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An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations

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  • PoTsang B. Huang

    (Chung-Yuan Christian University)

Abstract

In this research, a new intelligent neural-fuzzy in-process surface roughness monitoring (INF-SRM) system for an end milling operation was developed. The success of the INF-SRM system depends on an accurate decision-making algorithm, which can analyze the input factors and then generate an accurate output. A new neural-fuzzy model was proposed and implemented as decision-making algorithm for the INF-SRM system. The objective of the new model is to achieve higher accuracy for surface roughness prediction and solve the disadvantages of both neural networks and fuzzy logic. The neural-assisted method was implemented to generate the fuzzy IF-THEN rules for the model. To evaluate the performance of the new neural-fuzzy model, a neural networks model was applied to develop another surface roughness monitoring system for comparison. A statistical method was finally employed to analyze the accuracy between these systems.

Suggested Citation

  • PoTsang B. Huang, 2016. "An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 689-700, June.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:3:d:10.1007_s10845-014-0907-6
    DOI: 10.1007/s10845-014-0907-6
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    References listed on IDEAS

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    1. Vikas Upadhyay & P.K. Jain & N.K. Mehta, 2013. "Prediction of surface roughness using cutting parameters and vibration signals in minimum quantity coolant assisted turning of Ti-6Al-4V alloy," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 27(1/2/3), pages 33-46.
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    Cited by:

    1. Kuo Lu & Jin Xie & Risen Wang & Lei Li & Wenzhe Li & Yuning Jiang, 2022. "A closed-loop intelligent adjustment of process parameters in precise and micro hot-embossing using an in-process optic detection," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2341-2355, December.
    2. Shubham Vaishnav & Ankit Agarwal & K. A. Desai, 2020. "Machine learning-based instantaneous cutting force model for end milling operation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1353-1366, August.
    3. Gerardo Beruvides & Fernando Castaño & Rodolfo E. Haber & Ramón Quiza & Alberto Villalonga, 2017. "Coping with Complexity When Predicting Surface Roughness in Milling Processes: Hybrid Incremental Model with Optimal Parametrization," Complexity, Hindawi, vol. 2017, pages 1-11, December.
    4. PoTsang B. Huang & Huang-Jie Zhang & Yi-Ching Lin, 2019. "Development of a Grey online modeling surface roughness monitoring system in end milling operations," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1923-1936, April.

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