Artificial neural network modeling of grinding of ductile cast iron using water based sio2 nanocoolant
This paper presents optimization of the grinding progress of ductile cast iron using water-based SiO2 nanocoolant. Conventional and water-based nanocoolant grinding was performed using a precision surface grinding machine. The study is aimed to investigate the effect of table speed and depth of cut...
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Universiti Malaysia Pahang
2014
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| Online Access: | http://umpir.ump.edu.my/id/eprint/8171/ http://umpir.ump.edu.my/id/eprint/8171/ http://umpir.ump.edu.my/id/eprint/8171/ http://umpir.ump.edu.my/id/eprint/8171/1/15_Rahman_et_al.pdf |
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ump-81712018-01-25T02:43:09Z http://umpir.ump.edu.my/id/eprint/8171/ Artificial neural network modeling of grinding of ductile cast iron using water based sio2 nanocoolant M. M., Rahman K., Kadirgama Azma Salwani, Ab Aziz TJ Mechanical engineering and machinery This paper presents optimization of the grinding progress of ductile cast iron using water-based SiO2 nanocoolant. Conventional and water-based nanocoolant grinding was performed using a precision surface grinding machine. The study is aimed to investigate the effect of table speed and depth of cut on the surface roughness and material removal rate (MRR). Mathematical modeling is developed using the response surface method. An artificial neural network model is developed for predicting the surface roughness and MRR. Multi-layer perception and a batch back propagation algorithm are used. MLP is a gradient descent technique to minimize the error through a particular training pattern in which it adjusts the weight by a small amount at a time. From the experiment, the depth of cut is directly proportional to the surface roughness, but the table speed is inversely proportional to the surface roughness. The higher the value of the depth of cut, the lower the value of MRR, and vice versa for the table speed. It is concluded that the surface quality together with the material removal rate are the most affected by the depth of cut(s) and table speed. Universiti Malaysia Pahang 2014-06-30 Article PeerReviewed application/pdf en cc_by_nc_nd http://umpir.ump.edu.my/id/eprint/8171/1/15_Rahman_et_al.pdf M. M., Rahman and K., Kadirgama and Azma Salwani, Ab Aziz (2014) Artificial neural network modeling of grinding of ductile cast iron using water based sio2 nanocoolant. International Journal of Automotive and Mechanical Engineering (IJAME), 9. pp. 1649-1661. ISSN 1985-9325(Print); 2180-1606 (Online) http://dx.doi.org/10.15282/ijame.9.2013.15.0137 DOI: 10.15282/ijame.9.2013.15.0137 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Malaysia Pahang |
| building |
UMP Institutional Repository |
| collection |
Online Access |
| language |
English |
| topic |
TJ Mechanical engineering and machinery |
| spellingShingle |
TJ Mechanical engineering and machinery M. M., Rahman K., Kadirgama Azma Salwani, Ab Aziz Artificial neural network modeling of grinding of ductile cast iron using water based sio2 nanocoolant |
| description |
This paper presents optimization of the grinding progress of ductile cast iron using water-based SiO2 nanocoolant. Conventional and water-based nanocoolant grinding was performed using a precision surface grinding machine. The study is aimed to investigate the effect of table speed and depth of cut on the surface roughness and material removal rate (MRR). Mathematical modeling is developed using the response surface method. An artificial neural network model is developed for predicting the surface roughness and MRR. Multi-layer perception and a batch back propagation algorithm are used. MLP is a gradient descent technique to minimize the error through a particular training pattern in which it adjusts the weight by a small amount at a time. From the experiment, the depth of cut is directly proportional to the surface roughness, but the table speed is inversely proportional to the surface roughness. The higher the value of the depth of cut, the lower the value of MRR, and vice versa for the table speed. It is concluded that the surface quality together with the material removal rate are the most affected by the depth of cut(s) and table speed. |
| format |
Article |
| author |
M. M., Rahman K., Kadirgama Azma Salwani, Ab Aziz |
| author_facet |
M. M., Rahman K., Kadirgama Azma Salwani, Ab Aziz |
| author_sort |
M. M., Rahman |
| title |
Artificial neural network modeling of grinding of ductile cast iron using water based sio2 nanocoolant |
| title_short |
Artificial neural network modeling of grinding of ductile cast iron using water based sio2 nanocoolant |
| title_full |
Artificial neural network modeling of grinding of ductile cast iron using water based sio2 nanocoolant |
| title_fullStr |
Artificial neural network modeling of grinding of ductile cast iron using water based sio2 nanocoolant |
| title_full_unstemmed |
Artificial neural network modeling of grinding of ductile cast iron using water based sio2 nanocoolant |
| title_sort |
artificial neural network modeling of grinding of ductile cast iron using water based sio2 nanocoolant |
| publisher |
Universiti Malaysia Pahang |
| publishDate |
2014 |
| url |
http://umpir.ump.edu.my/id/eprint/8171/ http://umpir.ump.edu.my/id/eprint/8171/ http://umpir.ump.edu.my/id/eprint/8171/ http://umpir.ump.edu.my/id/eprint/8171/1/15_Rahman_et_al.pdf |
| first_indexed |
2023-09-18T22:05:29Z |
| last_indexed |
2023-09-18T22:05:29Z |
| _version_ |
1777414679058972672 |