Classification of non-normative errors in measuring instruments based on data mining : доклад, тезисы доклада | Научно-инновационный портал СФУ

Classification of non-normative errors in measuring instruments based on data mining : доклад, тезисы доклада

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Conference "Aviamechanical engineering and transport" (AVENT 2018); Irkutsk; Irkutsk

Год издания: 2018

Идентификатор DOI: 10.2991/avent-18.2018.83

Ключевые слова: induction soldering, waveguide path, measurment error classification, flux, neural networks, joints

Аннотация: The article deals with the problem of classification of non-normative errors in measuring instruments. The use of such high-tech methods for creating integral joints, like induction soldering, electron beam welding, diffusion welding, is complicated by the presence of errors in measuring instruments associated with high temperatures, the use of contactless temperature sensors, different thickness of the connected parts, different emissivity coefficients and human factor. Errors that arise when managing the process of creating permanent joints have a significant negative impact on the quality of products in various areas of engineering. In order to adequately control the technological process, it is necessary to identify and classify all types of errors in measuring instruments related to both the features of the technical means of measurement and the human factor. To solve the problem of classifying non-normative errors in measuring instruments, this article proposes the use of intelligent methods. The most suitable, effective and powerful means of solving the problem of non-normative errors classification is the use of artificial neural networks, which make it possible to develop the most effective control actions to compensate the arising non-normative errors.

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Издание

Журнал: Advances in Engineering Research

Выпуск журнала: 158

Номера страниц: 432-437

Издатель: Atlantis Press

Авторы

  • Milov A.V. (Reshetnev Siberian State University of Science and?Technology)
  • Tynchenko V.S. (Reshetnev Siberian State University of Science and?Technology)
  • Kukartsev V.V. (Reshetnev Siberian State University of Science and?Technology)
  • Tynchenko V.V. (Reshetnev Siberian State University of Science and?Technology)
  • Antamoshkin O.A. (Reshetnev Siberian State University of Science and?Technology)

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