EXPERIMENTAL INVESTIGATION AND PARAMETRIC OPTIMIZATION OF NEUROSURGICAL BONE GRINDING UNDER BIO-MIMIC ENVIRONMENT

Abstract

Bone grinding is a craniotomy procedure which is used to remove a bone flap from the skull to expose and create an access for the dissection of tumors. In this study, a computer-controlled neurosurgical bone grinding has been used to explore the effect of various neurosurgical bone grinding parameters, such as cutting forces, torque, grinding force ratio, and temperature generated during bone grinding have been investigated. Bone samples after grinding have been assessed for morphological analysis. Based on the outcomes, a multi-attribute decision-making methodology based on grey relational analysis has been adopted. Regression models have been developed and then validated to ensure the adequacy of the developed models. Subsequently, a comparative analysis of experimental and predicted results have been presented. It is revealed that grinding forces and torque decreased with the escalation of rotational speed from 35,000 revolutions per minute (rpm) to 55,000[Formula: see text]rpm. The optimum combination of process parameters found as rotational speed of 55,000[Formula: see text]rpm, feed rate of 20[Formula: see text]mm/min, and depth of cut of 0.50[Formula: see text]mm.