Table 8.

Developed machining process optimization for tool deflection.

Approach	Objective	Methods	Feedback	Machining Process
Offline	Tool deflection minimization [216]	A methodology was developed to reduce deflection errors in end milling. Parameters such as lubrication mode (flood, MQL, nano lubrication, dry), axial depth of cut, radial depth of cut, and feed rate were studied experimentally using the Taguchi method. The results showed that the cutting forces and the distance between the tool holder and workpiece have the greatest impact on deflection errors	-	Milling
Offline	Workpiece deflection constrained [217]	A methodology to maximize MRR is developed considering a penalty cost function of the deflections that occur during thin-wall machining. Radial depth of cut, axial depth of cut, spindle speed, feed per tooth, and number of flutes are considered as the input parameters	-	Milling
Offline	Tool and workpiece deflection [218]	An experimental design using RSM is conducted to minimize the tool and part deflection in the machining of a thin-wall workpiece considering feedrate, spindle speed, and depth of cut as the cutting parameters	-	Milling
Offline	Tool deflection [219]	Finite element modeling of the cutting tool and workpiece based on a mechanistic approach to determine cutting forces	-	Milling
Online	Tool deflection compensation [220]	A method is developed that utilizes the drive signals to compensate for tool deflections. Based on the evaluated forces from the machine tool’s drive signals, the tool path is compensated orthogonal to the feed direction	Drive signal	Milling