An effective model of the forging process is crucial for the optimal operation and health management of a hydraulic press machine (HPM). Modeling this forging process is difficult, because multiple localized nonlinear solutions and modeling of unknown complex interactions between localized regions are required. In this paper, a novel least squares support vector machine (LS-SVM) method is developed for modeling the forging process. The proposed method integrates the advantages of local LS-SVM modeling and global regularization. Local LS-SVM modeling is performed to capture the local dynamics for each local working region. Global regularization is performed to minimize the global error and improve the global generalization of the local models. These features guarantee continuity and smoothness between the local LS-SVM models and avoid over-fitting of each local LS-SVM model. The algorithm developed here is simple and may easily be added into existing HPM systems. Experiment data from a practical HPM demonstrate the effectiveness of the proposed method. Index Terms-Forging process, least squares support vector machine (LS-SVM), multiple working regions, regularization.Manuscript Xinjiang Lu (M'12) received the B.E. and M.E. degrees in process modeling and control, robust design, integration of design and control from the

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