Optimization of the Milling Parameters of a Robotic-based Bone Milling System

Abstract

Using a surgical robot for bone milling can reduce bone tissue damage due to the cutting process. Robotic systems can regulate bone cutting parameters at an optimized setting to reduce excessive bone milling force and temperature. This study investigates the possibility to minimize cortical bone milling force and temperature by optimizing the milling parameters. An artificial neural network (ANN) is constructed to estimate the mean milling force and bone temperature as a function of milling feed rate and spindle speed. The ANN outputs are then used as the objective functions in a genetic algorithm (GA) for optimizing two conflicting objectives, i.e. the milling force and bone temperature. The results indicate that minimizing the bone temperature requires increasing the feed rate that increases the milling force.