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All Journal Rekayasa Mesin Journal of Energy, Mechanical, Material and Manufacturing Engineering
Feriyanto, Richie
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Mechanical Engineering

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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.
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.
Co-Authors Abdul Rahim Abdul Rahim Hery Tri Waloyo Mujianto, Agus