ultrasonic data: the pre-processing technique necessary to normalize the signals from composite structures with different thicknesses and the classification techniques used to compare ultrasonic signals and to detect classes of similar points. The  second step was carried out by using a multilevel neural approach—firstly defective areas were separated from the sound ones and then they were classified on the basis of the defect type and localization in depth.


Mobile Robotic Platform for MultiSpar Box Inspection in Horizontal Stabilizers

The mobile robot has  to navigate inside each bay of the box, calculating its position throughout the operational environment,  carry sensors and acquire sensorial data (e.g. visual and ultrasonic) for Non-Destructive Testing.

The inspection robot.