Surface Roughness Optimization in Machining of Biodegradable Magnesium Alloys

Abstract

Magnesium alloys are suitable in automobile, aerospace and medical fields because of its low density, high-strength to weight ratio properties. These materials are now emerging as a promising material for temporary biodegradable medical implants. The implant surface is in direct contact with blood. This interaction makes surface integrity of magnesium implants a key factor in influencing the degradation rate. The surface integrity also influences the corrosion rate of the implants in the saline media present inside the human body. Surface roughness is an important component of surface integrity. Machining is a process to produce geometric features of implants. In the present work an attempt is made to investigate the study the outcomes of cutting speed, feed rate and depth of cut on surface roughness, during face milling under dry conditions on AZ31, which is a biodegradable magnesium alloy. The experiments have been conducted with uncoated carbide inserts. Two optimization algorithms namely firefly and PSO have been used to optimize the surface roughness. The objective function for these algorithms is generated using an Artificial neural network (ANN) model. The performance of the two optimization algorithms are compared for surface roughness optimization in machining biodegradable magnesium alloys.