Selection of optimal cutting conditions for pocket milling using genetic algorithm

Pocket milling is the most known machining operation in the domains of aerospace, die, and mold manufacturing. In the present work, GA-OptMill, a genetic algorithm (GA)-based optimization system for the minimization of pocket milling time, is developed. A wide range of cutting conditions, spindle speed, feed rate, and axial and radial depth of cut, are processed and optimized while respecting the important constraints during high-speed milling. Operational constraints of the machine tool system, such as spindle speed and feed limits, available spindle power and torque, acceptable limits of bending stress and deflection of the cutting tool, and clamping load limits of the workpiece system, are respected. Chatter vibration limits due to the dynamic interaction between cutting tool and workpiece are also embedded in the developed GA-OptMill system. Enhanced capabilities of the system in terms of encoded GA design variables and operators, targeted cutting conditions, and constraints are demonstrated for different pocket sizes. The automatically identified optimal cutting conditions are also verified experimentally. The developed optimization system is very appealing for industrial implementation to automate the selection of optimal cutting conditions to achieve high productivity.