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Multi-grid cutter shaft optimization method based on density clustering

A density clustering and optimization method technology, applied in the field of milling, can solve the problems of time-consuming solution of the tool axis optimization model and affecting software performance, etc.

Active Publication Date: 2019-08-09
HUAZHONG UNIV OF SCI & TECH
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Problems solved by technology

[0003] At present, those skilled in the art have done some research. For example, patent 201810745901.1 discloses a tool axis vector optimization method and system based on multi-objective constraints. It directly uses the constraint optimization method to solve the tool axis optimization model. When the trajectory is optimized for the tool axis, although high-quality multi-axis machining trajectories can be obtained, due to the large number of trajectories, the solution of the tool axis optimization model is extremely time-consuming. If it is integrated into the CAM software, it will greatly affect the software. performance

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  • Multi-grid cutter shaft optimization method based on density clustering

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[0053] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

[0054] see figure 1 and figure 2 , the multi-grid tool axis optimization method based on density clustering provided by the present invention mainly includes the following steps:

[0055] Step 1: Standardize the coordinates of the knife contact to avoid the problem of unreasonable clustering results caused by unreasonable variation in arc length.

[0056] see Figure 4 , the stan...

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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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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/19
CPCG05B19/19G05B2219/35349
Inventor 陈吉红李振瀚杨方召张玉明胡鹏程
Owner HUAZHONG UNIV OF SCI & TECH
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