A Novel Approach For Estimation of Profile Deviation Based on Poles and Pole Circles for the Inspection of 2D Free-form Profiles. (March 2023) - S., Hari Ganesh; Samuel, G.L.
Abstract: Coordinate Measuring Machine (CMM) is widely used for the accurate profile deviation evaluation of free-form components. Sampling, measurement and evaluation of the measured data are the most important steps of the CMM measurement process. Though various sampling strategies were proposed to reduce CMM measurement time, few error evaluation techniques have been reported to date. Generally, the CMM measured points are either fitted or registered with the continuous reference profile. The profile deviation is estimated using the point-inversion technique with relatively high time complexity. Hence, an alternate method is needed for quick and accurate profile deviation estimation. In this regard, a novel two-stage methodology for 2D-profile deviation estimation is proposed here. The proposed method is specific to profiles measured using CMM operated in discrete scanning mode and is based on poles and pole radii extracted from the discrete 2D-Voronoi diagram. The first stage is meant for reference profile discretization and poles data extraction, whereas the second stage is intended for profile deviation estimation that executes in O(m log n) time. The free-form profiles (line profile) in this work are modelled using the NURBS formulation. Implementations were done for the measured data extracted from simulated profiles and practically measured CMM data. From the implementation results, it is observed that the estimated profile deviation values are closer to the point-inversion method. Hence this method can be used in modern CMMs, and in-situ inspection of free-form profiles is accomplished using CNC machine. Highlights: A two-stage methodology is proposed for profile error estimation of 2D free-form profiles inspected using CMM. The methodology is based on the concept of poles and pole circles extracted from the 2D Voronoi diagram. The methodology's effective time complexity is O(m log n) and is at least 67.37% faster than the point-inversion method. The accuracy of the proposed methodology is on par with that of the point-inversion method.