Power consumption optimization strategy in micro ball-end milling of D2 steel via TLBO coupled with 3D FEM simulation

The present challenge in the manufacturing industry is to improve efficiency of production activities while reducing wastage of power consumption. Past research focused on multi response optimization of process parameters to improve performance of the process. The present study proposed an optimization-based strategy to reduce power consumption in micro ball end milling of D2 steel. As the power consumption is directly proportional to cutting forces, the process parameters such as cutting speed, feed and depth of cut were optimized to reduce cutting forces using teaching learning based optimization (TLBO) technique coupled with 3D finite element method (FEM) simulation. During the optimization, amplitude of cutter vibration and surface roughness were taken as constraints as 60 µm (ISO 10816) and 2 µm (ISO 1302) respectively. Three best combinations of cutting speed, feed and depth of cut were obtained for minimum cutting force. Among them, combination of cutting speed of 15 m/min, feed of 112.5 µm/tooth and depth of cut of 85.25 µm has low power consumption of 67 W with tool vibration of 36.5 µm. However, remaining two combinations were also considered to be the next best optimal cutting conditions. Numerical simulation was carried out for the three best solutions and the cutting forces and amplitude of cutter vibration were predicted. There was good agreement between simulation results and experimental results that verified the acceptance of the simulation. It was also found that the three best candidate solutions were having same the cutting speed of 15 m/min (minimum cutting speed). Hence, the induced stresses in the work piece were found to be with low values around 350 Mpa.