{"title":"Efficient Web-Learning Collision Detection Tool on Five-Axis Machine","authors":"Chia-Jung Chen, Rong-Shine Lin, Rong-Guey Chang","volume":79,"journal":"International Journal of Educational and Pedagogical Sciences","pagesStart":2045,"pagesEnd":2050,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/16447","abstract":"
As networking has become popular, Web-learning
\r\ntends to be a trend while designing a tool. Moreover, five-axis
\r\nmachining has been widely used in industry recently; however, it has
\r\npotential axial table colliding problems. Thus this paper aims at
\r\nproposing an efficient web-learning collision detection tool on
\r\nfive-axis machining. However, collision detection consumes heavy
\r\nresource that few devices can support, thus this research uses a
\r\nsystematic approach based on web knowledge to detect collision. The
\r\nmethodologies include the kinematics analyses for five-axis motions,
\r\nseparating axis method for collision detection, and computer
\r\nsimulation for verification. The machine structure is modeled as STL
\r\nformat in CAD software. The input to the detection system is the
\r\ng-code part program, which describes the tool motions to produce the
\r\npart surface. This research produced a simulation program with C
\r\nprogramming language and demonstrated a five-axis machining
\r\nexample with collision detection on web site. The system simulates the
\r\nfive-axis CNC motion for tool trajectory and detects for any collisions
\r\naccording to the input g-codes and also supports high-performance
\r\nweb service benefiting from C. The result shows that our method
\r\nimproves 4.5 time of computational efficiency, comparing to the
\r\nconventional detection method.<\/p>\r\n","references":"
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