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The research work carried out in this paper presents a novel intelligent tool condition monitoring solution for the turning process using an enhanced adaptive neural fuzzy inference system based on extended subtractive clustering. The hybrid system is constructed from training a takagi-sugeno-kang fuzzy logic system by integrating machining parameters- feed, cutting force, feed force and cutting tool...
This paper addresses a high precision discrete-time model-free PID adaptive neuro-fuzzy logic motion controller in case the physical models that describe a robot are not known. The advantage of this kind controller is that it uses an improved subtractive clustering technique to obtain the structure of the system model in order to ensure the high accuracy of the intelligent control. Moreover, the information...
Many modern and intelligent control methods had been developed for nonlinear systems in order to get better motion accuracy and dynamic performance for parallel robot. This paper aims to propose a nouvelle model-free adaptive neural fuzzy feed forward torque control for parallel mechanism. The advantage of this kind model-free control is that it uses the information directly from the nonlinear dynamics,...
Because of the difficulty in understanding the physics of the machining process, several different intelligence methods, which employ cutting forces for estimation tool wear, have been developed in the past few years. Unfortunately, none of them can overcome the difficulty to estimate the errors of approximation during tool wear monitoring. This paper aimed at presenting a tool wear monitoring method...
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