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.