| The non-circular hole of a piston can remarkable increase its useful life. Due to the particularity of non-circular hole of a piston, the problems for machining non-circular hole appear. In order to solve the difficult problems of precise machining a non-cylinder pin hole of a piston, a new machining method is presented using embedded giant magnetostrictive material (GMM) in the component. Some key technologies of GMM smart component design and precision position control are studied in depth in this dissertation, such as: the GMM smart component coupled electric, magnetic and mechanical fields finite element model suitable for engineering use, multi-objective optimization integrated above mentioned FE model method, temperature control strategy, coupled fluid and thermal fields FE model, hysteresis nonlinear modeling, control strategy eliminating hysteresis and temperature rising effects on the performance of GMM smart component. The GMM smart component test platform is constructed.In chapter 1, research on micro-displacement mechanism for machining noncircular hole of a piston is stated.Based on analysis on the GMA FEM modeling, optimization design, eliminating thermal error strategy and hysteresis nonlinear control, the problems are proposed in the GMA design, and then the main content of this dissertation and the project significance are proposed.In chapter 2, on the basis of the deficiencies of GMM FE analysis model at the present time, the coupled electric, magnetic and mechanical fields fractional step model suitable for engineering use is proposed. To reduce the freedom of node needed to solved, firstly the magnetic field of coil is solved, secondly the coupled magnetic and mechanical fields are calculated. According to Hamilton's principle, the finite element weak form formulations for purely magnetic field of coil and coupled magnetic and mechanical fields of the GMM are derived. The proposed model is implemented by using COMSOL Multiphysics 3.4. The effects on the GMM smart component designed deformation and the system resonance frequencies are studied. In chapter 3, the smart component multi-objective optimization model integrated the GMM electric, magnetic and mechanical fields FE model GMM is proposed. The optimum objects are the deformation of GMM smart component's top under 3 A input current, the first nature frequency, the inductance of driving coil, the responsive time constant and the energy efficiency constant of driving coil. On the basis of analysis of the multi-objective genetic algorithm NSGA-II in depth, the above mentioned optimization model is implemented using NSGA-II. According to the optimum results, the GMM smart component is designed.In chapter 4, base on the various measures for eliminating thermal effects on GMA, the reduced forced water cool control strategy is proposed for the giant magnetostrictive smart component and control schemes is provided in detail too. Through the characterizations of forced water cool analysis, a coupled fluid-thermal field finite element model is constructed. The model constructed is used to analyze the GMM smart component temperature distribution. The temperature control system for GMM smart component is constructed on the basis of the FE analysis.In chapter 5, on the basis of analysis on hysteresis nonlinear modeling, the input datas of neural network are the current smart component output and the output rate, the output of neural network is smart component input. Thus, the mathematical relation between these two outputs of smart component and its current input becomes an one-to-one mapping, which guarantees the complex rate-dependent inverse hysteresis model can be approximated by the neural network. The CMAC modeling method for hysteresis is proposed. The GMM smart component hysteresis model is constructed using this CMAC. A real-time hysteretic compensation control strategy combining a CMAC neural network feed forward controller and a proportional derivative (PD) feedback controller is proposed to implement the precision position tracking control of the smart component. Simulation shows that this control strategy can on-line obtain inverse hysteresis model of the smart component, eliminate the hysteretic nonlinear impact and achieve the precision control of the smart component.In chapter 6, the experimental table for GMM smart component based on the virtual instrument technologies is built up. The GMM smart component static stiffness, the relationships between input current and displacement, between input current and driving magnetic field, between driving magnetic field and displacement are studied through experiment, respectively. At various temperatures, the GMM smart component relationships between input current and displacement, between input current and driving magnetic field, between driving magnetic field and displacement are studied through experiment, respectively. Through this part's research efforts, the performance of GMM smart component is learned more. So these works can provide technical assistance for GMM smart component.In chapter 7, the main conclusions of this dissertation are summarized and the future research work is put forward.
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