Tool Wear Detection using Deep Learning

Tool wear detection plays a vital role in maintaining the efficiency and longevity of machining tools, which directly impacts production quality and cost. Accurate identification of tool wear levels is essential to prevent failures, ensure optimal performance, and schedule maintenance effectively. This study focuses on classifying tool wear into three distinct categories: Medium, Normal, and Severe. Leveraging the YOLOv9 (You Only Look Once version 9) model, a state-of-the-art deep learning algorithm, the project aims to detect and classify tool wear from images of cutting tools. The approach integrates image processing techniques for real-time wear detection, improving the accuracy of classification. The front-end interface is built using Streamlit for ease of use and interaction, while the back-end utilizes Python on Google Colab for model training and deployment. This work demonstrates the power of deep learning in industrial applications, offering insights into tool condition monitoring and providing actionable information for predictive maintenance strategies. The findings contribute to enhancing the effectiveness of automated tool wear detection systems, reducing downtime, and optimizing manufacturing operations.

Keywords:

Tool Wear Detection, YOLOv9, Deep Learning, Image Processing, Real-Time Monitoring, Streamlit, Python, Predictive Maintenance, Industrial Automation, Machine Learning, Cutting Tool Monitoring.