A robust method for axis identification

•A new method for axis identification in discrete geometric models, obtained from the measurement of real object, is proposed.•The working principle of the method is based on-a-never-used-before feature of the axially symmetrical surfaces.•Due to its working principle, the method is intrinsically robust to local defectiveness, measurement noise and outliers.•Compared with the state of the art, it proves to produce a more precise evaluation.•The method guarantees the trueness of the estimation also in critical cases where the other methods have produced estimations that are outliers.

The present paper proposes a new method for axis identification in discrete axially symmetrical geometric models. This method is based on-a-never-used-before property of the axially symmetrical surfaces for which the symmetry line of any section curve of the surface (or of a portion of it in the case of an incomplete axially symmetrical surface) always intersects the axis of symmetry of the surface. Thus the working principle of the method makes it very robust to local defectiveness, measurement noise and outliers.In order to compare it with the most cited methods presented in literature, several types of tests have been designed and performed. The robustness of those methods, on the one hand, has been evaluated by defining the Statistical Confidence Boundary at 1σ confidence level. The trueness of the method, on the other hand, has been evaluated on geometric models obtained by measuring real objects. The high robustness, which characterizes the proposed method, makes it particularly suitable for product geometric inspection where high accuracy is required.