关键词: 复杂产品, 质量预测, 双向长短期记忆网络, 注意力机制

Abstract: Due to the high-dimensional characteristics of process data and complex correlation between process data in the manufacturing process of complex products,it is difficult to explore the deep-level key process features in the process data,thus limiting the improvement of the accuracy of complex product quality prediction.Thus,a complex product quality prediction method based on attention mechanism and Bidirectional Long Short-Term Memory(BLSTM)network was proposed.A data pre-processing link for process data cleaning and mutual information feature screening was designed.The BLSTM network was used to simulate the complex transmission characteristics of errors in the product manufacturing process to mine the correlation between upstream and downstream process parameters,and to output the correlated process features extracted by BLSTM at all moments.The self-attention mechanism was designed to self-learn the differences in the contribution of correlated process features to final product quality at each moment,and assign different attention weights to process features at different moments to reinforce key features.Through the above two-stage feature processing method,the mining of deep-level key process features was achieved.Finally,the key features were used as the input layer to achieve accurate prediction of complex product quality through BPNN.Experiments showed that the proposed method had the best performance compared with the mainstream quality prediction methods such as BPNN,LSTM,BLSTM and so on.

Key words: complex product, quality prediction, bidirectional long short-term memory, attention mechanism