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Comprehensive Online Control Strategies for Plastic Injection Molding Process
Seo, J.,Khajepour, A.,Huissoon, J.P. American Society of Mechanical Engineers 2014 JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-T Vol.136 No.4
<P>This study proposes an effective thermal control for plastic injection molding (polymer: Santoprene 8211-45 with density of 790 kg/m(3), injection pressure: 1400 psi (9,652,660 Pa)) in a laminated die. For this purpose, a comprehensive control strategy is provided to cover various themes. First, a new method for determining the optimal sensor locations as a prerequisite step for modeling and controller design is introduced. Second, system identification through offline and online training with finite element analysis and neural network techniques are used to develop an accurate model by incorporating uncertain dynamics of the laminated die. Third, an additive feedforward control by adding direct adaptive inverse control to self-adaptive PID is developed for temperature control of cavity wall (cavity size: 52.9 x 32.07 x 16.03 mm). A verification of designed controller's performance demonstrates that the proposed strategy provides accurate online temperature tracking and faster response under thermal dynamics with various cycle-times in the injection mold process.</P>
Thermal Management in Laminated Die System
Seo, Jaho,Khajepour, Amir,Huissoon, Jan P. 제어로봇시스템학회 2014 Transaction on control, automation and systems eng Vol.12 No.4
The thermal control of a die is crucial for the development of high efficiency injection moulds. For an effective thermal management, this research provides a strategy to identify a thermal dynamic model and to design a controller. The neural network techniques and finite element analysis enable modeling to deal with various cycle-times for moulding process and uncertain dynamics of a die. Based on the system identification which is experimentally validated using a real system, controllers are designed using fuzzy-logic and self-tuning PID methods with backpropagation and radial basis function neural networks to tune control parameters. Through a comparative study, each controller's performance is verified in terms of response time and tracking accuracy under different moulding processes with multiple cycle-times.
Thermal Management in Laminated Die System
서자호,Amir Khajepour,Jan P. Huissoon 제어·로봇·시스템학회 2014 International Journal of Control, Automation, and Vol.12 No.4
The thermal control of a die is crucial for the development of high efficiency injection moulds. For an effective thermal management, this research provides a strategy to identify a thermal dynamic model and to design a controller. The neural network techniques and finite element analysis enable modeling to deal with various cycle-times for moulding process and uncertain dynamics of a die. Based on the system identification which is experimentally validated using a real system, controllers are designed using fuzzy-logic and self-tuning PID methods with backpropagation and radial basis function neural networks to tune control parameters. Through a comparative study, each controller’s performance is verified in terms of response time and tracking accuracy under different moulding processes with mul-tiple cycle-times.