Investigation on stability analysis of a boring tool on regenerative chatter free machining

Abstract

Vibration is an undesirable phenomenon in machining processes because it results in the reduction of material removal rate (MRR), poor surface finish and increased tool wear. Two major types of vibration occurring in machining are forced vibrations and self-excited vibrations. The imbalance of rotating members, servo instability, or force on a multi-tooth cutter may result in forced vibration. The cutting tool oscillates at the frequency of the cutting force. When this frequency is close to a natural frequency of the tool, large amplitude vibrations due to resonance occur. Selfexcited vibration or chatter is the most important type of vibration in machining processes. Two mechanisms known as regeneration and mode coupling are the major reasons for machine tool Chatter. The former is due to the interaction of the cutting force and the work piece surface undulations produced by preceding tool passes. Mode coupling is produced by relative vibration between the tool and the work piece that occur simultaneously in two different directions in the plane of cut. On-line chatter control technique using Magneto-rheological fluid is employed in this research work to control the vibration of a boring tool. On- line chatter reduction by employing MR fluid depends on the amount of magnetizing current applied to the fluid. By varying the current, the damping property of the MR fluid is varied. As a result the amount of vibration is controlled to a large extend. Now the question is, how much of current one needs to supply for suppressing the present level of vibration . Unless one measures the vibration level, it is difficult to say the amount of tool chatter. It becomes obvious that there is a need for a model which will measure the present chatter level and predict the corresponding current that is to be supplied to the damper to suppress the chatter in an online manner. This present study aims at reaching the above objective using linear regression, support vector regression and artificial neural networks.