OPTIMIZATION OF INJECTION MOLDING PARAMETERS IN MOLD SM20 USING THE TAGUCHI-GREY RELATIONAL ANALYSIS (GRA) METHOD FOR REDUCING SHORT-SHOT DEFECTS AND CYCLE TIME
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Abstract
Injection molding has become a favorite technique of manufacturing high quality of specific plastic components. Nevertheless, typical flaws like short-shot flaws and long cycle times may jeopardize the quality of a product and decrease the productivity. This project is aimed at the optimization of three major process parameters; that is, the nozzle temperature, injection pressure, and injection time, which will be the three set at four levels to achieve an improvement in the performance of Mold SM20. The Taguchi experimental design used is an L16 orthogonal array and this makes the experimental design to be successfully reducing the number of trial required. As part of dealing with the two-fold goals of QED reduction and cycle time, Grey Relational Analysis (GRA) would be used in multi-response optimization. Gra means normalization, calculating of grey relational coefficients and the calculation of grey relational grades (GRG). The outcomes show that injection pressure can play an important role in combined response, whereas nozzle temperature and injection time present a less important role. The best combination of parameters (temperature of the nozzle 225o C, injection pressure 55 bar and injection time 2.0 sec) gave the best value of GRG, 0.841278 which indicates better performance of the process, both quality-wise as well as efficiency-wise.
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