Multi-objective optimization of cortical bone grinding parameters based on particle swarm optimization

Abstract

Grinding is a fundamental operation in craniotomy. Suitable grinding parameters will not only reduce force damage, but also ensure grinding efficiency. In this study, the regression equations of material removal rate and grinding force were obtained based on the theory of cortical bone grinding and full factorial test results, a multi-objective optimization based on the particle swarm algorithm was proposed for optimizing the grinding parameters: spindle speed, feed speed, and grinding depth in the grinding process. Two conflicting objectives, minimum grinding force and maximum material removal rate, were optimized simultaneously. The results revealed that the optimal grinding parameter combination and optimization results were as follows: spindle speed of 5000 rpm, feed rate of 60 mm/min, grinding depth of 0.6 mm, grinding force of 15.1 N, and material removal rate of 113.8 mm3/min. The parameter optimization result can provide theoretical guidance for selecting cortical bone grinding parameters in actual craniotomy.