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All Journal Rekayasa Mesin Proceeding of the Electrical Engineering Computer Science and Informatics Journal of Energy, Mechanical, Material and Manufacturing Engineering Jurnal Abdimas Mahakam Manutech : Jurnal Teknologi Manufaktur Jurnal Teknik Mesin Indonesia Teknosains : Jurnal Sains,Teknologi dan Informatika JTTM: Jurnal Terapan Teknik Mesin National Multidisciplinary Sciences
Hery Tri Waloyo
Universitas Muhammadiyah Kalimantan Timur
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences

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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.
OPTIMIZATION OF TENSILE STRENGTH OF EMPTY OIL PALM FRUIT BUNCH FIBER REINFORCED COMPOSITES USING GENETIC ALGORITHMS Rahim, Abdul; Mujianto, Agus; Feriyanto, Richie; Waloyo, Hery Tri
Jurnal Rekayasa Mesin Vol. 15 No. 3 (2024)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v15i3.1898

Abstract

The use of natural materials such as oil palm empty fruit bunch fibers can provide a solution to increase value-added and manage plantation waste. Fibers are combined with a matrix to create composite materials. Instead of glass fibers, environmentally friendly natural fibers serve as the reinforcement in the composite material. Implementing natural fiber composites must consider the primary construction requirement, which is tensile strength. Artificial intelligence like genetic algorithms (GA) can simplify and reduce costs in the search for optimal values in composite material engineering. Data is obtained through experimental testing prepared samples and subsequently used as input for GA. The input parameters consist of three variables such as soaking time, volume fraction, and fiber length. The output of the optimization process is tensile strength. The maximum tensile strength has already been achieved with genetic crossover by the 125th generation. Based on GA calculations, the optimal parameters obtained are soaking time of 6.2 hours, volume fraction of 29.6%, and fiber length of 6.9 cm. The predicted optimal tensile strength value is 4.78 MPa.
Proses Produksi Soft Magnetic Composite (SMC) Berbahan Dasar Fe Waloyo, Hery Tri; Firmansyah, Muhammad Gilang; Setiyawan, Khanif; Mujianto, Agus
Jurnal Teknik Mesin Indonesia Vol. 20 No. 1 (2025): Vol. 20 No. 1 (2025): Jurnal Teknik Mesin Indonesia
Publisher : Badan Kerja Sama Teknik Mesin Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36289/jtmi.v20i1.765

Abstract

This study discuss in various production methods of iron-based Soft Magnetic Composite (SMC) and to identify the most effective production. The increase in carbon emissions and the need for alternative energy drive the development of electromagnetic technology, including the use of SMC in electric motors. The method used in this study is a literature review, by reviewing relevant articles from national and international journals. The results of the study indicate that traditional production methods and Additive Manufacturing (AM) are the two main methods used in SMC production. Among these methods, the traditional production method with Powder Metallurgy (PM) technology proved to be more effective in producing SMC with better quality and more efficient production processes. This research provides in-depth insights into various SMC production techniques and can serve as a foundation for future research in the development of electromagnetic materials.
OPTIMIZATION OF TENSILE STRENGTH OF EMPTY OIL PALM FRUIT BUNCH FIBER REINFORCED COMPOSITES USING GENETIC ALGORITHMS Rahim, Abdul; Mujianto, Agus; Feriyanto, Richie; Waloyo, Hery Tri
Jurnal Rekayasa Mesin Vol. 15 No. 3 (2024)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v15i3.1898

Abstract

The use of natural materials such as oil palm empty fruit bunch fibers can provide a solution to increase value-added and manage plantation waste. Fibers are combined with a matrix to create composite materials. Instead of glass fibers, environmentally friendly natural fibers serve as the reinforcement in the composite material. Implementing natural fiber composites must consider the primary construction requirement, which is tensile strength. Artificial intelligence like genetic algorithms (GA) can simplify and reduce costs in the search for optimal values in composite material engineering. Data is obtained through experimental testing prepared samples and subsequently used as input for GA. The input parameters consist of three variables such as soaking time, volume fraction, and fiber length. The output of the optimization process is tensile strength. The maximum tensile strength has already been achieved with genetic crossover by the 125th generation. Based on GA calculations, the optimal parameters obtained are soaking time of 6.2 hours, volume fraction of 29.6%, and fiber length of 6.9 cm. The predicted optimal tensile strength value is 4.78 MPa.
SOFT MAGNETIC COMPOSITE (SMC) ANALISIS DENGAN BIBLIOMETRIC Waloyo, Hery Tri; Kurniawan, Krisna Budi; Ahmad, Fauzi; Putra, Ferdiansyah; Fauzan, M. Afif; Firmansyah, Muhammad Gilang; Mujianto, Agus
Jurnal Rekayasa Mesin Vol. 16 No. 1 (2025)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v16i1.1917

Abstract

Soft Magnetic Composite (SMC) is a composite material made by binding iron (Fe) powder with non-magnetic materials such as epoxy resin. The main properties of SMC, which have high electrical resistance yet can efficiently conduct magnetic fields, make it a primary choice for various applications that require efficiency, reliability, and high performance. Extensive research has been conducted on SMC, making it necessary to perform a bibliometric analysis to categorize and observe developments in SMC research. The main objectives of this study are to understand patterns in scientific publications, identify the most productive research developments, determine research linkages, and identify collaboration networks related to SMC. A total of 1067 documents, 347 sources, and 2113 authors were recorded in the Scopus database between 2005 and 2024. The number of article publications has increased annually by 4.36%. China is the most productive country and also has the most extensive international collaboration networks. The majority of publications are published in journals with specific topics, primarily focusing on magnetics and magnetic materials.
Co-Authors ., Inayati Abdul Rahim Abdul Rahim Agus Mujianto Agus Mujianto Ahmad, Fauzi Akbar Ramadhan, Muhaimin Andi Nugroho Andi Nugroho, Andi Anis Siti Nurrohkayati Arbansyah Binyamin Binyamin, Binyamin Fajar David Aminuddin Fauzan, M. Afif Ferdiansyah Putra Feriyanto, Richie Firmansyah, Muhammad Gilang Ilham, Muhammad Fauzan Nur Inayati . Khanif Setiyawan Kurniawan, Krisna Budi Muhammad Nizam Muhammad Taufiq Sumadi Mujianto, Agus Naufal Azmi Verdikha Nizam, Muhammad Nur Prapanca, Agusti Perdinaresa, Gredino Ramadhani, Reza Sudirman Sudirman Syam Ramadhani Saputra