Researchers from Tomsk Polytechnic University and their German colleagues have jointly worked out a new method for searching for defects in semi-transparent materials.
According to Tomsk Polytechnic University researchers, the accuracy of measurements when using a newly-designed neuronet-based method will surpass all the others. The results of the research have been published in the Journal of Nondestructive Evaluation.
Fibre-reinforced plastic is a composite (material that consists of several components) made of polymer matrix and reinforced by fibreglass. It is widely used in the aerospace and automobile industries, energy production and other spheres, because it boasts a good hardness-weight ratio.
An indispensable aspect of the production and use of any modern materials, including fibre-reinforced plastic, is non-destructive testing – checks of hardness, reliability and other parameters, as well as the detection of structural defects in an object which avoids knocking it out of service or destroying it.
One of the wide-spread methods of non-destructive testing is infrared thermography, whereby materials are subjected to warming (usually with powerful optic lamps), and the surface temperature is controlled with a thermographic camera. If there are defects in a material, it will warm up and cool either slower or quicker, than an undamaged sample, the researchers noted.
According to them, this method allows to quickly control vast surfaces without contacting a material, with a good visualisation of the results, but the semi-transparency of fibre-reinforced plastic limits the use of the method of non-destructive testing.
The researchers designed the new method of detecting the depth of defects in semi-transparent materials with the help of artificial neural networks. They say that the efficacy of the method depends on the quantity and quality of data used to “teach” a neuronet. It is possible to train a neuronet to work with a certain material and equipment to such an extent that the accuracy of measuring will be way ahead of all the rest of methods.
The authors of the research are incorporating the newly obtained algorithms into software, which is now being worked on in the laboratory. In the future, the scientists plan to modernise the algorithm in order to boost its accuracy, while also optimising the cost of the procedure and its timeframe.
Sourse: sputniknews.com
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