Processing, Please wait...

  • Publisher Home
  • Home
  • 🔙 Back
  • 📚 Journals
    • ⚙️ IJIEC - Industrial Engineering Computations
    • 🌐 IJDNS - Data and Network Science
    • 🧪 CCL - Current Chemistry Letters
    • 💹 AC - Accounting
    • 🎯 DSL - Decision Science Letters
    • 🚛 USCM - Uncertain Supply Chain Management
    • 🏗️ JPM - Journal of Project Management
    • 🏥 HE - Healthcare Engineering
    • 📈 SCI - Scientometrica
    • 🔩 ESM - Engineering Solid Mechanics
    • 🌿 JFS - Journal of Future Sustainability
    • 💼 MSL - Management Science Letters
  • 📝 Submit Article
  • 📊 Statistics
  • 📋 About
    • 📄 About Us
    • 📰 Blog
    • 📢 News
    • 📧 Contact
  • 📺 Tutorial
  • Search:
  • Advanced Search

Growing Science » International Journal of Industrial Engineering Computations » Multi criteria decision making of machining parameters for Die Sinking EDM Process

⭐ Highly Cited Articles

  • Jaya Algorithm
  • Rao Algorithm
  • TLBO Algorithm
  • ChatGPT and Blended Learning

Journals

  • IJIEC (840)
  • IJDS (1032)
  • DSL (759)
  • ESM (434)
  • CCL (563)
  • JPM (350)
  • AC (572)
  • JFS (101)
  • MSL (2658)
  • USCM (1104)
  • HE (52)
  • SCI (52)

IJIEC Volumes

    • ▼ Volume 17 (113)
      • Issue 1 (21)
      • Issue 2 (30)
      • Issue 3 (26)
      • Issue 4 (36)
    • ▼ Volume 16 (75)
      • Issue 1 (12)
      • Issue 2 (15)
      • Issue 3 (19)
      • Issue 4 (29)
    • ▼ Volume 15 (55)
      • Issue 1 (19)
      • Issue 2 (15)
      • Issue 3 (12)
      • Issue 4 (9)
    • ▼ Volume 14 (50)
      • Issue 1 (11)
      • Issue 2 (15)
      • Issue 3 (9)
      • Issue 4 (15)
    • ▼ Volume 13 (41)
      • Issue 1 (10)
      • Issue 2 (8)
      • Issue 3 (10)
      • Issue 4 (13)
    • ▼ Volume 12 (29)
      • Issue 1 (9)
      • Issue 2 (6)
      • Issue 3 (8)
      • Issue 4 (6)
    • ▼ Volume 11 (36)
      • Issue 1 (9)
      • Issue 2 (8)
      • Issue 3 (9)
      • Issue 4 (10)
    • ▼ Volume 10 (34)
      • Issue 1 (8)
      • Issue 2 (10)
      • Issue 3 (8)
      • Issue 4 (8)
    • ▼ Volume 9 (32)
      • Issue 1 (9)
      • Issue 2 (6)
      • Issue 3 (7)
      • Issue 4 (10)
    • ▼ Volume 8 (30)
      • Issue 1 (9)
      • Issue 2 (7)
      • Issue 3 (8)
      • Issue 4 (6)
    • ▼ Volume 7 (47)
      • Issue 1 (10)
      • Issue 2 (14)
      • Issue 3 (10)
      • Issue 4 (13)
    • ▼ Volume 6 (39)
      • Issue 1 (7)
      • Issue 2 (12)
      • Issue 3 (10)
      • Issue 4 (10)
    • ▼ Volume 5 (47)
      • Issue 1 (13)
      • Issue 2 (12)
      • Issue 3 (12)
      • Issue 4 (10)
    • ▼ Volume 4 (50)
      • Issue 1 (14)
      • Issue 2 (10)
      • Issue 3 (12)
      • Issue 4 (14)
    • ▼ Volume 3 (77)
      • Issue 1 (10)
      • Issue 2 (15)
      • Issue 3 (20)
      • Issue 4 (12)
      • Issue 5 (20)
    • ▼ Volume 2 (68)
      • Issue 1 (12)
      • Issue 2 (20)
      • Issue 3 (20)
      • Issue 4 (16)
    • ▼ Volume 1 (17)
      • Issue 1 (9)
      • Issue 2 (8)

🔑 Keywords

Jordan(175)
Supply chain management(172)
Vietnam(154)
Customer satisfaction(124)
Performance(117)
Supply chain(115)
Artificial intelligence(108)
Service quality(101)
Competitive advantage(100)
SMEs(95)
Tehran Stock Exchange(94)
Sustainability(93)
optimization(88)
Financial performance(86)
Trust(85)
TOPSIS(85)
Job satisfaction(81)
Genetic Algorithm(81)
Organizational performance(81)
Social media(80)


» Show all keywords

✍️ Authors

Naser Azad(82)
Zeplin Jiwa Husada Tarigan(70)
Mohammad Reza Iravani(65)
Endri Endri(45)
Hotlan Siagian(43)
Muhammad Alshurideh(42)
Dmaithan Almajali(39)
Jumadil Saputra(37)
Muhammad Turki Alshurideh(35)
Ahmad Makui(33)
Sautma Ronni Basana(33)
Barween Al Kurdi(32)
Basrowi Basrowi(31)
Mohammad Khodaei Valahzaghard(30)
Haitham M. Alzoubi(30)
Ni Nyoman Kerti Yasa(30)
Hassan Ghodrati(30)
Shankar Chakraborty(29)
Sulieman Ibraheem Shelash Al-Hawary(28)
Mahmoud Allahham(28)


» Show all authors

🌍 Countries

1. Algeria (52)
2. Angola (2)
3. Argentina (22)
4. Armenia (2)
5. Australia (52)
6. Austria (2)
7. Bahrain (26)
8. Bangladesh (58)
9. Belarus (4)
10. Belgium (3)
11. Benin (2)
12. Benin Republic (1)
13. Bhutan (1)
14. Bosnia and Herzegovina (1)
15. Botswana (9)
16. Brazil (40)
17. Brunei (1)
18. Bulgaria (1)
19. Burkina Faso (1)
20. Cameroon (1)
Total: 121 countries

Show all countries
International Journal of Industrial Engineering Computations
ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print)
Quarterly Publication
Volume 6 Issue 2 pp. 241-252, 2015

Multi criteria decision making of machining parameters for Die Sinking EDM Process Pages 241-252 PDF Download PDF

Authors: G. K. Bose, K. K. Mahapatra

📋 Author Affiliations:
G.K. Bose ORCID 1, K.K. Mahapatra2
1 Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, 721657, India
2 Central Institute of Plastic Engineering Technology, Bhubaneswar, 751024, India
doi 10.5267/j.ijiec.2014.10.005
6 Source: Scopus
Crossref 4 Source: CrossRef

🔑 Keywords: ANOVA, EDM, GRA, Material removal rate, Overcut, Surface Roughness

Abstract: Electrical Discharge Machining (EDM) is one of the most basic non-conventional machining processes for production of complex geometries and process of hard materials, which are difficult to machine by conventional process. It is capable of machining geometrically complex or hard material components, that are precise and difficult-to-machine such as heat-treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels etc. The present study is focusing on the die sinking electric discharge machining (EDM) of AISI H 13, W.-Nr. 1.2344 Grade: Ovar Supreme for finding out the effect of machining parameters such as discharge current (GI), pulse on time (POT), pulse off time (POF) and spark gap (SG) on performance response like Material removal rate (MRR), Surface Roughness (Ra) & Overcut (OC) using Square-shaped Cu tool with Lateral flushing. A well-designed experimental scheme is used to reduce the total number of experiments. Parts of the experiment are conducted with the L9 orthogonal array based on the Taguchi methodology and significant process parameters are identified using Analysis of Variance (ANOVA). It is found that MRR is affected by gap current & Ra is affected by pulse on time. Moreover, the signal-to-noise ratios associated with the observed values in the experiments are determined by which factor is most affected by the responses of MRR, Ra and OC. These experimental data are further investigated using Grey Relational Analysis to optimize multiple performances in which different levels combination of the factors are ranked based on grey relational grade. The analysis reveals that substantial improvement in machining performance takes place following this technique.

How to cite this paper
APA: Bose, G & Mahapatra, K. (2015). Multi criteria decision making of machining parameters for Die Sinking EDM Process. International Journal of Industrial Engineering Computations, 6(2), 241-252.
Chicago/Turabian: Bose, G & Mahapatra, K. 2015. "Multi criteria decision making of machining parameters for Die Sinking EDM Process." International Journal of Industrial Engineering Computations 6, no. 2 (2015): 241-252.
AMA: Bose, G & Mahapatra, K. Multi criteria decision making of machining parameters for Die Sinking EDM Process. International Journal of Industrial Engineering Computations. 2015;6(2):241-252.

References
Bose, G. K., Jana, T. K., & Mitra, S. (2011). Identification of the significant process parameters by Taguchi methodology during electrochemical grinding of Al 2 O 3/Al? interpenetrating phase composite. International Journal of Computational Materials Science and Surface Engineering, 4(3), 232-246.

Bose, G. K., & Mitra, S. (2013). Study of ECG process while machining Al2O3/Al–IPC using grey-Taguchi methodology. Advances in Production Engineering & Management, 8(1), 41-51.

Chakravorty, R., Gauri, S., & Chakraborty, S. (2013). Optimization of Multiple Responses of Ultrasonic Machining (USM) Process: A Comparative Study. International Journal of Industrial Engineering Computations, 4(2), 285-296.

Debroy, A., & Chakraborty, S. (2013). Non-conventional optimization techniques in optimizing non-traditional machining processes: a review. Management Science Letters, 3(1), 23-38.

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

Ding, S., & Shi, Z. (2005). Studies on incident pattern recognition based on information entropy. Journal of Information Science, 31(6), 497-294.

El-Hofy, H. (2005). Advanced Machining Processes. McGraw Hill.

Gupta, M., & Kumar, S. (2013). Multi-objective optimization of cutting parameters in turning using grey relational analysis. International Journal of Industrial Engineering Computation, 4(4), 547 – 558.

Ghosh, A., & Mallik, A. K. (1991). Manufacturing Science. Affiliated East-West Press, New Delhi.

Ho. K.H., & Newman. S.T. (2003). State of art electrical discharge machining (EDM). International Journal of Machine Tools and Manufacturing, 43(13), 1287 – 1300.

Jangra, K. (2012). Study of unmachined area in intricate machining after rough cut in WEDM. International Journal of Industrial Engineering Computation, 3(5), 887 – 892.

Jangra, K., Grover, S., & Aggarwal, A. (2011). Simultaneous optimization of material removal rate and surface roughness for WEDM of WC-Co composite using grey relational analysis along with Taguchi method. International Journal of Industrial Engineering Computations, 2(3), 479-490.

Jana, T.K., Bose, G.K., Sarkar, B. & Saha, J. (2011). Multi-objective decision-making in single-pass turning using response surface methodology, International Journal of Computational Materials Science and Surface Engineering, 4(1), 87–108.#
Kaladhar, M., Subbaiah, K.V., Rao, C.S. (2012). Parametric optimization during machining of AISI 304 Austenitic Stainless Steel using CVD coated DURATOMIC cutting insert. International Journal of Industrial Engineering Computation, 3(4), 577 – 586.

Kiyak, M. & Cakir, O. (2007). Examination of machining parameters on surface roughness in EDM tool steel. Journal of Materials Processing Technology, 191(1-3), 141 – 144.

Kumar, R., Sahoo, A., Satyanarayana, K., & Rao, G. (2013). Some studies on cutting force and temperature in machining Ti-6Al-4V alloy using regression analysis and ANOVA. International Journal of Industrial Engineering Computations, 4(3), 427-436.

Nadam, S.R., Abhilash, D.E., Ranadheer, P., Srinivas, P.L.V., Kumar, R.S., & Rao, A. A. (2012). An Experimental investigation on machining of D2 tool steel by EDM sinking process. In 4th International & 25th All India Manufacturing Technology, Design and Research Conference, 505 – 512.

Phadke, M. S. (1989). Quality Engineering using Robust Design, Prentice Hall, New Jersey.

Ross, P.J. (2005). Taguchi techniques for quality engineering. 2nd edition, Tata Mc Graw Hill.

Saha, A., & Mandal, N.K. (2013). Optimization of machining parameters of turning operations based on multi performance criteria. International Journal of Industrial Engineering Computation, 4(1), 51 – 60.

Sahoo, A. K., Mohanty, T. (2013). Optimization of multiple performance characteristics in turning using Taguchi’s quality loss function: An experimental investigation. International Journal of Industrial Engineering Computation, 4(3), 325 – 336.

Varun, A., Venkaiah, N., & Kotiveerachari, B. (2012). Multi – objective optimization of powder mixed EDM. In 4th International & 25th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2012), 453 – 459.

Yan, B. H., Tsai, H.C., & Huang, F.Y. (2005). The effect of EDM of a dielectric of a urea solution in water on modifying the surface of titanium. International journal of Machine Tools and Manufacturing, 45(2), 194 – 200.
  • 17
  • 1
  • 2
  • 3
  • 4
  • 5

📚 Journal: International Journal of Industrial Engineering Computations | 📅 Year: 2015 | 📖 Volume: 6 | 📄 Issue: 2 | 👁️ Views: 2718 | 📊 Crossref: 4

Related Articles:
  • Multiple characteristics optimization in machining of GFRP composites using Grey relational analysis
  • Analysis of machining characteristics in drilling of GFRP composite with application of fuzzy logic approach
  • Optimization of machining parameters of turning operations based on multi performance criteria
  • Effect of machining conditions on MRR and surface roughness during CNC Turning of different Materials Using TiN Coated Cutting Tools – A Taguchi approach
  • Study of unmachined area in intricate machining after rough cut in WEDM

📝 Ready to share your research?

International Journal of Industrial Engineering Computations is accepting new submissions for upcoming issues. Join our community of authors and publish your work with us.

✓ Open access
✓ Rigorous peer review
✓ Fast publication
📤 Submit Your Manuscript →

📖 Author Guidelines

® 2010-2026 GrowingScience.Com