The use of collections of artificial neural networks to improve the control quality of the induction soldering process

Описание

Тип публикации: статья из журнала

Год издания: 2021

Идентификатор DOI: 10.3390/s21124199

Ключевые слова: artificial neural networks, induction brazing, industrial sensors, intelligent technologies, measurements reliability, non‐contact temperature measurement, pyrometers, waveguide paths

Аннотация: In industries that implement the technology of induction soldering, various sensors, including non‐contact pyrometric ones, are widely used to control the technological process. The use of this type of sensor implies the need to choose a solution that is effective in different operating conditions in terms of the accuracy of the daПоказать полностьюta obtained and the reliability of the measurement equipment and duplication in case of a failure. The present article discusses the development of intelligent technology based on a collection of artificial neural networks, which allows a number of problems associated with technological process control when using pyrometric sensors to be solved: assessing the quality of measurements, correcting measurements when non‐standard errors are detected, and controlling the process of induction heating in the absence of reliable readings of the measurement instruments. The collection of artificial neural networks is self‐configuring with the use of multicriterion genetic algorithms. The use of the proposed intelligent technology made it possible to improve the control quality of the technological process of the induction brazing of waveguide paths of spacecraft: the overregulation was decreased from 0–20 to 0, and the difference in the heating temperatures of the elements of the brazed waveguide assembly was decreased from 20–100 to 0–10. In addition, the overall process duration decreased and became more stable. When using the classical control technology, the time varied in the range of 20–60 s; when using the proposed technology, it stabilized in the range of 30–35 s. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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Издание

Журнал: Sensors

Выпуск журнала: Vol. 21, Is. 12

Номера страниц: 4199

ISSN журнала: 14248220

Издатель: MDPI AG

Персоны

  • Milov Anton Vladimirovich (Reshetnev Siberian State Univ Sci & Technol, Inst Comp Sci & Telecommun, Informat Control Syst Dept, Krasnoyarsk 660037, Russia)
  • Tynchenko Vadim Sergeevich (Reshetnev Siberian State Univ Sci & Technol, Inst Comp Sci & Telecommun, Informat Control Syst Dept, Krasnoyarsk 660037, Russia; Siberian Fed Univ, Sch Petr & Nat Gas Engn, Dept Technol Machines & Equipment Oil & Gas Compl, Krasnoyarsk 660041, Russia)
  • Kurashkin Sergei Olegovich (Reshetnev Siberian State Univ Sci & Technol, Inst Comp Sci & Telecommun, Informat Control Syst Dept, Krasnoyarsk 660037, Russia; Siberian Fed Univ, Sch Petr & Nat Gas Engn, Dept Technol Machines & Equipment Oil & Gas Compl, Krasnoyarsk 660041, Russia)
  • Tynchenko Valeriya Valerievna (Reshetnev Siberian State Univ Sci & Technol, Inst Comp Sci & Telecommun, Informat Control Syst Dept, Krasnoyarsk 660037, Russia; Siberian Fed Univ, Sch Petr & Nat Gas Engn, Dept Technol Machines & Equipment Oil & Gas Compl, Krasnoyarsk 660041, Russia)
  • Kukartsev Vladislav Viktorovich (Reshetnev Siberian State Univ Sci & Technol, Inst Comp Sci & Telecommun, Informat Control Syst Dept, Krasnoyarsk 660037, Russia; Siberian Fed Univ, Sch Petr & Nat Gas Engn, Dept Technol Machines & Equipment Oil & Gas Compl, Krasnoyarsk 660041, Russia)
  • Bukhtoyarov Vladimir Viktorovich (Reshetnev Siberian State Univ Sci & Technol, Inst Comp Sci & Telecommun, Informat Control Syst Dept, Krasnoyarsk 660037, Russia; Siberian Fed Univ, Sch Petr & Nat Gas Engn, Dept Technol Machines & Equipment Oil & Gas Compl, Krasnoyarsk 660041, Russia)
  • Sergienko Roman (Gini Gmbh, Machine Learning Dept, D-80339 Munich, Germany)
  • Kukartsev Viktor Alekseevich (Siberian Fed Univ, Sch Petr & Nat Gas Engn, Dept Technol Machines & Equipment Oil & Gas Compl, Krasnoyarsk 660041, Russia)
  • Bashmur Kirill Aleksandrovich (Siberian Fed Univ, Sch Petr & Nat Gas Engn, Dept Technol Machines & Equipment Oil & Gas Compl, Krasnoyarsk 660041, Russia)