Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining

End milling is one of the most common metal removal operations encountered in industrial processes. Product quality is a critical issue as it plays a vital role in how products perform and is also a factor with great influence on manufacturing cost. Surface roughness usually serves as an indicator o...

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Main Authors:	Maher, I., Eltaib, M.E.H., Sarhan, A.A.D., El-Zahry, R.M.
Format:	Article
Published:	Springer London Ltd, 236 Grays Inn Rd, 6th Floor, London Wc1x 8hl, England 2015
Subjects:	T Technology (General) TJ Mechanical engineering and machinery
Online Access:	http://link.springer.com/article/10.1007/s00170-014-6379-1 http://link.springer.com/article/10.1007/s00170-014-6379-1 http://eprints.um.edu.my/13815/1/Cutting_force%2Dbased_adaptive_neuro%2Dfuzzy_approach_for.pdf

id	um-13815
recordtype	eprints
spelling	um-138152015-07-25T02:15:59Z Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining Maher, I. Eltaib, M.E.H. Sarhan, A.A.D. El-Zahry, R.M. T Technology (General) TJ Mechanical engineering and machinery End milling is one of the most common metal removal operations encountered in industrial processes. Product quality is a critical issue as it plays a vital role in how products perform and is also a factor with great influence on manufacturing cost. Surface roughness usually serves as an indicator of product quality. During cutting, surface roughness measurement is impossible as the cutting tool is engaged with the workpiece, chip and cutting fluid. However, cutting force measurement is easier and could be used as an indirect parameter to predict surface roughness. In this research work, a correlation analysis was initially performed to determine the degree of association between cutting parameters (speed, feed rate, and depth of cut) and cutting force and surface roughness using adaptive neuro-fuzzy inference system (ANFIS) modeling. Furthermore, the cutting force values were employed to develop an ANFIS model for accurate surface roughness prediction in CNC end milling. This model provided good prediction accuracy (96.65 average accuracy) of surface roughness, indicating that the ANFIS model can accurately predict surface roughness during cutting using the cutting force signal in the intelligent machining process to achieve the required product quality and productivity. Springer London Ltd, 236 Grays Inn Rd, 6th Floor, London Wc1x 8hl, England 2015-02 Article PeerReviewed application/pdf http://eprints.um.edu.my/13815/1/Cutting_force%2Dbased_adaptive_neuro%2Dfuzzy_approach_for.pdf http://link.springer.com/article/10.1007/s00170-014-6379-1 Maher, I.; Eltaib, M.E.H.; Sarhan, A.A.D.; El-Zahry, R.M. (2015) Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining. International Journal of Advanced Manufacturing Technology <http://eprints.um.edu.my/view/publication/International_Journal_of_Advanced_Manufacturing_Technology.html>, 76 (5-8). pp. 1459-1467. ISSN 0268-3768 http://eprints.um.edu.my/13815/
repository_type	Digital Repository
institution_category	Local University
institution	University Malaya
building	UM Research Repository
collection	Online Access
topic	T Technology (General) TJ Mechanical engineering and machinery
spellingShingle	T Technology (General) TJ Mechanical engineering and machinery Maher, I. Eltaib, M.E.H. Sarhan, A.A.D. El-Zahry, R.M. Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining
description	End milling is one of the most common metal removal operations encountered in industrial processes. Product quality is a critical issue as it plays a vital role in how products perform and is also a factor with great influence on manufacturing cost. Surface roughness usually serves as an indicator of product quality. During cutting, surface roughness measurement is impossible as the cutting tool is engaged with the workpiece, chip and cutting fluid. However, cutting force measurement is easier and could be used as an indirect parameter to predict surface roughness. In this research work, a correlation analysis was initially performed to determine the degree of association between cutting parameters (speed, feed rate, and depth of cut) and cutting force and surface roughness using adaptive neuro-fuzzy inference system (ANFIS) modeling. Furthermore, the cutting force values were employed to develop an ANFIS model for accurate surface roughness prediction in CNC end milling. This model provided good prediction accuracy (96.65 average accuracy) of surface roughness, indicating that the ANFIS model can accurately predict surface roughness during cutting using the cutting force signal in the intelligent machining process to achieve the required product quality and productivity.
format	Article
author	Maher, I. Eltaib, M.E.H. Sarhan, A.A.D. El-Zahry, R.M.
author_facet	Maher, I. Eltaib, M.E.H. Sarhan, A.A.D. El-Zahry, R.M.
author_sort	Maher, I.
title	Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining
title_short	Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining
title_full	Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining
title_fullStr	Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining
title_full_unstemmed	Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining
title_sort	cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining
publisher	Springer London Ltd, 236 Grays Inn Rd, 6th Floor, London Wc1x 8hl, England
publishDate	2015
url	http://link.springer.com/article/10.1007/s00170-014-6379-1 http://link.springer.com/article/10.1007/s00170-014-6379-1 http://eprints.um.edu.my/13815/1/Cutting_force%2Dbased_adaptive_neuro%2Dfuzzy_approach_for.pdf
first_indexed	2018-09-06T06:17:46Z
last_indexed	2018-09-06T06:17:46Z
_version_	1610837829844205568

Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining

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