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Research Papers

A Graphics Processor Unit-Accelerated Freeform Surface Offsetting Method for High-Resolution Subtractive Three-Dimensional Printing (Machining)

[+] Author and Article Information
Mohammad M. Hossain

College of Computing,
Georgia Institute of Technology,
Atlanta, GA 30332-0765

Chandra Nath

School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405
e-mail: nathc2@asme.org

Thomas M. Tucker

Tucker Innovations, Inc,
Charlotte, NC 28173

Richard W. Vuduc

School of Computational Science
and Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0765

Thomas R. Kurfess

Professor
School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405

1Present address: Applied Machine Learning lab at Facebook, Inc. Menlo Park, CA.

2Present address: Hitachi America, Ltd. Research & Development Division, Farmington Hills, MI 48335.

3Corresponding author.

Manuscript received June 13, 2017; final manuscript received November 15, 2017; published online February 13, 2018. Assoc. Editor: Zhijian J. Pei.

J. Manuf. Sci. Eng 140(4), 041012 (Feb 13, 2018) (11 pages) Paper No: MANU-17-1371; doi: 10.1115/1.4038599 History: Received June 13, 2017; Revised November 15, 2017

Machining is one of the major manufacturing methods having very wide applications in industries. Unlike layer-by-layer additive three-dimensional (3D) printing technology, the lack of an easy and intuitive programmability in conventional toolpath planning approach in machining leads to significantly higher manufacturing cost for direct computer numerical control (CNC)-based prototyping (i.e., subtractive 3D printing). In standard computer-aided manufacturing (CAM) packages, general use of B-rep (boundary representation) and non-uniform rational basis spline (NURBS)-based representations of the computer-aided design (CAD) interfaces make core computations of tool trajectories generation process, such as surface offsetting, difficult. In this work, the problem of efficient generation of freeform surface offsets is addressed with a novel volumetric (voxel) representation. It presents an image filter-based offsetting algorithm, which leverages the parallel computing engines on modern graphics processor unit (GPU). The compact voxel data representation and the proposed computational acceleration on GPU together are capable to process voxel offsetting at four-fold higher resolution in interactive CAM application. Additionally, in order to further accelerate the offset computation, the problem of offsetting with a large distance is decomposed into successive offsetting using smaller distances. The performance trade-offs between accuracy and computation time of the offset algorithms are thoroughly analyzed. The developed GPU implementation of the offsetting algorithm is found to be robust in computation, and demonstrates a 50-fold speedup on single graphics card (NVIDIA GTX780Ti) relative to prior best-performing algorithms developed for multicores central processing units (CPU). The proposed offsetting approach has been validated for a variety of complex parts produced on different multi-axis CNC machine tools including turning, milling, and compound turning-milling.

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References

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Figures

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Fig. 5

Processing of the HDT leaf grid in CUDA: (a) CUDA framework (courtesy of NVIDIA) and (b) leaf grid layout in a CUDA threadblock

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Fig. 4

HDT representation and illustration: (a) layout of an HDT and (b) HDT in a sample 2D cross section

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Fig. 3

Illustration of a tiled grid structure: (a) two-Level (tiled) grid and (b) tiled grid in a sample 2D cross section of a 3D solid

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Fig. 2

Illustration of a quadtree: (a) A quadtree representation of a triangle and (b) resulting quadtree

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Fig. 1

Application of surface offsetting in CNC machining: (a) target part, (b) part shown inside stock, (c) shrunk stock, (d) expanded part, (e) contact volume, and (f) stock after toolpass

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Fig. 6

Offsetting illustration: (a) A 2D ring template (or structuring element), (b) cross section of input HDT swept with ring template, and (c) cross section of dilated HDT overlaid with the original HDT

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Fig. 11

Successive offsetting error analysis: (a) normalized average error and (b) normalized maximal error

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Fig. 16

Ball joint (multi-axis turning and milling): (a) balljoint CAD model and (b) machined balljoint

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Fig. 7

Dilations of candle holder: (a) 10 voxels, (b) 20 voxels, and (c) 40 voxels

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Fig. 8

Erosions of candle holder: (a) 10 voxels, (b) 20 voxels, and (c) 40 voxels

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Fig. 9

Offsetting error analysis: (a) normalized average error and (b) normalized maximal error

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Fig. 10

Successive offsetting performance

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Fig. 12

Surface dilation of a Buddha model (polygonal mesh comprising 1.1 million triangles): (a) original and (b) dilated

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Fig. 13

Wiggle CAD model to part: (a) target voxel model, (b) toolpaths superimposed, (c) contact volume, and (d) machined part

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Fig. 14

Pawn with two-axis turning: (a) pawn CAD model and (b) machined pawn

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Fig. 15

Swirled prism (three axis milling): (a) prism CAD model and (b) machined prism

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Fig. 17

Propeller (multi-axis turning and milling): (a) propeller CAD model and (b) machined propeller

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