عنوان المقالة:Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining
Ibrahem Maher, MEH Eltaib, Ahmed AD Sarhan, RM El-Zahry
الملخص العربي
End milling is one of the most common metal
removal operations encountered in industrial processes. Prod-
uct 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 pa-
rameter 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) model-
ing. 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.
ابراهيم ماهر | Ibrahem Maher | 13427