Multi-Response Optimization of Additively Manufactured Ti6Al4V Component Using Grey Relational Analysis Coupled with Entropy Weights

Due to its near-net-shape manufacturing and ability to treat challenging-to-manufacture materials such as titanium alloys, Additive manufacturing (AM) is growing in popularity. However, due to the poor surface quality of AM components, finishing processes such as machining are required. One of the most difficult aspects of finishing AM components is the fact that even when using the same machining parameters, the surface roughness can vary significantly depending on the orientation of the part. In this study, electron beam melting (EBM) Ti6Al4V parts are subjected to the finishing (milling) process in three potential orientations relative to the direction of the tool feed. The impact of the feed rate, radial depth of cut, and cutting speed on the surface roughness and cutting force of the Ti6Al4V EBM part is studied while taking the orientations of the EBM components into consideration. It is found that the machined surface changes in noticeable ways with respect to orientation. A factorial design is used for the experiments, and analysis of variance (ANOVA) is used to evaluate the results. Furthermore, the grey relational analysis (GRA) method coupled with entropy weights is utilized to determine the optimal process variables for machining a Ti6Al4V EBM component. The results show that the feed rate has the greatest impact on the multi-response optimization, followed by the cutting speed, faces, and radial depth of cut.