DESAIN PARAMETER EKSPERIMEN UNTUK OPTIMASI NILAI FRANGIBILITY FACTOR MATERIAL KOMPOSIT DENGAN METODE TAGUCHI DAN NEURAL NETWORK

DAMAYANTI, MIA KRISTINA (2017) DESAIN PARAMETER EKSPERIMEN UNTUK OPTIMASI NILAI FRANGIBILITY FACTOR MATERIAL KOMPOSIT DENGAN METODE TAGUCHI DAN NEURAL NETWORK. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Material komposit adalah jenis material yang banyak diaplikasikan untuk senjata api dalam latihan penembakan karena memiliki kemampuan mudah pecah (fragibility factor) yang tinggi. Nilai fragibility factor (FF) bergantung dengan setting level parameter proses selama fabrikasi dengan metalugi serbuk. Parameter proses dalam penelitian ini antara lain komposisi %wt Sn, tekanan kompaksi dan temperatur sintering. Penelitian ini menggunakan data sekunder nilai FF dari penelitian tim riset LPDP laboratorium Fisika Material Jurusan Teknik Material dan Metalurgi FTI ITS. Metode optimasi nilai FF untuk desain parameter ini menggunakan Orthogonal Array dari Taguchi dan prediksi Neural Network. Dari kedua metode ini diperoleh hasil optimasi setting level parameter yang sama yaitu pada kombinasi 20%wt Sn, tekanan kompaksi 450 MPa dan temperatur sintering 500 0C dengan prediksi nilai FF 19,70. Dari analisa ANOVA untuk mean pada Taguchi menunjukkan bahwa faktor tekanan kompaksi yang paling berpengaruh sebesar 45,49%, komposisi Sn 27,65% dan temperatur sintering 21,65%. Hasil setting level optimasi melalui desain ini selanjutnya dikonfirmasi dengan melakukan eksperimen dan menghasilkan nilai FF rata-rata 19,29. Dari hasil pengujian confidence interval nilai eksperimen ini diterima karena interval saling bersinggunan, yang artinya hasil desain optimasi memenuhi hasil eksperimen. ===================================================================================================== Composite material is a widely applied materials for shooting drilling because of high frangibility factor. Frangibility factor (FF) value depends on the setting of process parameter level in fabrication through powder metallurgy. The process parameters used in this research are %wt Sn composition, compaction pressure and sintering temperature. This research uses FF value as secondary data from the researches conducted by the previous composite material team, laboratory of physics materials, department of materials and metallurgical engineering. The method of optimization for FF value ,as parameters design, uses orthogonal array from Taguchi and Neural Network prediction. From both methods, it is obtained optimization result of parameter level setting with the same value on the combination 20%wt Sn, compaction pressure 450 MPa and sintering temperature 500 0C with FF value prediction 19.70. From ANOVA analysis for mean in Taguchi shows that the compaction pressure has the most significant factor at 45.49%, Sn composition at 27.65% and sintering temperature at 21.65%. Optimization level setting result through this design is confirmed by conducting experiment and obtained FF value average at 19.29. From the result of interval confidence testing, this experiment result is acceptable because of the interval intersection, which means that the result of optimization design is suitable with experimental result. The result show that Taguchi and neural network method are able to predict the FF value.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Material Komposit, Metalurgi Serbuk, Taguchi, Neural Network, composite material, powder metallurgy
Subjects: Q Science > QA Mathematics > QA278.2 Regression Analysis
Divisions: Faculty of Industrial Technology > Material & Metallurgical Engineering > (S1) Undergraduate Theses
Depositing User: Mia Kristina Damayanti
Date Deposited: 30 Jan 2017 06:42
Last Modified: 30 Jan 2017 06:42
URI: http://repository.its.ac.id/id/eprint/1760

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