Abstract
The study aims to examine the challenges and approaches to intelligent monitoring in diversified automated manufacturing in the context of its digitalization as well as to provide a quality monitoring case based on neural networks. Perspectives of artificial neural networks application to real-time monitoring the produced part quality are discussed. The analysis of network structures and a number of algorithms prove that a counter-propagation network can be used as the selected neural network. The work proposes a modification of the counter-propagation network topology for solving the problem of determining the quality parameters of a manufactured part, as well as a structure for intelligent machining quality monitoring. The paper analyzes the challenges of intelligent monitoring in digitalized diversified automated production. A system for intelligent quality monitoring based on neural networks (counter-propagation network) has been developed. Real time quality monitoring together with the control correction will ensure the improved quality and will make the manufacturing as a whole more efficient.
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Brovkova, M., Martynov, V. (2023). Intelligent Monitoring System Formation in Modern Production in the Context of Manufacturing Digitalization. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Algorithms in Systems. CoMeSySo 2022. Lecture Notes in Networks and Systems, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-031-21438-7_49
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DOI: https://doi.org/10.1007/978-3-031-21438-7_49
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