Title:
Scalable path planning and simulation via numerical ring prediction and path optimization through model-centric GPU data analysis

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Author(s)
Wu, Zhengkai
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Advisor(s)
Meliopoulos, A. P. Sakis
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Abstract
The major research purpose is model-based 3D visualization by path parameter tuning via GPU simulation and model fitting as well as remodeling of system integration for path planning by local scalable solution towards efficiency. Through Hybrid Dynamic Tree(HDT) with branch topology mapping and numerical depth analysis with grid modeling measurement for geometry edges and features, we optimize ring model of 3D CAD design and computer aided manufacturing(CAM) towards controllable path planning with user interactive decision towards efficiency and optimization in embedded system software. This is one of the motivations. The purpose of 3D STL model is for understanding optimal variable selection and limitations of linearization methods for nonlinear path planning. The model also helps path model fitting, which are through simulation parameters and machining. Stochastic models enable evaluation of path validation and data distribution. Path validation and protection help reduce path collision and error as well as avoid noneffective path retraction of travel distance. Accurate simulation saves actual machining time and power towards energy efficiency while reducing material waste and cost. The innovations of our scheme are a scalable path planning solution based on local ring prediction and accessibility map sequence data analysis towards parallel optimization. We manage to extract 2D image features into 3D printing product through subtractive 3D printing on Computer Numerical Control(CNC) machine. The benefit of discussed solution is both flexibility of 3D printing product combined with intelligent control feature of CNC machine with high cutting speed and power. Graphical Processing Unit(GPU) speeds up the interactive simulation of material removal process for path iteration layer by layer in an adaptive way. Smart data modeling is going to drive low-cost and high-quality machining for both time-saving and virtual event planning. Math models enable digital optimization and cloud computing of distributed path as well as understanding the path planning limits. Digital parallel data metrics such as efficiency in Service-Oriented Cloud is discussed. Path planning strategy for path partition and post-processing in G-code optimization as well as scalability study of local and global path are also studied.
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Date Issued
2019-03-11
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Dissertation
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