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Experimental and fuzzy modelling analysis on dynamic cutting force in micro milling.

, , , , and . Soft Comput., 17 (9): 1687-1697 (2013)

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Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to a neural network., , and . J. Intell. Manuf., 9 (5): 447-455 (1998)TSK fuzzy modeling for tool wear condition in turning processes: An experimental study., , , and . Eng. Appl. Artif. Intell., 24 (2): 260-265 (2011)Reliable Tool Life Estimation with Multiple Acoustic Emission Signal Feature Selection and Integration Based on Type-2 Fuzzy Logic., , , and . Advances in Type-2 Fuzzy Sets and Systems, volume 301 of Studies in Fuzziness and Soft Computing, Springer, (2013)An enhanced adaptive neural fuzzy tool condition monitoring for turning process., , , and . FUZZ-IEEE, page 1976-1982. IEEE, (2016)Fuzzy cutting force modelling in micro-milling., , , and . J. Intell. Fuzzy Syst., 25 (4): 1027-1035 (2013)Tool Condition Monitoring Using the TSK Fuzzy Approach Based on Subtractive Clustering Method., , , and . IEA/AIE, volume 5027 of Lecture Notes in Computer Science, page 52-61. Springer, (2008)Modelling of dynamic micromilling cutting forces using type-2 fuzzy rule-based system., , , and . FUZZ-IEEE, page 1-7. IEEE, (2010)Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling., , , , , and . Inf. Sci., (2014)Experimental and fuzzy modelling analysis on dynamic cutting force in micro milling., , , , and . Soft Comput., 17 (9): 1687-1697 (2013)