Artificial intelligence model to predict surface roughness of Ti-15-3 alloy in EDM process
Conventionally the selection of parameters depends intensely on the operator’s experience or conservative technological data provided by the EDM equipment manufacturers that assign inconsistent machining performance. The parameter settings given by the manufacturers are only relevant with common s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
World Academy of Science, Engineering and Technology
2011
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/50011/1/P49b_2011_UMP.pdf http://irep.iium.edu.my/50011/4/50011_Artificial%20intelligence%20model%20to%20predict%20surface.pdf |
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| author | Khan, Md. Ashikur Rahman Maleque, Md. Abdul Rahman, M.M. Hafizur Kadirgama, K. Abu Bakar, Rosli |
| author_facet | Khan, Md. Ashikur Rahman Maleque, Md. Abdul Rahman, M.M. Hafizur Kadirgama, K. Abu Bakar, Rosli |
| author_sort | Khan, Md. Ashikur Rahman |
| collection | IIUM |
| description | Conventionally the selection of parameters depends
intensely on the operator’s experience or conservative technological data provided by the EDM equipment manufacturers that assign inconsistent machining performance. The parameter settings given by
the manufacturers are only relevant with common steel grades. A single parameter change influences the process in a complex way. Hence, the present research proposes artificial neural network (ANN) models for the prediction of surface roughness on first commenced Ti-15-3 alloy in electrical discharge machining (EDM) process. The
proposed models use peak current, pulse on time, pulse off time and
servo voltage as input parameters. Multilayer perceptron (MLP) with three hidden layer feedforward networks are applied. An assessment is carried out with the models of distinct hidden layer. Training of the models is performed with data from an extensive series of
experiments utilizing copper electrode as positive polarity. The predictions based on the above developed models have been verified with another set of experiments and are found to be in good agreement with the experimental results. Beside this they can be
exercised as precious tools for the process planning for EDM. |
| first_indexed | 2024-03-05T23:49:54Z |
| format | Article |
| id | oai:generic.eprints.org:50011 |
| institution | International Islamic University Malaysia |
| language | English English |
| last_indexed | 2024-03-05T23:49:54Z |
| publishDate | 2011 |
| publisher | World Academy of Science, Engineering and Technology |
| record_format | dspace |
| spelling | oai:generic.eprints.org:500112020-09-11T04:07:23Z http://irep.iium.edu.my/50011/ Artificial intelligence model to predict surface roughness of Ti-15-3 alloy in EDM process Khan, Md. Ashikur Rahman Maleque, Md. Abdul Rahman, M.M. Hafizur Kadirgama, K. Abu Bakar, Rosli T173.2 Technological change TA213 Engineering machinery, tools, and implements TA401 Materials of engineering and construction TS200 Metal manufactures. Metalworking Conventionally the selection of parameters depends intensely on the operator’s experience or conservative technological data provided by the EDM equipment manufacturers that assign inconsistent machining performance. The parameter settings given by the manufacturers are only relevant with common steel grades. A single parameter change influences the process in a complex way. Hence, the present research proposes artificial neural network (ANN) models for the prediction of surface roughness on first commenced Ti-15-3 alloy in electrical discharge machining (EDM) process. The proposed models use peak current, pulse on time, pulse off time and servo voltage as input parameters. Multilayer perceptron (MLP) with three hidden layer feedforward networks are applied. An assessment is carried out with the models of distinct hidden layer. Training of the models is performed with data from an extensive series of experiments utilizing copper electrode as positive polarity. The predictions based on the above developed models have been verified with another set of experiments and are found to be in good agreement with the experimental results. Beside this they can be exercised as precious tools for the process planning for EDM. World Academy of Science, Engineering and Technology 2011 Article PeerReviewed application/pdf en http://irep.iium.edu.my/50011/1/P49b_2011_UMP.pdf application/pdf en http://irep.iium.edu.my/50011/4/50011_Artificial%20intelligence%20model%20to%20predict%20surface.pdf Khan, Md. Ashikur Rahman and Maleque, Md. Abdul and Rahman, M.M. Hafizur and Kadirgama, K. and Abu Bakar, Rosli (2011) Artificial intelligence model to predict surface roughness of Ti-15-3 alloy in EDM process. International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 5 (2). pp. 503-507. ISSN 2010-376X E-ISSN 2010-3778 http://waset.org/Publication/artificial-intelligence-model-to-predict-surface-roughness-of-ti-15-3-alloy-in-edm-process/5730 |
| spellingShingle | T173.2 Technological change TA213 Engineering machinery, tools, and implements TA401 Materials of engineering and construction TS200 Metal manufactures. Metalworking Khan, Md. Ashikur Rahman Maleque, Md. Abdul Rahman, M.M. Hafizur Kadirgama, K. Abu Bakar, Rosli Artificial intelligence model to predict surface roughness of Ti-15-3 alloy in EDM process |
| title | Artificial intelligence model to predict surface roughness of Ti-15-3 alloy in EDM process |
| title_full | Artificial intelligence model to predict surface roughness of Ti-15-3 alloy in EDM process |
| title_fullStr | Artificial intelligence model to predict surface roughness of Ti-15-3 alloy in EDM process |
| title_full_unstemmed | Artificial intelligence model to predict surface roughness of Ti-15-3 alloy in EDM process |
| title_short | Artificial intelligence model to predict surface roughness of Ti-15-3 alloy in EDM process |
| title_sort | artificial intelligence model to predict surface roughness of ti 15 3 alloy in edm process |
| topic | T173.2 Technological change TA213 Engineering machinery, tools, and implements TA401 Materials of engineering and construction TS200 Metal manufactures. Metalworking |
| url | http://irep.iium.edu.my/50011/1/P49b_2011_UMP.pdf http://irep.iium.edu.my/50011/4/50011_Artificial%20intelligence%20model%20to%20predict%20surface.pdf |
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