MATEC Web Conf.
Volume 224, 2018International Conference on Modern Trends in Manufacturing Technologies and Equipment (ICMTMTE 2018)
|Number of page(s)||8|
|Section||General problems of mechanical engineering: design, optimization, maintenance|
|Published online||30 October 2018|
The analysis of methods effectiveness of automated non-destructive testing of products based on Data Mining methods
Orenburg State University, Orenburg, Russian Federation
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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