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 st...

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Main Authors:	Khan, Md. Ashikur Rahman, M. M., Rahman, K., Kadirgama, R. A., Bakar
Format:	Article
Language:	English
Published:	2011
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/1223/ http://umpir.ump.edu.my/id/eprint/1223/ http://umpir.ump.edu.my/id/eprint/1223/1/Rahman_Khan.PDF

id	ump-1223
recordtype	eprints
spelling	ump-12232018-04-04T06:00:20Z http://umpir.ump.edu.my/id/eprint/1223/ Artificial Intelligence Model to Predict Surface Roughness of Ti-15-3 Alloy in EDM Process Khan, Md. Ashikur Rahman M. M., Rahman K., Kadirgama R. A., Bakar TJ Mechanical engineering and machinery 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. 2011 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1223/1/Rahman_Khan.PDF Khan, Md. Ashikur Rahman and M. M., Rahman and K., Kadirgama and R. A., Bakar (2011) Artificial Intelligence Model to Predict Surface Roughness of Ti-15-3 Alloy in EDM Process. World Academy of Science, Engineering and Technology, 74. ISSN 2010-3778 http://www.waset.org/journals/waset/v74/v74-35.pdf
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 Khan, Md. Ashikur Rahman M. M., Rahman K., Kadirgama R. A., Bakar Artificial Intelligence Model to Predict Surface Roughness of Ti-15-3 Alloy in EDM Process
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.
format	Article
author	Khan, Md. Ashikur Rahman M. M., Rahman K., Kadirgama R. A., Bakar
author_facet	Khan, Md. Ashikur Rahman M. M., Rahman K., Kadirgama R. A., Bakar
author_sort	Khan, Md. Ashikur Rahman
title	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_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_sort	artificial intelligence model to predict surface roughness of ti-15-3 alloy in edm process
publishDate	2011
url	http://umpir.ump.edu.my/id/eprint/1223/ http://umpir.ump.edu.my/id/eprint/1223/ http://umpir.ump.edu.my/id/eprint/1223/1/Rahman_Khan.PDF
first_indexed	2023-09-18T21:54:10Z
last_indexed	2023-09-18T21:54:10Z
_version_	1777413968031121408

Artificial Intelligence Model to Predict Surface Roughness of Ti-15-3 Alloy in EDM Process

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