An Approach to Improved CNC Machining Using Vision-Based System

Abstract

CNC machines are still suffering from machine blindness. They cannot automatically assess the performance of applied machined tasks. In this article, an approach is made to improve the performance of CNC machining by utilizing on-line vision-based monitoring and control system. To facilitate the integration of computer vision with CNC machines a system is proposed and developed to tackle a number of pinpointed issues that obstruct such integration. A practical executable methodology of these steps is developed to enable their beneficial implementation on lab-scale CNC milling machines. Two different models of bench type CNC machines are employed to generalize the findings. Two cameras are mounted on the machine spindle of each of the two employed CNC machines to provide valid image data according to the cutting direction. Proper selection and activation of relative camera is achieved automatically by the developed system which analyze the most recent conducted tool path movement to decide on which camera is to be activated. In order to assess the machining surface quality and cutting tool status, image data are processed to evaluate resulting tool imprints on the machined surface. An indicating parameter to assess resulting tool imprints is proposed and used. The overall results show the validity of the approach and encourage further development to realize real industrial scale intelligent vision-controlled CNC machines.

Keywords:

• Improved CNC machining
• Intelligent machining
• Surface roughness
• Vision system