Intelligent Monitoring System Formation in Modern Production in the Context of Manufacturing Digitalization

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.