A Comparative Study on Various ANN Optimization Algorithms for Magnetorheological Elastomer Carbonyl Iron Particle Concentration Estimation

Authors

  • Kasma Diana Saharuddin Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, Malaysia
  • Mohd Hatta Mohammed Ariff Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, Malaysia
  • Irfan Bahiuddin Department of Mechanical Engineering, Vocational College Universitas Gadjah Mada, Yogyakarta, Indonesia
  • Nurhazimah Nazmi Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, Malaysia
  • Mohd Azizi Abdul Rahman Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, Malaysia
  • Mohd Ibrahim Shapiai Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, Malaysia
  • Fauzan Ahmad Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, Malaysia
  • Sarah ‘Atifah Saruchi Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia

DOI:

https://doi.org/10.37934/armne.16.1.124133

Keywords:

Magnetorheological elastomer, carbonyl iron particle, artificial neural network, Adam optimizer, machine learning

Abstract

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. 

Author Biography

Mohd Hatta Mohammed Ariff, Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, Malaysia

mohdhatta.kl@utm.my

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Published

2024-03-03

How to Cite

Kasma Diana Saharuddin, Mohd Hatta Mohammed Ariff, Irfan Bahiuddin, Nurhazimah Nazmi, Mohd Azizi Abdul Rahman, Mohd Ibrahim Shapiai, 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):124-33. https://doi.org/10.37934/armne.16.1.124133.
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