This paper presents a novel approach for active, on-line suppression of chatter in machining operations. The presence of chatter limits the material removal rate and therefore, lowers the productivity of machining processes. The goal of chatter suppression is to minimize the chatter amplitude and therefore extend the chatter stability boundary. Once the presence of chatter is detected the suppression system will be activated. A neural network model is used to calculate current gradient values with respect to the parameters of the active vibration source. These gradient information will be used by an optimization module to find the optimal set of parameters for the active vibration source. The overall result is an on-line, adaptable chatter suppression system. The methodology described is evaluated through simulation studies. The results show that the system is capable of suppressing chatter in single point cutting process.
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