IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i6p922-d367952.html
   My bibliography  Save this article

Using a Fuzzy Inference System to Obtain Technological Tables for Electrical Discharge Machining Processes

Author

Listed:
  • C. J. Luis Pérez

    (Materials and Manufacturing Engineering Research Group, Engineering Department, Public University of Navarre, Campus de Arrosadía s/n, Pamplona, 31006 Navarra, Spain)

Abstract

Technological tables are very important in electrical discharge machining to determine optimal operating conditions for process variables, such as material removal rate or electrode wear. Their determination is of great industrial importance and their experimental determination is very important because they allow the most appropriate operating conditions to be selected beforehand. These technological tables are usually employed for electrical discharge machining of steel, but their number is significantly less in the case of other materials. In this present research study, a methodology based on using a fuzzy inference system to obtain these technological tables is shown with the aim of being able to select the most appropriate manufacturing conditions in advance. In addition, a study of the results obtained using a fuzzy inference system for modeling the behavior of electrical discharge machining parameters is shown. These results are compared to those obtained from response surface methodology. Furthermore, it is demonstrated that the fuzzy system can provide a high degree of precision and, therefore, it can be used to determine the influence of these machining parameters on technological variables, such as roughness, electrode wear, or material removal rate, more efficiently than other techniques.

Suggested Citation

  • C. J. Luis Pérez, 2020. "Using a Fuzzy Inference System to Obtain Technological Tables for Electrical Discharge Machining Processes," Mathematics, MDPI, vol. 8(6), pages 1-26, June.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:6:p:922-:d:367952
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/6/922/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/6/922/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fausto Cavallaro, 2015. "A Takagi-Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomass," Sustainability, MDPI, vol. 7(9), pages 1-13, 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. Mehrbakhsh Nilashi & Fausto Cavallaro & Abbas Mardani & Edmundas Kazimieras Zavadskas & Sarminah Samad & Othman Ibrahim, 2018. "Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    2. Fausto Cavallaro & Edmundas Kazimieras Zavadskas & Saulius Raslanas, 2016. "Evaluation of Combined Heat and Power (CHP) Systems Using Fuzzy Shannon Entropy and Fuzzy TOPSIS," Sustainability, MDPI, vol. 8(6), pages 1-21, June.
    3. C. J. Luis Pérez, 2020. "A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering," Mathematics, MDPI, vol. 8(9), pages 1-38, August.
    4. Mahmoudabadi, Parvin & Tavakoli-Kakhki, Mahsan, 2021. "Tracking control with disturbance rejection of nonlinear fractional order fuzzy systems: Modified repetitive control approach," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    5. Garofalo, Pasquale & Mastrorilli, Marcello & Ventrella, Domenico & Vonella, Alessandro Vittorio & Campi, Pasquale, 2020. "Modelling the suitability of energy crops through a fuzzy-based system approach: The case of sugar beet in the bioethanol supply chain," Energy, Elsevier, vol. 196(C).
    6. Mehdi Poornikoo & Kjell Ivar Øvergård, 2022. "Levels of automation in maritime autonomous surface ships (MASS): a fuzzy logic approach," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 24(2), pages 278-301, June.
    7. Omid Ghorbanzadeh & Hashem Rostamzadeh & Thomas Blaschke & Khalil Gholaminia & Jagannath Aryal, 2018. "A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(2), pages 497-517, November.
    8. Garofalo, Pasquale & Campi, Pasquale & Vonella, Alessandro Vittorio & Mastrorilli, Marcello, 2018. "Application of multi-metric analysis for the evaluation of energy performance and energy use efficiency of sweet sorghum in the bioethanol supply-chain: A fuzzy-based expert system approach," Applied Energy, Elsevier, vol. 220(C), pages 313-324.
    9. Ramin Gharizadeh Beiragh & Reza Alizadeh & Saeid Shafiei Kaleibari & Fausto Cavallaro & Sarfaraz Hashemkhani Zolfani & Romualdas Bausys & Abbas Mardani, 2020. "An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies," Sustainability, MDPI, vol. 12(3), pages 1, January.

    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:gam:jmathe:v:8:y:2020:i:6:p:922-:d:367952. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.