Author:
Kłos Sławomir,Patalas-Maliszewska Justyna,Bazel Michal
Abstract
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.
Publisher
Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
Subject
Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality
Cited by
2 articles.
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