Processing, Please wait...

  • Home
  • About Us
  • Search:
  • Advanced Search

Growing Science » Decision Science Letters » A study on the machinability of some metal alloys using grey TOPSIS method

Journals

  • IJIEC (747)
  • MSL (2643)
  • DSL (668)
  • CCL (508)
  • USCM (1092)
  • ESM (413)
  • AC (562)
  • JPM (271)
  • IJDS (912)
  • JFS (91)
  • HE (32)
  • SCI (26)

DSL Volumes

    • Volume 1 (10)
      • Issue 1 (5)
      • Issue 2 (5)
    • Volume 2 (30)
      • Issue 1 (5)
      • Issue 2 (6)
      • Issue 3 (9)
      • Issue 4 (10)
    • Volume 3 (53)
      • Issue 1 (15)
      • Issue 2 (10)
      • Issue 3 (19)
      • Issue 4 (9)
    • Volume 4 (48)
      • Issue 1 (10)
      • Issue 2 (12)
      • Issue 3 (14)
      • Issue 4 (12)
    • Volume 5 (39)
      • Issue 1 (12)
      • Issue 2 (10)
      • Issue 3 (8)
      • Issue 4 (9)
    • Volume 6 (30)
      • Issue 1 (8)
      • Issue 2 (6)
      • Issue 3 (9)
      • Issue 4 (7)
    • Volume 7 (41)
      • Issue 1 (8)
      • Issue 2 (8)
      • Issue 3 (8)
      • Issue 4 (17)
    • Volume 8 (38)
      • Issue 1 (8)
      • Issue 2 (6)
      • Issue 3 (14)
      • Issue 4 (10)
    • Volume 9 (39)
      • Issue 1 (8)
      • Issue 2 (9)
      • Issue 3 (14)
      • Issue 4 (8)
    • Volume 10 (43)
      • Issue 1 (7)
      • Issue 2 (8)
      • Issue 3 (20)
      • Issue 4 (8)
    • Volume 11 (49)
      • Issue 1 (9)
      • Issue 2 (9)
      • Issue 3 (14)
      • Issue 4 (17)
    • Volume 12 (64)
      • Issue 1 (12)
      • Issue 2 (24)
      • Issue 3 (13)
      • Issue 4 (15)
    • Volume 13 (78)
      • Issue 1 (21)
      • Issue 2 (18)
      • Issue 3 (19)
      • Issue 4 (20)
    • Volume 14 (87)
      • Issue 1 (21)
      • Issue 2 (23)
      • Issue 3 (25)
      • Issue 4 (18)
    • Volume 15 (19)
      • Issue 1 (19)

Keywords

Supply chain management(166)
Jordan(161)
Vietnam(149)
Customer satisfaction(120)
Performance(113)
Supply chain(110)
Service quality(98)
Competitive advantage(95)
Tehran Stock Exchange(94)
SMEs(87)
optimization(86)
Financial performance(83)
Trust(83)
TOPSIS(83)
Sustainability(81)
Job satisfaction(80)
Factor analysis(78)
Social media(78)
Knowledge Management(77)
Artificial intelligence(77)


» Show all keywords

Authors

Naser Azad(82)
Mohammad Reza Iravani(64)
Zeplin Jiwa Husada Tarigan(63)
Endri Endri(45)
Muhammad Alshurideh(42)
Hotlan Siagian(39)
Jumadil Saputra(36)
Dmaithan Almajali(36)
Muhammad Turki Alshurideh(35)
Barween Al Kurdi(32)
Ahmad Makui(32)
Basrowi Basrowi(31)
Hassan Ghodrati(31)
Mohammad Khodaei Valahzaghard(30)
Sautma Ronni Basana(29)
Shankar Chakraborty(29)
Ni Nyoman Kerti Yasa(29)
Sulieman Ibraheem Shelash Al-Hawary(28)
Prasadja Ricardianto(28)
Haitham M. Alzoubi(27)


» Show all authors

Countries

Iran(2183)
Indonesia(1290)
India(787)
Jordan(786)
Vietnam(504)
Saudi Arabia(453)
Malaysia(441)
United Arab Emirates(220)
China(206)
Thailand(153)
United States(111)
Turkey(106)
Ukraine(104)
Egypt(98)
Canada(92)
Peru(88)
Pakistan(85)
United Kingdom(80)
Morocco(79)
Nigeria(78)


» Show all countries

Decision Science Letters

ISSN 1929-5812 (Online) - ISSN 1929-5804 (Print)
Quarterly Publication
Volume 5 Issue 1 pp. 31-44 , 2016

A study on the machinability of some metal alloys using grey TOPSIS method Pages 31-44 Right click to download the paper Download PDF

Authors: Swarat Dey, Shankar Chakraborty

DOI: 10.5267/j.dsl.2015.9.002

Keywords: Alloy steel, Alloys, Aluminium, Copper, Grey theory, Machinability, TOPSIS

Abstract: The machinability of a material can be defined as the ease with which it can be machined. Materials with good machinability property require less power to cut, can be cut quickly, and easily obtain a good finish without wearing the tooling much. Therefore, to manufacture components economically, production engineers are challenged to discover ways to determine machinability of materials which mainly depends on their mechanical properties, as well as on other cutting conditions. In this paper, the machinability characteristics of alloys of three materials, i.e. aluminium, copper and steel are studied applying grey TOPSIS (technique for order preference by similarity to ideal solution) method. For each case, eight different alloys are considered whose machinability is evaluated based on different mechanical properties which are expressed in grey numbers. Using the adopted methodology, it now becomes easier for the manufacturers to select a particular alloy that can be easily machined. It is observed that A357RC, CuCr1Zr and AISI 5140 are the best machinable aluminium, copper and steel alloys, respectively. It is also found that the ranking performance of grey TOPSIS method remains unaffected with the variation in greyness of the considered mechanical property values.

How to cite this paper
Dey, S & Chakraborty, S. (2016). A study on the machinability of some metal alloys using grey TOPSIS method.Decision Science Letters , 5(1), 31-44.

Refrences
Alexopoulos, N.D. & Pantelakis, S.G. (2004). A new quality index for characterizing aluminum cast alloys with regard to aircraft structure design requirements. Metallurgical and Materials Transactions A, 35A, 301-308.

Alexopoulous, N.D. (2007). Generation of quality maps to support material selection by exploiting the quality indices concept of cast aluminium alloys. Materials & Design, 28(2), 534-543.

Boubekri, N., Rodriguez, J., & Asfour, S. (2003). Development of an aggregate indicator to assess the machinability of steels. Journal of Materials Processing Technology, 134(2), 159-165.

Davim, J.P., & Reis, P. (2004). Machinability study on composite (polyetheretherketone reinforced with 30% glass fibre–PEEK GF 30) using polycrystalline diamond (PCD) and cemented carbide (K20) tools. International Journal of Advanced Manufacturing Technology, 23(5-6), 412-418.

Davim, J. P., & Mata, F. (2005). A new machinability index in turning fiber reinforced plastics. Journal of Materials Processing Technology, 170(1-2), 436-440.

Davim, J.P., & Figueira, L. (2007). Machinability evaluation in hard turning of cold work tool steel (D2) with ceramic tools using statistical techniques. Materials & Design, 28(4), 1186-1191.

Deng, J. L. (1982). Control problem of grey system. System and Control Letters, 1(5), 288-294.

Deng, J. L. (1989). Introduction to grey system theory. The Journal of Grey System, 1(1), 1-24.

Enache, S., Str?jescu, E., Opran, C., Minciu, C., & Zamfirache, M. (1995). Mathematical model for the establishment of the materials machinability. Annals of the CIRP, 44(1), 79-82.

Hoseiny, H., H?gman, B., Klement, U., & Kinnander, A. (2012). Machinability evaluation of pre-hardened plastic mould steels. International Journal of Machining and Machinability of Materials, 11(4), 327-341.

Jadidi, O., Hong, T.S., Firouzi, F., & Yusuff, R.M. (2008). An optimal grey based approach based on TOPSIS concepts for supplier selection problem. International Journal of Management Science and Engineering Management, 4(2), 104-117.

Kim, K-K., Kang, M-C., Kim, J-S., Jung, Y-H., & Kim, N-K. (2002). A study on the precision machinability of ball end milling by cutting speed optimization. Journal of Materials Processing Technology, 130-131, 357-362.

Li, G-D., Yamaguchi, D., & Nagai, M. (2007). A grey-based decision-making approach to the supplier selection problem. Mathematical and Computer Modelling, 46(3-4), 573-581.

Lin, Y-H., Lee, P-C., & Ting, H-I. (2008). Dynamic multi-attribute decision making model with grey number evaluations. Expert Systems with Applications, 35(4), 1638-1644.

Lobato, F.S., Sousa, M.N., Silva, M.A.. & Machado, A.R. (2014). Multi-objective optimization and bio-inspired methods applied to machinability of stainless steel. Applied Soft Computing, 22, 261-271.

Manna, A., & Bhattacharayya, B. (2003). A study on machinability of Al/SiC-MMC. Journal of Materials Processing Technology, 140(1-3), 711-716.

Mills, B. (1983). Machinability of Engineering Materials. Essex: Applied Science Publishers Ltd.

Morehead, M., Huang, Y., & Hartwig, K.T. (2007). Machinability of ultrafine-grained copper using tungsten carbide and polycrystalline diamond tools. International Journal of Machine Tools & Manufacture, 47(2), 286-293.

Rao, R.V., & Gandhi, O.P. (2002). Digraph and matrix methods for the machinability evaluation of work materials. International Journal of Machine Tools & Manufacture, 42(3), 321-330.

Rao, R.V. (2006). Machinability evaluation of work materials using a combined multiple attribute decision-making method. International Journal of Advanced Manufacturing Technology, 28(3-4), 221-227.

Rech, J., Le Calvez, C., & Dessoly, M. (2004). A new approach for the characterization of machinability - application to steels for plastic injection molds. Journal of Materials Processing Technology, 152(1), 66-70.

Sadeghi, M., Razavi, S.H., & Saberi, N. (2013). Application of grey TOPSIS in preference ordering of action plans in balanced scorecard and strategy map. Informatica, 24(4), 619-635.

?alak, A., Vasilko, K., Seleck?, M., & H. Danninger (2006). New short time face turning method for testing the machinability of PM steels. Journal of Materials Processing Technology, 176(1-3), 62-69.

Sameer Kumar, D., & Suman, K.N.S. (2014). Selection of magnesium alloy by MADM methods for automobile wheels. International Journal of Engineering and Manufacturing, 4(2), 31-41.

?eker, U., & Hasirci, H. (2006). Evaluation of machinability of austempered ductile irons in terms of cutting forces and surface quality. Journal of Materials Processing Technology, 173(3), 260-268.

Sridharan, V., & Muthukrishnan, N. (2013). Optimization of machinability of polyester/modified jute fabric composite using grey relational analysis (GRA). Procedia Engineering, 64, 1003- 1012.

Stoi?, A., Kopa?, J., & Cukor, G. (2005). Testing of machinability of mould steel 40CrMnMo7 using genetic algorithm. Journal of Materials Processing Technology, 164-165, 1624-1630.

Turskis, Z., & Zavadskas, E.K. (2010). A novel method for multiple criteria analysis: grey additive ratio assessment (ARAS-G) method. Informatica, 21(4), 597-610.

Wang, X., Huang, C., Zou, B., Liu, H., Zhu, H., & Wang, J. (2014). A new method to evaluate the machinability of difficult-to-cut materials. International Journal of Advanced Manufacturing Technology, 75(1-4), 91-96.

Xu, L., Schultheiss, F., Andersson, M., & St?hl, J-E. (2013). General conception of polar diagrams for the evaluation of the potential machinability of workpiece materials. International Journal of Machining and Machinability of Materials, 14(1), 24-44.

Zavadskas, E.K., Kaklauskas, A., Turskis, Z., & Tamo?aitien?, J. (2009). Multi-attribute decision-making model by applying grey numbers. Informatica, 20(2), 305-320.

Zolfani, S.H., & Antucheviciene, J. (2012). Team member selecting based on AHP and TOPSIS grey. Inzinerine Ekonomika - Engineering Economics, 23(4), 425-434.
  • 51
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: Decision Science Letters | Year: 2016 | Volume: 5 | Issue: 1 | Views: 2859 | Reviews: 0

Related Articles:
  • Statistical analysis of AISI304 austenitic stainless steel machining using ...
  • Multi-response optimization of process parameters using Taguchi method and ...
  • Multiple characteristics optimization in machining of GFRP composites using ...
  • Application of Taguchi and regression analysis on surface roughness in mach ...
  • Optimization of machining parameters of turning operations based on multi p ...

Add Reviews

Name:*
E-Mail:
Review:
Bold Italic Underline Strike | Align left Center Align right | Insert smilies Insert link URLInsert protected URL Select color | Add Hidden Text Insert Quote Convert selected text from selection to Cyrillic (Russian) alphabet Insert spoiler
winkwinkedsmileam
belayfeelfellowlaughing
lollovenorecourse
requestsadtonguewassat
cryingwhatbullyangry
Security Code: *
Include security image CAPCHA.
Refresh Code

® 2010-2026 GrowingScience.Com