Electron beam melting of titanium alloy and surface finish improvement through rotary ultrasonic machining

• Published in: International journal of advanced manufacturing technology Vol. 92; no. 9-12; pp. 3349 - 3361
• Main Authors: Ahmed, Naveed, Abdo, Basem M., Darwish, Saied, Moiduddin, Khawaja, Pervaiz, Salman, Alahmari, Abdulrahman M., Naveed, Madiha
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2017; Springer Nature B.V
• Subjects: Aerospace industry; Aircraft components; Artificial neural networks; Automobile industry; Automotive engineering; Automotive parts; Biocompatibility; CAE) and Design; Computer-Aided Engineering (CAD; Design of experiments; Electron beam melting; Engineering; Feed rate; Industrial and Production Engineering; Machine shops; Machine tools; Machining; Mechanical Engineering; Media Management; Neural networks; Original Article; Parameters; Power supplies; Product design; Surface finish; Surface properties; Surface roughness; Surgical implants; Titanium alloys; Titanium base alloys; Ultrasonic machining; Ultrasonic testing; Vanadium; Vanadium base alloys; Artificial neural network (ANN); Surface roughness; Additive manufacturing; Rotary ultrasonic machining (RUM); Electron beam melting (EBM); Taguchi design
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-017-0365-3

Electron beam melting (EBM), an additive method and rotary ultrasonic machining (RUM), a subtractive technique have been in great demand owing to their innumerable benefits. These techniques can manufacture components from titanium alloys (such as Ti-6Al-4 V) for industries such as aerospace, medical, and automotive, etc. However, these techniques have their own limitations since they are not yet fully developed for many materials including Ti-6Al-4 V. For example, the RUM involves high machining time due to the extremely low MRR while EBM suffers with poor surface quality of final parts. Therefore, in this work, an attempt has been made to combine the additive (EBM) and subtractive (RUM) techniques. The two techniques have been integrated to overcome the limitations of one over the other. In fact, this research has aimed to minimize the surface roughness of EBM fabricated parts using RUM. The design of experiment (DOE) has been adopted to get the best combination of RUM parameters which produce a high surface finish for EBM parts. Moreover, the artificial neural network (ANN) model has been developed to predict the surface roughness effectively. The machining parameters such as coolant pressure, frequency, spindle speed, depth of cut, feed rate, and power supply of RUM have been investigated for better surface finish. It has been confirmed from this study that the surfaces with R a value less than 0.3 μm can be achieved using the proposed methodology.