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This paper presents a new and fully automatic method to extract cross-sectional contour profiles of a physical object from the point cloud data scanned from its surface. Correctly extracting the sectional contours is of particular importance in the quality inspection of airfoil blades as the tolerances specified on a manufactured aero-engine blade are generally imposed at specific blade sections. The collected point cloud via 3D laser scanning is, however, distributed all over the blade surface rather than at the desired specific sections. In fact, no point in the point cloud is located exactly on the sectional planes. The desired sectional data have to be extracted from the nearby data points. If the underlying smooth surface geometry of the point cloud in the vicinity of a nearby data point can be approximated by a mathematical function, the approximated local surface formulation can be used to project the nearby point onto the desired sectional plane along a curvilinear trajectory. This is achieved in this work by fitting a local quadric surface to the neighbouring points of the point of interest. A systematic approach to establish a balanced set of neighbouring points is employed to avoid bias in fitting the local quadric surface as well as to guide the selection of points to be projected onto the sectional plane. The projected points are then used to construct the desired sectional contour profile. Implementation results have demonstrated the superior performance of the proposed fully automatic method in comparison with the existing methods. [ABSTRACT FROM AUTHOR]

Airfoil blades are typically inspected in sections to verify their conformance to the geometric tolerances specified on their nominal design. To maintain the accuracy of geometric error evaluation, in particular, for the position and orientation errors of the airfoil sections, sectional airfoil profiles should be reconstructed from the inspection data points. This paper presents a new method to automatically reconstruct the airfoil profile from unorganized noisy sectional data points of 3D scanned blades. A three-step airfoil profile reconstruction approach is presented. First, the algorithm thins the scattered set of sectional data points by projecting them onto the local curves fitted to them. For this purpose, a recursive weighted local least-squares scheme is proposed to fit local curves within the measurement uncertainty constraint of inspection data. Then, to order the thinned set of data points, the profile polygon is generated and imperfect nodes are modified by evaluation of the angular deviation of edges. Finally, a closed nonperiodic B-spline curve is fitted to the thinned and ordered set of data points to construct the smooth airfoil profile. A series of case studies have been carried out to demonstrate the effectiveness of the proposed airfoil profile reconstruction method. Implementation results have demonstrated that the proposed method is accurate and robust to noise. In addition to blade inspection, other applications such as repair and adaptive machining of aero-engine blades can equally benefit from the proposed method for automatic airfoil profile reconstruction. [ABSTRACT FROM AUTHOR]