Experimental Study on Application of Tuned Mass Dampers for Chatter in Turning of a Thin-Walled Cylinder

Chatter is more likely to occur during the turning process of a thin-walled cylindrical workpiece owing to the low rigidity of such workpieces. Chatter causes intensive vibration, deterioration of the surface finish accuracy, tool damage, and tool wear. Tuned mass dampers (TMD) are usually applied as a passive damping technique to induce a large damping effect using a small mass. This study experimentally investigated the effect of the mounting arrangement and tuning parameters of the TMDs on the production of chatter during the turning process of a thin-walled cylinder, wherein multiple TMDs with extremely small mass ratios were attached to the rotating workpiece. The results of the cutting tests performed by varying the circumferential and axial mounting positions of the TMDs exhibited different characteristics of the chatter suppression effect. Conclusively, the TMDs could suppress the chatter generated by the vibration mode with circumferential nodes if they were mounted on the workpiece to avoid the coincidence of the circumferential arrangement with the pitch of the vibration nodes, regardless of the extremely small mass of the TMDs.

Experimental implementation of artificial neural network-based active vibration control & chatter suppression

Vibration control strategies strive to reduce the effect of harmful vibrations such as machining chatter. In general, these strategies are classified as passive or active. While passive vibration control techniques are generally less complex, there is a limit to their effectiveness. Active vibration control strategies, which work by providing an additional energy supply to vibration systems, on the other hand, require more complex algorithms but can be very effective. In this work, a novel artificial neural network-based active vibration control system has been developed. The developed system can detect the sinusoidal vibration component with the highest power and suppress it in one control cycle, and in subsequent cycles, sinusoidal signals with the next highest power will be suppressed. With artificial neural networks trained to cover enough frequency and amplitude ranges, most of the original vibration can be suppressed. The efficiency of the proposed methodology has been verified experimentally in the vibration control of a cantilever beam. Artificial neural networks can be trained automatically for updated time delays in the system when necessary. Experimental results show that the developed active vibration control system is real time, adaptable, robust, effective and easy to be implemented. Finally, an experimental setup of chatter suppression for a lathe has been successfully implemented, and the successful techniques used in the previous artificial neural network-based active vibration control system have been utilized for active chatter suppression in turning.

Experimental implementation of artificial neural network-based active vibration control & chatter suppression

Vibration control strategies strive to reduce the effect of harmful vibrations such as machining chatter. In general, these strategies are classified as passive or active. While passive vibration control techniques are generally less complex, there is a limit to their effectiveness. Active vibration control strategies, which work by providing an additional energy supply to vibration systems, on the other hand, require more complex algorithms but can be very effective. In this work, a novel artificial neural network-based active vibration control system has been developed. The developed system can detect the sinusoidal vibration component with the highest power and suppress it in one control cycle, and in subsequent cycles, sinusoidal signals with the next highest power will be suppressed. With artificial neural networks trained to cover enough frequency and amplitude ranges, most of the original vibration can be suppressed. The efficiency of the proposed methodology has been verified experimentally in the vibration control of a cantilever beam. Artificial neural networks can be trained automatically for updated time delays in the system when necessary. Experimental results show that the developed active vibration control system is real time, adaptable, robust, effective and easy to be implemented. Finally, an experimental setup of chatter suppression for a lathe has been successfully implemented, and the successful techniques used in the previous artificial neural network-based active vibration control system have been utilized for active chatter suppression in turning.

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.