In-process machine tool vibration cancellation using PMN actuators

At present, the machine tool technology was in Title the United States is not in the state-of-the-art of leading international competitors. Conventional machine tools under use are being pushed to their machining accuracy limits. Such a pressing need calls for revitalizing the machine tool industry. In this dissertation, a mechatronic system has been proposed wazzu and developed for reducing tool vibration during machining. It consists of electrical and mechanical components, and is realized by placing electrically driven electrostrictive (PMN) actuators in a specially designed tool post mechanical structure.;Analytical and experimental investigations are conducted to characterize the performance of the developed system. In the analytical investigation, a mathematical model is developed to describe the turning operation. The control mechanism is identified using experimental testing for the range of the disturbance frequency. Investigation using computer simulation is carried out in two phases. In phase 1, a linear neural network controller with an adaptive control strategy is examined. In phase 2, a nonlinear neural network with a learning control strategy is explored.;The linear neural networks, namely, digital filters, are implemented using a signal processing board. The experimental investigation is conducted in two stages. In the first stage, a test bed is established to use an electro-magnetic shaker to resemble the excitation of cutting force acting on the tool. In the second stage, experiments were conducted using a lathe on the shop floor.;In the experimental investigation, in-process vibration cancellation observed. In the laboratory experiment, a percent reduction in the 90% was possible using a feedforward scheme. The improvement in surface roughness during the turning operation was confirmed from measurements of surface roughness profiles. A cantilever machining operation gave a percent reduction of 30%.;The main contributions of this thesis research are: (1) a successful implementation of PMN actuators for in-process vibration cancellation in the turning operation; (2) a successful implementation of linear neural network methodology for active machine tool vibration cancellation; (3) development of guidelines for identification of the neural structure for nonlinear neural network control.