Abstract—Dimensional changes because of shrinkage is one of the most important problems in production of plastic parts using injection molding. In this study, effect of injection molding parameters on the shrinkage in polypropylene (PP) and polystyrene (PS) is investigated. The relationship between input and output of the process is studied using regression method and Analysis of Variance (ANOVA) technique. To do this, existing data is used. The selected input parameters are melting temperature, injection pressure, packing pressure and packing time. Effect of these parameters on the shrinkage of above mentioned materials is studied using mathematical modeling. For modeling the process, different types of regression equations including linear polynomial, Quadratic polynomial and logarithmic function, are used to interpolate experiment data. Next, using step backward elimination and 95% confidence level (CL), insignificant parameters are eliminated from model. To check validity of the PP model, correlation coefficient of each model is calculated and the best model is selected. The same procedure is repeated for the PS model. Finally, optimum levels of the input parameters that minimize shrinkage, for both materials are determined. Invasive Weed Optimization (IWO) algorithm is applied on the developed mathematical models. The optimization results show that the proposed models and algorithm are effective in solving the mentioned problems.
Index Terms—IWO algorithm, Optimization, Plastic injection molding, Regression, shrinkage.
Alireza Akbarzadeh is with the Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. Tel/Fax: 98-511-876-3304; E-mail: Ali_Akbarzadeh_T@ yahoo.com.
Mohammad Sadeghi is with the Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. E-mail: H.Sadeghi@ymail.com.
Cite: Alireza Akbarzadeh and Mohammad Sadeghi, "Parameter Study in Plastic Injection Molding Process using Statistical Methods and IWO Algorithm," International Journal of Modeling and Optimization vol. 1, no. 2, pp. 141-145, 2011.