关键词: 注塑过程, 产品质量预测, 拉普拉斯特征映射, Mean Shift聚类, 偏最小二乘支持向量机

Abstract: Taking into consideration the high-dimensional,correlated and nonlinear process data in the injection molding process with multiple operating modes,an online product quality prediction method was developed based on offline clustering and online recognition of the operating modes.Firstly,a nonlinear dimension reduction method,Laplacian Eigenmap (LE),was used to project the high-dimensional process data onto a low-dimensional feature space,where a Mean Shift based clustering algorithm was used to obtain the underlying operating patterns in the injection molding process.Meanwhile,an online operating mode recognition algorithm was proposed by making use of the principle of Mean Shift.After that,a PLS-LSSVM prediction method,where the particle swarm optimization (PSO)method was used for parameter determination,was used to develop the soft-sensor model of product quality for each operating mode.A multi-mode quality prediction method was finally developed by combining LE,Mean-Shift clustering and PLS-LSSVM algorithms.The experimental results showed that the proposed method outperformed the standard PLS-LSSVM method,and it could become a useful tool for online quality prediction for the industrial injection molding process with multiple operating modes.

Key words: injection molding process, product quality prediction, Laplacian Eigenmap, Mean-Shift clustering, partial least square-least square support vector machine