UTILIZING VISION-BASED OBJECT DETECTION ALGORITHMS IN RECOGNIZING UNCOMMON OPERATING CONDITIONS FOR CNC MILLING MACHINE

Although Computer Numerical Control (CNC) machines were designed to perform tasks with the least human intervention, operator involvement is mandatory to ensure fault-free operations. Numerous technological solutions utilizing Artificial Intelligence, sensor fusion, Internet of Things (IoT), machine vision, etc., have been developed for process, component, and machine monitoring to impart smartness and autonomous operating abilities. The primary focus of these solutions is to monitor process faults such as tool wear, chatter, static deflections, and cutting forces to assist the operator in minimizing the consequences. The present work develops a vision-based solution for identifying uncommon process abnormalities like improper coolant flow, chip clogging, and tool breakage during CNC milling. The proposed solution replicates the task of a machine operator in identifying these faults and assists in fault-free operations. The study explores the feasibility of utilizing classical and deep learning-based object detection algorithms while developing these solutions. The classical image processing algorithm is ineffective during dynamic process conditions. The deep learning-based algorithm, with an average precision of about 0.75, showed proficiency in abnormalities detection. A Graphical User Interface (GUI) has been developed and integrated with the CNC milling machine to provide an interactive in-process monitoring tool. It is demonstrated that the proposed solution can reduce dependence on a machine operator while monitoring these faults.