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

Abstract

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.

A Hybrid Design of Experiment Approach in Analyzing the Electrical Discharge Machining Influence on Stir Cast Al7075/B4C Metal Matrix Composites

“…In the multi-objective optimization process, Grey relational analysis (GRA), the weighted Grey relational analysis (WGRA) technique for order of preference by similarity to ideal solution (TOPSIS), desirability function (DF), entropy weight method (EWM), and many other techniques are applied for optimize the process parameters [11,12]. GRA and TOPSIS are not specific optimization tools, yet they can be used as part of an optimization process to rank and select the most efficient solution based on multiple criteria.…”

Optimization of the Effect of Laser Power Bed Fusion 3D Printing during the Milling Process Using Hybrid Artificial Neural Networks with Particle Swarm Optimization and Genetic Algorithms

“…Some studies have shown that the 3D printing layer orientations considerably affect the final visual appearance of the additively manufactured parts. Milling of the Ti6Al4V [38] and γ-TiAl EBM [33] components and turning of the Ti6Al4V EBM [39] components are examples where the effects of layer orientations have been studied and published. It has been found through investigations that the same machining parameters can produce varying degrees of surface roughness depending on the orientation of the EBM component being machined [40].…”