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Vivien Suphandani Djanali
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JMES The International Journal of Mechanical Engineering and Sciences Editorial Office Jurusan Teknik Mesin, ITS Kampus ITS Sukolilo Surabaya 60111 Building C, Floor 2 Indonesia
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JMES The International Journal of Mechanical Engineering and Sciences
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : -     EISSN : 25807471     DOI : https://dx.doi.org/10.12962/j25807471
Core Subject : Science, Engineering,
Energy Materials Science & Nanotechnology Mechanical Engineering
Topics covered by JMES include most topics related to mechanical sciences including energy conversion (wind, turbine, and power plant), mechanical structure and design (solid mechanics, machine design), manufacturing (welding, industrial robotics, metal forming), advanced materials (composites, nanotube, metal foam, ceramics, polymer), metallurgy (corrosion, non-destructive testing, heat treatment, metal casting), heat transfer, fluid mechanics, thermodynamics, mechatronics and controls, advanced energy storage and devices (fuel cell, electric vehicle, battery), numerical modelling (FEM, BEM).
Arjuna Subject : Teknik (semua kategori) - Teknik Mesin
Articles 5 Documents
 1 
Search results for , issue "Vol 5, No 2 (2021)" : 5 Documents clear
Design and Analysis of ECVT on Electric Powered Vehicles for Determining the Speed Ratio Himmawan Sabda Maulana; I Nyoman Sutantra
JMES The International Journal of Mechanical Engineering and Sciences Vol 5, No 2 (2021)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v5i2.8923

Abstract

Electronics Continuous Variable Transmission (ECVT) is a smart transmission that has unlimited gear ratios. By analyzing vehicle conditions, ECVT can choose the most efficient gear ratio. ECVT mode requires a Planetary Gear Set (PGS) as a power splitter. This research will use PGS and double electric motor to combine the ECVT mode with electric vehicles to determine the desired speed ratio. The PGS was designed by an analytical model and simulated using CAD software. The simulation will provide several input variations to get the right speed ratio, such as speed variations and rotational direction. The analytical model obtained a PGS ratio of 1:6 with 19 sun gear teeth, 95 ring gear teeth, 38 planet gear teeth, and a motor power of 47 kW. Simulation results will be applied to build the prototype that will be made with a 3D printer. This study shows that ECVT can be a transmission system for electric vehicles with 2 to 5 levels of transmission, and using double electric motors with small power can replace an electric motor with large power. To obtain maximum efficiency, a good control strategy is needed. The control strategy will be discussed in further research.
Quasi-Static Cyclic Response of Unidirectional Thin-Ply Hybrid Composites Putu Suwarta; Gergely Czel; Mohamad Fotouhi; Marco L. Longana; Sutikno Sutikno; Michael R. Wisnom
JMES The International Journal of Mechanical Engineering and Sciences Vol 5, No 2 (2021)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v5i2.8614

Abstract

Quasi-static cyclic loading of unidirectional (UD) thin-ply hybrid composites was conducted to assess the extent of stiffness loss with increasing applied strain. For this study, three types of hybrid configuration were examined: SG1/MR401/SG1, SG1/TR301/SG1, SG1/TR302/SG1, where SG is a high strength glass fibre and MR40 is an intermediate modulus carbon fibre while TR30 is a standard modulus carbon fibre. The strain at first carbon ply failure and the knee point strain (εk) for the SG1/TR301/SG1 hybrid is higher than for the SG1/TR302/SG1 hybrid. This is due to the ‘hybrid effect’ which provides a delay in damage initiation due to a constraint on broken carbon cluster development. For SG1/MR401/SG1 and SG1/TR302/SG1 configurations, the stiffness reduction over the course of loading was governed by fragmentation of the carbon plies and delamination between the carbon and glass plies. A smaller stiffness reduction for the SG1/TR301/SG1 configuration compared to the other hybrid configurations was observed with the fragmentation of the carbon ply as the main damage mechanism responsible for the reduction. With each loading cycle, there was a small amount of hysteresis and residual strain. The response of the UD thin-ply hybrid laminates are considered pseudo-ductile because the damage in the form of ply fragmentation and stable delamination, leads to gradual loss of stiffness. The stable delamination of this hybrid material is due to the low energy release rate of the thin carbon ply.
Determination of Injection Molding Process Parameters using Combination of Backpropagation Neural Network and Genetic Algorithm Optimization Method Arif Wahjudi; Thenny Daus Salamoni; I Made Londen Batan; Dinny Harnany
JMES The International Journal of Mechanical Engineering and Sciences Vol 5, No 2 (2021)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v5i2.8592

Abstract

The polymer matrix composite (PMC) in use today is generally made of synthetic fibers which are expensive and not environmentally friendly. The use of synthetic fibers can be replaced with natural fibers, which are more environmentally friendly at a lower price. The natural fiber material used in this study is made from husks, with a particle size of 500 µm (mesh 35). In the PMC manufacturing process, rice husks are mixed with polypropylene (PP) and maleic anhydride polypropylene (MAPP) with a composition of 10 wt% RH, 85 wt% PP and 5 wt% MAPP. PMC materials using natural fibers are called biocomposite materials. The result of mixing PMC with natural fibers in the form of pellets is then carried out by the injection process using an injection molding machine. The printed results are in the form of tensile test specimens based on ASTM D 638-03 type V testing standards and impact test specimens based on ASTM D 256-04 testing standards. The research was conducted by optimizing the responses i.e. tensile strength and impact strength of the biocomposite material in the injection molding machine process, whereas varied process parameters, namely barrel temperature, injection pressure, holding pressure, injection velocity were selected as process parameters. The backpropagation neural network (BPNN) training method is used to recognize the pattern of the relationship between process parameters and response parameters based on the previous experiment, while the genetic algorithm (GA) optimization method is to determine the variation settings for process parameters that can optimize tensile and impact strength. The results of the BPNN training have a 4-9-9-2 network architecture consisting of 4 input layers, 2 hidden layers with 9 neurons, and 2 neurons in the output layer. Optimization with GA produces a combination of variable process parameters barrel temperature 217◦C, injection pressure 55 Bar, holding pressure 41 Bar and injection velocity 65 mm/sec. The results of statistical validation using one sample T test show that the average value of tensile strength and impact strength from the results of the confirmation experiment is the same as the value of the tensile strength and impact strength of the optimization prediction.
Comparative Study of Hydro-Magneto-Electric Regenerative Shock Absorber (HMERSA) with Two Outputs Hydraulic Generator Installed Series And Parallels Taufik Kurniawan; Harus Laksana Guntur
JMES The International Journal of Mechanical Engineering and Sciences Vol 5, No 2 (2021)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v5i2.8934

Abstract

Through the Regenerative Shock Absorber (RSA) mechanism, wasted energy will be utilized into electrical energy. Hydro-Magneto-Electric Regenerative Shock Absorber (HMERSA) was designed and analyzed with 2 generator outputs installed in series and parallel. The twin-tube shock absorber was modified, so that fluid flow in the chamber is only unidirectional flow. It is passed to four check valves that keep the fluid flow in one direction and towards the 2 hydro-generators installed on the system in series and parallel positions. The hydro-generator converts the linear fluid flow into a rotational motion which causes the generator to rotate and generate energy. HMERSA was tested on minibus with speed bumps types of roads. In the bump test, the harvested energy were 8.2 Volts and 5.97 Volts for HMERSA with 2 generator outputs installed in series, and 5.169 Volts and 4.33 Volts for HMERSA with 2 parallel output generators. From the result, we can conclude that HMERSA with 2 generator outputs installed in series is better than HMERSA with 2 parallel output generators.
Multi-objective Optimization Using Neural Network, Differential Evolution, and Teaching Learning Based Optimization in Drilling Process of Glass Fiber Reinforced Polymer Kirana Alif Fatika; Mohammad Khoirul Effendi
JMES The International Journal of Mechanical Engineering and Sciences Vol 5, No 2 (2021)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v5i2.10382

Abstract

This experiment focused on the drilling process of Glass Fiber Reinforced Polymer (GFRP) composites. The data was obtained from an experiment carried out by Production Engineering Laboratory, Mechanical Engineering Department, Faculty of Industrial Technology and Systems Engineering, Institut Sepuluh Nopember Surabaya in 2019. The experiment was done with an artificial intelligence method called Backpropagation Neural Network (BPNN) as an approach to predict the response parameters (thrust force, torque, hole roundness, and hole surface roughness). The parameter inputs are drill point geometry, drill point angle, feed rate, and spindle speed. Hence the prediction would be used to gain the minimum input parameters by applying metaheuristic methods called Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO). Then the result from both methods was compared to determine which method gained the better optimization values. Since BPNN-DE and BPNN-TLBO with type X drill point geometry was considerably better than type S drill point geometry, type X drill point geometry could be used to optimize the drilling process of GFRP.

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