Multi-grid cutter shaft optimization method based on density clustering

Abstract

The invention, which belongs to the related technical field of milling processing, discloses a multi-grid cutter shaft optimization method based on density clustering. The optimization method comprises the following steps: (1), carrying out standardization processing on a cutter contact coordinate to solve a problem of an unreasonable clustering result caused by unreasonable variation value of thearc length; (2), carrying out density clustering on a cutter contact after standardization processing; and (3), calculating a coarse grid corresponding to the cutter contact based on an obtained clustering group, optimizing cutter shafts of coarse grid points based on the coarse grid, establishing a refined grid between every two adjacent coarse grid points, optimizing cutter shafts of the refined grid points based on all refined grids, and thus completing cutter shaft optimization. According to the invention, standardization processing and density clustering are carried out on the cutter contacts successively; and the cutter shafts after overall optimization are obtained by integration of coarse grid optimization and refined grid optimization. Therefore, the convergence speed of the algorithm is greatly improved while the cutter shaft optimization effect is not reduced; and the practicability is high.

Description

technical field

[0001] The invention belongs to the technical field related to milling, and more specifically relates to a multi-grid tool axis optimization method based on density clustering. Background technique

[0002] Compared with three-axis CNC machining, five-axis CNC machining can ensure better surface quality and higher processing efficiency. At present, most complex curved surface parts are processed by five-axis CNC machining. Common complex surface parts include aircraft fuselage, propeller blades, mobile phone mold contour surfaces, steam turbine blades, automobile bodies, etc. In multi-axis NC machining, tool axis vector optimization is a very challenging problem. The optimization effect depends not only on the optimization model, but also on the solution algorithm of the optimization model. The same tool axis vector optimization model uses different solving methods, and the final planned tool axis vector field may be completely different. Different optimiza...

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