Abstract
The 5-axis Coordinate Measuring Machine (CMM) is widely used for quality data collection of the machining parts, such as cylinder blocks and heads of the engines. High efficient inspection path planning for multiple feature groups from different stations is one of the key tasks for CMM application. In engineering practice, the inspection planning of diverse feature groups accounts for large labor cost and process development cycle. To improve the efficiency of path generation for the complex machining part, a five-axis CMM inspection path planning method considering path length, probe rotation and path reusability is proposed. Firstly, the measuring points (MPs) are classified based on feasible inspection direction cone and accessibility of the MPs to achieve the minimum times of probe rotation. Then, the rapidly exploring random trees with multi-root node (RRT-MRNC) algorithm is proposed to implement local path planning considering inspection path reuse. Furthermore, intra-group and inter-group path is generated simultaneously based on the proposed enhanced Genetic Algorithm (GA) algorithm. In order to evaluate the effectiveness of the proposed method, the cylinder block path planning case is used. Compared with the benchmark methods, the total planning time based on the proposed planning method for the dynamic tasks was reduced by 55.2% and 54.9% respectively.
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