Field Dependent Force Predictions of a Meandering Valve Based-Magnetorheological Damper Using Multilayer Artificial Neural Networks
A meandering magnetorheological damper's produced damping force or pressure drop is correlated with the inputted current. Although the relationship can be considered proportional, the predicted force's hysteresis influence led to the difficulty of the modeling process. Artificial Neural Ne...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/281944/1/Chazim_SV.pdf |
| _version_ | 1797037506080800768 |
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| author | Chazim, Ryandhi R Bahiuddin, Irfan Prabhakara, Hafizh A Nugroho, Rizki S Imaduddin, Fitrian Mazlan, Saiful Amri |
| author_facet | Chazim, Ryandhi R Bahiuddin, Irfan Prabhakara, Hafizh A Nugroho, Rizki S Imaduddin, Fitrian Mazlan, Saiful Amri |
| author_sort | Chazim, Ryandhi R |
| collection | UGM |
| description | A meandering magnetorheological damper's produced damping force or pressure drop is correlated with the inputted current. Although the relationship can be considered proportional, the predicted force's hysteresis influence led to the difficulty of the modeling process. Artificial Neural Networks (ANN) can be a solution to predict the behavior of a meandering magnetorheological damper. This paper presents an investigation of the application of ANN on the force prediction of a magnetorheological damper using various hidden layer numbers. The model contains three inputs: displacement, velocity, and electrical current, and one output, which is force. Firstly, the experimental data is prepared and pre-processed to eliminate outliers and to normalize the input range. The prepared data is divided into training and testing data and inputted into the training algorithm. Various hidden layer numbers' effects on the model accuracy have been investigated. The visual observations on the figures of force-displacement and force-velocity also have been conducted to check the predicted hysteresis and pattern at various currents. The results show that layers two, three, and four can produce better results than one layer ANN with mean square errors above 0.010. In conclusion, the proposed method can accurately predict the damping force, considering the R-squared value of 0.9997. |
| first_indexed | 2024-03-14T00:04:19Z |
| format | Conference or Workshop Item |
| id | oai:generic.eprints.org:281944 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-03-14T00:04:19Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2819442023-11-15T07:27:04Z https://repository.ugm.ac.id/281944/ Field Dependent Force Predictions of a Meandering Valve Based-Magnetorheological Damper Using Multilayer Artificial Neural Networks Chazim, Ryandhi R Bahiuddin, Irfan Prabhakara, Hafizh A Nugroho, Rizki S Imaduddin, Fitrian Mazlan, Saiful Amri Engineering Mechanical Engineering A meandering magnetorheological damper's produced damping force or pressure drop is correlated with the inputted current. Although the relationship can be considered proportional, the predicted force's hysteresis influence led to the difficulty of the modeling process. Artificial Neural Networks (ANN) can be a solution to predict the behavior of a meandering magnetorheological damper. This paper presents an investigation of the application of ANN on the force prediction of a magnetorheological damper using various hidden layer numbers. The model contains three inputs: displacement, velocity, and electrical current, and one output, which is force. Firstly, the experimental data is prepared and pre-processed to eliminate outliers and to normalize the input range. The prepared data is divided into training and testing data and inputted into the training algorithm. Various hidden layer numbers' effects on the model accuracy have been investigated. The visual observations on the figures of force-displacement and force-velocity also have been conducted to check the predicted hysteresis and pattern at various currents. The results show that layers two, three, and four can produce better results than one layer ANN with mean square errors above 0.010. In conclusion, the proposed method can accurately predict the damping force, considering the R-squared value of 0.9997. 2023-01-16 Conference or Workshop Item PeerReviewed application/pdf en https://repository.ugm.ac.id/281944/1/Chazim_SV.pdf Chazim, Ryandhi R and Bahiuddin, Irfan and Prabhakara, Hafizh A and Nugroho, Rizki S and Imaduddin, Fitrian and Mazlan, Saiful Amri (2023) Field Dependent Force Predictions of a Meandering Valve Based-Magnetorheological Damper Using Multilayer Artificial Neural Networks. In: 2nd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2022, 4-5 November 2022, Yogyakarta, Indonesia. https://ieeexplore.ieee.org/document/10010062 |
| spellingShingle | Engineering Mechanical Engineering Chazim, Ryandhi R Bahiuddin, Irfan Prabhakara, Hafizh A Nugroho, Rizki S Imaduddin, Fitrian Mazlan, Saiful Amri Field Dependent Force Predictions of a Meandering Valve Based-Magnetorheological Damper Using Multilayer Artificial Neural Networks |
| title | Field Dependent Force Predictions of a Meandering Valve Based-Magnetorheological Damper Using Multilayer Artificial Neural Networks |
| title_full | Field Dependent Force Predictions of a Meandering Valve Based-Magnetorheological Damper Using Multilayer Artificial Neural Networks |
| title_fullStr | Field Dependent Force Predictions of a Meandering Valve Based-Magnetorheological Damper Using Multilayer Artificial Neural Networks |
| title_full_unstemmed | Field Dependent Force Predictions of a Meandering Valve Based-Magnetorheological Damper Using Multilayer Artificial Neural Networks |
| title_short | Field Dependent Force Predictions of a Meandering Valve Based-Magnetorheological Damper Using Multilayer Artificial Neural Networks |
| title_sort | field dependent force predictions of a meandering valve based magnetorheological damper using multilayer artificial neural networks |
| topic | Engineering Mechanical Engineering |
| url | https://repository.ugm.ac.id/281944/1/Chazim_SV.pdf |
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