Although Computerized Numerical Controlled (CNC) machines are currently regarded as the heart of machining workshops they are still suffering from machine blindness, hence, they cannot automatically judge the performance of applied machining parameters or monitor tool wear. Therefore, parts produced on these machines may not be as precise as expected. In this research an innovative system is developed and successfully tested to improve the performance of CNC machines. The system utilizes twin-camera computer vision system. This vision system is integrated with the CNC machine controller to facilitate on-line monitoring and assessment of machined surfaces. Outcome from the monitoring and assessment task of is used to real-time control of the applied machining parameters by a developed decision making subsystem which automatically decides whether to keep or alter the employed machining parameters or to apply tool change. To facilitate the integration of computer vision with CNC machines a comprehensive system is developed to tackle a number of pinpointed issues that obstruct such integration including scene visibility issue (e.g. effects of coolant and cut chips as well as camera mounting and lighting), effects of machine vibration on the quality of obtained roughness measurement, selection of a the most proper roughness parameter to be employed, and assessment of machining parameters effects on acquired roughness measurement. Two system rigs employing different models of CNC machines are practically developed and employed in the conducted tests to beneficially 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 wider scale applications of vision-based-CNC machines.


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