Application research of machine manufacturing process monitoring based on neural network

Abstract

In the process of mechanical manufacturing, parameters such as geometry, size and surface quality are important factors affecting product quality. The existing technology makes it difficult to conduct product monitoring and data monitoring during machine manufacturing. At the same time, the uncertainty of the manufacturing process also brings great challenges. Therefore, it is necessary to design corresponding monitoring schemes according to different situations. Therefore, neural network based machinery manufacturing process monitoring technology is of great significance. The neural network model established in this paper can automatically learn and establish complex mapping relationships, and can predict and detect anomalies according to historical data. In addition, deep learning technology can be used to carry out adaptive feature learning on a large amount of monitoring data, and complete efficient training and real-time decision-making in a distributed environment. Compared with the common mechanical manufacturing process, neural network monitoring has a lower loss rate and error rate. The neural network based machinery manufacturing process monitoring technology will become an important direction of the future development of machinery manufacturing industry, and will have great practical application value in improving production efficiency, reducing production cost and ensuring product quality.