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Multi-objective optimization for sustainable turning Ti6Al4V alloy using grey relational analysis (GRA) based on analytic hierarchy process (AHP)

  • Muhammad YounasEmail author
  • Syed Husain Imran Jaffery
  • Mushtaq Khan
  • Muhammad Ali Khan
  • Riaz Ahmad
  • Aamir Mubashar
  • Liaqat Ali
ORIGINAL ARTICLE
  • 88 Downloads

Abstract

Sustainable machining necessitates energy-efficient processes, longer tool lifespan, and greater surface integrity of the products in modern manufacturing. However, when considering Ti6Al4V alloy, these objectives turn out to be difficult to achieve as titanium alloys pose serious machinability challenges, especially at elevated temperatures. In this research, we investigate the optimal machining parameters required for turning of Ti6Al4V alloy. Turning experiments were performed to optimize four response parameters, i.e., specific cutting energy (SCE), wear rate (R), surface roughness (Ra), and material removal rate (MRR) with uncoated H13 carbide inserts in the dry cutting environment. Grey relational analysis (GRA) combined with the analytic hierarchy process (AHP) was performed to develop a multi-objective function. Response surface optimization was used to optimize the developed multi-objective function and determine the optimal cutting condition. As per the ANOVA, the interaction of feed rate and cutting speed (f × V) was found to be the most significant factor influencing the grey relational grade (GRG) of the multi-objective function. The optimized machining conditions increased the MRR and tool life by 34% and 7%, whereas, reducing the specific cutting energy and surface roughness by 6% and 2% respectively. Using Taguchi-based GRA by analytic hierarchy process (AHP) weights method, the benefits of high-speed machining Ti6Al4V through multi-response optimization were achieved.

Keywords

Sustainable machining Ti6Al4V alloy Multi-objective optimization Grey relational grade Analytic hierarchy process 

Abbreviations

AHP

Analytic hierarchy process

ANOVA

Analysis of variance

d

Depth of cut

f

Feed (mm/rev)

GRA

Grey relational analysis

GRC

Grey relational coefficients

GRG

Grey relational grade

MOO

Multi-objective optimization

MRR

Material removal rate

R

Wear rate

Ra

Surface roughness

RSM

Response surface methodology

SCE

Specific cutting energy

TOPSIS

The technique for order of preference by similarity to ideal solution

V

Cutting speed

VB

Flank wear

HSM

High-speed machining

Notes

Compliance with ethical standards

Conflict of interests

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Muhammad Younas
    • 1
    Email author
  • Syed Husain Imran Jaffery
    • 1
  • Mushtaq Khan
    • 1
  • Muhammad Ali Khan
    • 1
  • Riaz Ahmad
    • 1
  • Aamir Mubashar
    • 1
  • Liaqat Ali
    • 2
  1. 1.Department of Design and Manufacturing Engineering (DME), School of Mechanical and Manufacturing Engineering (SMME)National University of Sciences and Technology (NUST)IslamabadPakistan
  2. 2.University of Technology NowsheraNowsheraPakistan

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