Chatter Suppression in Machining Processes

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 thatn is 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 suppresion 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 vibratration 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.

Chatter Suppression in Machining Processes

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 thatn is 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 suppresion 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 vibratration 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.

Analyzing chatter vibration during turning on computer numerical control lathe using ensemble local mean decomposition and probabilistic approach

Chatter vibration is an undesired and indispensable phenomenon in turning operation, which cannot be completely avoided. However, it can be suppressed by early identification and with the proper choice of input turning parameters. The key issue of chatter detection is to process the acquired signals and extract the features pertaining to it. In the present work, a methodology has been proposed for exploring tool chatter features in the incipient stage during turning on lathe. Chatter signals generated during the turning of Al 6061-T6 have been acquired using a microphone. A stability lobe diagram has been plotted to access the stability regime. Further, in order to study the effect of feed rate on stability, the recorded signals have been processed using a local mean decomposition signal processing technique, followed by the selection of dominating product functions using the Fourier transform. The decomposed signals have been used to evaluate the new output parameter, that is, chatter index. Further, the Nakagami probability distribution has been used to ascertain stability region (threshold). From the experimental validation, it has been inferred that cutting combinations obtained from the Nakagami probability distribution are significant and capable of limiting chatter vibrations. The present methodology will serve as guidelines to the researchers and machinist for the identification of tool chatter in the incipient stage, explore its severity, and finally suppress it with the proper selection of input turning parameters.