Multi-temporal process quality prediction based on graph neural network

Abstract

For the complex dependencies of production data in time and space, a multi-temporal processing process quality prediction model GLSTM based on graph neural networks is proposed, which uses graph structure data to model the process relationships among production indicators, uses graph neural networks to aggregate spatial information among production indicators, and uses long and short term memory networks to model the complex dependencies of shop floor processing quality indicator sequences in time, and the experimental The results show that the model is able to achieve relative performance improvements of 5.40%, 15.04% and 0.30% compared to time series analysis methods.