Machining assessment of nano-crystalline hydroxyapatite bio-ceramic

Abstract

The demand of synthetic implants for good quality of life is high and increasing continuously due to limitations of autogenous bone grafting. Development of various synthetic bio materials and their manufacturing methods in the fields of orthopedics and dentistry has been done and still under way. Close physical properties with human bone make sintered hydroxyapatite (HAP) a suitable bioceramic material for hard tissue replacement. Newly developed fully dense nanocrystalline hydroxyapatite (nHAP) bioceramic has better mechanical properties than porous hydroxyapatite and has potential to be used alone, without metallic support in certain applications. When being used as implant devices in the human body, the nHAP bioceramic needs to be machined to the closest possible configuration with minimal surface roughness. This study investigates the machinability of nHAP bioceramic in milling operations. Efforts are focused on the effects of various machining conditions on surface integrity. Surface roughness is measured using a surface profilometer and the machined surface is observed using an optical microscope and a scanning electron microscope (SEM). Chip morphology and tool wear are examined using an optical microscope. Machined surface analysis showed that the surface integrity was good and the required surface roughness value (R[alpha]) of 1 - 1.5 [mu]m was achieved in many experiments. It was found that material removal is caused by brittle fracture without plastic flow. A first order surface roughness model for the end milling of nHAP under dry condition has been described. The mathematical model for surface roughness has been developed based on the cutting parameters: cutting speed, feed and depth of cut. The effects of these parameters on surface roughness have been studied using factorial designs and response surface method. Model analysis showed that all three cutting parameters have significant effect on surface roughness. However the current model has limited statistical power for prediction purposes and it demands a higher order model for accurate prediction of surface roughness value.