IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v32y2021i1d10.1007_s10845-020-01555-4.html
   My bibliography  Save this article

Fuzzy logic and sub-clustering approaches to predict main cutting force in high-pressure jet assisted turning

Author

Listed:
  • Dragan Rodić

    (University of Novi Sad)

  • Milenko Sekulić

    (University of Novi Sad)

  • Marin Gostimirović

    (University of Novi Sad)

  • Vladimir Pucovsky

    (University of Novi Sad)

  • Davorin Kramar

    (University of Ljubljana)

Abstract

Due to the complexity of the high-pressure jet assisted turning, knowledge, and prediction of the cutting forces are essential for the planning of machining operations for maximum productivity and quality. However, it is well known that during processing using this procedure there are difficulties in collecting data. It is required to establish an adequate model that would make it possible to predict the cutting force based on the input parameters. During machining to avoid difficulties in acquisition data, two models have developed based on fuzzy logic that will allow indirect monitoring of the cutting force. This research uses the improved fuzzy logic methods for modeling, whereby it can make predictions of the main cutting force according to the different input parameters. The contribution of this work reflected through the application of two innovative methods based on reducing the number of rules, which leads to better interpretability of models. First is the Mamdani with rule reduction method, and second is the Sugeno sub-clustering method based on the identification of the model structure, it comes down to finding the required number of rules by forming specific clusters. Both approaches differ by reducing the number of rules without affecting the accuracy of the models. The ability to predict the model determined by applying different statistical parameters. It concluded that Mamdani and Sugeno models give an approximate quality of the prediction. The resulting models also have an acceptable error to predict data that did not participate in their creation. Furthermore, obtained models can be used at the generalization stage where the cutting force information is required and where direct measurement is not possible.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:joinma:v:32:y:2021:i:1:d:10.1007_s10845-020-01555-4
    DOI: 10.1007/s10845-020-01555-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01555-4
    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/s10845-020-01555-4?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. Damien McParland & Szymon Baron & Sarah O’Rourke & Denis Dowling & Eamonn Ahearne & Andrew Parnell, 2019. "Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1259-1270, March.
    2. Jagadish & Sumit Bhowmik & Amitava Ray, 2019. "Prediction of surface roughness quality of green abrasive water jet machining: a soft computing approach," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2965-2979, December.
    3. D. Yu. Pimenov & A. Bustillo & T. Mikolajczyk, 2018. "Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1045-1061, June.
    4. Emel Kuram & Babur Ozcelik, 2016. "Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 817-830, August.
    5. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Danil Yu Pimenov & Andres Bustillo & Szymon Wojciechowski & Vishal S. Sharma & Munish K. Gupta & Mustafa Kuntoğlu, 2023. "Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2079-2121, June.

    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. Danil Yu Pimenov & Andres Bustillo & Szymon Wojciechowski & Vishal S. Sharma & Munish K. Gupta & Mustafa Kuntoğlu, 2023. "Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2079-2121, June.
    2. Andres Bustillo & Danil Yu. Pimenov & Mozammel Mia & Wojciech Kapłonek, 2021. "Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 895-912, March.
    3. 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.
    4. Andres Bustillo & Roberto Reis & Alisson R. Machado & Danil Yu. Pimenov, 2022. "Improving the accuracy of machine-learning models with data from machine test repetitions," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 203-221, January.
    5. Ardamanbir Singh Sidhu & Sehijpal Singh & Raman Kumar & Danil Yurievich Pimenov & Khaled Giasin, 2021. "Prioritizing Energy-Intensive Machining Operations and Gauging the Influence of Electric Parameters: An Industrial Case Study," Energies, MDPI, vol. 14(16), pages 1-39, August.
    6. Pauline Ong & Choon Sin Ho & Desmond Daniel Vui Sheng Chin & Chee Kiong Sia & Chuan Huat Ng & Md Saidin Wahab & Abduladim Salem Bala, 2019. "Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1957-1972, April.
    7. Ruiyang Hao & Bingyu Lu & Ying Cheng & Xiu Li & Biqing Huang, 2021. "A steel surface defect inspection approach towards smart industrial monitoring," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1833-1843, October.
    8. Omojola Awogbemi & Daramy Vandi Von Kallon & Kazeem Aderemi Bello, 2022. "Resource Recycling with the Aim of Achieving Zero-Waste Manufacturing," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    9. Yanxi Zhang & Deyong You & Xiangdong Gao & Congyi Wang & Yangjin Li & Perry P. Gao, 2020. "Real-time monitoring of high-power disk laser welding statuses based on deep learning framework," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 799-814, April.
    10. Ahmed Elsheikh & Soumaya Yacout & Mohamed-Salah Ouali & Yasser Shaban, 2020. "Failure time prediction using adaptive logical analysis of survival curves and multiple machining signals," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 403-415, February.
    11. Ki Bum Lee & Chang Ouk Kim, 2020. "Recurrent feature-incorporated convolutional neural network for virtual metrology of the chemical mechanical planarization process," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 73-86, January.
    12. Durga Prasad Penumuru & Sreekumar Muthuswamy & Premkumar Karumbu, 2020. "Identification and classification of materials using machine vision and machine learning in the context of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1229-1241, June.
    13. Ammar H. Elsheikh & Taher A. Shehabeldeen & Jianxin Zhou & Ezzat Showaib & Mohamed Abd Elaziz, 2021. "Prediction of laser cutting parameters for polymethylmethacrylate sheets using random vector functional link network integrated with equilibrium optimizer," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1377-1388, June.
    14. Zengya Zhao & Sibao Wang & Zehua Wang & Shilong Wang & Chi Ma & Bo Yang, 2022. "Surface roughness stabilization method based on digital twin-driven machining parameters self-adaption adjustment: a case study in five-axis machining," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 943-952, April.
    15. Christian Kubik & Sebastian Michael Knauer & Peter Groche, 2022. "Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 259-282, January.
    16. J. Santhakumar & U. Mohammed Iqbal, 2021. "Role of trochoidal machining process parameter and chip morphology studies during end milling of AISI D3 steel," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 649-665, March.
    17. Soheyl Khalilpourazari & Saman Khalilpourazary & Aybike Özyüksel Çiftçioğlu & Gerhard-Wilhelm Weber, 2021. "Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1621-1647, August.
    18. Victor Flores & Brian Keith, 2019. "Gradient Boosted Trees Predictive Models for Surface Roughness in High-Speed Milling in the Steel and Aluminum Metalworking Industry," Complexity, Hindawi, vol. 2019, pages 1-15, July.
    19. Wo Jae Lee & Kevin Xia & Nancy L. Denton & Bruno Ribeiro & John W. Sutherland, 2021. "Development of a speed invariant deep learning model with application to condition monitoring of rotating machinery," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 393-406, February.
    20. Soo-Bong Cho & Seung-Kook Ro & Byung-Sub Kim & Sung-Cheul Lee & Jong-Kweon Park, 2021. "The development of a micro-pattern manufacturing method using rotating active tools with compensation of estimated errors and an LMS algorithm," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 51-59, 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:spr:joinma:v:32:y:2021:i:1:d:10.1007_s10845-020-01555-4. 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.