Effect of process parameters settings and thickness on surface roughness of EBM produced Ti-6Al-4V

Purpose - Ti-6Al-4V is one of the most attractive materials being used in aerospace, automotive and medical implant industries. Electron beam melting (EBM) is one of the direct digital manufacturing methods to produce complex geometries of fully dense and near net shape parts. The EBM system provides an opportunity to built metallic objects with different processing parameter settings like beam current, scan speed, probe size on powder, etc. The purpose of this paper is to determine and understand the effect of part's thickness and variation in process parameter settings of the EBM system on surface roughness/topography of EBM fabricated Ti-6Al-4V metallic parts. Design/methodology/approach - A mathematical model based upon response surface methodology (RSM) is developed to study the variation of surface roughness with changing process parameter settings. Surface roughness of the test slabs produced with different parameter settings and thickness has been studied under confocal microscope. Response surface methodology was used to develop a multiple regression model to correlate the effect of variation in EBM process parameters settings and thickness of parts on surface roughness of EBM produced Ti-6Al-4V. Findings - It has been observed that every part produced by EBM system has detectable surface roughness. The surface roughness parameter Ra varies between 1-20 mu m for different samples depending upon the process parameter setting and thickness. The Ra value increases with increasing sample thickness and beam current, and decreases with increase in offset focus and scan speed. Originality/value - Surface roughness is related to wear and friction property of the material and hence is related to the life time and performance of the part. Surface roughness is an important property of any material to be considered as biomaterial. The surface roughness of the material depends upon the manufacturing method and environment and hence it is controllable either during fabrication or by post processing. From the 1st order regression model developed in this study, it is also evident that sample thickness, scan speed and beam current have relatively more effect on roughness value then the offset focus. With the model obtained equation, a designer can subsequently select the best combination of sample thickness and process parameter values to achieve desired surface roughness.