Novel and cost-effective CNC tool condition monitoring through image processing techniques

Abstract

Abstract CNC machining is an important part of the manufacturing industry. This paper introduces a novel and efficient approach for tool condition monitoring in CNC machine operations through the application of image processing techniques. By utilizing a consumer-grade camera capable of recording videos at 60 frames per second, the study demonstrates a cost-effective method to detect tool breakage and identify edge fractures. Basic image processing techniques, including frame extraction, background subtraction, thresholding, and morphological operations, are applied to analyze captured images and videos. This research not only offers a practical solution to enhance the efficiency and accuracy of CNC machine operations but also aligns with advancements in smart manufacturing and Industry 4.0. The findings will showcase the proposed system’s effectiveness in actual CNC machine environments, underscoring its potential to enhance maintenance strategies and operational productivity for small and medium-sized manufacturers without incurring high costs. Furthermore, it sets the stage for future investigations in this domain, indicating the possibility for enhancements through machine learning and an expanded application of these monitoring techniques.

Keywords

• CNC tool monitoring
• Image processing
• Machine vision
• Smart manufacturing
• Predictive maintenance