Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network
This report presents the artificial neural network model to predict the optimal machining parameters for Ti-6Al-4V through electrical discharge machining (EDM) using copper as an electrode and positive polarity of the electrode. The objective of this paper is to investigate how the peak current, ser...
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| Format: | Undergraduates Project Papers |
| Language: | English |
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2010
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| Online Access: | http://umpir.ump.edu.my/id/eprint/1403/1/Optimization%20of%20machining%20parameters%20of%20titanium%20alloy%20in%20electric%20discharge%20machining%20based%20on%20%20artificial%20neural%20network.pdf |
| _version_ | 1825821116087664640 |
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| author | Liew, Annie Ann Nee |
| author_facet | Liew, Annie Ann Nee |
| author_sort | Liew, Annie Ann Nee |
| collection | UMP |
| description | This report presents the artificial neural network model to predict the optimal machining parameters for Ti-6Al-4V through electrical discharge machining (EDM) using copper as an electrode and positive polarity of the electrode. The objective of this paper is to investigate how the peak current, servor voltage, pulse on- and off-time in EDM effect on material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). Radial basis function neural network (RBFN) is used to develop the Artificial Neural Network (ANN) modeling of MRR, TWR and SR. Design of experiments (DOE) method and response surface methodology (RSM) techniques are implemented. The validity test of the fit and adequacy of the proposed models has been carried out by doing confirmation test. The optimum machining conditions are estimated and verified with proposed ANN model. It is observed that the developed model is within the limits of the agreeable error with experimental results. Sensitivity analysis is carried out to investigate the relative influence of factors on the performance measures. It is observed that peak current effectively influences the performance measures. The reported results indicate that the proposed ANN models can satisfactorily evaluate the MRR, TWR as well as SR in EDM. Therefore, the proposed model can be considered as valuable tools for the process planning for EDM and leads to economical industrial machining by optimizing the input parameters. |
| first_indexed | 2024-03-06T11:36:41Z |
| format | Undergraduates Project Papers |
| id | UMPir1403 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T11:36:41Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | UMPir14032023-10-19T06:45:51Z http://umpir.ump.edu.my/id/eprint/1403/ Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network Liew, Annie Ann Nee TJ Mechanical engineering and machinery This report presents the artificial neural network model to predict the optimal machining parameters for Ti-6Al-4V through electrical discharge machining (EDM) using copper as an electrode and positive polarity of the electrode. The objective of this paper is to investigate how the peak current, servor voltage, pulse on- and off-time in EDM effect on material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). Radial basis function neural network (RBFN) is used to develop the Artificial Neural Network (ANN) modeling of MRR, TWR and SR. Design of experiments (DOE) method and response surface methodology (RSM) techniques are implemented. The validity test of the fit and adequacy of the proposed models has been carried out by doing confirmation test. The optimum machining conditions are estimated and verified with proposed ANN model. It is observed that the developed model is within the limits of the agreeable error with experimental results. Sensitivity analysis is carried out to investigate the relative influence of factors on the performance measures. It is observed that peak current effectively influences the performance measures. The reported results indicate that the proposed ANN models can satisfactorily evaluate the MRR, TWR as well as SR in EDM. Therefore, the proposed model can be considered as valuable tools for the process planning for EDM and leads to economical industrial machining by optimizing the input parameters. 2010-12 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/1403/1/Optimization%20of%20machining%20parameters%20of%20titanium%20alloy%20in%20electric%20discharge%20machining%20based%20on%20%20artificial%20neural%20network.pdf Liew, Annie Ann Nee (2010) Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network. Faculty of Mechanical Engineering, Universiti Malaysia Pahang. |
| spellingShingle | TJ Mechanical engineering and machinery Liew, Annie Ann Nee Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network |
| title | Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network |
| title_full | Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network |
| title_fullStr | Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network |
| title_full_unstemmed | Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network |
| title_short | Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network |
| title_sort | optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network |
| topic | TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/1403/1/Optimization%20of%20machining%20parameters%20of%20titanium%20alloy%20in%20electric%20discharge%20machining%20based%20on%20%20artificial%20neural%20network.pdf |
| work_keys_str_mv | AT liewannieannnee optimizationofmachiningparametersoftitaniumalloyinelectricdischargemachiningbasedonartificialneuralnetwork |