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

Author:

Hossain Mohammad M.1,Nath Chandra2,Tucker Thomas M.3,Vuduc Richard W.4,Kurfess Thomas R.5

Affiliation:

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

2. School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405 e-mail:

3. Tucker Innovations, Inc, Charlotte, NC 28173

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

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

Abstract

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.

Funder

National Science Foundation

Publisher

ASME International

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Control and Systems Engineering

Reference25 articles.

1. The Challenges for CAM Systems and Users in 5-Axis Machining,2012

2. Voxel-Based Offsetting at High Resolution With Tunable Speed and Precision Using Hybrid Dynamic Trees,2016

3. A Graphical Approach for Freeform Surface Offsetting With GPU Acceleration for Subtractive 3D Printing,2016

4. Computing Offsets of NURBS Curves and Surfaces;Comput.-Aided Des.,1999

5. An Overview of Offset Curves and Surfaces;Comput.-Aided Des.,1999

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