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| Dimension prediction method of injection molded parts based on multi-feature fusion of DL-BiGRU |
Qingjie QIAN1( ),Junhe YU1,*(),Hongfei ZHAN1,Rui WANG1,Jian HU2 |
1. Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China
2. The First Industrial Design and Research Institute of CMCU Engineering Co. Ltd, Chongqing 400039, China |
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Abstract A multi-feature fusion injection molded part dimension prediction method based on double-layer bidirectional gated cyclic unit network (DL-BiGRU) was proposed, to fully explore the high-frequency time series features inside the mold cavity and the state features of the injection molding machine in the injection molding process. The relationship between the high-frequency time series features obtained from sensors inside the mold cavity and the dimensions of injection molded parts was analyzed. A DL-BiGRU network was utilized to automatically extract the time series features from the high-frequency data, representing the dynamic characteristics of the molding process of the injection molded parts. The instantaneous features of the injection molding process can be represented, by sampling the high-frequency time series data inside the mold cavity and flattening it. An end-to-end deep learning multi-feature fusion framework was constructed by integrating the time series features, instantaneous features, and molding machine state features. The above three characteristics were fused and jointly trained to improve the dimension prediction accuracy of injection molded parts. The model was verified on the data set of injection molding, and the results showed that the average mean square error of the predicted dimension was 4.7×10?4 mm2, and the minimum error fluctuation was on the order of 10?5 mm2. The model has high prediction accuracy and stability.
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Received: 28 March 2023
Published: 05 March 2024
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| Fund: 国家自然科学基金资助项目(71671097); 国家重点研发计划资助项目(2019YFB1707101, 2019YFB1707103); 浙江省省属高校基本科研业务费资助项目(SJLZ2023001); 浙江省公益技术应用研究计划资助项目(LGG20E050010). |
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Corresponding Authors:
Junhe YU
E-mail: qianqingjie@foxmail.com;yujunhe@nbu.edu.cn
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基于DL-BiGRU多特征融合的注塑件尺寸预测方法
为了充分挖掘注塑成型过程中模腔内的高频时序特征和注塑成型机状态特征，提出基于双层双向门控循环单元网络(DL-BiGRU)的多特征融合注塑件尺寸预测方法. 分析膜腔内传感器高频时序特征与注塑件尺寸间的关联性，采用DL-BiGRU网络从高频数据中自动提取时序特征，表征注塑件成型过程状态变化特性. 通过采样模腔内高频时序数据进行展成平铺，表征注塑成型的瞬时特征. 融合时序特征、瞬时特征和成型机状态特征，构建端到端的深度学习多特征融合框架. 将上述3种特征融合并联合训练，提升注塑件尺寸预测精度. 在注塑成型数据集上进行模型验证，预测尺寸平均均方误差为4.7×10?4 mm2，最小误差波动为10?5 mm2量级，模型具有较高的预测精度和稳定性.
关键词：
注塑成型,
深度学习,
双向门控循环单元网络 (BiGRU),
多特征融合,
尺寸预测
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