Analysis of Shrinkage on Thick Plate Part using Genetic Algorithm

Injection moulding is the most widely used processes in manufacturing plastic products. Since the quality of injection improves plastic parts are mostly influenced by process conditions, the method to determine the optimum process conditions becomes the key to improving the part quality. This paper presents a systematic methodology to analyse the shrinkage of the thick plate part during the injection moulding process. Genetic Algorithm (GA) method was proposed to optimise the process parameters that would result in optimal solutions of optimisation goals. Using the GA, the shrinkage of the thick plate part was improved by 39.1% in parallel direction and 17.21% in the normal direction of melt flow.

[1]  Yuehua Gao,et al.  An effective warpage optimization method in injection molding based on the Kriging model , 2008 .

[2]  B. Ozcelik,et al.  Determination of effecting dimensional parameters on warpage of thin shell plastic parts using integrated response surface method and genetic algorithm , 2005 .

[3]  Qian Li,et al.  Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method , 2007 .

[4]  Christopher R. Houck,et al.  A Genetic Algorithm for Function Optimization: A Matlab Implementation , 2001 .

[5]  Samson Seged Teklehaimanot.  Simulation and Design of a plastic injection mold , 2012 .

[6]  S. M. Sapuan,et al.  Optimizing the parameters in durian skin fiber reinforced polypropylene composites by response surface methodology , 2014 .

[7]  A. Sulong,et al.  Analysis of warpage and shrinkage properties of injection-molded micro gears polymer composites using numerical simulations assisted by the Taguchi method , 2012 .

[8]  Tao Chen,et al.  Response surface methodology with prediction uncertainty: A multi-objective optimisation approach , 2012 .

[9]  Vijay K. Stokes,et al.  Solidification of thermoviscoelastic melts. Part 4: Effects of boundary conditions on shrinkage and residual stresses , 1995 .

[10]  Ming-Chih Huang,et al.  The effective factors in the warpage problem of an injection-molded part with a thin shell feature , 2001 .

[11]  S. M. Sapuan,et al.  Design and thermal analysis of plastic injection mould , 2006 .

[12]  L. Hua,et al.  A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters , 2011 .

[13]  Chun-Yin Wu,et al.  Injection molding optimization with weld line design constraint using distributed multi-population genetic algorithm , 2011 .

[14]  Habibollah Haron,et al.  Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process , 2010, Expert Syst. Appl..

[15]  Babur Ozcelik,et al.  Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm , 2006 .

[16]  Babur Ozcelik,et al.  Warpage and structural analysis of thin shell plastic in the plastic injection molding , 2009 .