The development of an untended machining system has been the subject of research for quite some time. Today, the need for such a system is greater than it once was because of the shortage of skilled workers, higher machining speeds, increase in precision machining, and the need to lower downtime. One aspect of machining process has been under investigation is tool chatter.

Chatter is a machining instability resulting from self-excited vibration caused by interaction of the chip removal process, the cutting tool, and the structure of the machine tool. Chatter can severely reduce the material rate by putting limits to cutting speed and width of cut.

This thesis describes a novel approach for active, on line suppression of chatter in machining operations. 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. This gradient information will be used by an optimization module to find the optimal set of parameters for the active vibration source. The methodology described is evaluated through simulation studies and simulation results confirmed the effectiveness of the approach.