Gorbunov Vladimir Vladimirovich, Candidate of engineering sciences, head of department of automation, Research and production enterprise “Podshipnik-STOMA” (43 Entuziastov avenue, Saratov, Russia), email@example.com
Samoylova Elena Mikhaylovna, Candidate of engineering sciences, associate professor, sub-department of automation, control, mechatronics, Yuri Gagarin State Technical University of Saratov (77 Politekhnicheskaya street, Saratov, Russia), firstname.lastname@example.org
Ignat'ev Aleksandr Anatol'evich, Doctor of engineering sciences, professor, sub-department of automation, control, mechatronics, Yuri Gagarin State Technical University of Saratov (77 Politekhnicheskaya street, Saratov, Russia), email@example.com
Background. The problem of automation of eddy current testing of surface layers of bearing parts using intelligent technologies is quite topical in modern manufacturing with the aim of improving the quality of products and technological equipment. Creating an effective technological process monitoring system, in particular in the bearing production, involves solution of a whole complex of interrelated tasks, including organizational, methodological, technical, informational and human resource support that ensures improvement of the quality of bearings and prevents occurrence of defects, i.e. decreases production costs.
Methodology. Of scientific and practical interest is the question of application of the methodology of automatic recognition of surface defects of parts using neural networks as part of a monitoring system substantiating the use of four distinctive signs, capable of identifying all the main defects of the rolling surface of bearing rings.
Results. The article describes the results of case studies of the proposed methodology of automatic recognition of surface defects of parts using neural networks with substantiation of the use of four distinctive signs, capable of identifying an item with defects in the composition of a monitoring system. The authors present a new complex of structural solutions – automated eddy current test benches ASVKND and ASVK-2VD built into the production line manufacturing rings of axlebox bearings for railway rolling stock.
Conclusions. The article displays design solutions and software products for implementation of the method of surface defects detection in rolling rings of large-size bearings to solve the problem of efficient non-destructive testing.
intelligent technologies, eddy current, defect, bearing, automation, recognition, sign, ring
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