Efficient Web-Learning Collision Detection Tool on Five-Axis Machine

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.

• Chia-Jung Chen
• Rong-Shine Lin
• Rong-Guey Chang

• Collision detection
• Five-axis machining
• Separating axis.

<p>[1] I. Palmer and R. Grimsdale, &ldquo;Collision detection for animation using
sphere-trees,&rdquo; Computer Graphics Forum, Vol. 14, no. 2 (1995), pp.
105-116.
[2] G. Bradshaw and C. O&#39;Sullivan, &ldquo;Adaptive Medial-Axis Approximation
For Sphere-Tree Construction,&rdquo; ACM Translations on Graphics, Vol. 23,
no. 1 (2004), pp. 1-26.
[3] J. D. Cohen, M. C. Lin, D. Manocha, and M. Ponamgi, &ldquo;I-COLLIDE: An
Interactive and Exact Collision Detection System for Large-Scale
Environments,&rdquo; ACM Interactive 3D Graphics Conference (1995), pp.
189-196.
[4] V. Bergen, &ldquo;Efficient Collision Detection of Complex Deformable
Models Using AABB Trees,&rdquo; Journal of Graphics Tools, Vol. 2, no. 4
(1997), pp.1-14.
[5] S. Gottschalk, M. Lin, and D. Manocha, &ldquo;OBB-tree: A Hierarchical
structure for Rapid Interference Detection,&rdquo; In: Proc. SIGGRAPH (1996),
pp. 171-180.
[6] J. Chang, W. Wang, and M. Kima, &ldquo;Efficient Collision Detection Using a
Dual OBB-Sphere Bounding Volume Hierarchy,&rdquo; Computer-Aided
Design Vol. 42 (2010), pp. 50-57.
[7] R.D. Owston, &ldquo;The World Wide Web: A Technology to Enhance
Teaching and Learning?&rdquo;, In American Educational Research
Association, 1997.
[8] M.F. Shiratuddin, W. Thabet, &rdquo;Virtual office walkthrough using a 3D
game engine&rdquo;, International Journal of Design Computing, Volume 4,
2002.
[9] M.Woo, J. Neider, T. Davis, D. Shreiner, &ldquo;OpenGL Programming Guide:
The Official Guide to Learning OpenGL, Version 1.2&rdquo;, 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&ndash;187.</p>