A GPU-Accelerated Freeform Surface Offsetting Method for High-Resolution Subtractive 3D Printing (Machining)

[+] Author and Article Information
Mohammad Hossain

College of Computing, Georgia Institute of Technology, Atlanta, GA, USA

Chandra Nath

Postdoctoral Research Fellow, School of Mechanical Engineering, Georgia Institute of Technology, GA, USA

Thomas M. Tucker

Principal Investigator, Tucker Innovations, Inc., Charlotte, NC, USA

Richard Vuduc

Associate Professor, School of Computational Science and Engineering, Georgia Institute of Technology, GA, USA

Thomas Kurfess

Professor, School of Mechanical Engineering, Georgia Institute of Technology, GA, USA

1Corresponding author.

ASME 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. The lack of an easy and intuitive programmability in conventional toolpath planning approach in machining leads to significantly higher manufacturing cost for CNC-based prototyping. In standard computer aided manufacturing (CAM) packages, general use of B-rep or NURBS-based representations of the CAD interfaces challenge core computations of tool trajectories generation process, such as, surface offsetting to be completely automated. In this work, the problem of efficient generation of free-form 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). 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 is thoroughly analyzed. The developed GPU implementation of the offsetting algorithm is found to be robust in computation, and has demonstrated a 50-fold speedup on single graphics card (NVIDIA GTX780Ti) relative to prior best-performing dual sockets quad-cores CPU implementation. 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.

Copyright (c) 2017 by ASME
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