Abstract
Purpose of the study: the analysis of the effectiveness of automated nondestructive testing methods within the objectives of data clustering on the use of short-wave electromagnetic radiation in flaw detection. Research methods: Kohonen self-organizing maps (SOM). The relevance of the work is that due to the increased demand for quality and reliability of products are becoming increasingly important physical methods for automated control of metals and products thereof that do not require cutting or fracture specimens of finished productes. The article noted common features of methods of short-wave electromagnetic control of products. The effectiveness of the Data Mining approach to the construction of a hypothesis on the interrelationships of data groups on non-destructive testing of products is substantiated. As an instrument, the method of self-organizing Kohonen maps was chosen. An example of a part of training data and neural network configuration parameters performing the task of visualization and clustering is given. It is concluded about the lead electromagnetic methods of automated control of complex products in production. The resulting distance matrix and the cluster map are shown. An example of applying the results of analysis to the problem of testing spot welded joints is considered. Given the further directions of research is to develop a computer image processing techniques in the framework of automated non-destructive testing systems.
Highlights
Due to the increased demand for quality and reliability of manufactured products, physical methods of control of metals and products from them, which do not require cutting out samples or destroying finished products, become more important
In view of the above, one may conclude that leadership in the field of electromagnetic control
Further lines of research are that the use of short-wavelength electromagnetic nondestructive inspection based on image processing and pattern recognition methods, obtained as a result of the technological process control
Summary
Due to the increased demand for quality and reliability of manufactured products, physical methods of control of metals and products from them, which do not require cutting out samples or destroying finished products, become more important. As a result of solving the clustering problem, a cluster map is obtained, where closelying clusters correspond to the input vectors of the neural network that are closer to each other. All this allows you to perform a visual multi-parametric ordering information. As an input network, clustering was carried out in 6 regions according to the following groups of methods for nondestructive testing (Fig. 3, from left to right, from top to bottom: ultrasonic inspection, thermal imaging control, magnetic particle inspection, short-wave electromagnetic control methods, eddy current methods, visual inspection)
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