关键词: 流程制造, 多工序耦合, 注意力机制—门控循环单元-BP神经网络, 时序特征融合, 关联预测

Abstract: Due to the serious coupling of energy,mass flow between process manufacturing processes and significant time series characteristics of process parameters,the accurate prediction of product quality has become an urgent problem to be solved under the existing manufacturing mode.Based on analyzing the characteristics of multi-dimension,strong time sequence and correlation coupling of process manufacturing process performance index,an Attention Model—Gated Recurrent Unit—Back Propagation Neural Network (AM-GRU-BPNN) based multi-process coupling parameter correlation prediction method was proposed.The polymorphic and heterogeneous production data were screened by mutual information method as input to establish a ConvGRU autoencoder.The temporal features of process data,process parameters,operating parameters and other data through unsupervised learning were extracted.At the same time,attention mechanism was introduced to extract the coupling correlation features of different processes and embed the vectors,so as to allocate attention weights to the process parameters of different processes.On this basis,the attention network self-learning was designed to study the influence of process correlation characteristics on quality performance indicators at different times.Then,the important correlation features were enhanced through GRU network,and the single process prediction model was aggregated according to the time sequence characteristics to realize the time sequence coupling feature fusion of multiple processes.The accurate prediction of product process quality was completed through the output layer BPNN.Experimental results showed that AM-GRU-BPNN could effectively improve the prediction accuracy and provide the basis for the process control of production line from the angle of multi-procedure.

Key words: process manufacturing, multi-process coupling, attention model—gated recurrent unit—back propagation neural network, temporal sequence feature fusion, correlation prediction