Abstract
To reduce dynamic deflection and suppress vibration during the end-milling process of large thin-plate workpiece, an intelligent mirror-milling machining system is established in this paper. This system includes two movable support heads, and both of them are periodically repositioned which affix themselves to the workpiece by the way of vacuum adsorption in order to improve stability of the workpiece during the milling process. One critical point of this task is how to ensure support heads move reasonably and simultaneously and support to the workpiece stably. Therefore, this paper developed a practical method for the planning of support head moving path (SHMP) under a given cutting path. This method involves the finite difference method (FDM), artificial neural network (ANN), and genetic algorithm (GA). In particular, dynamic vibration response of four-edge clamped thin-plate workpiece is calculated with FDM; to reduce computing time, ANN is applied to establish the relation between support head location and workpiece dynamic vibration amplitude; and as SHMP is a multi-objective optimization problem, GA is implemented with the classical weighted sum method to find a solution. Finally, this method is verified and simulated under two specific cases; the final paths obviously show that there will always be at least one support head support workpiece during the machining process.
Published Version
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