机械科学与技术2025，Vol.44Issue(3)：453-464,12.DOI:10.13433/j.cnki.1003-8728.20230181
融合Seq2Seq与时序注意力机制的工艺质量预测
Process Quality Prediction Combining Seq2Seq and Temporal Attention Mechanisms
摘要
Abstract
Aiming at the problems of many processes,serious coupling between processes,and complexity of multivariable processing data,a high-dimensional and multi-scale process quality prediction method based on Seq2Seq temporal attention mechanism is proposed.Based on the analysis of the characteristics of multi-process process data and the problems encountered in the process of encoding and decoding by using Seq2Seq model,the sequential attention mechanism was introduced to construct the time-domain information matrix of long-distance variation,and the convolutional neural network and BiLSTM were designed as the encoder components.At the same time,the potential depth features such as process parameter correlation and bidirectional sequence relationship of process timing data were learned,and key information was extracted by using sequence attention mechanism,so as to realize adaptive learning of nonlinear correlation characteristics and sequence dependence of process parameter timing data related to process quality.Finally,the practicability and effectiveness of the proposed method were verified by prediction experiments on the quality of silk manufacturing process.The method provides the implementation approach for accurate quality prediction of multi-process coupling process.关键词
多工序时序耦合/工艺质量预测/Seq2Seq/时序注意力机制/自适应学习Key words
multi-process temporal coupling/process quality forecast/Seq2Seq/temporal attention mechanism/adaptive learning分类
信息技术与安全科学引用本文复制引用
阴艳超,施成娟,邹朝普,刘孝保..融合Seq2Seq与时序注意力机制的工艺质量预测[J].机械科学与技术,2025,44(3):453-464,12.基金项目
国家自然科学基金项目(52065033) （52065033）
