Optimization of Micro Metal Injection Molding By Using Grey Relational Grade

2011 ◽  
Author(s):  
M. H. I. Ibrahim ◽  
N. Muhamad ◽  
A. B. Sulong ◽  
N. H. M. Nor ◽  
K. R. Jamaludin ◽  
...  
Keyword(s):  
Injection Molding ◽  
Metal Injection Molding ◽  
Grey Relational Grade ◽  
Grey Relational

Application of the Grey-Taguchi Method to the Optimization of Metal Injection Molding (MIM) Process

2010 ◽  
Vol 443 ◽  
pp. 63-68 ◽  
Author(s):  
Khairur Rijal Jamaludin ◽  
Norhamidi Muhamad ◽  
Mohd Nizam Ab. Rahman ◽  
Sufizar Ahmad ◽  
Mohd Halim Irwan Ibrahim ◽  
...  
Keyword(s):  
Injection Molding ◽  
Taguchi Method ◽  
Mold Temperature ◽  
Parameter Design ◽  
Metal Injection Molding ◽  
Quality Performance ◽  
Surface Appearance ◽  
Grey Relational Grade ◽  
Grey Relational ◽  
Grey Taguchi

The Grey-Taguchi method was adopted in this study to optimize the injection molding parameters for the MIM green compacts with multiple quality performance. A Grey relational grade obtained from the Grey relational analysis is used as the quality performance in the Taguchi method. Then, the optimum injection molding parameters are determined using the parameter design proposed by the Taguchi method. The result concluded that the mold temperature (D) is very significant, by the fact that the ANOVA shows its contribution to excellent surface appearance as well as strong and dense green compacts is 38.82%.

Optimizing the Sintering Parameter of Metal Injection Molding Compact Using Robust Engineering Technique

2012 ◽  
Vol 445 ◽  
pp. 357-361
Author(s):  
Khairur Rijal Jamaludin ◽  
Norhamidi Muhamad ◽  
Sufizar Ahmad ◽  
Mohd Halim Irwan Ibrahim ◽  
Nor Hafiez Mohd Nor ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Injection Molding ◽  
Quality Characteristics ◽  
Metal Injection Molding ◽  
Sintered Part ◽  
Multiple Quality Characteristics ◽  
Sintering Time ◽  
Engineering Technique ◽  
Grey Relational Grade ◽  
Grey Relational

Sintering is a key step in the metal injection molding (MIM) process, which affects the mechanical properties of the sintered part. The mechanical properties of the sintered compacts are resulted from tremendous sintered part densification. This work utilizes robust engineering technique in optimizing sintering parameters of metal injection molding compacts. Three quality characteristics; shrinkage, density and flexure strength is optimized using Taguchi method-based grey analysis. The modified algorithm adopted here was successfully used for both detraining the optimum setting of the process parameters and for combining multiple quality characteristics into one integrated numerical value called grey relational grade. The sintering parameters investigated are: sintering temperature, sintering time, and heating rate. The result concluded that sintering time is the most significant for the combination of the quality characteristics.

Multi-Objective Optimization in Manufacturing Engineering for Slender Pen Rod Injection Molding Quality Based on Grey Correlation

2013 ◽  
Vol 345 ◽  
pp. 486-493
Author(s):  
Xiao Hong Tan ◽  
Lei Gang Wang ◽  
Wen Shen Wang
Keyword(s):  
Injection Molding ◽  
Grey Relational Analysis ◽  
Injection Molding Process ◽  
Multi Objective Optimization ◽  
Molding Process ◽  
Melt Temperature ◽  
Multi Objective ◽  
Relational Analysis ◽  
Grey Relational Grade ◽  
Grey Relational

In this paper a new approach for the optimization of the multi-objective injection molding process based on the Taguchi robust design combined with the grey relational analysis has been studied. A grey relational grade obtained from the multi-objective grey relational analysis is used to solve the injection molding process with the multiple performance characteristics including volume shrinkage (R1) and axial deformation (R2), the injecting parameters, namely mold temperature, melt temperature, holding pressure and holding time are optimized. By orthogonal polar difference analysis and statistical analysis of variance (ANOVA) of grey relational grade, main factors influencing and the best process parameters were determined: A=50°C,B=250°C,C=30MPa,D=9s.Under the case of continuity factor, Fitting the response surface further the optimal combination of in continuous space r is identified: A=50.3°C,B=250°C,C=29MPa,D=8.3s. Experimental results have shown that the Taguchi combined with the grey relational analysis can avoid human evaluation of the multi-objective optimization, and Injection molding multi-objective optimization is implemented more objectively, and product performance in the process can be improved effectively through this approach.

Preparation and Mechanical Properties of Inconel718 Alloy by Metal Injection Molding

2010 ◽  
Vol 39 (5) ◽  
pp. 775-780 ◽  
Author(s):  
Hu Youhua ◽  
Li Yimin ◽  
He Hao ◽  
Lou Jia ◽  
Tang Xiao
Keyword(s):  
Mechanical Properties ◽  
Injection Molding ◽  
Metal Injection Molding

scholarly journals Grey Relational Grade Based Quantitative Analysis of the Factors Influencing the Load Characteristics of A Power Grid

2018 ◽  
Vol 53 ◽  
pp. 01012 ◽  
Author(s):  
Wei Pan ◽  
Caijia Lei ◽  
Wei Jia ◽  
Hui Gao ◽  
Binghua Fang
Keyword(s):  
Quantitative Analysis ◽  
Influencing Factors ◽  
Power Grid ◽  
Grey System Theory ◽  
Grey System ◽  
Factors Affecting ◽  
Grey Relational Grade ◽  
Load Characteristics ◽  
Grey Relational ◽  
Main Factor

Regarding analysis of load characteristics of a power grid, there are multiple factors that influence the variation of load characteristics. Among these factors, the influence of different ones on the change of load characteristic is somewhat different, thus the degree of influence of various factors needs to be quantified to distinguish the main and minor factors of load characteristics. Based on this, the grey relational analysis in the grey system theory is employed as the basis of mathematical model in this paper. Firstly, the main factors affecting the load characteristics of a power grid are analysed. Then, the principle of quantitative analysis of the influencing factors by using grey relational grade is introduced. Lastly, the load of Guangzhou power grid is selected as the research object, thereby the main factor of temperature affecting the load characteristics is quantitatively analysed, such that the correlation between temperature and load is established. In this paper, by investigating the influencing factors and the degree of influence of load characteristics, the law of load characteristics changes can be effectively revealed, which is of great significance for power system planning and dispatching operation.

Application of Taguchi Method for Parameters Optimization in Micro Metal Injection Molding

2011 ◽  
Vol 52-54 ◽  
pp. 244-248
Author(s):  
Haw Pei Li ◽  
Norhamidi Muhamad ◽  
Abu Bakar Sulong ◽  
Heng Shye Yunn ◽  
Hooman Abolhasani
Keyword(s):  
Injection Molding ◽  
Signal To Noise Ratio ◽  
Steel Powder ◽  
Mold Temperature ◽  
Parameters Optimization ◽  
Simultaneous Optimization ◽  
Metal Injection Molding ◽  
Surface Appearance ◽  
Optimal Injection ◽  
Acetate Butyrate

Optimization of injection parameters in Micro Metal Injection Molding (μMIM) was described in this study. Stainless steel powder was mixed with Polyethelena Glycol (PEG), Polymethyl Methacrilate (PMMA) and Cellulose Acetate Butyrate (CAB) to produce feedstock. Design of Experiments (DOE) of Taguchi L-27 (313) orthogonal array technique has been used to investigate the significance and optimal injection molding parameters. The signal-to-noise ratio and analysis of variance (ANOVA) are applied to study the optimum levels and effects of process parameters. Simultaneous optimization to obtain the highest green density and excellent surface appearance was discussed. The result concluded that the mold temperature (D) is the most statistically significant process parameter and its contribution to the best appearance and density is the highest.

Metal injection molding: 3D modeling of nonisothermal filling

2002 ◽  
Vol 42 (4) ◽  
pp. 760-770 ◽  
Author(s):  
Florin Ilinca ◽  
Jean-François Hétu ◽  
Abdessalem Derdouri ◽  
James Stevenson
Keyword(s):  
Injection Molding ◽  
3D Modeling ◽  
Metal Injection Molding
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