Proceedings of International Conference on Mechanical & Manufacturing Engineering (ICME2008), 21– 23 May 2008, Johor Bahru, Malaysia. © Faculty of Mechanical & Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia. ISBN: 97–98 –2963–59–2 Analysis of Variance on the Metal Injection molding parameters using a bimodal particle size distribution feedstock Khairur Rijal Jamaludin 1 , Norhamidi Muhamad 2 , Mohd Nizam Ab. Rahman 2 , Sri Yulis M. Amin 2 , Muntadhahadi 2 1 Department of Mechanical Engineering, College of Science and Technology, University of Technology Malaysia, City Campus, 54100 Kuala Lumpur, Malaysia. 2 Precision Process Research Group, Dept. of Mechanical and Materials Engineering, Faculty of Engineering, National University of Malaysia, 43600 Bangi, Selangor Darul Ehsan, Malaysia. Email: khairur@citycampus.utm.my Abstract: The paper describes author’s work on the investigation of the molding parameter for the metal injection molding (MIM) feedstock. Bimodal particle size distribution of SS316 L powder was used in the investigation and the metal powder was covered with PMMA and PEG as its binder. Taguchi’s L 27 orthogonal array has been used as DOE while green defects, green density and green strength were assumed to be the quality characteristic (response). A green defect has been measured using parameter design for discrete data technique while the green density and green strength were measured according to the MPIF 42 and MPIF 15 respectively. Classical analysis of variance (ANOVA) was used to investigate the significance of each molding parameters and finally propose the optimum molding parameter. Keywords: Analysis of variance (ANOVA), Metal injection molding, bimodal powder distribution, Taguchi method, Design of experiment (DOE). 1. Introduction Metal injection molding (MIM) is expected to be very efficient for manufacturing small and complex metallic components in large batch. Research on MIM concerns three main stages: injection molding of a feedstock, thermal or catalytic debinding, and sintering [1]. The determination and optimization of the process parameters have motivated numerous research works, as it needs deep knowledge on different processes and accurate modeling techniques for each stage. Traditional approach to experimental work is to vary one factor at a time, holding all other factors fixed. This method does not produce satisfactory results in a wide range of experimental settings. Other workers [2-5], used classical Design of Experiment (DOE) technique to study the effects of injection parameters on the green part quality characteristics (response) such as density, strength and defects. Furthermore, for many experimental situations in practice, more than one response will be measured for the different combination of values which a set of design variables may take. When it involved several responses, the optimum condition for one response is not very likely equal to the optimum condition for the other response [6]. In this paper, authors will discuss how to find the overall optimum condition for several responses when parameter designs using orthogonal arrays was employed. Thus, simultaneous optimization using analysis of variance (ANOVA) is the best method for the optimization of multiple characteristics problem.