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Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing

Author

Listed:
  • Doriana M. D’Addona

    (University of Naples Federico II)

  • A. M. M. Sharif Ullah

    (Kitami Institute of Technology)

  • D. Matarazzo

    (University of Naples Federico II)

Abstract

Managing tool-wear is an important issue associated with all material removal processes. This paper deals with the application of two nature-inspired computing techniques, namely, artificial neural network (ANN) and (in silico) DNA-based computing (DBC) for managing the tool-wear. Experimental data (images of worn-zone of cutting tool) has been used to train the ANN and, then, to perform the DBC. It is demonstrated that the ANN can predict the degree of tool-wear from a set of tool-wear images processed under a given procedure whereas the DBC can identify the degree of similarity/dissimilar among the processed images. Further study can be carried out while solving other complex problems integrating ANN and DBC where both prediction and pattern-recognition are two important computational problems that need to be solved simultaneously.

Suggested Citation

  • Doriana M. D’Addona & A. M. M. Sharif Ullah & D. Matarazzo, 2017. "Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1285-1301, August.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:6:d:10.1007_s10845-015-1155-0
    DOI: 10.1007/s10845-015-1155-0
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    Cited by:

    1. Woong-Gi Kim & Namhyuk Ham & Jae-Jun Kim, 2021. "Enhanced Subcontractors Allocation for Apartment Construction Project Applying Conceptual 4D Digital Twin Framework," Sustainability, MDPI, vol. 13(21), pages 1-21, October.
    2. Antonio Del Prete & Rodolfo Franchi & Stefania Cacace & Quirico Semeraro, 2020. "Optimization of cutting conditions using an evolutive online procedure," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 481-499, February.
    3. Chayma Sellami & Carlos Miranda & Ahmed Samet & Mohamed Anis Bach Tobji & François de Beuvron, 2020. "On mining frequent chronicles for machine failure prediction," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1019-1035, April.
    4. Antonio Armillotta, 2021. "On the role of complexity in machining time estimation," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2281-2299, December.
    5. 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.
    6. 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.
    7. Yaxuan Liu, 2021. "Developing the network social media in graphic design based on artificial neural network," 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. 12(4), pages 640-653, August.
    8. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
    9. 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.

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