OPTIMIZING FRANGIBILITY FACTOR OF CU-SN COMPOSITE MATERIAL BY TAGUCHI AND NEURAL NETWORK METHODS

Widyastuti Widyastuti, Mia Kristina Damayanti, Mas Irfan Hidayat, Hosta Ardhyananta

Abstract


CuSn composite material is widely applied to fabricate frangible bullets. This work employs secondary data of Frangibility Factor (FF) value. The optimization method for the design is performed with orthogonal array by Taguchi and neural network method. It is obtained that the optimum parameter is combination of 20% wt Sn, compaction pressure of 450 MPa and sintering temperature of 500 oC with the prediction value of Frangibility Factor (FF) 19.70. The ANOVA analysis for Taguchi shows that the compaction pressure factor is 45.49%, Sn 27.65% composition and sintering temperature of 21.65%. The result of the optimization design is further confirmed by experiment and yielded an average Frangibility Factor (FF) value of 19.29. From the experimental data, confidence interval test results are accepted because of the interlock interval. It means that the optimization design agrees with the experimental results.

 


Keywords


CuSn, Composite materials, Powder Metallurgy, Taguchi, Neural Network.

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DOI: http://dx.doi.org/10.12962/j2746279X.v1i1.6551

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