Prediction of magnetorheological grease compositions using extreme learning machine methods
This paper presents a data-driven model to predict magnetorheological (MR) grease composition as a function of its rheological properties using several machine learning methods. The methods are Single Hidden Layer Feedforward Neural Networks (SLFNs) and Kernel Based-Extreme Learning Ma-chine (KELM)....
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creator | Bahiuddin, Irfan Pratama, Nico Imaduddin, Fitrian Mazlan, Saiful Amri Ubaidillah Mohamad, Norzilawati |
description | This paper presents a data-driven model to predict magnetorheological (MR) grease composition as a function of its rheological properties using several machine learning methods. The methods are Single Hidden Layer Feedforward Neural Networks (SLFNs) and Kernel Based-Extreme Learning Ma-chine (KELM). The approach provides high accuracy prediction and the easiness of changing the inputs or outputs as long as the data is available. While the model output is carbonyl iron particles weight percentage, the model in-puts are the slope of the magnetic field density-dependent-yield stress change over the magnetic fields and the off-state yield stress. The kernel functions are varied from radial basis function, wavelet, linear, and polynomial functions. The simulation results of KELM show that R-squared values are more than 90% for both training and testing data. The root mean square errors also show relatively small values. With a relatively lower number of parameters than SLFNs-ELM, KELM can show comparable performance with SLFNs-ELM and Back Propagation neural networks. |
doi_str_mv | 10.1063/5.0228147 |
format | Conference Proceeding |
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The methods are Single Hidden Layer Feedforward Neural Networks (SLFNs) and Kernel Based-Extreme Learning Ma-chine (KELM). The approach provides high accuracy prediction and the easiness of changing the inputs or outputs as long as the data is available. While the model output is carbonyl iron particles weight percentage, the model in-puts are the slope of the magnetic field density-dependent-yield stress change over the magnetic fields and the off-state yield stress. The kernel functions are varied from radial basis function, wavelet, linear, and polynomial functions. The simulation results of KELM show that R-squared values are more than 90% for both training and testing data. The root mean square errors also show relatively small values. With a relatively lower number of parameters than SLFNs-ELM, KELM can show comparable performance with SLFNs-ELM and Back Propagation neural networks.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0228147</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Artificial neural networks ; Back propagation networks ; Carbonyls ; Composition ; Greases ; Kernel functions ; Machine learning ; Magnetic fields ; Magnetic properties ; Neural networks ; Polynomials ; Radial basis function ; Rheological properties ; Yield strength ; Yield stress</subject><ispartof>AIP Conference Proceedings, 2024, Vol.3124 (1)</ispartof><rights>Author(s)</rights><rights>2024 Author(s). Published under an exclusive license by AIP Publishing.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://pubs.aip.org/acp/article-lookup/doi/10.1063/5.0228147$$EHTML$$P50$$Gscitation$$H</linktohtml><link.rule.ids>310,311,315,782,786,791,792,796,4514,23937,23938,25147,27931,27932,76392</link.rule.ids></links><search><contributor>Prabowo, Aditya Rio</contributor><contributor>Tjahjana, Dominicus Danardono Dwi Prija</contributor><contributor>Imaddudin, Fitrian</contributor><contributor>Ubaidillah</contributor><contributor>Yaningsih, Indri</contributor><creatorcontrib>Bahiuddin, Irfan</creatorcontrib><creatorcontrib>Pratama, Nico</creatorcontrib><creatorcontrib>Imaduddin, Fitrian</creatorcontrib><creatorcontrib>Mazlan, Saiful Amri</creatorcontrib><creatorcontrib>Ubaidillah</creatorcontrib><creatorcontrib>Mohamad, Norzilawati</creatorcontrib><title>Prediction of magnetorheological grease compositions using extreme learning machine methods</title><title>AIP Conference Proceedings</title><description>This paper presents a data-driven model to predict magnetorheological (MR) grease composition as a function of its rheological properties using several machine learning methods. The methods are Single Hidden Layer Feedforward Neural Networks (SLFNs) and Kernel Based-Extreme Learning Ma-chine (KELM). The approach provides high accuracy prediction and the easiness of changing the inputs or outputs as long as the data is available. While the model output is carbonyl iron particles weight percentage, the model in-puts are the slope of the magnetic field density-dependent-yield stress change over the magnetic fields and the off-state yield stress. The kernel functions are varied from radial basis function, wavelet, linear, and polynomial functions. The simulation results of KELM show that R-squared values are more than 90% for both training and testing data. The root mean square errors also show relatively small values. With a relatively lower number of parameters than SLFNs-ELM, KELM can show comparable performance with SLFNs-ELM and Back Propagation neural networks.</description><subject>Artificial neural networks</subject><subject>Back propagation networks</subject><subject>Carbonyls</subject><subject>Composition</subject><subject>Greases</subject><subject>Kernel functions</subject><subject>Machine learning</subject><subject>Magnetic fields</subject><subject>Magnetic properties</subject><subject>Neural networks</subject><subject>Polynomials</subject><subject>Radial basis function</subject><subject>Rheological properties</subject><subject>Yield strength</subject><subject>Yield stress</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNotkM9LwzAcxYMoOKcH_4OAN6EzP5qkOcrQKQz0sIPgIaTpt11G29SkBf3vXdlODx4f3uM9hO4pWVEi-ZNYEcYKmqsLtKBC0ExJKi_RghCdZyznX9foJqUDIUwrVSzQ92eEyrvRhx6HGne26WEMcQ-hDY13tsVNBJsAu9ANIfkZTHhKvm8w_I4ROsAt2NjPRmfd3veAOxj3oUq36Kq2bYK7sy7R7vVlt37Lth-b9_XzNhskVxnVjOe0qkHWhFupGThdK-dkYQlTymlXkgq0tLzQDmhV6DJ3vCyPmOB1WfElejjFDjH8TJBGcwhT7I-NhlNKmSaiEEfq8UQl50c7zzBD9J2Nf4YSM39nhDl_x_8BUTNjKg</recordid><startdate>20240930</startdate><enddate>20240930</enddate><creator>Bahiuddin, Irfan</creator><creator>Pratama, Nico</creator><creator>Imaduddin, Fitrian</creator><creator>Mazlan, Saiful Amri</creator><creator>Ubaidillah</creator><creator>Mohamad, Norzilawati</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20240930</creationdate><title>Prediction of magnetorheological grease compositions using extreme learning machine methods</title><author>Bahiuddin, Irfan ; Pratama, Nico ; Imaduddin, Fitrian ; Mazlan, Saiful Amri ; Ubaidillah ; Mohamad, Norzilawati</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p637-192341dfe6f03a692ec9f7cc68a0277c9cb0de96a389ce1d89b4c3bbec953fbd3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Artificial neural networks</topic><topic>Back propagation networks</topic><topic>Carbonyls</topic><topic>Composition</topic><topic>Greases</topic><topic>Kernel functions</topic><topic>Machine learning</topic><topic>Magnetic fields</topic><topic>Magnetic properties</topic><topic>Neural networks</topic><topic>Polynomials</topic><topic>Radial basis function</topic><topic>Rheological properties</topic><topic>Yield strength</topic><topic>Yield stress</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bahiuddin, Irfan</creatorcontrib><creatorcontrib>Pratama, Nico</creatorcontrib><creatorcontrib>Imaduddin, Fitrian</creatorcontrib><creatorcontrib>Mazlan, Saiful Amri</creatorcontrib><creatorcontrib>Ubaidillah</creatorcontrib><creatorcontrib>Mohamad, Norzilawati</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bahiuddin, Irfan</au><au>Pratama, Nico</au><au>Imaduddin, Fitrian</au><au>Mazlan, Saiful Amri</au><au>Ubaidillah</au><au>Mohamad, Norzilawati</au><au>Prabowo, Aditya Rio</au><au>Tjahjana, Dominicus Danardono Dwi Prija</au><au>Imaddudin, Fitrian</au><au>Ubaidillah</au><au>Yaningsih, Indri</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Prediction of magnetorheological grease compositions using extreme learning machine methods</atitle><btitle>AIP Conference Proceedings</btitle><date>2024-09-30</date><risdate>2024</risdate><volume>3124</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>This paper presents a data-driven model to predict magnetorheological (MR) grease composition as a function of its rheological properties using several machine learning methods. The methods are Single Hidden Layer Feedforward Neural Networks (SLFNs) and Kernel Based-Extreme Learning Ma-chine (KELM). The approach provides high accuracy prediction and the easiness of changing the inputs or outputs as long as the data is available. While the model output is carbonyl iron particles weight percentage, the model in-puts are the slope of the magnetic field density-dependent-yield stress change over the magnetic fields and the off-state yield stress. The kernel functions are varied from radial basis function, wavelet, linear, and polynomial functions. The simulation results of KELM show that R-squared values are more than 90% for both training and testing data. The root mean square errors also show relatively small values. With a relatively lower number of parameters than SLFNs-ELM, KELM can show comparable performance with SLFNs-ELM and Back Propagation neural networks.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0228147</doi><tpages>7</tpages></addata></record> |
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source | AIP Journals Complete |
subjects | Artificial neural networks Back propagation networks Carbonyls Composition Greases Kernel functions Machine learning Magnetic fields Magnetic properties Neural networks Polynomials Radial basis function Rheological properties Yield strength Yield stress |
title | Prediction of magnetorheological grease compositions using extreme learning machine methods |
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