Quality Prediction for Injection Molding Product Based on Broad Learning System

LIN Jianghao; WU Zongze; LI Jiajun; XIE Shengli

doi:10.11999/JEIT211414

Volume 44 Issue 5

May 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(5): 1581-1590

LIN Jianghao, WU Zongze, LI Jiajun, XIE Shengli. Quality Prediction for Injection Molding Product Based on Broad Learning System[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1581-1590. doi: 10.11999/JEIT211414

Citation:

LIN Jianghao, WU Zongze, LI Jiajun, XIE Shengli. Quality Prediction for Injection Molding Product Based on Broad Learning System[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1581-1590. doi: 10.11999/JEIT211414

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Quality Prediction for Injection Molding Product Based on Broad Learning System

doi: 10.11999/JEIT211414

LIN Jianghao^{1, 2},
WU Zongze^{1, 3
,
,},
LI Jiajun¹,
XIE Shengli^{1, 3}

1.
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
2.
Laboratory for Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou 510006, China
3.
Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangdong University of Technology, Guangzhou 510006, China

Funds: The National Natural Science Foundation of China(62073088, U1911401), The National Key R&D Program of China (2020AAA0108300), The Key-Area Research and Development Program of Guangdong Province (2021B0101200005), The Guangdong Basic and Applied Basic Research Foundation (2019A1515011606)

Received Date: 2021-12-01
Rev Recd Date: 2022-03-31

Available Online: 2022-04-12

Publish Date: 2022-05-25

Abstract

Abstract

Automatic monitoring of product quality has always been the core of intelligent development of injection molding industry. Practical factors like high cost of data collection, small sizes of sample and unbalanced sample categories require higher challenges for quality prediction of injection molded products. Therefore, a quality prediction model for injection molded products based on Broad Learning System (BLS) is proposed. Specifically, with the three-dimensional sizes of products as predicted targets, p-Norm is applied into the general BLS model to handle the problems of small samples and unbalanced data. The dataset from task two of the fourth industrial big data innovation competition is adopted. 192 parameter features are collected, among which 17 basic features, 4 derived features and 2 injection machine adjusting parameters are extracted as the input of the model via correlation analysis. The comparative experiments are then carried out between the proposed method and methods like Support Vector Machines (SVM), K-Nearest Neighbor (KNN), MultiLayer Perceptron (MLP) and BLS, with a respective sample size of 8300 data in the training and testing sets. Experimental results show that pN-BLS has the most accurate and fast effect of prediction. In practical defect detection applications, pN-BLS can predict abnormal data more accurately and has higher robustness.
- Injection molding,
- Production quality prediction,
- Broad Learning System(BLS),
- Least p-Norm

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References(13)

References

[1]	LI Xiaoli, HU Bin, and DU Ruxu. Predicting the parts weight in plastic injection molding using least squares support vector regression[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) , 2008, 38(6): 827–833. doi: 10.1109/TSMCC.2008.2001707
[2]	OGORODNYK O, LYNGSTAD O V, LARSEN L, et al. Prediction of Width and Thickness of Injection Molded Parts Using Machine Learning Methods[M]. KISHITA Y, MATSUMOTO M, INOUE M, et al. EcoDesign and Sustainability I. Singapore: Springer, 2021: 455–469.
[3]	郑生荣, 辛勇, 杨国泰, 等. 人工神经网络在注塑参数预测中的应用[J]. 塑料工业, 2003, 31(10): 26–28,33. doi: 10.3321/j.issn:1005-5770.2003.10.009 ZHENG Shengrong, XIN Yong, YANG Guotai, et al. Application of artificial neural network in prediction of injection parameters[J]. China Plastics Industry, 2003, 31(10): 26–28,33. doi: 10.3321/j.issn:1005-5770.2003.10.009
[4]	王博, 蔡安江, 孟广慧, 等. 采用组合算法的注塑制品翘曲变形预测[J]. 西安交通大学学报, 2020, 54(8): 84–90. doi: 10.7652/xjtuxb202008011 WANG Bo, CAI Anjiang, MENG Guanghui, et al. Warpage deformation prediction of injection products with combinatorial algorithm[J]. Journal of Xi’an Jiaotong University, 2020, 54(8): 84–90. doi: 10.7652/xjtuxb202008011
[5]	季宁, 张卫星, 于洋洋, 等. 基于径向基函数神经网络和多岛遗传算法的注射成型质量控制与预测[J]. 工程塑料应用, 2020, 48(4): 62–68. doi: 10.3969/j.issn.1001-3539.2020.04.011 JI Ning, ZHANG Weixing, YU Yangyang, et al. Quality control and prediction of injection molding based on RBF neural network and MIGA[J]. Engineering Plastics Application, 2020, 48(4): 62–68. doi: 10.3969/j.issn.1001-3539.2020.04.011
[6]	宋建, 陈广森, 陈敬福, 等. 基于特征选择和贝叶斯优化LightGBM的注塑制品尺寸预测[J]. 工程塑料应用, 2021, 49(8): 54–60. doi: 10.3969/j.issn.1001-3539.2021.08.010 SONG Jian, CHEN Guangsen, CHEN Jingfu, et al. Size prediction of injection molded products based on feature selection and Bayesian optimized LightGBM[J]. Engineering Plastics Application, 2021, 49(8): 54–60. doi: 10.3969/j.issn.1001-3539.2021.08.010
[7]	刘永兴, 唐小琦, 钟靖龙, 等. 基于LightGBM的非对称风险注塑成型产品尺寸预测模型[J/OL]. 中国机械工程. http://kns.cnki.net/kcms/detail/42.1294.TH.20210913.1448.002.html, 2022. LIU Yongxing, TANG Xiaoqi, ZHONG Jinglong, et al. Asymmetric risk injection molding product size prediction based on LightGBM[J/OL]. China Mechanical Engineering. http://kns.cnki.net/kcms/detail/42.1294.TH.20210913.1448.002.html, 2022.
[8]	LIU Jiahuan, GUO Fei, HUANG Gao, et al. Defect detection of injection molding products on small datasets using transfer learning[J]. Journal of Manufacturing Processes, 2021, 70: 400–413. doi: 10.1016/j.jmapro.2021.08.034
[9]	KIM E, CHO S, LEE B, et al. Fault detection and diagnosis using self-attentive convolutional neural networks for variable-length sensor data in semiconductor manufacturing[J]. IEEE Transactions on Semiconductor Manufacturing, 2019, 32(3): 302–309. doi: 10.1109/TSM.2019.2917521
[10]	KIM G, CHOI J G, KU M, et al. A multimodal deep learning-based fault detection model for a plastic injection molding process[J]. IEEE Access, 2021, 9: 132455–132467. doi: 10.1109/ACCESS.2021.3115665
[11]	CHEN C L P and LIU Zhulin. Broad learning system: An effective and efficient incremental learning system without the need for deep architecture[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(1): 10–24. doi: 10.1109/tnnls.2017.2716952
[12]	CHEN C L P, LIU Zhulin, and FENG Shuang. Universal approximation capability of broad learning system and its structural variations[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(4): 1191–1204. doi: 10.1109/TNNLS.2018.2866622
[13]	郑云飞, 陈霸东. 基于最小p-范数的宽度学习系统[J]. 模式识别与人工智能, 2019, 32(1): 51–57. doi: 10.16451/j.cnki.issn1003-6059.201901007 ZHENG Yunfei and CHEN Badong. Least p-norm based broad learning system[J]. Pattern Recognition and Artificial Intelligence, 2019, 32(1): 51–57. doi: 10.16451/j.cnki.issn1003-6059.201901007