Gate Locations Optimization of an Automotive Instrument Panel for Minimizing Cavity Pressure

Cavity pressure, an important factor in injection molding process, should be minimized to enhance injection molding quality. In this study, we decided the locations of valve gates to minimize the maximum cavity pressure. To solve this problem, we integrated MAPS-3D (Mold Analysis and Plastic Solution-3Dimension), a commercial injection molding analysis CAE tool, using the file parsing method of PIAnO (Process Integration, Automation and Optimization) as a commercial process integration and design optimization tool. In order to reduce the computational time for obtaining the optimal design solution, we performed an approximate optimization using a meta-model that replaced expensive computer simulations. To generate the meta-model, computer simulations were performed at the design points selected using the optimal Latin hypercube design as an experimental design. Then, we used micro genetic algorithm equipped in PIAnO to obtain the optimal design solution. Using the proposed design approach, the maximum cavity pressure was reduced by 17.3% compared to the initial one, which clearly showed the validity of the proposed design approach.

[1]  Jong-Hun Kim,et al.  Applications of Micro Genetic Algorithms to Engineering Design Optimization , 2003 .

[2]  G. Matheron.  Principles of geostatistics , 1963 .

[3]  Shinichi Nagaoka,et al.  New method of designing runner system for injection mold using CAO technique , 2004 .

[4]  D. Krige.  A statistical approach to some basic mine valuation problems on the Witwatersrand, by D.G. Krige, published in the Journal, December 1951 : introduction by the author , 1951 .

[5]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[6]  Roberto Spina.  Injection moulding of automotive components: comparison between hot runner systems for a case study , 2004 .

[7]  Kalmanje Krishnakumar,et al.  Micro-Genetic Algorithms For Stationary And Non-Stationary Function Optimization , 1990, Other Conferences.

[8]  Jeong‐Soo Park.  Optimal Latin-hypercube designs for computer experiments , 1994 .

[9]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[10]  Yong-Jeong Huh,et al.  Intelligent Design System for Gate and Runner in Injection Molding , 2001 .

[11]  Thomas J. Santner,et al.  Design and analysis of computer experiments , 1998 .

[12]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[13]  Kuang-Ting Hsiao,et al.  Branch and bound search to optimize injection gate locations in liquid composite molding processes , 2002 .