IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v9y2018i5d10.1007_s13198-018-0717-x.html
   My bibliography  Save this article

Prediction of cutting force by using ANFIS

Author

Listed:
  • Vineet Jain

    (Amity University Haryana)

  • Tilak Raj

    (YMCA University of Science and Technology)

Abstract

The aim of this research is to develop a model to predict the cutting forces of a turning operation. This paper focuses on to design a monitoring system that can recognize cutting force on the basis of cutting parameters like spindle speed, feed and depth of cut by using adaptive neuro-fuzzy inference system (ANFIS). Cutting force is one of the important characteristic variables to be watched and controlled in the cutting processes to determine tool life and surface roughness of the work piece. The principal assumption was that the cutting forces increase due to the wearing of the tool. So, ANFIS model is used to express the cutting force signal. In this paper, ANFIS is used to predict the cutting force. The correlation coefficient (R) and average percentage error found in this modeling are 0.9976 and 2.59% respectively. The predicted cutting force values derived from ANFIS were compared with experimental data. The comparison indicates that the ANFIS achieved very satisfactory accuracy. The correlation coefficient (R) and average percentage error found in this modeling are 0.9976 and 2.59% respectively. The prediction accuracy of ANFIS reached is as high as 97%.

Suggested Citation

  • Vineet Jain & Tilak Raj, 2018. "Prediction of cutting force by using ANFIS," 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. 9(5), pages 1137-1146, October.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:5:d:10.1007_s13198-018-0717-x
    DOI: 10.1007/s13198-018-0717-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-018-0717-x
    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/s13198-018-0717-x?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. Himanshu Chaudhary & Vikas Panwar & Rajendra Prasad & N. Sukavanam, 2016. "Adaptive neuro fuzzy based hybrid force/position control for an industrial robot manipulator," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1299-1308, December.
    2. Jain, Vineet & Raj, Tilak, 2016. "Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 84-96.
    3. Vineet Jain & Tilak Raj, 2017. "Tool life management of unmanned production system based on surface roughness by ANFIS," 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. 8(2), pages 458-467, June.
    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. Dragan Rodić & Milenko Sekulić & Marin Gostimirović & Vladimir Pucovsky & Davorin Kramar, 2021. "Fuzzy logic and sub-clustering approaches to predict main cutting force in high-pressure jet assisted turning," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 21-36, January.
    2. Amin Vafadarnikjoo & Hadi Badri Ahmadi & Benjamin Thomas Hazen & James J. H. Liou, 2020. "Understanding Interdependencies among Social Sustainability Evaluation Criteria in an Emerging Economy," Sustainability, MDPI, vol. 12(5), pages 1-21, March.
    3. Amit Kumar Gupta & Harshit Goyal, 2021. "Framework for implementing big data analytics in Indian manufacturing: ISM-MICMAC and Fuzzy-AHP approach," Information Technology and Management, Springer, vol. 22(3), pages 207-229, September.
    4. Vineet Jain & Tilak Raj, 2017. "Tool life management of unmanned production system based on surface roughness by ANFIS," 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. 8(2), pages 458-467, June.
    5. Vineet Jain & Puneeta Ajmera, 2019. "Modelling of the factors affecting lean implementation in healthcare using structural equation modelling," 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. 10(4), pages 563-575, August.
    6. Sadia Samar Ali & Rajbir Kaur & Shahbaz Khan, 2023. "Identification of innovative technology enablers and drone technology determinants adoption: a graph theory matrix analysis framework," Operations Management Research, Springer, vol. 16(2), pages 830-852, June.
    7. Puneeta Ajmera & Vineet Jain, 2019. "Modelling the barriers of Health 4.0–the fourth healthcare industrial revolution in India by TISM," Operations Management Research, Springer, vol. 12(3), pages 129-145, December.
    8. Lu Han & Haijun Bao & Yi Peng, 2017. "Which Factors Affect Landless Peasants’ Intention for Entrepreneurship? A Case Study in the South of the Yangtze River Delta, China," Sustainability, MDPI, vol. 9(7), pages 1-18, July.

    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:9:y:2018:i:5:d:10.1007_s13198-018-0717-x. 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.