Oetomo, Ghassani Darystyo (2021) Optimasi Metaheuristik Menggunakan Metode Back Propagation Neural Network, Genetic Algorithm Dan Particle Swarm Optimization Proses Hot Forging Pada Produksi Roda Kereta Cepat. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Transportasi merupakan kebutuhan dasar masyarakat dalam kehidupan sehari-hari. Perkembangan pembangunan sistem transportasi di Indonesia saat ini sedang digencarkan guna memenuhi kebutuhan masyarakat terutama sektor transportasi darat. Transportasi darat yang disediakan pemerintah dalam memenuhi kebutuhan transportasi masyarakat yang layak, terjangkau,aman dan efisien salah satunya adalah kereta cepat. Sistem roda dari kereta cepat berbeda dengan kereta kecepatan rendah dan kecepatan sedang dan merupakan komponen penting dalam pengoperasian kereta cepat, perbedaan kebutuhan memerlukan optimalisasi dari proses manufaktur dalam pembentukan roda kereta untuk menghindari terjadinya kegagalan dan over-design roda kereta yang dapat membahayakan pengguna dan membebani dari segi ekonomi. Penelitian ini berfokus pada proses hot forging yang digunakan dalam proses pembentukan roda kereta yang dipengaruhi temperatur proses forging, dimensi bilet dan dimensi dies yang digunakan dalam proses hot forging.
Penelitian dilakukan dengan metode optimalisasi Backpropagation Neural Network (BPNN) sebagai alat untuk merespon nilai minimal forging force dengan parameter input variasi dimensi billet dan temperatur. Prediksi tersebut kemudian digunakan untuk mendapatkan parameter input dengan hasil respon yang optimal dengan menggunakan metode metaheuristik yaitu Genetic Algorithm (GA) dan Particle Swarm Optimization (PSO) yang akan digunakan untuk mencari nilai global optimum minimal forging force dan akurasi dimensi.
Hasil dari penelitian ini didapatkan nilai optimum dari dimensi diameter billet dan tebal billet untuk minimum forging force. Dengan ketebalan billet 73,5 mm dan diameter billet 880 mm. Untuk nilai optimal berdasarkan simulasi ANSYS nilai optimum dari temperatur berada pada temperatur 1000°C dimana minimal forging force berbanding lurus dengan peningkatan temperatur. Hasil dari optimasi yang dilakukan menggunakan metode BPNN-GA untuk mencari nilai global optimum dari parameter forging didapatkan parameter optimum pada temperatur 950°C ketebalan billet 73,5 mm dan diameter billet 880 mm dan untuk BPNN-PSO didapatkan parameter optimum pada temperatur 950°C dan terdapat perbedaan prediksi nilai global minimum pada akurasi dimensi web dimana temperatur optimal sebsar 1000°C hal ini terjadi karena PSO tidak mempunyai kemampuan keluar dari nilai lokal optimum sehingga terdapat perbedaan pada prediksi temperatur optimal. Untuk ketebalan billet 73,5 mm dan diameter billet 880 mm.
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Transportation is a basic need of society in everyday life. The
development of the transportation system in Indonesia is currently
being intensified to meet the needs of the community, especially
the land transportation sector. Land transportation provided by
the government in meeting the needs of public transportation that
is feasible, affordable, safe and efficient, one of which is the fast
train. The wheel system of the high-speed train is different from
the low-speed and medium-speed trains and is an important
component in the operation of the high-speed train, the different
requirements require optimization of the manufacturing process in
the formation of the train wheels to avoid failure and over-design
of the train wheels which can endanger the user and burden the
carriage. economics. This study focuses on the hot forging
process used in the train wheel formation process which is
influenced by the forging process temperature, billet dimensions
and dies dimensions used in the hot forging process.
The research was conducted using the Backpropagation Neural
Network (BPNN) optimization method as a tool to respond to the
minimum value of forging force with the input parameters of billet
dimensions and temperature variations. These predictions are then
used to obtain input parameters with optimal response results using
metaheuristic methods, namely Genetic Algorithm (GA) and Particle
Swarm Optimization (PSO) which will be used to find the
global optimum value for minimum forging force and dimensional
accuracy.
The results of this study obtained the optimum value of the
dimensions of the billet diameter and billet thickness for minimum
forging force. With a billet thickness of 73.5 mm and a billet
diameter of 880 mm. For the optimal value based on the ANSYS
simulation, the optimum value of temperature is at a temperature
of 1000°C where the minimum forging force is directly
proportional to the increase in temperature. The results of the
optimization carried out using the BPNN-GA method to find the
global optimum value of the forging parameter obtained the
optimum parameter at a temperature of 950°C, billet thickness of
73.5 mm and billet diameter of 880 mm and for BPNN-PSO the
optimum parameter was obtained at a temperature of 950°C. and
there is a difference in the prediction of the minimum global value
on the accuracy of the web dimensions where the optimal
temperature is 1000°C this happens because PSO does not have
the ability to get out of the optimum local value so that there is a
difference in the optimal temperature prediction. For billet
thickness 73.5 mm and billet diameter 880 mm.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Roda Kereta, Hot Forging, Back Propagation Neural Network, Genetic Algorithm, Particle Swarm Optimization, Wheel Train, Hot Forging |
| Subjects: | T Technology > T Technology (General) > T57.62 Simulation T Technology > TF Railroad engineering and operation T Technology > TF Railroad engineering and operation > TF1327.O58 High speed ground transportation (train) T Technology > TS Manufactures T Technology > TS Manufactures > TS171 Product design T Technology > TS Manufactures > TS176 Manufacturing engineering. Process engineering (Including manufacturing planning, production planning) |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Mechanical Engineering > 21201-(S1) Undergraduate Thesis |
| Depositing User: | Ghassani Darystyo Oetomo |
| Date Deposited: | 01 Mar 2022 02:09 |
| Last Modified: | 07 Apr 2023 23:45 |
| URI: | http://repository.its.ac.id/id/eprint/94768 |
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