Abstract
Geometric characteristics and curvature of free-form surfaces have a great influence on multi-axis computer numerical control (CNC) machining performances such as processing efficiency, surface quality and energy consumption. The conventional machining methods with the whole tool path generation results in poor low machining efficiency, surface quality, and high energy consumption, due to the repeated and crossing tool path. Therefore, this paper proposes an energy-aware milling method for complex surface based on clustering features. Firstly, the geometric features of free-form surfaces are classified according to their curvature characteristics. Then, free-form surface is discretized and initially divided into different regions, and further divided into patches based on K-means clustering algorithm to generate the same or similar surface type. Moreover, an optimization model for the sub-regional milling tool path is established, and the optimal result is obtained using adaptive dynamic genetic algorithm. Finally, the effectiveness of the proposed method is verified by comparison of traditional milling methods.
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