Research On Subdivision And Adaptive Ocalization Machining Technology Of Super-Large Components With A Developed Movable Machine Tool

Super-large components are required increasingly in major science and technology projects for better performance and higher quality. However, it is difficult to machine super-large components because of their huge volume, large weight, poor mobility and small-batch production. Currently, super-large components machining method and machining error theory are lacked. Therefore, the research in machining super-large components and analyzing machining errors has practical significance for improving machining accuracy, efficiency and cost-effectiveness.According to the characteristics of large components, a method using small movable machine tool to machine super-large components by adaptive subdivision was proposed in this thesis. The principle of the method, system architecture and reconfigurable scheme were provided. Wheeled movable structure was designed for roughly localization in large space, and large-space measuring technology was utilized for precisely localization. Parallel machining head implemented adaptive machining. The machine tool enhanced the machining flexibility and efficiency as well as reduced production costs because of its mobility, flexibility, reconfigurability.To achieve accurate machining of super-large components, the precise location of the workpiece should be obtained firstly. Studying adaptive localization theories of super-large component combined with rigid body motion Matrix and Lie algebra theory, a self-adaptive localization model was established by analyzing the relationship among multi-coordinate systems during super-large components machining. In this thesis, a complete localization method based on workpiece machining features was proposed. The objective function solving adaptive positioning model was derived, and a general solution method, tangent plane algorithm based on Euler angle methods was applied to solve the super-large components adaptive localization. The accuracy, convergence rate and convergence condition of the method were evaluated by simulation for verifying the correctness and reliability of adaptive localization method.In this thesis, machining area subdivision rules for were introduced referring adaptive machining principle of super-large component. According to geometrical features, machining features subdivision rules were introduced and the tree model of subdivision was constructed. Simultaneously, regular geometrical features subdivision method for super-large component and machining area subdivision method based on machine tool workspace were proposed. Theoretical tool path generation method based on subdivision region was presented. Aiming at to obtain the shortest machining path, a tool path optimization method based on genetic algorithms was also discussed and validated by simulation. Finally, actual tool path and NC program were generated on the basis of theoretical tool path and adaptive localization model.To ensure the reliability of super-large component machining, the influence of error factors to super-large component machining was theoretically analyzed in the thesis. Combined with spiral theory, a series of error models in super-large component adaptive localization were established, including global position and orientation error model, flatness error prediction model, spatial straightness error prediction model and roundness error prediction model in machining plane. The influence of errors were further analyzed from the aspects of adaptive localization error, machine tool geometric error and machine stiffness error. A workpiece automatic positioning error prediction method based on statistical analysis was presented, and the prediction model of the workpiece automatic orientation errors was validated by simulation. Analyzed the influences of geometric errors in the movable small machine tools, the error model of parallel mechanism was described. Referring multi-linear interpolation and Kalman filter off-line calibration theory, un-calibrated errors were predicted and a compensation method was provided. Finally, an integrated error prediction based on Monte Carlo method for super-large component machining was presented and it was verified by simulation.A movable machine tool for machining super-large flange was developed on the basis of theoretical analysis, and machining experiment was conducted. The experiment results demonstrate that the method of subdivision by adaptive localization for machining super-large component using movable machine tool is feasible, and its error predication methods are correct.