Representations and Algorithms for Manufacturing Coordinate Metrology Point Clouds

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Successful design and manufacturing requires minimum material use with repeatable material properties and shape production. Increased memory and parallel processing has led to demand for new Computer Aided Design and Manufacturing mesh and point cloud based representations and algorithms that can provide analyses at factory floor production rates. Addressing this demand gap is the focus of the research program. Specific short term emphasis is outlined below. 1) In part topology optimization, “non-optimization volume” (such as assembly mating surfaces) constraints are specified, but the remaining “optimization volume” may, depending on expected loads and deflection/strain requirements, be reduced. Software available in the applicant’s laboratory will be used to design Additive Manufacturing (AM) parts that will be 3D printed and measured to determine both free state geometry and deflection/strain under applied loads. The outcome will be validation and greater confidence in the topology optimization / AM process, and more rapid industry adoption of this competitive emerging technology. 2) When using metrology digitizers, an essential task is registration of the point cloud to the nominal part surface. For very thin parts, such as sheet metal, the surface to which the point matches can be ambiguous. Using the applicant's custom installation, for each surface point, the digitizer direction vector towards the digitizer will be determined. Surfaces with opposing normal vectors can then be rejected as matches. 3) A recently acquired laboratory digitizer collects point clouds at rates approaching 500,000 samples / second. Networking up to 4 digitizers, a data point collection rate of 2 million samples / second is achievable. For an automobile mirror sized part, 12 million points must, within 6 seconds, be registered with the nominal geometry. Sole use of serial desktop computer algorithms requires 5 minutes or more for the related computations. Development of a 448 parallel core Graphical Processing Unit video card algorithm has reduced the calculation time to 15 seconds, and recent laboratory purchase of 2880 core technology offers potential for more speed-up. The proposed research will exploit this new technology with the goal of developing parallel algorithms that will process point clouds at factory floor rates. The proposed research program involves highly employable student training in the use of state-of-the-art software, coordinate metrology digitizers, and parallel computing hardware. It addresses important AM design optimization and measurement issues essential to maintain Canadian competitiveness in lightweight design and manufacture of medical appliance, automotive, aerospace, and consumer goods.