中国机械工程2026，Vol.37Issue(2)：416-427,12.DOI:10.3969/j.issn.1004-132X.2026.02.017
基于TCN-BiGRU-SE两阶段特征提取与多特征融合的注塑质量预测方法
Injection Molding Quality Prediction Method Based on Two-stage Feature Extraction and Multi-feature Fusion Using TCN-BiGRU-SE Model
摘要
Abstract
During the injection molding processes,the dimensions of molded parts were easily affected by the coupling of various complex factors.To improve prediction accuracy,a quality prediction method was proposed based on temporal convolutional networks(TCN),Bidirectional gated recurrent units(Bi-GRU),and squeeze-and-excitation(SE)attention mechanism(TCN-BiGRU-SE).The TCN-BiGRU-SE network was utilized to extract deep features from time-series data,characterizing the dynamic changes during the injection molding processes.Quantitative feature values and dimensionless values from the injec-tion and holding phases were extracted and stacked into a three-dimensional matrix,which was then dimen-sionally reduced using convolutional neural networks(CNN)to capture the changing trends at critical phases.By integrating high-frequency data,statistical features,and machine state information,an end-to-end deep prediction model was constructed for the prediction of molded part size.Comparative,ablation,and stability tests were conducted on the Foxconn injection molding dataset,along with generalization tests on three types of injection experimental datasets.The results show that the model outperforms other meth-ods on multiple evaluation metrics,demonstrating strong robustness and generalization capability.关键词
注塑成形/质量预测/时序数据/多特征融合/深度学习Key words
injection molding/quality prediction/time-series data/multi-feature fusion/deep learning分类
信息技术与安全科学引用本文复制引用
邓晓强,战韬阳,项薇,林文文,余军合,郑志鹏..基于TCN-BiGRU-SE两阶段特征提取与多特征融合的注塑质量预测方法[J].中国机械工程,2026,37(2):416-427,12.基金项目
国家重点研发计划(2019YFB1707101,2019YFB1707103) （2019YFB1707101,2019YFB1707103）
