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

打开文本图片集

关键词：注塑成形；质量预测；时序数据；多特征融合；深度学习

中图分类号：TP181

DOI：10.3969/j.issn.1004-132X.2026.02.017 开放科学（资源服务）标识码（OSID）：

Injection Molding Quality Prediction Method Based on Two-stage Feature Extraction and Multi-feature Fusion Using TCN-BiGRU-SE Model

DENG Xiaoqiang ZHAN Taoyang XIANG Wei LIN Wenwen YU Junhe ZHENG Zhipeng School of Mechanical Engineering and Inteligent Manufacturing，Ningbo University，Ningbo， Zhejiang，315211

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 （BiGRU），and squeeze-and-excitation （SE） attention mechanism （TCN-BiGRU-SE）. The TCN-BiGRUSE 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 injection and holding phases were extracted and stacked into a three-dimensional matrix，which was then dimensionally 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-toend 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 methods on multiple evaluation metrics， demonstrating strong robustness and generalization capability.

Key words ： injection molding;quality prediction; time-series data; multi-feature fusion;deep learning

0 引言

注塑成形是塑料制品制造中最常见的工艺之一，全球约 40% 的塑料制品通过注塑机加工完成[]。（剩余16981字）