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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 93 Documents
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Numerical Study of Blended Winglet Geometry Variations on Unmanned Aerial Vehicle Aerodynamic Performance Fungky Dyan Pertiwi; Arif Wahjudi
JMES The International Journal of Mechanical Engineering and Sciences Vol 6, No 1 (2022)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v6i1.12317

Abstract

An unmanned aerial vehicle (UAV) is an unmanned aircraft that can be controlled remotely or flown automatically. Nowadays, the use of UAVs is extensive, not only limited to the military field but also in civilian tasks such as humanitarian search and rescue (SAR) tasks, railroad inspections, and environmental damage inspections. Therefore, study on UAV becomes essential to answer the challenges of its increasingly widespread use. This study explores the addition of a blended winglet on the swept-back wing of the UAV. It is to predict the effect of the aerodynamic performance. The backpropagation neural network (BPNN) method helps to predict the aerodynamic performance of the UAV in the form of a lift-drag coefficient ratio (CL/CD) and drag coefficient at 0O angle of attack (CD0). It is based on blended winglet parameters such as height, tip chord, and cant angle. The obtained BPNN modeling has a network architecture of 3 inputs, 2 hidden layers, and 1 output with a mean square error (MSE) of 4.9462e-08 and 4.4756e-06 for the relationships between blended winglet parameters with CL/CD and CD0, respectively.
Study of Coal Drying Characteristics Using Boiler Blowdown in a Rotary Coal Dryer Aripin Gandi Marbun; Bambang Arip Dwiyantoro; Alvin Mizrawan Tarmizi
JMES The International Journal of Mechanical Engineering and Sciences Vol 6, No 1 (2022)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v6i1.12053

Abstract

Drying lignite coal to reduce its moisture content has become popular in the last decade. Previously, coal dryers used typical energy such as steam, fuel, or electrical as heat sources. Waste energy had never been implemented in a coal dryer while using it would reduce the cost of production and raise the economic value of the coal itself. An experimental study of drying low-rank coal was conducted using waste energy boiler blowdown in a rotary coal dryer. With variations of 0.595 mm, 1.18 mm, and 4.75 mm coal particle size and the flow's changes of 20 kg/hour, 30 kg/hour, and 40 kg/hour. The hot air temperature of 70oC, mass flow rate of 36 kg/hour, and pressure of 0,03 MPa were the constant parameters on the 15 rpm rotary drum. The results found that the coal moisture decreased significantly at 0.595 mm particle size and 20 kg/hour of flow. The final coal moisture dropped by 20.685%, and the calorific value increased by 879.6 kcal/kg from its initial value. In addition, the efficiency of the rotary coal dryer is 81.8%.
Numerical Analysis on Flexibility of Unexpanded Balloon-Expandable Stent Ilham Agung Aribowo; Varien Janitra Nuralif Susanto
JMES The International Journal of Mechanical Engineering and Sciences Vol 6, No 1 (2022)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v6i1.11371

Abstract

A stent is a mesh of micro metal tube commonly used to provide support to an enlarged blood vessels that are narrowed due to plaque growth. To function correctly, a stent must have specific characteristics, which includes good flexibility. The flexibility of the stent can be predicted using the finite element method simulation. The type of stent studied are the sinusoidal and spiral type balloon-expandable stent. The 3D model is created in Solidworks 2016, while the structural analysis is performed with ANSYS Workbench Student R18. The simulation carried out is a four-point bending test. The analyzed parameters are the von Mises stress and the flexibility value of the stent. The material model for the stent is isotropic SS 316 L, while the balloon was polyurethane which is modeled as hyper-elastic material. The results obtained from this study are sinusoidal type stents can be deflected up to 0.221 mm to remain in the elastic area, while spiral type stents can be deflected up to 0.109 mm. The maximum flexibility value of the sinusoidal type stent is 0.003526 N-1.mm-2 while the spiral type stent is 0.002478 N-1.mm-2.
Numerical Study of Heat Transfer Characteristics in High Pressure Steam Turbine During Stop Unit Process with Sliding Pressure Budi Santoso; Bambang Arip Dwiyantoro
The International Journal of Mechanical Engineering and Sciences Vol 6, No 2 (2022)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v6i2.12048

Abstract

During maintenance of the turbine and its auxiliary equipment, which requires a stop of the turbine oil system equipment and an open turbine casing, the first stage metal temperature requirement must be below 150°C. The normal stop unit method with natural cooling takes about 14 to 17 days. In order to speed up the cooling time to 5 days, a forced cooling turbine is needed using the stop unit method with sliding pressure. The heat transfer that occurs in the high-pressure turbine during the stop unit process with sliding pressure was investigated using the numerical method of CFD simulation. The 2D geometry design was made from high-pressure turbine cutouts images. Then meshing was made. The solver stage and the post-processing stage were set. The simulation was running in a steady state and followed by transients. The validation method was to compare the first stage metal temperature parameter between the actual process and the results of the CFD simulation at a load of 350 MW, then re-simulate it at 500 MW and 645 MW. The stop unit process with sliding pressure starting at 645MW resulted in the best final cooling compared to the stop unit at 500 MW and 350MW loads. By increasing the main steam flow, the resulting cooling increases. By increasing the value of the fluid flow velocity, the Reynolds number increases, so the convection heat coefficient also increases.
The Effect of Rice Husk as Additive in Injection Molding Process Dinny Harnany; I Made Londen Batan; Arif Wahjudi; Sylvia Ayu Pradanawati
The International Journal of Mechanical Engineering and Sciences Vol 6, No 2 (2022)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v6i2.14182

Abstract

This study investigated the moldability and the mechanical properties of bio-composite with rice husk as natural reinforcement. Natural materials that are abundant in nature can be used as reinforcement for polymer materials. Natural materials as reinforcement in plastic materials were used to obtain alternative materials in an injection molding process. With rice husk, polypropylene, and MAPP, four compositions of bio-composite materials were made and used as raw material injection molding process. The moldability from this material was observed through visualization of the product. The mechanical properties of the materials were observed by the tensile strength and impact test on the injection molding product. The result showed that these materials could be injected to form ASTM D638-03 Type V tensile test and ASTM D256-04 impact test specimens. Visually, the more rice husk on the bio-composite material, the darker the product color. The differences in tensile strength values decreased along with increased rice husk content. All bio-composite materials had roughly the same tensile strength value and were lower than polypropylene, except RH-5%. The impact value of bio-composites was lower than polypropylene impact value and tended to decline along with the increase in the rice husk content. Scanning electron microscope (SEM) analyzes were done on the fracture side of the impact specimen. Microscale voids decreased and were rarely found by adding rice husk to the material bio-composite. On the other hand, rice husk breakage and pullout phenomenon on bio-composite material were found.
Engine RPM and Battery SOC Activation Optimization in Hybrid Vehicle Energy Management System Utilizing BPNN - Genetic Algorithm and BPNN – Particle Swarm Optimization Rhema Adi Magiza Wicaksana; Bambang Sudarmanta; Mohammad Khoirul Effendi
The International Journal of Mechanical Engineering and Sciences Vol 6, No 2 (2022)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v6i2.12360

Abstract

The energy used in the hybrid vehicle needs to be regulated to gain further mileage and lower fuel consumption. It is achieved by selecting the correct levels of hybrid energy management system (EMS) parameters (i.e., vehicle speed, engine RPM, and activation State of Charge (SOC) of battery). This study focused on the modeling and optimization of Sepuluh Nopember Institute of Technology (ITS)’s series plug-in hybrid electric vehicle (PHEV) car mileage and fuel consumption by comparing the backpropagation neural network (BPNN) method – genetic algorithm (GA) and BPNN – particle swarm optimization (PSO). The BPNN was used to model the character of ITS’s series PHEV EMS and predict mileage and fuel consumption. The BPNN’s model obtained the best EMS parameters, most extended mileage, and minimum fuel consumption. The result of the validation experiment showed that both the integration of BPNN - GA and BPNN - PSO were able to predict and optimize the multi-objective characteristic with the same results.
Numerical Analysis of Translational Vibration Reduction Response on Drilling Process due to Additional Mass-Rubber Dynamic Vibration Absorber (MR-DVA) Dika Andini Suryandari; Wiwiek Hendrowati
The International Journal of Mechanical Engineering and Sciences Vol 6, No 2 (2022)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v6i2.13441

Abstract

Machining is an important part of the industry, one of which is the drilling process. The drilling process is utilized in the making of holes in a material. When the drill-bit touches the material, it causes a vibration that can affect the quality of the hole surface. Therefore, the analysis of the addition of MR-DVA with a natural frequency of 1,630.2 Hz on a workpiece Aluminium Alloy 7075-T6 as the main system is conducted. Simulations were carried out in the natural frequency range of 1,675 Hz – 1,680 Hz with various workpiece dimension ratios of 2⁄5, 3⁄5, 4⁄5, and 5⁄5, along with the different ratios of an MR-DVA placement between the clamp and hole of 1⁄4, 2⁄4, and 3⁄4. Based on the conducted simulation, it has been found that the MR-DVA with a mass ratio of 1⁄20 can dampen well. The largest reduction for a workpiece dimension ratio of 2⁄5 with an MR-DVA placement ratio of 1⁄4 is 92%. In contrast, the smallest reduction for a workpiece dimension ratio of 3⁄5 is 7.2%. These are because the damping area and the increase in the workpiece's dimensions ratio are inverses, affecting the workpiece area that touches the clamp.
Effect of Diffusers Installation in Inlet Primary Air Coal Pulverizer on Airflow Characteristic and Wear Concentration using CFD Modeling R. Panji Satrio Wening Galih; Bambang Arip Dwiyantoro
The International Journal of Mechanical Engineering and Sciences Vol 6, No 2 (2022)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v6i2.12045

Abstract

This paper investigates the effect of installing several diffuser models in the pulverizer ZGM123-GII inlet ducting on air flow characteristics and wear concentration. Results of the internal check found that an area of the wall and the surrounding components were wearing abnormally or faster than usual. This condition will affect the availability of the pulverizer. There are 6 variations model used in this study to solve this problem. The 2 variations in the number of blade, there are 2 and 3 blades, combined with 3 angle variations, namely 30o, 45o, and 50o. In this study, the viscous k-omega SST model was used to simulate airflow from the primary inlet to the area above the throat ring. The results show the contours of velocity of the air and the velocity vector on the pulverizer. Model with an angle of 45 degrees and the number of blade 3, is able to circulate air dominantly on the left side of the primary air inlet ducting, according to the reference study. From all variations, the model 45o angle with 3 blades and 50o angle with 3 blades are able to overcome the wear concentration problem.
Analysis of Seal Face Formation Parameters using Powder Metallurgy Technology with Taguchi Method and Gray Relational Analysis Kurniawan Kurniawan; Mohammad Nurdin; Otto Purnawarman; Fachrul Rozy
The International Journal of Mechanical Engineering and Sciences Vol 6, No 2 (2022)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v6i2.11890

Abstract

The seal face is the main component of a mechanical seal to prevent leakage in a system with fluid flow. Seal face manufacture is generally produced by the material removal process, which causes some raw material waste. Powder metallurgy is the process of manufacturing products from metal powders with raw material efficiency of up to 97%. This study discusses the relationship between the manufacturing process parameters of seal face with SiC material through a powder metallurgy process as a substitute for manufacturing by material removal. The approach used in this research was the design of experiments with the Taguchi method and the technique of Gray Relational Analysis. Process parameters controlled were compaction pressure (CF), compaction time (CH), sintering temperature (ST), and sintering time (SH). Responses were measured in the form of surface hardness (HV) and density. The combination of process parameters that produces the optimum response is CF = 408 N/mm2 (level 3), CH = 2 min (level 1) ST = 1050°C (level 3), SH = 120 min (level 2) with contribution of process parameters CF = 38.06%, CH = 2.53%, ST = 49.50%, and SH = 9.91%. The optimum surface hardness and density values were 513.03 HV and 3.04 gr/mm3. 
Automated Corrosion Detection on Steel Structures Using Convolutional Neural Network Mohammad Khoirul Effendi; Bara Atmaja; Arif Wahjudi; Dedi Budi Purwanto
JMES The International Journal of Mechanical Engineering and Sciences Vol 7, No 1 (2023)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v7i1.15881

Abstract

Steel is a material that is widely used in industry and construction. The tensile and compressive force of steel is relatively high compared to other materials. On the opposite, low corrosion resistance is the main weakness of steel, which can encourage steel deterioration and fatal accidents for the user. Furthermore, regular visual inspection by a human should be performed to prevent catastrophic incidents. However, human visual inspection increases the risk of work accidents and reduces work effectiveness. Therefore, a drone with a camera is one solution to increase efficiency, increase security levels, and minimize difficulties or risks during corrosion inspection. In this research, the drone has been used to capture corroded video of a construction structure. The convolutional neural network (CNN) method is then used to detect the location of the corroded images. This study has been conducted on Surabaya’s Petekan-bridge with the Mobilenet V1 SSD pre-training model. In this study, the distance between a drone and the detected object varied between 1 and 2 m. Next, the drone speed was varied into 0.6 m/s, 0.9m/s, and 1.3m/s. As a result, CNN can detect corrosion on the surface of steel materials with the best accuracy is 84.66% and minimum total loss value of 1.673 by applying 200 images, 200000 epochs, batch size at 4, learning rate at 0.001 and 0.1, the distance at 1 m, drone speed at 0.6 m/s. 

Page 6 of 10 | Total Record : 93

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