Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30375
Efficient Web-Learning Collision Detection Tool on Five-Axis Machine

Authors: Chia-Jung Chen, Rong-Shine Lin, Rong-Guey Chang

Abstract:

As networking has become popular, Web-learning tends to be a trend while designing a tool. Moreover, five-axis machining has been widely used in industry recently; however, it has potential axial table colliding problems. Thus this paper aims at proposing an efficient web-learning collision detection tool on five-axis machining. However, collision detection consumes heavy resource that few devices can support, thus this research uses a systematic approach based on web knowledge to detect collision. The methodologies include the kinematics analyses for five-axis motions, separating axis method for collision detection, and computer simulation for verification. The machine structure is modeled as STL format in CAD software. The input to the detection system is the g-code part program, which describes the tool motions to produce the part surface. This research produced a simulation program with C programming language and demonstrated a five-axis machining example with collision detection on web site. The system simulates the five-axis CNC motion for tool trajectory and detects for any collisions according to the input g-codes and also supports high-performance web service benefiting from C. The result shows that our method improves 4.5 time of computational efficiency, comparing to the conventional detection method.

Keywords: collision detection, five-axis machining, Separating axis

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1087247

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1827

References:


[1] I. Palmer and R. Grimsdale, “Collision detection for animation using sphere-trees,” Computer Graphics Forum, Vol. 14, no. 2 (1995), pp. 105-116.
[2] G. Bradshaw and C. O'Sullivan, “Adaptive Medial-Axis Approximation For Sphere-Tree Construction,” ACM Translations on Graphics, Vol. 23, no. 1 (2004), pp. 1-26.
[3] J. D. Cohen, M. C. Lin, D. Manocha, and M. Ponamgi, “I-COLLIDE: An Interactive and Exact Collision Detection System for Large-Scale Environments,” ACM Interactive 3D Graphics Conference (1995), pp. 189-196.
[4] V. Bergen, “Efficient Collision Detection of Complex Deformable Models Using AABB Trees,” Journal of Graphics Tools, Vol. 2, no. 4 (1997), pp.1-14.
[5] S. Gottschalk, M. Lin, and D. Manocha, “OBB-tree: A Hierarchical structure for Rapid Interference Detection,” In: Proc. SIGGRAPH (1996), pp. 171-180.
[6] J. Chang, W. Wang, and M. Kima, “Efficient Collision Detection Using a Dual OBB-Sphere Bounding Volume Hierarchy,” Computer-Aided Design Vol. 42 (2010), pp. 50-57.
[7] R.D. Owston, “The World Wide Web: A Technology to Enhance Teaching and Learning?”, In American Educational Research Association, 1997.
[8] M.F. Shiratuddin, W. Thabet, ”Virtual office walkthrough using a 3D game engine”, International Journal of Design Computing, Volume 4, 2002.
[9] M.Woo, J. Neider, T. Davis, D. Shreiner, “OpenGL Programming Guide: The Official Guide to Learning OpenGL, Version 1.2”, In 3rd Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA 1999.
[10] Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med 1985;13:818-829.
[11] W.T. Sung, S.C. Ou, Web-based learning in the computer-aided design curriculum, In Journal of Computer Assisted Learning, 18 (2) (2002), pp. 175–187.