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...

Full description

Bibliographic Details
Main Authors:	Khan, Md. Ashikur Rahman, Maleque, Md. Abdul, Rahman, M.M. Hafizur, Kadirgama, K., Abu Bakar, Rosli
Format:	Article
Language:	English
Published:	World Academy of Science, Engineering and Technology 2011
Subjects:	T173.2 Technological change TA213 Engineering machinery, tools, and implements TA401 Materials of engineering and construction TS200 Metal manufactures. Metalworking
Online Access:	http://irep.iium.edu.my/50011/ http://irep.iium.edu.my/50011/ http://irep.iium.edu.my/50011/1/P49b_2011_UMP.pdf

id	iium-50011
recordtype	eprints
spelling	iium-500112016-07-15T01:56:18Z 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 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 http://waset.org/Publication/artificial-intelligence-model-to-predict-surface-roughness-of-ti-15-3-alloy-in-edm-process/5730
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T173.2 Technological change TA213 Engineering machinery, tools, and implements TA401 Materials of engineering and construction TS200 Metal manufactures. Metalworking
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
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 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
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
publisher	World Academy of Science, Engineering and Technology
publishDate	2011
url	http://irep.iium.edu.my/50011/ http://irep.iium.edu.my/50011/ http://irep.iium.edu.my/50011/1/P49b_2011_UMP.pdf
first_indexed	2018-09-07T07:00:10Z
last_indexed	2018-09-07T07:00:10Z
_version_	1610931094663725056

Artificial intelligence model to predict surface roughness of Ti-15-3 alloy in EDM process

Similar Items