Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network
Surface roughness is one of the most important properties in any machining process and in micro milling it is really critical as the product needs to be of a very high surface quality. Therefore the present research is aimed at finding the optimal process parameters for end milling process and op...
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| Format: | Thesis |
| Language: | English English |
| Published: |
2014
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| Online Access: | http://eprints.uthm.edu.my/1701/1/24p%20YAZID%20ABDULSAMEEA%20MOHAMMED%20SAIF.pdf http://eprints.uthm.edu.my/1701/2/YAZID%20ABDULSAMEEA%20MOHAMMED%20SAIF%20WATERMARK.pdf |
| _version_ | 1825709588010237952 |
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| author | Mohammed Saif, Yazid Abdulsameea |
| author_facet | Mohammed Saif, Yazid Abdulsameea |
| author_sort | Mohammed Saif, Yazid Abdulsameea |
| collection | UTHM |
| description | Surface roughness is one of the most important properties in any machining process and
in micro milling it is really critical as the product needs to be of a very high surface
quality. Therefore the present research is aimed at finding the optimal process
parameters for end milling process and optimum surface roughness. In this study by
using regression model and Artificial Neural Networks (ANN) which are widely used
for both modeling and optimizing the performance of the manufacturing technologies.
Optimum machining parameters are of great concern in manufacturing environments,
where economy of machining operation plays a key role in competitiveness in the
market. The End milling process is a widely used machining process in aerospace
industries and many other industries ranging from large manufacturers to a small tool
and die shops, because of its versatility and efficiency. The present work involves the
estimation of optimal values of the process variables like, speed, feed and depth of cut,
whereas the surface roughness was taken as the output. The obtained results proved the
ability of ANN method for End milling process modeling and optimization. The final
measurement experiment and predicting the error of surface roughness in neural network
have been performed to verify the surface roughness optimum error percentage 1.71µm. For this study, the accuracy of artificial neural network and regression model 98.2% and
96.3 respectively. |
| first_indexed | 2024-03-05T21:40:52Z |
| format | Thesis |
| id | uthm.eprints-1701 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English English |
| last_indexed | 2024-03-05T21:40:52Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | uthm.eprints-17012021-10-04T08:44:20Z http://eprints.uthm.edu.my/1701/ Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network Mohammed Saif, Yazid Abdulsameea TJ Mechanical engineering and machinery TJ1125-1345 Machine shops and machine shop practice Surface roughness is one of the most important properties in any machining process and in micro milling it is really critical as the product needs to be of a very high surface quality. Therefore the present research is aimed at finding the optimal process parameters for end milling process and optimum surface roughness. In this study by using regression model and Artificial Neural Networks (ANN) which are widely used for both modeling and optimizing the performance of the manufacturing technologies. Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. The End milling process is a widely used machining process in aerospace industries and many other industries ranging from large manufacturers to a small tool and die shops, because of its versatility and efficiency. The present work involves the estimation of optimal values of the process variables like, speed, feed and depth of cut, whereas the surface roughness was taken as the output. The obtained results proved the ability of ANN method for End milling process modeling and optimization. The final measurement experiment and predicting the error of surface roughness in neural network have been performed to verify the surface roughness optimum error percentage 1.71µm. For this study, the accuracy of artificial neural network and regression model 98.2% and 96.3 respectively. 2014-06 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/1701/1/24p%20YAZID%20ABDULSAMEEA%20MOHAMMED%20SAIF.pdf text en http://eprints.uthm.edu.my/1701/2/YAZID%20ABDULSAMEEA%20MOHAMMED%20SAIF%20WATERMARK.pdf Mohammed Saif, Yazid Abdulsameea (2014) Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network. Masters thesis, Universiti Tun Hussein Onn Malaysia. |
| spellingShingle | TJ Mechanical engineering and machinery TJ1125-1345 Machine shops and machine shop practice Mohammed Saif, Yazid Abdulsameea Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network |
| title | Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network |
| title_full | Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network |
| title_fullStr | Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network |
| title_full_unstemmed | Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network |
| title_short | Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network |
| title_sort | modelling and simulation of surface roughness obtain from micro milling by using artificial neural network |
| topic | TJ Mechanical engineering and machinery TJ1125-1345 Machine shops and machine shop practice |
| url | http://eprints.uthm.edu.my/1701/1/24p%20YAZID%20ABDULSAMEEA%20MOHAMMED%20SAIF.pdf http://eprints.uthm.edu.my/1701/2/YAZID%20ABDULSAMEEA%20MOHAMMED%20SAIF%20WATERMARK.pdf |
| work_keys_str_mv | AT mohammedsaifyazidabdulsameea modellingandsimulationofsurfaceroughnessobtainfrommicromillingbyusingartificialneuralnetwork |