To improve the working efficiency and safety of steel round bar sawing equipment, a grinding-wheel-saw automatic replacement system has been employed to replace grinding-wheel saws. In this paper, we report a machine-vision-based spindle positioning system for spindle localization in this replacement system. The hardware and software for the spindle positioning system were established, as well as an imaging model of the camera and the calibration of the vision system. The template matching approach applied to identify the shaft nut of the spindle is also described. Experimental results clearly demonstrate the effectiveness of the proposed machine-vision-based system.

In the factory, the steel round bar sawing machine is applied to finish the sawing mission. Due to the process of the sawing steel is operating in high speed, the grinding-wheel-saw (GWS) is quickly worn by the friction and replaced frequently. The automatic replacement system of GWSs is seldom built yet. To improve the efficiency of the replacement for GWSs, the machine-vision-based intelligent robotic replacement system (MIRRS) for GWSs is developed and reported in the study. The MIRRS is mainly consisted of the six degrees-of-freedom (DOF) manipulator and the six-axes mechanical arm system to successfully complete the task of the replacement. The finite element method (FEM) is applied to analyze and design the subsystem of the MIRRS. Besides, the machine-vision positioning system associated with the digital camera are also setup to provide the required feedback detections for the MIRRS. The hardware system architecture and the control flow chart are described in detail and the hundreds of realistic testing results are collected to verify the reliability and offer the well working efficiency of the designed MIRRS.