IDEAS home Printed from
   My bibliography  Save this article

Tool life management of unmanned production system based on surface roughness by ANFIS


  • Vineet Jain

    () (Amity University Gurgaon)

  • Tilak Raj

    () (YMCA University of Science and Technology)


Abstract This research focuses on to develop monitoring systems that can detect surface roughness by using adaptive neuro-fuzzy inference system (ANFIS) for the unmanned production system. Cutting force is one important characteristic variable to be monitored in the cutting processes to determine tool life regarding tool breakage, tool wear, and surface roughness (Ra) of the workpiece. The principal presumption was that the cutting forces are normally increased by the wear of the tool. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. Input parameters for making an ANFIS model are Speed, feed, depth of cut, cutting force and output in term of surface roughness. A piezoelectric dynamometer measured the forces. The experimental forces and surface roughness were utilized to train the developed simulation environment based on ANFIS modeling. By tool condition monitoring system, the machining process can be on-line monitored for the unmanned production system. The achieved Correlation coefficient (R) is 0.9528 and average percentage error is 7.38 %. In this research, we predict the surface roughness of a workpiece by using the ANFIS modeling and surface roughness can be used for tool life management and enables it for monitoring of unmanned production system.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-016-0450-2
    DOI: 10.1007/s13198-016-0450-2

    Download full text from publisher

    File URL:
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

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

    References listed on IDEAS

    1. 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.
    Full references (including those not matched with items on IDEAS)


    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:8:y:2017:i:2:d:10.1007_s13198-016-0450-2. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.