Automatic Detection of Tool Wear in Machining Tools Using Deep Learning

The proposed research project aims to create an AI-based automatic system for monitoring and characterizing the condition of cutting tools by integrating robotic systems and smart cameras. This innovative approach addresses the limitations of traditional tool wear monitoring methods, which often lead to unnecessary costs and machine downtime. By developing advanced machine learning algorithms and real-time image analysis techniques, the project seeks to improve the accuracy and efficiency of tool wear detection and prediction. This will help extend tool life, reduce maintenance costs, and enhance overall production efficiency. Participating institutions will benefit from cutting-edge research that aligns with Industry 4.0 trends, promoting smart manufacturing practices and contributing to the development of sustainable, cost-effective machining technologies.

Faculty Supervisor:

Dan Wu

Student:

Partner:

Karlsruher Institut für Technologie

Discipline:

Computer science

Sector:

Education

University:

University of Windsor

Program: