The Research And Development Of RTLinux-based Open Architecture CNC System Of PCB NC-drilling Machine

The technology of Printed Circuit Board (PCB) manufacturing is one of the most important parts of electronic information manufacturing industry. The NC-drilling machine used in PCB processing is the key equipment when perform the precise PCB manufacturing. Currently, to attain the high speed, high precise PCB NC-drilling machine with intellectual property right is the major restriction of the development of the nation's electronic information manufacturing industry. As the CNC system is the core of NC machine, the developing of CNC system of PCB NC-drilling machine with self-owned intellectual property right has a great significance for the development of the nation's electronic information manufacturing industry.Nowadays, the PC-based open-architecture CNC system is an inevitable trend in NC development. DOS or Windows operating system are usually used as the development platform. But as these OS can not well meet the real-time requirement, they are not the ideal platform for developing CNC system. Under this situation, this paper does the research in developing the open architecture CNC system for PCB NC-drilling machine under RTLinux platform.RTLinux is a real-time OS which is achieved by modifying the standard Linux kernel. With the help of RTLinux's multi-tasks property and preemptive scheduling based on priority, RTLinux is the ideal developing platform for CNC system. This paper describes the structure of CNC system software and defines the function modules in details. The discussion of the relationship and communication mechanism between modules is also involved. The coding language is Qt lib using C/C++.During the procedure of PCB processing, the selection of drilling path will greatly affect the manufacturing efficiency. This paper introduces the Genetic Algorithm into the optimizing drilling path issue for PCB processing. After establishing the math module of drilling path, this paper well found the nearly best optimizing solution. Also improving the efficiency of the algorithm by redesigning the mutation approach and choosing better mutation rate is also concerned.