IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v11y2020i6d10.1007_s13198-020-00951-6.html
   My bibliography  Save this article

Multidimensional optimization of electrical discharge machining for high speed steel (AISI M2) using Taguchi-fuzzy approach

Author

Listed:
  • Alaa M. Ubaid

    (University of Sharjah)

  • Shukry H. Aghdeab

    (University of Technology)

  • Ahmed Ghazi Abdulameer

    (University of Technology)

  • Laith Abdullah Al-Juboori

    (Higher Colleges of Technology)

  • Fikri T. Dweiri

    (University of Sharjah)

Abstract

Electrical discharge machining (EDM) is one of the non-traditional machining processes characterized by its ability to machine parts that electrically conductive but difficult to be machined in the traditional machining processes due to its high hardness, complex geometry, and low tolerances. To minimize EDM process cost, ensure the highest process efficiency and achieve the highest product quality, the EDM process needs to be optimized. The aim of this research is optimizing EDM process parameters for machining HSS (AISI M2) material by use of copper electrode and brass electrode considering conflicting performance measures in one and multidimensional levels. The performance measures used in the current research are material removal rate (MRR) and electrode wear rate (EWR), while machining parameters that will subject to optimization process are current (A), pulse on (TON), and pulse off (TOFF). During optimization stages, the Taguchi method, signal to noise ratio (S/N ratio), and analysis of variances (ANOVA) will be used in the first stage to find the optimal machining parameters for each performance measure and for each electrode material. In the second stage, multidimensional optimization approach encompasses using the calculated S/N ratio as input from the first stage, fuzzy logic and ANOVA to calculate multi response performance index which will be used to select optimal machining parameters for each electrode and then select the best electrode and optimal machining parameters for machining AISI M2 steel. In one-dimensional optimization, for brass electrode, to maximize MRR value, the optimal machining parameters combination is A3TON1TOFF3 and to minimize EWR value, the optimal machining parameters combination is A1TON1TOFF2. For copper electrode, to maximize MRR value, the optimal machining parameters combination is A1TON1TOFF3 and to minimize EWR value, the optimal machining parameters combination is A1TON3TOFF1. In multidimensional optimization, for the copper electrode, the optimal machining parameters combination was A1TON1TOFF3 and for the brass electrode, the optimal machining parameters combination is A3TON1TOFF3. It could be concluded that EDM machining of HSS (AISI M2) by using the copper electrode will give the best results and the optimal machining parameters combination is A1TON1TOFF3. Yet, further research needs to be conducted to study these results and analyze machining process effectiveness in terms of cost and process sustainability, i.e. environmental impact. This research has both theoretical and practical implications.

Suggested Citation

  • Alaa M. Ubaid & Shukry H. Aghdeab & Ahmed Ghazi Abdulameer & Laith Abdullah Al-Juboori & Fikri T. Dweiri, 2020. "Multidimensional optimization of electrical discharge machining for high speed steel (AISI M2) using Taguchi-fuzzy approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(6), pages 1021-1045, December.
  • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:6:d:10.1007_s13198-020-00951-6
    DOI: 10.1007/s13198-020-00951-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-020-00951-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13198-020-00951-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mohan Kumar Pradhan, 2018. "Optimisation of EDM process for MRR, TWR and radial overcut of D2 steel: a hybrid RSM-GRA and entropy weight-based TOPSIS approach," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 29(3), pages 273-302.
    2. Mary M. Crossan & Marina Apaydin, 2010. "A Multi‐Dimensional Framework of Organizational Innovation: A Systematic Review of the Literature," Journal of Management Studies, Wiley Blackwell, vol. 47(6), pages 1154-1191, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Swen Nadkarni & Reinhard Prügl, 2021. "Digital transformation: a review, synthesis and opportunities for future research," Management Review Quarterly, Springer, vol. 71(2), pages 233-341, April.
    2. Tal Shahor, 2018. "Is the Marginal Effect of Education on Income Diminishing?," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 4, May - Aug.
    3. Piñeiro-Chousa, Juan & López-Cabarcos, M. Ángeles & Romero-Castro, Noelia María & Pérez-Pico, Ada María, 2020. "Innovation, entrepreneurship and knowledge in the business scientific field: Mapping the research front," Journal of Business Research, Elsevier, vol. 115(C), pages 475-485.
    4. Bentivoglio, Deborah & Bucci, Giorgia & Belletti, Matteo & Finco, Adele, 2022. "A theoretical framework on network’s dynamics for precision agriculture technologies adoption," Revista de Economia e Sociologia Rural (RESR), Sociedade Brasileira de Economia e Sociologia Rural, vol. 60(4), January.
    5. Silvia Cosimato & Roberto Vona, 2021. "Digital Innovation for the Sustainability of Reshoring Strategies: A Literature Review," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    6. van den Broek, Tijs & van Veenstra, Anne Fleur, 2018. "Governance of big data collaborations: How to balance regulatory compliance and disruptive innovation," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 330-338.
    7. George A Shinkle & Jo-Ann Suchard, 2019. "Innovation in newly public firms: The influence of government grants, venture capital, and private equity," Australian Journal of Management, Australian School of Business, vol. 44(2), pages 248-281, May.
    8. Schiuma, Giovanni & Santarsiero, Francesco, 2023. "Innovation labs as organisational catalysts for innovation capacity development: A systematic literature review," Technovation, Elsevier, vol. 123(C).
    9. Rubio-Andrés, Mercedes & Ramos-González, Mª del Mar & Sastre-Castillo, Miguel Ángel & Gutiérrez-Broncano, Santiago, 2023. "Stakeholder pressure and innovation capacity of SMEs in the COVID-19 pandemic: Mediating and multigroup analysis," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    10. Dimitrios Kafetzopoulos & Evangelos Psomas, 2016. "ORGANISATIONAL LEARNING, NON-TECHNICAL INNOVATION AND CUSTOMER SATISFACTION OF SMEs," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 20(03), pages 1-28, April.
    11. Sheth, Ananya & Sinfield, Joseph V., 2022. "An analytical framework to compare innovation strategies and identify simple rules," Technovation, Elsevier, vol. 115(C).
    12. Christel Lane & Daniela Lup, 2015. "Cooking under Fire: Managing Multilevel Tensions between Creativity and Innovation in Haute Cuisine," Industry and Innovation, Taylor & Francis Journals, vol. 22(8), pages 654-676, November.
    13. Julia Naranjo-Valencia & Ricardo Vidal-Patiño & Gregorio Calderón-Hernández, 2019. "Characterization of Innovation Research Published in Latin American Journals Indexed in WoS," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(07), pages 1-38, November.
    14. Donald F. Kuratko & Greg Fisher & James M. Bloodgood & Jeffrey S. Hornsby, 2017. "The paradox of new venture legitimation within an entrepreneurial ecosystem," Small Business Economics, Springer, vol. 49(1), pages 119-140, June.
    15. Georgios Giotis & Evangelia Papadionysiou, 2022. "The Role of Managerial and Technological Innovations in the Tourism Industry: A Review of the Empirical Literature," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    16. Gregori, Patrick & Ukobitz, Desiree V. & Parastuty, Zulaicha, 2018. "A Conceptual Framework on Entrepreneurial Team Member Exits: A Starting Point for Further Research," 6th International OFEL Conference on Governance, Management and Entrepreneurship. New Business Models and Institutional Entrepreneurs: Leading Disruptive Change (Dubrovnik, 2018), in: 6th International OFEL Conference on Governance, Management and Entrepreneurship. New Business Models and Institutional Entrepreneurs: Leading Disrupt, pages 453-474, Governance Research and Development Centre (CIRU), Zagreb.
    17. Yang Gao, 2022. "The Belt and Road Initiative and cascading innovation in China’s domestic railway ecosystem," Journal of International Business Policy, Palgrave Macmillan, vol. 5(2), pages 236-258, June.
    18. Liwen Wang & Jin Jason Lu & Kevin Zhou, 2023. "Technological Capability Strength/Asymmetry and Supply Chain Process Innovation: The Contingent Roles of Institutional Environments in China," Post-Print hal-03954124, HAL.
    19. Kong YuSheng & Masud Ibrahim, 2020. "Innovation Capabilities, Innovation Types, and Firm Performance: Evidence From the Banking Sector of Ghana," SAGE Open, , vol. 10(2), pages 21582440209, May.
    20. Bordunos, A. & Kosheleva, S., 2016. "High Involvement Work System from organizational and individual perspective," Working Papers 6445, Graduate School of Management, St. Petersburg State University.

    More about this item

    Keywords

    EDM; Optimization; Fuzzy logic; Taguchi; ANOVA; AISI M2;
    All these keywords.

    JEL classification:

    • M2 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:11:y:2020:i:6:d:10.1007_s13198-020-00951-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.