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INDONESIA
Journal of Energy, Mechanical, Material and Manufacturing Engineering
Published by Universitas Muhammadiyah Malang
ISSN : 25416332     EISSN : 25484281     DOI : -
Core Subject : Engineering,
Mechanical Engineering
Journal of Energy, Mechanical, Material and Manufacturing Engineering Scientific (JEMMME) is a scientific journal in the area of renewable energy, mechanical engineering, advanced material, dan manufacturing engineering. We are committing to invite academicians and scientiests for sharing ideas, knowledges, and experiences in our online publishing for free of charge. It would be our pleasure to accept your manuscripts submission to our journal site.
Arjuna Subject : -
Articles 6 Documents
 1 
Search results for , issue "Vol. 9 No. 2 (2024)" : 6 Documents clear
Tensile strength prediction of empty palm oil bunch fiber composite with artificial neural network Waloyo, Hery Tri; Mujianto, Agus; Feriyanto, Richie
Journal of Energy, Mechanical, Material, and Manufacturing Engineering Vol. 9 No. 2 (2024)
Publisher : University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jemmme.v9i2.35619

Abstract

As the leading global producer of palm oil, Indonesia encounters substantial environmental challenges arising from the waste generated by empty palm oil fruit bunches (EPOFB). This research aims to develop an accurate Artificial Neural Network (ANN) model to predict the tensile strength of EPOFB fiber-reinforced composites. The method involves two types of ANN, namely Radial Basis Function (RBF) and Backpropagation, with testing using variations in immersion time, volume fraction, and length of EPOFB fibers. The research results show that both ANN models can predict tensile strength with a Mean Absolute Error (MAE) below 10%. However, the Backpropagation ANN shows superior performance with a training MAE of 0.0078 and a testing MAE of 0.45, compared to the RBF ANN, which has a training MAE of 0.371 and a testing MAE of 0.53. In conclusion, ANN Backpropagation is superior in prediction accuracy and characterization efficiency of EFB fiber-reinforced composites, offering an economical solution and supporting sustainable palm oil waste management.
The performance improvement of the combustion process in diesel engines with fuel heater wagiman, Acep
Journal of Energy, Mechanical, Material, and Manufacturing Engineering Vol. 9 No. 2 (2024)
Publisher : University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jemmme.v9i2.36203

Abstract

The incomplete combustion process will be a problem in the development effort of the diesel engine's performance. The non-homogeneous air-fuel mixing process is one of the factors which causes incomplete combustion. Heating the diesel fuel to a certain temperature before it goes through the high-pressure injection pump will lower its density and viscosity. Therefore, when injected in the combustion chamber, it forms smaller droplets of fuel spray which results in a more homogeneous air-fuel mixture. Moreover, using higher temperatures will make the diesel fuel easier to ignite to compensate for the limited time that is available in high-speed operating conditions. Diesel fuel heating can improve the combustion process to increase the power and decrease fuel consumption optimally.  
Modeling and simulation of magnet-coil arrays for vibrational energy harvesting in agricultural electric vehicles Divine Kobbi, Mbanwei; Alombah , Njimboh Henry; Ngwabie, Ngwa Martin
Journal of Energy, Mechanical, Material, and Manufacturing Engineering Vol. 9 No. 2 (2024)
Publisher : University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jemmme.v9i2.36498

Abstract

Electric vehicles have advantages such as reduced maintenance and fuel costs compared to internal combustion engines. However, their limited driving range still hinders their widespread adoption compared to internal combustion engines. Harvesting wasted energies through vibrations in electric vehicles is a good approach to complement the energy of their batteries. Space constraints in electric vehicles require devices with high power output per unit volume. This study aimed to design a novel vibration energy harvesting using the geometrical model for electric vehicles. Different configurations and their performance in maximum flux linkage, electromagnetic coupling coefficient, induced voltage, and generated power were investigated. The modeling, excitement, and analysis were conducted using ANSYS Maxwell software with four configurations under similar conditions. These were the Halbach array with three magnets, one coil, and flat back shield; the Halbach array with three magnets and one coil with a stepped back shield; the double magnet array with two magnets, one coil, and flat back shield; and the fourth one was a double magnet array with two magnets, one coil and stepped back shield. The MATLAB Simulink software was used to obtain further results and power output analysis. The results of the analysis show that the Halbach array with three magnets, one coil, and a stepped-back shield is the best configuration for harvesting energy from vibrations, producing an electromagnetic coupling coefficient of up to 110 Wb/m, a voltage of up to 36 V, and generated power density of 0.13 W/cm. A reasonable increase in output using less volume was obtained compared to the other studies. The energy harvested will be applied in future studies to extend the range of agricultural electric vehicles, reducing farmers’ income spent on fuel and maintenance.
The effect of adding borax on the Oxy-Acetylene Welding (OAW) process on tensile strength of ST42 steel Moh. Jufri; Hendaryati, Heni; Nur Subeki; Pramojo, Mita Putri Bambang; Baiq Firyal Salsabila Safitri
Journal of Energy, Mechanical, Material, and Manufacturing Engineering Vol. 9 No. 2 (2024)
Publisher : University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jemmme.v9i2.34988

Abstract

The welding process aims at joining materials, especially metals or thermoplastic for certain purposes. It melts the materials in the joining process. In some cases, such material is difficult to be joined with the welding process. Therefore, an appropriate flux is needed in this process. The effect of adding borax in the welding process of OAW is the focus of this research. The research was conducted using the experimental method of adding 1 gram, 3 grams, and 5 grams of borax to the brass and ST42 steel welding process. Borax was added to aid the adhesion process between brass and steel as both have different properties. This research results in the highest elongation value in 1 gram of borax addition; the elongation is 3.9935 cm. Meanwhile, adding 1 gram of borax also affects the welding joints’ ultimate tensile strength (UTS). It results in the highest UTS value of 9.961779 kN/mm2 among the other weight variations of borax addition. It indicates that the borax addition with proper weight in the welding process affects the joints. Moreover, the borax addition in the welding process influences the elongation values, ultimate tensile strength, and modulus of elasticity of the welding joints.
The implementation of innovative IoT models in machine failure detection and risk mitigation Hendra, Franka; Effendi, Riki; Supriyono
Journal of Energy, Mechanical, Material, and Manufacturing Engineering Vol. 9 No. 2 (2024)
Publisher : University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jemmme.v9i2.34121

Abstract

In the era of Industry 4.0, the integration of advanced technologies like the Internet of Things (IoT) into risk-based maintenance planning systems has become crucial for optimizing operational efficiency. This research explores methods to enhance maintenance decision-making by integrating real-time IoT data with risk-based maintenance models. Traditional risk-based maintenance often relies on historical data, which can be insufficient for responding to dynamic operational conditions. By leveraging IoT's ability to collect continuous, real-time data, this study aims to improve the accuracy and responsiveness of maintenance strategies. The research employs a systematic methodology, including data collection through IoT sensors, data preprocessing, and the development of predictive models using machine learning techniques such as Random Forest and Neural Networks. The results indicate that IoT integration reduces downtime by predicting equipment failures with higher accuracy, leading to a 30% reduction in maintenance costs and a 25% increase in productivity. This study demonstrates the significant potential of IoT in transforming maintenance strategies from reactive to proactive, ultimately enhancing equipment reliability and extending operational lifespan.
Steam requirements and mass balance in digesters and screw presses at palm oil mill Zulfatri Aini; Tengku, Tengku Reza Suka Alaqsa; Sri Basriati
Journal of Energy, Mechanical, Material, and Manufacturing Engineering Vol. 9 No. 2 (2024)
Publisher : University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jemmme.v9i2.37043

Abstract

Fresh Fruit Bunches (FFB) are the primary component in Crude Palm Oil (CPO) production. Palm oil mills face challenges in optimizing CPO yield, particularly in reducing oil losses during processing, which affects efficiency and profitability. The pressing station, including the digester and screw press, plays an important role in oil extraction. The digester uses steam to heat and soften the fruit for better oil release, while the screw press performs the mechanical extraction of oil. Insufficient steam can hinder oil separation, leading to increased losses. This research aimed to analyze steam requirements for the digester and evaluate the mass balance of the screw press. Using energy and mass balance methods, the optimal steam requirement was 359,870 kg/hour with a mass balance error of 6.58%. Corrective actions in steam valve settings reduced oil losses to 1.57%, which improved processing efficiency and product quality.

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