Optimization of Micro Metal Injection Molding SS 316L for the Highest Green Strength by Using Taguchi Method

Abstract

Micro metal injection molding which is a new develop technology has attract most researcher where it becomes among the promising method in powder metallurgy research to produce small-scale intricate part at an effective process and competitive cost for mass production. Due to highly stringent characteristics of micro MIM feedstock,the study has been emphasized in investigating the optimization of highest green strength which plays an important characteristic in determining the successful of micro MIM. Stainless steel SS 316L with D50 = 5.96μm was used with composite binder, which consists of PEG, PMMA and Stearic Acid. From rheological characteristic and highly significant parameter through screening experiment, feedstock with 61.5% with several injection parameters were optimized such as injection pressure(A), injection temperature(B), mold temperature(C), injection time(D) and holding time(E). Besides that, interaction effects between injection pressure, injection temperature and mold temperature were also considered to optimize in the Taguchi’s orthogonal array. Analysis of variance (ANOVA) in terms of signal-to-noise ratio (S/N-larger is better) for green strength was also presented in this paper. Result shows that interaction between injection temperature and mold temperature(BxC) give highest significant factor followed by interaction between injection pressure and injection temperature(AxB). Single factor that also contributes to significant optimization are mold temperature(C), injection time(D) and injection pressure(A). This study shows that Taguchi method would be among the best method to solve the problem with minimum number of trials.

Green Strength Optimization in Metal Injection Molding Applicable with a Taguchi Method L9 (3)⁴

“…The graph predicted that the best optimum variable is A 0 , B 1 , C 1 and D 1 or in other words the injection temperature 185 ˚C, barrel temperature 190 ˚C, injection pressure 11 Mpa, and speed 90 %. The result supported by Ibrahim et al [4] where as temperature and pressure give the best effect of variable in determine the highest green strength. By using this Taguchi method orthogonal array it can be reduce the cost effectively.…”

“…Moreover the adjustment of injection parameter in smart rules could retrieved the best performance sample where as by maximize or minimize the characteristic [4]. The results analyzed by using Signal to Noise Ratio (S/N ratio) terms In detailed explanation, S/N ratios is help in prediction the optimum result besides improve the quality via variability reduction and improved the measurement based on repetition [4]. Thus, paper are discussed the optimization of highest green strength with 0.55 Powder loading.…”

“…This technology are based on injection molding but respect to focus on metal powder [1]Currently the areas of application can be applied in the biochemical, aerospace, computer peripherals and automobile industries. In MIM, binder systems play an important role as it acts as a temporary vehicle as homogeneously factor in order to packing the powder into the desired shape [2]The green strength means the mechanical strength of parts where as a compacted powder compacted powder must have in order to withstand mechanical operations and it is subjected after pressing and before sintering, without damaging its fine details and sharp edges [3] The objective of this paper is to evaluate the strength of green part from MIM process before it's undergoes to debinding or sintering process based on the different composition ratio between waste garbage bag, Palm Kernel and Stainless Steel 316L…”

Pengoptimuman Proses Penyemperitan Gentian Karbon Terkisar dan Polipropilena bagi Komposit Polimer Pengalir

“…Susunan orthogonal L 9 direkodkan dalam Jadual 2. Pengulangan nilai bacaan keberaliran elektrik yang telah direkodkan kemudiannya diubah kepada nilai tunggal menggunakan nisbah SN bagi menggambarkan jumlah varian yang hadir serta respons bagi min (Ibrahim et al 2010). Seterusnya, interaksi antara parameter A dan B dianalisa menerusi analisis varian (ANOVA) bagi menentukan sumbangan oleh setiap parameter (Arslanoglu & Yigit 2016;Kaytakoğlu & Akyalçın 2007).…”

Optimization of Micro Metal Injection Molding By Using Grey Relational Grade

“…However, there is limitation of µMIM due to oxidation during handling where submicron powders tend to be pyrophoric and a low viscosity feedstock is desirable for filling the micro details during injection molding rapidly before the feedstock solidified [2,3]. Due to the problem of submicron powders, several optimization techniques have been published where it tends to reduce defects and give higher mechanical properties plus better shape retention [6]. Due to the problem of submicron powders, several optimization techniques have been published where it tends to reduce defects and give higher mechanical properties plus better shape retention [6].…”