Modelling of tool life, torque and thrust force in drilling: a neuro-fuzzy approach

Abstract

This paper presents the application of neuro-fuzzy approach for modelling tool life, torque and thrust force in drilling operation for set of given process parameters, namely cutting speed, feed rate and drill diameter. The proposed approach uses a hybrid-learning algorithm i.e., combination of the back-propagation gradient descent method and least squares method, to identify premise and consequent parameters of the first-order Sugeno-fuzzy inference system. The least square method is used to optimize the consequent parameters with the premise parameters fixed. Once the optimal consequent parameters are found, the back-propagation algorithm gradient descent method is used to adjust optimally the premise parameters corresponding to the fuzzy set in the input domain. The predicted tool life, torque and thrust force values obtained from neuro-fuzzy system were compared with the experimental data. This comparison indicates that the proposed approach can produce optimal knowledge base of fuzzy system for predicting tool life, torque and thrust force in drilling operation. (Received in August 2009, accepted in November 2009. This paper was with the author 1 month for 1 revision.)