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

  • Ibrahem Maher
  • M. E. H. Eltaib
  • Ahmed A. D. Sarhan
  • R. M. El-Zahry
ORIGINAL ARTICLE
First Online:
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Accepted:

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.

Keywords

Intelligent machining End milling Cutting forces Surface roughness CNC ANFIS 

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Ibrahem Maher
    • 1
    • 2
  • M. E. H. Eltaib
    • 3
  • Ahmed A. D. Sarhan
    • 1
    • 3
  • R. M. El-Zahry
    • 3
  1. 1.Centre of Advanced Manufacturing and Material Processing, Department of Mechanical EngineeringUniversity of MalayaKuala LumpurMalaysia
  2. 2.Department of Mechanical Engineering, Faculty of EngineeringKafrelsheikh UniversityKafrelsheikhEgypt
  3. 3.Department of Mechanical Engineering, Faculty of EngineeringAssiut UniversityAssiutEgypt

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