Geometric Feature-Based Partial Retrieval of On-Machine Measurement Models

To realize the effective reuse of the existing model measurement process planning and to improve the efficiency of the preparation of the measurement program, this paper proposes an on-machine measurement model partial retrieval method based on geometric features. First, the B-Rep information and measurement procedures of the model are extracted, and combined with the analysis of the geometric features of the on-machine measurement, the model is transformed into an attribute adjacency labeling graph; next, the index tree is constructed by using hash coding, and the result set is obtained through the filtering-verification framework to realize the synthesis similarity evaluation of the geometrical level of the on-machine measurement model; lastly, different reuse methods of the measurement process planning are selected based on the model’s similarity value. Fast programming based on partial retrieval of on-machine measurement models is realized by directly selecting programs, calling macros, and assigning values to variables. The experimental results show that the method can realize the effective reuse of the existing model measurement process planning, which can improve the efficiency of the preparation of the measurement program and meet the actual needs of on-machine measurement.