Analysis and prediction of leak detection in the low-pressure heat treatment of metal equipment

• Autorzy: Kłos Sławomir , Patalas-Maliszewska Justyna , Bazel Michal

Identyfikatory

DOI

10.17531/ein.2022.4.12

• Języki publikacji: EN

Abstrakty

EN

The low-pressure heat treatment of metals enables the continuous improvement of the mechanical and plastic properties of products, such as hardness, abrasion resistance, etc. A significant problem related to the operation of vacuum furnaces for heat treatment is that they become unsealed during operation, resulting from the degradation of seals or the thermal expansion of the construction materials. Therefore, research was undertaken to develop a prediction model for detecting leaks in vacuum furnaces, the use of which will reduce the risk of degradation in the charge being processed. Unique experimental studies were carried out to detect leakages in a vacuum pit furnace, simulated using the ENV 116 reference slot. As a consequence, a prediction model for the detection of leaks in vacuum furnaces- which are used in the heat treatment of metals- was designed, using an artificial neural network. (93% for MLP 15-10-1) was developed. The model was implemented in a predictive maintenance system, in a real production company, as an element in the monitoring of the operation of vacuum furnaces.

Słowa kluczowe

EN

vacuum heat treatment; vacuum furnace leaks; predictive maintenance; artificial neural networks

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2022
• Tom: Vol. 24, no. 4
• Strony: 719--727
• Opis fizyczny: Bibliogr. 33 poz., rys., tab.

Twórcy

autor

Kłos Sławomir

• University of Zielona Góra, Institute of Mechanical Engineering, ul. prof. Z. Szafrana 4, 65-516 Zielona Góra, Polska

• https://orcid.org/0000-0001-7110-9052

autor

Patalas-Maliszewska Justyna

• University of Zielona Góra, Institute of Mechanical Engineering, ul. prof. Z. Szafrana 4, 65-516 Zielona Góra, Polska

• https://orcid.org/0000-0003-2439-2865

autor

Bazel Michal

• Seco/Warwick S.A. , ul. Sobieskiego 8, 66-200 Świebodzin, Poland

• https://orcid.org/0000-0002-1460-5110

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).