Optimization of Injection Molding Process to Minimize Weld-line and Sink-mark Defects Using Taguchi based Grey Relational Analysis

2018 ◽  
Vol 5 (5) ◽  
pp. 12615-12622
Author(s):  
J. Sreedharan ◽  
A.K. Jeevanantham
Keyword(s):  
Injection Molding ◽  
Grey Relational Analysis ◽  
Weld Line ◽  
Injection Molding Process ◽  
Molding Process ◽  
Relational Analysis ◽  
Grey Relational ◽  
Sink Mark

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.

scholarly journals Analysis of injection molding for computer cooling fans by Taguchi method and grey relational analysis

Filomat ◽  
2016 ◽  
Vol 30 (15) ◽  
pp. 4199-4211
Author(s):  
Chi-Hung Lo
Keyword(s):  
Injection Molding ◽  
Taguchi Method ◽  
Grey Relational Analysis ◽  
Operating Conditions ◽  
Influential Factor ◽  
Injection Molding Process ◽  
Melt Temperature ◽  
Relational Analysis ◽  
Grey Relational

Various factors affect the quality of a plastic product during the injection molding process. In this study, the quality engineering planning method, Taguchi Method, and grey relational analysis were used in this study to determine these factors and the Moldflow Plastics Insight (MPI) was used to conduct the moldflow analysis. By varying different types of operating conditions, the results obtained from the experiments were analyzed so as to verify the influence of each factor on the quality of the final product. The optimal processing parameters which can reduce the mold tryout time and the analysis cost were then determined. The plastic impeller of a computer cooling fan is selected as the case study and the goal is to resolve the warping problems. The deviations in the shear stress distribution as obtained by varying the S/N ratio of variance factors during the experiments are in agreement with the results of analyzing the grey information relational degrees. The most influential factor is the mold temperature, followed sequentially by the fill time, fill pressure, and melt temperature.

scholarly journals Prevention of Defects in Injection Molding Process in the Manufacturing of Ballpoint Pen

2019 ◽  
Vol 8 (3) ◽  
pp. 4932-4937
Keyword(s):  
Injection Molding ◽  
Weld Line ◽  
Close Relation ◽  
Injection Molding Process ◽  
Molding Machine ◽  
Molding Process ◽  
Injection Molding Machine ◽  
Preventive Actions ◽  
Sink Mark ◽  
Ball Point

Injection molding is one of the major manufacturing processes for thermoplastic polymers. In injection molding machine, process variables play a very important role in generating defects in products. In the present paper manufacturing of ball point pen is taken up to investigate the prevention of defects through design and controlling various process parameters. Two molds are designed and fabricated specially for this experimental work. The analysis showed that the defect occurrence has close relation with pressure, speed and the temperature of the injection molding machine in addition to the basic design of the mold. The paper suggests preventive actions for the defects like flash, burn marks, short shot, shrinkage, weld line, warpage and sink mark. The preventive actions were successfully implemented in manufacturing the ball point pen.

scholarly journals The Study of Optimal Molding of a LED Lens with Grey Relational Analysis and Molding Simulation

2019 ◽  
Vol 63 (4) ◽  
pp. 278-294 ◽  
Author(s):  
Min-Wen Wang ◽  
Fatahul Arifin ◽  
Van-Hanh Vu
Keyword(s):  
Injection Molding ◽  
Mold Temperature ◽  
Injection Molding Process ◽  
Molding Process ◽  
Melt Temperature ◽  
Injection Speed ◽  
Relational Analysis ◽  
Total Displacement ◽  
Packing Pressure ◽  
Grey Relational

Injection molding technology is known as the most widely used method in mass production of plastic products. To meet the quality requirements, a lot of methods were applied in optimization of injection molding process parameter. In this study the optimization based on Taguchi orthogonal array and Grey relational analysis (GRA) is used to optimize the injection molding process parameters on a LED lens. The four process parameters are: packing pressure, injection speed, melt temperature and mold temperature. The multi-response quality characteristics are total displacement, volumetric shrinkage, and thermal residual stress. The optimal molding parameters are packing pressure (90 MPa), injection speed (300 mm/sec), melt temperature (270 °C) and mold temperature (90 °C). The luminous uniformity of the LED is 92.61 % and the viewing angle of the LED is 124.76°. Among the four factors, packing pressure plays the key role in reducing total displacement, volumetric shrinkage, and thermal residual stress.

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