Abstract
In this work, an attempt has been made to design an intelligence technique-based expert system using adaptive neuro-fuzzy inference system (ANFIS) for predicting tool wear in milling operation. An artificial neural network is used for designing an optimized fuzzy logic system, so that the tool wear can be modeled for a set of input cutting parameters, namely feed rate, depth of cut, and cutting force. The proposed method uses two different learning approaches, namely back-propagation gradient descent method alone and hybrid method (i.e., combination of the least squares method and back-propagation algorithm) for training of first-order Sugeno-type fuzzy system. The predicted tool wear values derived from proposed ANFIS were compared with the experimental data.
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Roy, S.S. (2015). An Application of ANFIS-Based Intelligence Technique for Predicting Tool Wear in Milling. In: Mandal, D., Kar, R., Das, S., Panigrahi, B. (eds) Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 343. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2268-2_32
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DOI: https://doi.org/10.1007/978-81-322-2268-2_32
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