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

Maher, I. and Eltaib, M.E.H. and Sarhan, A.A.D. and 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, 76 (5-8). pp. 1459-1467. ISSN 0268-3768

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Official URL: http://link.springer.com/article/10.1007/s00170-01...

Abstract

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.

Item Type: Article
Additional Information: Az6da Times Cited:0 Cited References Count:29
Uncontrolled Keywords: Intelligent machining, end milling, cutting forces, surface roughness, cnc, anfis, multiple-regression, tool wear, system, parameters, networks, quality, models,
Subjects: T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 25 Jul 2015 02:15
Last Modified: 25 Jul 2015 02:15
URI: http://eprints.um.edu.my/id/eprint/13815

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