Artificial Intelligence Techniques for Machining Performance: a Review

This paper reviews the approaches of artificial neural network (ANN) on machining performance. ANN considered as a successful approach to modelling the machining process for predicting performance measures through the development of an expert system. An expert system is an interactive intelligence p...

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Main Authors: M. M., Rahman, K., Kadirgama, M. M., Noor
Format: Conference or Workshop Item
Language:English
Published: 2010
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/1762/1/ARTIFICIAL_INTELLIGENCE_TECHNIQUES_FOR_MACHINING.pdf
http://umpir.ump.edu.my/id/eprint/1762/
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spelling my.ump.umpir.17622018-01-25T07:12:30Z http://umpir.ump.edu.my/id/eprint/1762/ Artificial Intelligence Techniques for Machining Performance: a Review M. M., Rahman K., Kadirgama M. M., Noor TJ Mechanical engineering and machinery This paper reviews the approaches of artificial neural network (ANN) on machining performance. ANN considered as a successful approach to modelling the machining process for predicting performance measures through the development of an expert system. An expert system is an interactive intelligence program with an expert-like performance in solving a particular type of problem using knowledge base, inference engine and user interface. The approaches of ANN in past years with respect to cutting forces, surface roughness of the machined work piece, tool wear and material removal rate were reviewed. Results from literatures indicated that the ANN has the ability in generalizing the system characteristics by predicting values close to the actual measured ones. 2010 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1762/1/ARTIFICIAL_INTELLIGENCE_TECHNIQUES_FOR_MACHINING.pdf M. M., Rahman and K., Kadirgama and M. M., Noor (2010) Artificial Intelligence Techniques for Machining Performance: a Review. In: National Conference in Mechanical Engineering Research and Postgraduate Studies (2nd NCMER 2010), 3-4 December 2010 , UMP Pekan, Pahang. .
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
M. M., Rahman
K., Kadirgama
M. M., Noor
Artificial Intelligence Techniques for Machining Performance: a Review
description This paper reviews the approaches of artificial neural network (ANN) on machining performance. ANN considered as a successful approach to modelling the machining process for predicting performance measures through the development of an expert system. An expert system is an interactive intelligence program with an expert-like performance in solving a particular type of problem using knowledge base, inference engine and user interface. The approaches of ANN in past years with respect to cutting forces, surface roughness of the machined work piece, tool wear and material removal rate were reviewed. Results from literatures indicated that the ANN has the ability in generalizing the system characteristics by predicting values close to the actual measured ones.
format Conference or Workshop Item
author M. M., Rahman
K., Kadirgama
M. M., Noor
author_facet M. M., Rahman
K., Kadirgama
M. M., Noor
author_sort M. M., Rahman
title Artificial Intelligence Techniques for Machining Performance: a Review
title_short Artificial Intelligence Techniques for Machining Performance: a Review
title_full Artificial Intelligence Techniques for Machining Performance: a Review
title_fullStr Artificial Intelligence Techniques for Machining Performance: a Review
title_full_unstemmed Artificial Intelligence Techniques for Machining Performance: a Review
title_sort artificial intelligence techniques for machining performance: a review
publishDate 2010
url http://umpir.ump.edu.my/id/eprint/1762/1/ARTIFICIAL_INTELLIGENCE_TECHNIQUES_FOR_MACHINING.pdf
http://umpir.ump.edu.my/id/eprint/1762/
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score 13.211869