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Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling

Author

Listed:
  • Emel Kuram

    (Gebze Institute of Technology)

  • Babur Ozcelik

    (Gebze Institute of Technology)

Abstract

In this study, micro-milling of AISI 304 stainless steel with ball nose end mill was conducted using Taguchi method. The influences of spindle speed, feed rate and depth of cut on tool wear, cutting forces and surface roughness were examined. Taguchi’s signal to noise ratio was utilized to optimize the output responses. The influence of control parameters on output responses was determined by analysis of variance. In this study, the models describing the relationship between the independent variables and the dependent variables were also established by using regression and fuzzy logic. Efficiency of both models was determined by analyzing correlation coefficients and by comparing with experimental values. The results showed that both regression and fuzzy logic modelling could be efficiently utilized for the prediction of tool wear, cutting forces and surface roughness in micro-milling of AISI 304 stainless steel.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:4:d:10.1007_s10845-014-0916-5
    DOI: 10.1007/s10845-014-0916-5
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    Citations

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    Cited by:

    1. 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.
    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. 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.
    4. 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.
    5. 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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