Optimization of Injection Moulding Process via Design of Experiment (DOE) Method basedon Rice Husk (RH) Reinforced Low Density Polyethylene (LDPE) Composite Properties

Archives of Metallurgy and Materials

article

Haliza Jaya¹  Nik Noriman Zulkepli¹  Mohd Firdaus Omar¹  Shayfull Zamree Abd Rahim¹  Marcin Nabiałek⁴  Kinga Jeż⁴  Mohd Mustafa Al Bakri AbdulLah¹ 

[1] Universiti Malaysia Perlis, Centre of Excellence Geopolymer and Green Technology;Universiti Malaysia Perlis ,(UniMAP), Faculty of Chemical Engineering Technology;Universiti Malaysia Perlis ,(UniMAP), Faculty of Mechanical Engineering Technology;Częstochowa University of Technology, Department of Physics

关键词: Injection Moulding;    Design of Experiments (DOE);    Central Composite Design;    response surface methodology(RSM);    Shrinkage;    Tensile Strength;   

DOI  :  10.24425/amm.2022.137810

学科分类：物理（综合）

来源: Akademia Gorniczo-Hutnicza im. Stanislawa Staszica / University of Mining and Metallurgy

【 摘 要 】

Optimal parameters setting of injection moulding (IM) machine critically effects productivity, quality, and cost productionof end products in manufacturing industries. Previously, trial and error method were the most common method for the productionengineers to meet the optimal process injection moulding parameter setting. Inappropriate injection moulding machine parametersettings can lead to poor production and quality of a product. Therefore, this study was purposefully carried out to overcome thoseuncertainty. This paper presents a statistical technique on the optimization of injection moulding process parameters through centralcomposite design (CCD). In this study, an understanding of the injection moulding process and consequently its optimization iscarried out by CCD based on three parameters (melt temperature, packing pressure, and cooling time) which influence the shrinkage and tensile strength of rice husk (RH) reinforced low density polyethylene (LDPE) composites. Statistical results and analysisare used to provide better interpretation of the experiment. The models are form from analysis of variance (ANOVA) method andthe model passed the tests for normality and independence assumptions.

【 授权许可】

CC BY-NC