An Evaluation of Artificial Neural Networks Applied to Infrared Thermography Inspection of Composite Aerospace Structures

The increasing use of composite materials on aircraft structures as well as their increasing average age have led to the search and the development of several global nondestructive testing techniques to scan large portions of the aircraft externally. One such technique is Infrared thermography. If rapid inspection can be expected, the size of the data and the complexity of the thermograms make the interpretation difficult. So in order to help the operator in the fulfilment of his job to achieve rapid, reliable and repeatable non destructive evaluation, we have caried out for the last four years a project named SEQUOIA, in which Artificial Intelligence has been integrated. The first approach presented at QNDE 93 was based on spatial analysis which revealed itself to be encouraging but insufficient and with not enough versatility [1]. A complementary approach is presented, it is based on the use of multi-layered Neural Networks. This classification technique is used to correlate temporal thermal signatures with sound and defected regions of an inspected part. As thermal modelling is now well developed and comprehensive, the investigative study relies on the training of the neural network on theoretical thermograms so that we can produce as many examples as one can think of. Different inputs for the neural network have been studied: raw data (temperature curves), derived data (derivative of temperature curves), contrast data (subtraction of reference from raw data). Multi-layer neural networks, as well as related algorithms such as Nearest Neighbour (KNN, Kmeans) and Learning Vector Quantization (L.V.Q) have been tested. The evaluation of the neural network process has mainly been based on its ability to reduce the errors, prior to uncertainties.</p

Thermal aging of power module assemblies based on ceramic heat sink and multilayers pressureless silver sintering

International audienceAn assembly based on the deposition and pressureless sintering of successive silver layers on an Aluminum Nitride heat sink is studied in this paper. Sintered silver layers act as die attach, current tracks and adhesion layer on the ceramic. Compared to previous studies where silver sintering is only used as die attach, an additional degree of freedom on the sintering temperature of the ceramic adhesion and track layers is obtained. This allows the investigation of silver sintering at non-conventional temperatures (>400 °C). The presented assembly is expected to endure high operating temperature since all its constituent materials are able to withstand such temperatures. The porosity of the sintered layer is controlled by the dwell time and the sintering temperature, and porosity values between 19% and 41% are obtained. The electrical and thermal conductivities of sintered layers as functions of joint porosity are in good agreement with the literature data and the assembly presents good mechanical properties with a shear strength value higher than 18 MPa. The assembly shows a high robustness during thermal storage at 200 °C for 1000 h. The microstructure and the electrical conductivity of track layers are both stable during the whole aging period. However, a slight improvement of the thermal response and the mechanical properties of the assembly is detected after 200 h of aging followed by a stabilization until the end of the aging tests (1000 h). Such observation is correlated to the microstructure coarsening of the sintered silver under the device and it is shown that this evolution is favored by the presence of the device

An Evaluation of Artificial Neural Networks Applied to Infrared Thermography Inspection of Composite Aerospace Structures

The increasing use of composite materials on aircraft structures as well as their increasing average age have led to the search and the development of several global nondestructive testing techniques to scan large portions of the aircraft externally. One such technique is Infrared thermography. If rapid inspection can be expected, the size of the data and the complexity of the thermograms make the interpretation difficult. So in order to help the operator in the fulfilment of his job to achieve rapid, reliable and repeatable non destructive evaluation, we have caried out for the last four years a project named SEQUOIA, in which Artificial Intelligence has been integrated. The first approach presented at QNDE 93 was based on spatial analysis which revealed itself to be encouraging but insufficient and with not enough versatility [1]. A complementary approach is presented, it is based on the use of multi-layered Neural Networks. This classification technique is used to correlate temporal thermal signatures with sound and defected regions of an inspected part. As thermal modelling is now well developed and comprehensive, the investigative study relies on the training of the neural network on theoretical thermograms so that we can produce as many examples as one can think of. Different inputs for the neural network have been studied: raw data (temperature curves), derived data (derivative of temperature curves), contrast data (subtraction of reference from raw data). Multi-layer neural networks, as well as related algorithms such as Nearest Neighbour (KNN, Kmeans) and Learning Vector Quantization (L.V.Q) have been tested. The evaluation of the neural network process has mainly been based on its ability to reduce the errors, prior to uncertainties.</p

Monolithic integration in CVD diamond: Schottky power diodes and integrated temperature sensor

International audienc

Electrothermal Characterization of Double-Sided Cooling Si Power Module

International audienceThis paper presents an electrothermal characterization of a prototype double-sided cooling power module. The junction temperature T j is an important parameter of power devices. Different methods exist for junction temperature measurement. In this work, an electrical method based on temperature sensitive electrical parameter (TSEP) is conducted to estimate the junction temperature of the power module. A 3D thermal model was built to better comprehend thermal behavior within the module. A comparison between simulation and measurement results is performed and analyzed. Results have shown that 3D numerical modeling help understanding several manufacturing defects (soldering, sintering, die defaults, etc.)