Determination of Optimal Process Parameters for Plastic Injection Molding of Polymer Materials Using Multi-Objective Optimization


ÖKTEM H. , Shinde D.

Journal of Materials Engineering and Performance, 2021 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume:
  • Publication Date: 2021
  • Doi Number: 10.1007/s11665-021-06029-z
  • Title of Journal : Journal of Materials Engineering and Performance
  • Keywords: flexural strength, multi-objective optimization, plastic injection molding, process parameters, NEURAL-NETWORK, MECHANICAL-PROPERTIES, GENETIC ALGORITHM, SHRINKAGE, WARPAGE, WELDLINES, PRODUCTS

Abstract

© 2021, ASM International.The present work concentrated on the determination of optimal plastic injection molding process parameters for two polymer materials, i.e. polyethylene and polypropylene, in order to achieve the highest mechanical properties and lowest volumetric shrinkage. The study used the Taguchi design of experiments with five process parameters (melt temperature, injection pressure, packing pressure, packing time, and cooling time) for the manufacture of 27 polymer sample parts via PIM. The flexural strength, flexural modulus, and yield stress were measured using a three-point bending test, whereas the volumetric shrinkage was measured according to the volume. The results were interpreted by variance analysis (ANOVA) and 3D surface plots. A predictive model was developed using stepwise regression analysis. Two multi-objective optimization methodologies were implemented to evaluate the optimal PIM process parameters for both materials: multi-criteria decision making (MCDM) and the multi-objective genetic algorithm (MOGA). The results showed that the melt temperature was the most significant parameter, followed by packing time, injection pressure, cooling time, and packing pressure. The selected model effectively predicted the responses at an error rate of less than 4% for both materials. The MCDMs found that trial Number-9 exhibited the optimal set of process parameters, and compared to the MCDMs, the MOGA results showed improvement of 7-9%. Significant contribution of the present study is to obtain the optimal manufacturing process parameters in injection molding of the polymeric materials considering two outcomes simultaneously, viz. maximize the mechanical properties and minimize the volumetric shrinkage with injection molding parameters by using multi-objective optimization methodologies with ANOVA and Regression analysis.