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Electro Discharge Machining of Ti-Alloy (Ti6Al4V) and 316L Stainless Steel and Optimization of Process Parameters by Grey Relational Analysis (GRA) Method

  • Anshuman Kumar SahuEmail author
  • Pragyan Paramita Mohanty
  • Sarat Kumar Sahoo
Chapter

Abstract

Increasing demand on micro-product leads to the development of innovative manufacturing process in nonconventional machining process to these micro-scale applications. In the medical field a huge variety of products can be found in prosthesis, surgery devices and tissue engineering, which required the application of the EDM process to manufacture micro cavities. Now-a-days the materials like Ti-alloy (Ti6Al4V) and 316L Stainless Steel are widely used in biomedical fields, which are very difficult to machine. These materials are also used in additive manufacturing process. Here it presents an experimental study of electro-discharge machining (EDM) of titanium alloy (Ti6Al4V) and 316L Stainless Steel. The objective of this work is to study the effect and optimization of machining process parameters like pulse-on-time, discharge current and duty cycle on process performance parameters such as material removal rate (MRR), tool wear rate (TWR) and Radial over cut (ROC). A Taguchi L9 design of experiment (DOE) has been applied and three levels of process parameters have been taken. The optimization method Grey relational analysis (GRA) method was used to optimize the parameters. The Analysis of Variance (ANOVA) also indicated the percentage contribution of machining parameters that influence response performance parameters. By the GRA method it was found that for Ti-alloy the machining parameter duty cycle (DC) has maximum percentage contribution on the output responses followed by discharge current (I p) and pulse on time (T ON). Similarly for 316L Stainless Steel the machining parameter discharge current (I p) has maximum percentage contribution on the output responses followed by pulse-on-time (T ON) and duty cycle (DC).

Keywords

EDM Taguchi design Multi-response optimization method Grey relational analysis method ANOVA 

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Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Anshuman Kumar Sahu
    • 1
    Email author
  • Pragyan Paramita Mohanty
    • 1
  • Sarat Kumar Sahoo
    • 1
  1. 1.Department of Mechanical EngineeringVeer Surendra Sai University of TechnologyBurlaIndia

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