Today, machine vision is widely spread in manufacturing applications such as tool monitoring, workpiece recognition, collision detection, and measuring and inspection, offering digitized information to manufacturing systems. The main process of machin...
Today, machine vision is widely spread in manufacturing applications such as tool monitoring, workpiece recognition, collision detection, and measuring and inspection, offering digitized information to manufacturing systems. The main process of machine visioning consists of several steps: i) object image obtaining, ii) image processing and analysis, iii) feedback of image information to a control system, and iv) actuator manipulation. Especially, in the case of recognizing printed 2D shapes and generating machining data, the image processing and analysis step affects much to manufacturing quality and shop floor productivity. For instance, the patterns of a cover lens of a mobile phone camera are printed into a tempered glass, being measured through a vision system. Machining data for the camera lens is then generated by image processing and analysis. However, thermal effects and printing machine resolution force printed shapes to get errors such as irregular arrangement, rotated patterns, miss-printed patterns, and so on. Thus, this paper identifies the patterns of errors occurred in the image processing and analysis step, proposing an error compensation method based on error patterns for vision based manufacturing systems. A prototype vision-based manufacturing system is shown.