基于宽度学习的注塑产品质量预测方法

在注塑成型工业中，产品质量自动监测一直是注塑工业智能化发展的核心问题。高品质和大规模的产品质量数据采集成本高昂，导致数据样本量少、不同类别样本数据不平衡，为注塑产品质量预测提出了更高的挑战。为此，该文提出一种基于宽度学习方法的注塑产品质量预测模型，以产品的3维尺寸为预测目标，在普通的宽度学习系统(BLS)中加入最小p范数来改进得到模型p范数宽度学习系统(pN-BLS)，解决小样本和不平衡数据的问题，提高模型对离群点的检测性能。在第4届工业大数据竞赛任务2《注塑成型工艺的虚拟量测和调机优化》数据集中，将192个参数特征与预测目标进行相关分析，提取相关性高的基础特征17个，衍生特征4个和调机参数2个作为模型的输入。将16600条数据平均分为训练集和测试集各8300条，与支持向量机 (SVM)、最近邻算法 (KNN)、多层感知机 (MLP)和BLS进行对比实验，实验结果显示pN-BLS具有更快速和更准确的预测效果。在实际缺陷检测应用中，pN-BLS能更准确地预测异常数据，具有更高的鲁棒性。

Quality Prediction for Injection Molding Product Based on Broad Learning System

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.