基于注塑机螺杆位置与压力曲线的注射成型过程监测方法  

Monitoring method of injection moulding process based on screw position and pressure curve of injection molding machine

作　　者：刁思勉 乔海玉 贺鉴 汪汝健 张云[2] 周华民[2] DIAO Si-mian;QIAO Hai-yu;HE Jian;WANG Ru-jian;ZHANG Yun;ZHOU Hua-min(Shenzhen Yejiawei Technology Co.,Ltd.,Shenzhen,Guangdong 518116,China;School of Materials Science and Engineering,Huazhong University of Science and Technology,Wuhan,Hubei 430074,China)

机构地区：[1]深圳市烨嘉为技术有限公司,广东深圳518116 [2]华中科技大学材料科学与工程学院,湖北武汉430074

出　　处：《模具工业》2022年第2期1-7,共7页Die & Mould Industry

基　　金：广东省重点领域研发计划项目(2019B090918001)。

摘　　要：提出基于注塑机螺杆位置与压力信号,采用非线性数据降维方法提取成型特征,建立原材料、模具温度与制品成型质量之间的神经网络模型,实现注射成型过程的故障监测和质量预测。试验结果表明:与未降维的数据和主成分分析等线性降维方法相比,拉普拉斯映射法和扩散系数图法相结合的非线性降维方法可从螺杆位置与压力信号中判断制品原材料、模具温度的异常及预测制品成型质量,在验证集和测试集均获得了高于0.92的回归系数,所提出的方法提取了原始曲线数据内在的高维度、非线性、强耦合的数据关系,有效进行了数据降维,提高了故障监测与质量预测模型的精度与效率。Based on the screw position and pressure signals of injection molding machine,the moulding characteristics were extracted by using the nonlinear data dimensionality reduction method,and the neural network model among raw material,mould temperature,and moulding quality was established,and a method of fault monitoring and quality prediction in injection moulding process was proposed.The test results showed that the nonlinear dimensionality reduction method combined with Laplace eigenmaps and diffusion coefficient graph could judge the abnormal material and mould temperature and predict product quality from screw position and pressure signals,compared with linear dimensionality reduction methods such as non-dimensionality reduction data and principal component analysis.The regression coefficients were higher than 0.92 in both validation and test sets,and the proposed method extracted the high-dimensional,nonlinear,and strongly coupling data relations inherent in the original curve data,effectively reduced the data dimension,and improved the accuracy and efficiency of the fault monitoring and quality prediction model.

关 键 词：塑料熔体 注射成型 过程监控 降维 数据采集 

分 类 号：TG76[金属学及工艺—刀具与模具] TP391.76[自动化与计算机技术—计算机应用技术]