Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
The International Journal of Advanced Manufacturing Technology
Published in: The International Journal of Advanced Manufacturing Technology 1-4/2019

27-08-2019 | ORIGINAL ARTICLE

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

Authors: Muhammad Younas, Syed Husain Imran Jaffery, Mushtaq Khan, Muhammad Ali Khan, Riaz Ahmad, Aamir Mubashar, Liaqat Ali

Published in: The International Journal of Advanced Manufacturing Technology | Issue 1-4/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article The effect of composite boring bars on vibration in machining process
next article An investigation of stress condition in vibration-assisted burnishing

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Tascioglu E, Gharibi A, Kaynak Y (2019) High speed machining of near-beta titanium Ti-5553 alloy under various cooling and lubrication conditions. Int J Adv Manuf Technol 102:4257–4271CrossRef
2.
Warsi SS et al (2018) Development and analysis of energy consumption map for high-speed machining of Al 6061-T6 alloy. Int J Adv Manuf Technol:1–12
3.
Niknam SA, Khettabi R, Songmene V (2014) Machinability and machining of titanium alloys: a review. In: Davim JP (ed) Machining of Titanium Alloys. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 1–30
4.
Jaffery SHI, Khan M, Sheikh NA, Mativenga P (2013) Wear mechanism analysis in milling of Ti-6Al-4V alloy. Proc Inst Mech Eng Part B-J Eng Manuf 227(8):1148–1156CrossRef
5.
Jaffery SHI, Mativenga PT (2012) Wear mechanisms analysis for turning Ti-6Al-4V-towards the development of suitable tool coatings. Int J Adv Manuf Technol 58(5-8):479–493CrossRef
6.
Jaffery SI, Mativenga PT (2009) Study of the use of wear maps for assessing machining performance. Proc Inst Mech Eng Part B-J Eng Manuf 223(9):1097–1105CrossRef
7.
Jaffery SI, Mativenga PT (2009) Assessment of the machinability of Ti-6Al-4V alloy using the wear map approach. Int J Adv Manuf Technol 40(7-8):687–696CrossRef
8.
Jaffery SHI et al (2015) Statistical analysis of process parameters in micromachining of Ti-6Al-4V alloy. Proc Inst Mech Eng B J Eng Manuf 230(6):1017–1034CrossRef
9.
Warsi SS et al (2015) Analysis of power and specific cutting energy consumption in orthogonal machining of Al 6061-T6 alloys at transitional cutting speeds. in ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers
10.
Mia M, Gupta MK, Lozano JA, Carou D, Pimenov DY, Królczyk G, Khan AM, Dhar NR (2019) Multi-objective optimization and life cycle assessment of eco-friendly cryogenic N2 assisted turning of Ti-6Al-4V. J Clean Prod 210:121–133CrossRef
11.
Moradnazhad M, Unver HO (2017) Energy consumption characteristics of turn-mill machining. Int J Adv Manuf Technol 91(5):1991–2016CrossRef
12.
Venugopal KA, Paul S, Chattopadhyay AB (2007) Growth of tool wear in turning of Ti-6Al-4V alloy under cryogenic cooling. Wear 262(9):1071–1078CrossRef
13.
Li W, Kara S (2011) An empirical model for predicting energy consumption of manufacturing processes: a case of turning process. Proc Inst Mech Eng B J Eng Manuf 225(9):1636–1646CrossRef
14.
Nguyen H-P, Pham V-D, Ngo N-V (2018) Application of TOPSIS to Taguchi method for multi-characteristic optimization of electrical discharge machining with titanium powder mixed into dielectric fluid. Int J Adv Manuf Technol 98(5):1179–1198CrossRef
15.
Camposeco-Negrete C (2015) Optimization of cutting parameters using response surface method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum. J Clean Prod 91:109–117CrossRef
16.
Ramesh S, Karunamoorthy L, Palanikumar K (2012) Measurement and analysis of surface roughness in turning of aerospace titanium alloy (gr5). Measurement 45(5):1266–1276CrossRef
17.
Gok A (2015) A new approach to minimization of the surface roughness and cutting force via fuzzy TOPSIS, multi-objective grey design and RSA. Measurement 70:100–109CrossRef
18.
Zhang H, Deng Z, Fu Y, Lv L, Yan C (2017) A process parameters optimization method of multi-pass dry milling for high efficiency, low energy and low carbon emissions. J Clean Prod 148:174–184CrossRef
19.
Warsi SS, Agha MH, Ahmad R, Jaffery SHI, Khan M (2019) Sustainable turning using multi-objective optimization: a study of Al 6061 T6 at high cutting speeds. Int J Adv Manuf Technol 100(1):843–855CrossRef
20.
Sarıkaya M, Güllü A (2015) Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25. J Clean Prod 91:347–357CrossRef
21.
Rajemi M, Mativenga P, Jaffery S (2009) Energy and carbon footprint analysis for machining titanium Ti-6Al-4V Alloy. J Mach Eng 9(1):103–112
22.
Mia M, Khan MA, Rahman SS, Dhar NR (2017) Mono-objective and multi-objective optimization of performance parameters in high pressure coolant assisted turning of Ti-6Al-4V. Int J Adv Manuf Technol 90(1):109–118CrossRef
23.
Escamilla-Salazar IG, Torres-Treviño LM, González-Ortíz B, Zambrano PC (2013) Machining optimization using swarm intelligence in titanium (6Al 4 V) alloy. Int J Adv Manuf Technol 67(1):535–544CrossRef
24.
Özel T, Karpat Y (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tools Manuf 45(4-5):467–479CrossRef
25.
Shen Y, Liu Y, Dong H, Zhang K, Lv L, Zhang X, Zheng C, Ji R (2017) Parameters optimization for sustainable machining of Ti6Al4V using a novel high-speed dry electrical discharge milling. Int J Adv Manuf Technol 90(9):2733–2740CrossRef
26.
Mia M, Khan MA, Dhar NR (2017) High-pressure coolant on flank and rake surfaces of tool in turning of Ti-6Al-4V: investigations on surface roughness and tool wear. Int J Adv Manuf Technol 90(5-8):1825–1834CrossRef
27.
Mia M, Khan MA, Dhar NR (2017) Study of surface roughness and cutting forces using ANN, RSM, and ANOVA in turning of Ti-6Al-4V under cryogenic jets applied at flank and rake faces of coated WC tool. Int J Adv Manuf Technol 93(1-4):975–991CrossRef
28.
Mia M, Khan MA, Rahman SS, Dhar NR (2017) Mono-objective and multi-objective optimization of performance parameters in high pressure coolant assisted turning of Ti-6Al-4V. Int J Adv Manuf Technol 90(1-4):109–118CrossRef
29.
Kumar R, Bilga PS, Singh S (2017) Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation. J Clean Prod 164:45–57CrossRef
30.
Younas M et al (2019) Tool Wear Progression and its Effect on Energy Consumption in Turning of Titanium Alloy (Ti-6Al-4V). Mech Sci 10(2):373–382CrossRef
31.
Sandvik-Coromant, Product Catalogue, Turning tools. 2015.
32.
Warsi SS, Jaffery SHI, Ahmad R, Khan M, Ali L, Agha MH, Akram S (2018) Development of energy consumption map for orthogonal machining of Al 6061-T6 alloy. Proc Inst Mech Eng B J Eng Manuf 232(14):2510–2522CrossRef
33.
ISO, I., 3685 (1993) Tool-life testing with single-point turning tools. International Organization for Standardization (ISO), Geneva, Switzerland
34.
Pervaiz S, Deiab I, Darras B (2013) Power consumption and tool wear assessment when machining titanium alloys. Int J Precis Eng Manuf 14(6):925–936CrossRef
35.
Razak NH, Chen ZW, Pasang T (2016) Progression of tool deterioration and related cutting force during milling of 718Plus superalloy using cemented tungsten carbide tools. Int J Adv Manuf Technol 86(9):3203–3216CrossRef
36.
Cho SS, Komvopoulos K (1998) Cutting force variation due to wear of multi-layer ceramic coated tools. J Tribol 120(1):75–81CrossRef
37.
Ezugwu EO, Wang ZM (1997) Titanium alloys and their machinability—a review. J Mater Process Technol 68(3):262–274CrossRef
38.
Sun S, Brandt M, Dargusch MS (2009) Characteristics of cutting forces and chip formation in machining of titanium alloys. Int J Mach Tools Manuf 49(7):561–568CrossRef
39.
Yan J, Li L (2013) Multi-objective optimization of milling parameters – the trade-offs between energy, production rate and cutting quality. J Clean Prod 52:462–471CrossRef
40.
Kuo Y, Yang T, Huang G-W (2008) The use of a grey-based Taguchi method for optimizing multi-response simulation problems. Eng Optim 40(6):517–528CrossRef
41.
Chalisgaonkar R, Kumar J (2015) Multi-response optimization and modeling of trim cut WEDM operation of commercially pure titanium (CPTi) considering multiple user’s preferences. Eng Sci Technol, Int J 18(2):125–134CrossRef
42.
Waldorf DJ (2006) A simplified model for ploughing forces in turning. J Manuf Process 8(2):76–82CrossRef
43.
Balogun VA, Edem IF, Adekunle AA, Mativenga PT (2016) Specific energy based evaluation of machining efficiency. J Clean Prod 116:187–197CrossRef
44.
Balogun VA, Mativenga PT (2014) Impact of un-deformed chip thickness on specific energy in mechanical machining processes. J Clean Prod 69:260–268CrossRef
45.
Bahçe E, Ozel C (2013) Experimental investigation of the effect of machining parameters on the surface roughness and the formation of built up edge (BUE) in the drilling of Al 5005, in Tribology in Engineering. IntechOpen, London
Metadata
Title
Multi-objective optimization for sustainable turning Ti6Al4V alloy using grey relational analysis (GRA) based on analytic hierarchy process (AHP)
Authors
Muhammad Younas
Syed Husain Imran Jaffery
Mushtaq Khan
Muhammad Ali Khan
Riaz Ahmad
Aamir Mubashar
Liaqat Ali
Publication date
27-08-2019
Publisher
Springer London
Published in
The International Journal of Advanced Manufacturing Technology / Issue 1-4/2019
Print ISSN: 0268-3768
Electronic ISSN: 1433-3015
DOI
https://doi.org/10.1007/s00170-019-04299-5

Other articles of this Issue 1-4/2019

The International Journal of Advanced Manufacturing Technology 1-4/2019 Go to the issue

ORIGINAL ARTICLE

Geometric accuracy enhancement of five-axis machine tool based on error analysis

ORIGINAL ARTICLE

Cutting performance and wear behavior of coated cermet and coated carbide tools when turning AISI 4340 steel

ORIGINAL ARTICLE

Robust adaptive exponentially weighted moving average control charts with applications of manufacturing processes

ORIGINAL ARTICLE

Effect of cutting parameters on tool wear under minimum quantity cooling lubrication (MQCL) conditions

ORIGINAL ARTICLE

A novel approach to recognize interacting features for manufacturability evaluation of prismatic parts with orthogonal features

ORIGINAL ARTICLE

Experimental investigation of a new low-temperature hot stamping process for boron steels

Premium Partners

    Image Credits