A comparative study on various ANN optimization algorithms for magnetorheological elastomer carbonyl iron particle concentration estimation
Estimation particle composition such as particle shape, size, and concentration are crucial prior to the fabrication process of magnetorheological elastomer (MRE) to avoid process repetition due to inaccurate formulation. Currently, most of MRE prediction model were purposely used to predict the rhe...
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Penerbit Akademia Baru
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/41685/1/A%20comparative%20study%20on%20various%20ANN%20optimization%20algorithms%20for%20magnetorheological.pdf http://umpir.ump.edu.my/id/eprint/41685/ https://doi.org/10.37934/armne.16.1.124133 |
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| author | Kasma Diana, Saharuddin Mohd Hatta, Mohammed Ariff Bahiuddin, Irfan Nurhazimah, Nazmi Mohd Azizi, Abdul Rahman Mohd Ibrahim, Shapiai Fauzan, Ahmad Sarah Atifah, Saruchi |
| author_facet | Kasma Diana, Saharuddin Mohd Hatta, Mohammed Ariff Bahiuddin, Irfan Nurhazimah, Nazmi Mohd Azizi, Abdul Rahman Mohd Ibrahim, Shapiai Fauzan, Ahmad Sarah Atifah, Saruchi |
| author_sort | Kasma Diana, Saharuddin |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Estimation particle composition such as particle shape, size, and concentration are crucial prior to the fabrication process of magnetorheological elastomer (MRE) to avoid process repetition due to inaccurate formulation. Currently, most of MRE prediction model were purposely used to predict the rheological properties such as shear stress and dynamic modulus, known as forward model. Nonetheless, very few studies have been reported to be capable able of predicting particle composition particularly in MR materials, which known as inverse model. Therefore, this paper proposed a carbonyl iron particle (CIP) concentration based MRE prediction model using neural network algorithm. Neural network-based machine learning model is more approachable compared to conventional mathematical modelling approach due to easily identify trends and pattern while handling multi-variety data. Various optimization algorithms have been employed such as Adam, RMSprop, SGD, AdaGrad, and Nadam throughout the modelling process. As the results, given shear strain amplitude, magnetic flux density, storage modulus, and loss factor as model input, SGD gave the maximum prediction accuracy with 0.95 and 3.038 MPa of R2 and RMSE, respectively. Hence, this model can be the basis to the MRE material and devices development particularly as the tool to reduce costing and time consuming. |
| format | Article |
| id | my.ump.umpir.41685 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2024 |
| publisher | Penerbit Akademia Baru |
| record_format | eprints |
| spelling | my.ump.umpir.416852024-07-31T03:22:39Z http://umpir.ump.edu.my/id/eprint/41685/ A comparative study on various ANN optimization algorithms for magnetorheological elastomer carbonyl iron particle concentration estimation Kasma Diana, Saharuddin Mohd Hatta, Mohammed Ariff Bahiuddin, Irfan Nurhazimah, Nazmi Mohd Azizi, Abdul Rahman Mohd Ibrahim, Shapiai Fauzan, Ahmad Sarah Atifah, Saruchi T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Estimation particle composition such as particle shape, size, and concentration are crucial prior to the fabrication process of magnetorheological elastomer (MRE) to avoid process repetition due to inaccurate formulation. Currently, most of MRE prediction model were purposely used to predict the rheological properties such as shear stress and dynamic modulus, known as forward model. Nonetheless, very few studies have been reported to be capable able of predicting particle composition particularly in MR materials, which known as inverse model. Therefore, this paper proposed a carbonyl iron particle (CIP) concentration based MRE prediction model using neural network algorithm. Neural network-based machine learning model is more approachable compared to conventional mathematical modelling approach due to easily identify trends and pattern while handling multi-variety data. Various optimization algorithms have been employed such as Adam, RMSprop, SGD, AdaGrad, and Nadam throughout the modelling process. As the results, given shear strain amplitude, magnetic flux density, storage modulus, and loss factor as model input, SGD gave the maximum prediction accuracy with 0.95 and 3.038 MPa of R2 and RMSE, respectively. Hence, this model can be the basis to the MRE material and devices development particularly as the tool to reduce costing and time consuming. Penerbit Akademia Baru 2024 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/41685/1/A%20comparative%20study%20on%20various%20ANN%20optimization%20algorithms%20for%20magnetorheological.pdf Kasma Diana, Saharuddin and Mohd Hatta, Mohammed Ariff and Bahiuddin, Irfan and Nurhazimah, Nazmi and Mohd Azizi, Abdul Rahman and Mohd Ibrahim, Shapiai and Fauzan, Ahmad and Sarah Atifah, Saruchi (2024) A comparative study on various ANN optimization algorithms for magnetorheological elastomer carbonyl iron particle concentration estimation. Journal of Advanced Research in Micro and Nano Engineering, 16 (1). pp. 124-133. ISSN 2756-8210. (Published) https://doi.org/10.37934/armne.16.1.124133 https://doi.org/10.37934/armne.16.1.124133 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Kasma Diana, Saharuddin Mohd Hatta, Mohammed Ariff Bahiuddin, Irfan Nurhazimah, Nazmi Mohd Azizi, Abdul Rahman Mohd Ibrahim, Shapiai Fauzan, Ahmad Sarah Atifah, Saruchi A comparative study on various ANN optimization algorithms for magnetorheological elastomer carbonyl iron particle concentration estimation |
| title | A comparative study on various ANN optimization algorithms for magnetorheological elastomer carbonyl iron particle concentration estimation |
| title_full | A comparative study on various ANN optimization algorithms for magnetorheological elastomer carbonyl iron particle concentration estimation |
| title_fullStr | A comparative study on various ANN optimization algorithms for magnetorheological elastomer carbonyl iron particle concentration estimation |
| title_full_unstemmed | A comparative study on various ANN optimization algorithms for magnetorheological elastomer carbonyl iron particle concentration estimation |
| title_short | A comparative study on various ANN optimization algorithms for magnetorheological elastomer carbonyl iron particle concentration estimation |
| title_sort | comparative study on various ann optimization algorithms for magnetorheological elastomer carbonyl iron particle concentration estimation |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/41685/1/A%20comparative%20study%20on%20various%20ANN%20optimization%20algorithms%20for%20magnetorheological.pdf http://umpir.ump.edu.my/id/eprint/41685/ https://doi.org/10.37934/armne.16.1.124133 https://doi.org/10.37934/armne.16.1.124133 |
| url_provider | http://umpir.ump.edu.my/ |