关键词: 流程制造, GRU-Attention多层神经网络, 云边协同, 联动优化

Abstract: To solve the optimization problems of process manufacturing process parameters,such as the complexity of multi process coupling model construction,the difficulty of multi-objective analysis and the difficulty of ensuring real-time and accuracy,a cloud edge linkage optimization method for process manufacturing parameters was proposed by combining Gated Recurrent Unit—Attention(GRU-Attention)with whale algorithm.A cloud edge collaborative architecture for training computing was designed for multi process coupling production.Based on the efficient collaboration between the equipment edge node and the cloud platform,the cloud training of the prediction model and optimization model,the edge data collection,the model download and call calculation were completed.On this basis,the production process quality prediction model based on GRU-Attention multi-layer neural network was established.The output quality index was taken as fitness,and the whale algorithm was invoked to optimize the production process parameters globally,so as to obtain the optimal process parameter combination of different processes and realize real-time prediction and comprehensive optimization of processing quality of different processes in process manufacturing.An example of a process silk production line was used for experimental verification.The results showed that the proposed cloud edge linkage method based on deep learning could achieve comprehensive dynamic optimization of production quality and reduce the completion time of process parameter control tasks.

Key words: process manufacturing, GRU-Attention multi-layer neural network, cloud edge collaboration, linkage optimization