Name
Abstract | ||
|---|---|---|
Machines for automated PCB depaneling have greatly improved the industrial production efficiency of electronic products. But the preparation for automated depaneling could be very complex and time-consuming. In this paper, we propose a novel systematic solution for this problem. Using a vision-based assistant system, called ”Auto-Teaching”, all connection tabs that should be milled off panels are detected automatically. Highly accurate milling curves are generated with respect to the geometry of the corresponding tabs. Then they could be easily converted into CNC code which drive milling cutters later. Moreover, placement suggestions of supporting pins used to fix panels are obtained. All image analysis functions are implemented in C++ and optimized to meet the minimal hardware and time requirements. Thus the whole process of the preparation is simplified for users and the PCB manufactures can benefit from reduced idle running of machines and labor costs. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/INDIN.2012.6300919 | INDIN |
Keywords | Field | DocType |
panoramic imaging,labor costs,production engineering computing,industrial image processing,morphological operation,vision-based assistant system,pcb manufactures,industrial production efficiency,vision,automated pcb depaneling,image analysis,c++ language,thinning,computer vision,printed circuit manufacture,c++,electronic products,vision-based auto-teaching,milling cutters,cnc code,geometry,electronic engineering computing,pruning,pcb depaneling,milling curves,c,accuracy,image segmentation | Industrial production,Idle,Image segmentation,Vision based,Engineering,Depaneling,Embedded system | Conference |
ISSN | ISBN | Citations |
1935-4576 | 978-1-4673-0312-5 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Li | 1 | 25 | 5.04 |
| Matthias Breier | 2 | 8 | 3.46 |
| Til Aach | 3 | 855 | 117.45 |