Papers by Keyword: Taguchi's Orthogonal Array

Abstract: Due to its versatility, micro metal injection molding has become an alternative method in powder metallurgy where it can produce small part with a minimal number of waste. The success of micro MIM is greatly influenced by feedstock characteristics. This paper investigated the characterization and optimization which both of them plays an important characteristic in determining the successful of micro MIM. In this paper, stainless steel SS 316L was used with composite binder, which consists of PEG (Polyethelena Glycol), PMMA (Polymethyl Methacrilate) and SA (Stearic Acid). The rheology properties are investigated using Shimadzu Flowtester CFT-500D capillary rheometer. The geometry of water atomised stainless steel powder are irregular shape, therefore it is expected significant changes in the rheological results that can influence the microcomponent, surface quality, shape retention and resolution capabilities. From rheological characteristics, feedstock with 61.5% shows a significant value with several injection parameters were optimized through screening experiment 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 Taguchis orthogonal array. Result shows that 61.5%vol contributes a significant stability over a range of temperature and the best powder loading from a critical powder volume percentage (CPVP) and rheological point of view. Furthermore interaction between injection temperature and mold temperature (BxC) give highest significant factor followed by interaction between injection pressure and mold temperature (AxC).

Abstract: Solid particle erosion (SPE) wear characteristics of particulate filled polymer matrix composites have been widely explored by different investigators. Through judicious control of reinforcing solid particulate phase, selection of matrix and suitable processing technique, composites can be prepared to tailor the properties needed for any specific application. Due to high cost of conventional ceramic fillers, it has become important to explore the potential of cheap materials like mineral ores and industrial wastes for utilization in preparing particle-reinforced polymer composites. Previous researchers have reported the use of industrial wastes such as fly ash and red mud as filler materials in polymeric matrices. But the reinforcing potential of blast furnace slag (BFS) particle, a solid waste generated from pig iron production route, has not been explored so far in polymeric materials. In this work, composite samples are prepared by reinforcing micro-sized blast furnace slag as the particulate filler in epoxy resin reinforced with bi-directional glass fibre. Different specimens with varied BFS content (0, 10, 20 and 30 wt %) are fabricated by simple hand lay-up technique. They are subjected to solid particle erosion using an air jet type erosion test rig. Erosion tests are carried out by following a well designed experimental schedule based on Taguchi’s orthogonal array. Here, factors like BFS content, impact velocity, erodent temperature and impingement angle in declining sequence are found to be significant to minimize the erosion rate. A prediction model based on artificial neural network is proposed to predict the erosion performance of the composites under a wide range of erosive wear conditions. This model is based on the database obtained from the experiments and involves training, testing and prediction protocols. This work shows that an ANN model helps in saving time and resources that are required for a large number of experimental trials and successfully predicts the erosion rate of composites both within and beyond the experimental domain.

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.

Abstract: Nowadays, micro metal injection molding has become among the promising method in powder metallurgy research to produce small-scale intricate part at an effective process and competitive cost for mass production. This paper investigated the optimization of highest green strength which plays an important characteristic in determining the successful of micro MIM. In this paper, stainless steel SS 316L with D50 = 5.96µm was used with composite binder, which consists of PEG (Polyethelena Glycol), PMMA (Polymethyl Methacrilate) and SA (Stearic Acid). Feedstock with 61.5% with several injection parameters were optimized which highly significant through screening experiment 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 density 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 mold temperature(AxC). Single factor that also contributes to significant optimization are mold temperature(C) and injection time(D). This study shows that Taguchi method would be among the best method to solve the problem with minimum number of trials.