A Blade Altering Toolbox for Automating Rotor Design Optimization

The Blade Altering Toolbox(BAT)described in this paper is a tool designed for fast reconstruction of an altered blade geometry for design optimization purposes.The BAT algorithm is capable of twisting a given rotor’s angle of attack and stretching the chord length along the span of the rotor.Several test cases were run using the BAT’s algorithm.The BAT code’s twisting,stretching,and mesh reconstruction capabilities proved to be able to handle reasonably large geometric alterations to a provided input rotor geometry.The test examples showed that the toolbox’s algorithm could handle any stretching of the blade’s chord as long as the blade remained within the original bounds of the unaltered mesh.The algorithm appears to fail when the net twist angle applied the geometry exceeds approximately 30 degrees,however this limitation is dependent on the initial geometry and other input parameters.Overall,the algorithm is a very powerful tool for automating a design optimization procedure.

A framework from point clouds to workpieces

Combining computer-aided design and computer numerical control(CNC)with global technical connections have become interesting topics in the manufacturing industry.A framework was implemented that includes point clouds to workpieces and consists of a mesh generation from geometric data,optimal surface segmentation for CNC,and tool path planning with a certified scallop height.The latest methods were introduced into the mesh generation with implicit geometric regularization and total generalized variation.Once the mesh model was obtained,a fast and robust optimal surface segmentation method is provided by establishing a weighted graph and searching for the minimum spanning tree of the graph for extraordinary points.This method is easy to implement,and the number of segmented patches can be controlled while preserving the sharp features of the workpiece.Finally,a contour parallel tool-path with a confined scallop height is generated on each patch based on B-spline fitting.Experimental results show that the proposed framework is effective and robust.

Surface reconstruction from unorganized point clouds based on edge growing

Owing to unorganized point cloud data,unexpected triangles,such as holes and slits,may be generated during mesh surface reconstruction.To solve this problem,a mesh surface reconstruction method based on edge growing from unorganized point clouds is proposed.The method first constructs an octree structure for unorganized point cloud data,and determines the k-nearest neighbor for each point.Subsequently,the method searches for flat areas in the point clouds to be used as the initial mesh edge growth regions,to avoid incorrect reconstruction of the mesh surface owing to the growth of initial sharp areas.Finally,the optimal mesh surface is obtained by controlling the mesh edge growing based on compulsive restriction and comprehensive optimization criteria.The experimental results of mesh surface reconstruction show that the method is feasible and shows high reconstruction performance without introducing holes or slits in the reconstructed mesh surface.

3D Face Reconstruction Using Images from Cameras with Varying Parameters

In this paper, we present a new technique of 3D face reconstruction from a sequence of images taken with cameras having varying parameters without the need to grid. This method is based on the estimation of the projection matrices of the cameras from a symmetry property which characterizes the face, these projections matrices are used with points matching in each pair of images to determine the 3D points cloud, subsequently, 3D mesh of the face is constructed with 3D Crust algorithm. Lastly, the 2D image is projected on the 3D model to generate the texture mapping. The strong point of the proposed approach is to minimize the constraints of the calibration system： we calibrated the cameras from a symmetry property which characterizes the face, this property gives us the opportunity to know some points of 3D face in a specific well-chosen global reference, to formulate a system of linear and nonlinear equations according to these 3D points, their projection in the image plan and the elements of the projections matrix. Then to solve these equations, we use a genetic algorithm which consists of finding the global optimum without the need of the initial estimation and allows to avoid the local minima of the formulated cost function. Our study is conducted on real data to demonstrate the validity and the performance of the proposed approach in terms of robustness, simplicity, stability and convergence.