Engineering parts usually deviate from their intended shapes during their manufacturing due to the inaccuracies of machine tools, deformation of various elements of machine tool, cutting tools and workpiece. Resulting geometric errors affect the functionality and assembly of the parts significantly. This demands a reliable strategy to accurately assess the errors during the final part quality inspection. Among all the geometric features, circular feature is very common on the majority of the engineering parts. Hence, the measurement and evaluation of circularity with a high degree of accuracy are of utmost importance. In the present work, a hybrid approach is proposed to accurately evaluate the circularity error. This approach comprises a least squares method (LSM) and a novel probabilistic global search Lausanne (PGSL) technique. The LSM is used to reduce the search space initially. Within the reduced search space, the PGSL performs efficient, fine and global search. In the process of establishing minimum zone circles to the measured data of the circular features, the proposed approach dynamically updates the probability distribution function of the circularity parameters continuously. The update procedure ensures that the probability of moving towards the potential optimal solutions is increased. The algorithm has been tested using several benchmark datasets for its generalization capability and robustness. The proposed strategy is found to be efficient in yielding accurate results. Therefore, the algorithm can be implemented in computer-aided circularity measuring instruments in order to minimize acceptance of bad parts and rejection of good parts.
The International Journal of Advanced Manufacturing Technology – Springer Journals
Published: Apr 10, 2017
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