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Optimal-Parameter Determination by Inverse Model Based on MANFIS: The Case of Injection Molding for PBGA

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2 Author(s)
Chung-Neng Huang ; Grad. Inst. of Mechatron. Syst. Eng., Nat. Univ. of Tainan, Tainan, Taiwan ; Chong-Ching Chang

This paper presents a novel method of integrating both optimization and inversely modeling methods to determine the optimal input-parameter for a multi-input multi-output (MIMO) system to realize the desired output-performance. First, the Taguchi method is employed to minimize experimental numbers and to collect experimental data representing the quality performances of a MIMO system. Next, the MANFIS is used to train the inverse model based on the data from the Taguchi experimental method. The adaptive neuro-fuzzy inference system (ANFIS) has been widely used for modeling different kinds of nonlinear systems. In this study, the method is further extended to MIMO-ANFIS (MANFIS) architecture to train the inverse model. The well-trained model has the ability of uniquely determining the inverse relationship for each input-output set. A case study involving statistical characterization and multiple criteria optimization on injection molding for plastic ball grid array (PBGA) is successfully presented to demonstrate the effectiveness of the proposed method.

Published in:

Control Systems Technology, IEEE Transactions on  (Volume:19 ,  Issue: 6 )

Date of Publication:

Nov. 2011

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