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.
Efficient Web-Learning Collision Detection Tool on Five-Axis Machine
2013-07-27
oai:zenodo.org:1087247
Article (Journal)
Electronic Resource
English
DDC: | 629 |
HAWDE Five Axis Wing Surface Drilling Machine
SAE Technical Papers | 2004
|A machining test to evaluate thermal influence on the kinematics of a five-axis machine tool
BASE | 2021
|Optimised Tool Path Generation Methods for Economic and Collision Free Multi-Axis Machining
British Library Conference Proceedings | 1998
|Optimised tool path generation methods for economic and collision free multi-axis machining
Automotive engineering | 1998
|