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Profile and thickness constrained adaptive localization for manufacturing curved thin-walled parts based on on-machine measurement

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Abstract

Localization plays an important role in manufacturing the curved thin-walled parts, which can determine the distribution of machining allowance and has great influence on the manufacturing accuracy. The registration algorithm is the most efficient way to locate the billet in the machining coordinate system of the machine tool by computing a transformation matrix. However, the localization of the curved thin-walled parts is complicated and challenging since the billets are individual, the shape and location of which are unknown. This paper attempts to develop an adaptive localization approach with the constraints of the profile and thickness based on on-machine measured data. The framework for constrained adaptive localization approach is illustrated, in which on-machine measurement, registration, isometric mapping and allowance optimization are involved. The details of the on-machine measurement for both the profile inspection and the thickness measurement are presented. An isometric mapping method is employed to build the separate point pairs between the measured points and the nominal shape of the part. A constrained optimization algorithm for the machining allowance is performed iteratively until meeting the constraints of the profile and thickness tolerance ranges. Finally, a case study of constrained adaptive localization was carried out, the results of which confirm the validity of the proposed approach.

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Funding

The authors received financial support from the National Natural Science Foundation of China (No. 51805258), Natural Science Foundation of Jiangsu Province (No. BK20180441), and Fundamental Research Funds for Central Universities (No.NT2019016).

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Correspondence to Zhengcai Zhao.

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Zhao, Z., Xu, T., Li, Y. et al. Profile and thickness constrained adaptive localization for manufacturing curved thin-walled parts based on on-machine measurement. Int J Adv Manuf Technol 110, 113–123 (2020). https://doi.org/10.1007/s00170-020-05860-3

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  • DOI: https://doi.org/10.1007/s00170-020-05860-3

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