An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations
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DOI: 10.1007/s10845-014-0907-6
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References listed on IDEAS
- 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:
- 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.
- 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.
- 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.
- 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.
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Keywords
Intelligent neural-fuzzy model; In-process surface roughness monitoring; End milling operations; Neural networks; Fuzzy logic;All these keywords.
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