Diagnostics of mechanical engineering products on several grounds

Authors

DOI:

https://doi.org/10.15276/opu.3.62.2020.02

Keywords:

non-destructive testing, neural network, diagnostics, frequency spectrum

Abstract

The article considers methods of non-destructive testing based on various physical laws and phenomena. The possibility of creating a new topical tool for obtaining a wide range of data of mechanical engineering products such as shape, size and location in space is considered. It is proposed to use sound diagnostics using a high-frequency broadband signal to capture the frequency characteristics of the object. The purpose of the study is to develop a method of non-contact measurement of mechanical engineering products on several grounds. With the help of vibroacoustic diagnostics and the method of quantitative control, the distribution of the entire volume of products was 100 pieces. on two parties: the main and control, quantitative parameters of each unit of a product are removed. A signal from 0 to 20,000 Hz was applied by means of a frequency generator. The frequency response of each sample was recorded in the Spectrum Analiyser program. Estimation of the deviation of the product size and its frequency spectrum was performed in the NeuroPro 0.25 software. The created neural network allows is predicted in real time values of several quantitative signs irrespective of their nature. A working model for collecting statistical data for the efficient operation of the neural network is obtained. The developed technique allows detecting the configuration of products on the basis of indirect measurements through the frequency spectrum. This technique can be used to diagnose parts by geometric features, physical properties, defects. This requires an increase in input data for neural network training. With a sufficient selection of parts with different defects of the neural network on the acoustic frequency characteristics will be able to divide the parts into groups of worthy and unworthy on various grounds.

Downloads

Download data is not yet available.

References

Назолін А.Л. Оцінка можливості виявлення дефектів статора турбогенератора по спектру віброакус-тичнго сигналу. Заводская лаборатория. Диагностика материалов. 2017. Вип. 4. 314 с.

Basaran F. U., Kurban M. A New Approach for the Short-Term Load Forecasting with Autoregressive and Artificial Neural Network Models. International Journal of Computational Intelligence Research. 2007. № 3. P. 66–71.

Коновалова І.О., Беркович Ю.А., Єрохін А.Н. Оптимізація світлодіодної системи освітлення вітамі-нами космічної теплиці. Авіакосмічна і екологічна медицина. 2016. Вип. 50, № 3. С. 17–22.

Кононюк А. Е. Обобщенная теория моделирования. Начала: Освіта України, 2012. 602 с.

Бигус Г.А., Даниев Ю.Ф., Быстрова Н.А. Диагностика технических устройств. М. : Изд-во МГТУ им. Н.Э. Баумана, 2014. 615 с.

Бобров В.Т., Самокрутов А.А., Шевалдыкин В.Г. Техническая диагностика и неразрушающий контроль Состояние и тенденции развития акустических (ультразвуковых) методов, средств и технологий неразрушающего контроля и технической диагностики. Территория NDT. 2018. № 2. С. 24–27.

Ермолов И.Н. Техническая диагностика и неразрушающий контроль. Достижения в теоретиче-ских вопросах ультразвуковой дефектоскопии, задачи и перспективы. Дефектоскопия. 2018. № 2. C. 13–48.

Терентьев В.Ф., Колмаков А.Г., Блинов В.М. Деформация и разрушение материалов. Деформа-ция и разрушение материалов. 2007. № 6. С. 2–9.

Белолапотков Д.А., Добровинский И.Р., Медведик Ю.Т. Повышение точности активного контро-ля размеров деталей в процессе изготовления. Мир измерений М. РИА «Стандарты и качество». 2007. № 7. С. 43–46.

Леньков С.В., Федорова Н.В. Резонансный электромагнитный  акустический метод измерения вязкоупругих свойств аморфных ферро магнитных лент, подвергнутых низкотемпературному отжигу. Физика металлов и металловедение. 2014. № 115. С. 749–755.

Верещака А.С., Высоцкий В.В., Мокрицкий Б.Я. Технологические процессы повышения работо-способности металлорежущего инструмента. Комсомольск-на-Амуре : ФГБОУ ВПО «КнАГТУ», 2013. 208 с.

Самотугин С.С., Лещинский Л.К. Плазменное упрочнение инструментальных материалов. До-нецк : Новый мир, 2002. 338 с.

Golesorkhtabar R. А. ElaStic: A tool for calculating second-order elastic constants from first principles. Computer Physics Communications. 2013. № 8. P. 1861–1873.

Kovalevska O. S., Kovalevskyy S. V. Application of acoustic analysis in control systems of robotic ma-chine tools. Radio Electronics, Computer Science, Control. 2018. № 2(45). С. 51–59.

Kovalevskyy S.V., Kovalevska O.S., Turmanidze R.A. Acoustic diagnostics of lever mechanisms with subsequent processing of data on neural networks. New technologies, development and application. Springer International Publishing AG, part of Springer Nature. 2018. Vol. 42. P. 202–210.

Kovalevskyy S. V., Kovalevska O.S. Resource optimization with systemic design of robotized techno-logical equipment. World Convention on Robots, Autonomous Vehicles and Deep Learning. 10-11 Sep-tember 2018, Singapore. 2018. P. 50. DOI: 10.4172/2168-9695-C3-0216th.

Downloads

Published

2020-12-17

How to Cite

[1]
Kovalevskyy, S., Kovalevska, O. and Postavnichyi, A. 2020. Diagnostics of mechanical engineering products on several grounds. Proceedings of Odessa Polytechnic University. 3(62) (Dec. 2020), 14–20. DOI:https://doi.org/10.15276/opu.3.62.2020.02.