A knowledge-based method for tool path planning of large-sized parts

Abstract

Proper tool paths ensure the high-efficiency and high-quality machining of workpiece. The current tool path planning method is inadequate to meet the complex process requirements for the complex workpiece of abundant local features. Due to the lack of ability of independent process decision-making, the planning effect is still heavily dependent on human experience and the planning is time-consuming and laborious, which is not conducive to shortening development cycle of new products. Therefore, a novel knowledge-based method for tool path planning is proposed in this paper. The proposed method can automatically plan the tool path that meets the requirements of the machining process by the adaptive process decision-making according to the perceived features of complex workpiece. The knowledge extraction method based on the syntactic dependency analysis is used to extract named entities in the process document. And the correctly classified entities are filled in the nodes of knowledge model represented by the form of frame to finish the establishment of process knowledge base. Then, with the help of feature extraction module based on the hybrid clustering method, the semantic segmentation of the point cloud of the workpiece is completed, and the Feature Matrix (FM) is formed. This matrix is used to provide feature information required for process decision, including Feature Vector (FV) and Structure Description Matrix (SDM). Finally, the tool path is generated in the Path Generator. At last, an automatic path planning system is developed for the painting process of automotive exterior. The planned paths are tested for 20 types of bumpers, where the chromatic aberration ΔE of each product is less than 1.4; both the average thickness of each paint and the requirement of production rate are satisfactory. These test results verify the effectiveness of the proposed tool path planning method.