Digital Twin Framework for Lathe Tool Condition Monitoring in Machining of Aluminium 5052

Keywords: Digital twin, Machine learning, Tool wear, Cutting forces, CNC


Digital Twin (DT) is a virtual representation of a product system that exhibits the properties and analyzes the system’s functions. The significant impact of DT extends to several fields, which increases productivity and reduces wastage. This article focuses on developing a Digital twin model of a Lathe machine for Tool Condition Monitoring (TCM). DT implementation in industries is challenging due to simulating online cutting forces and wear. Even though several pieces of research have been carried out in the prediction of tool conditions using machine learning, Artificial Neural network models, only a few pieces of research have been made in digital twins for TCM. This article provides the technique for implementing the DT model of a lathe tool. The feasibility of the DT Model framework is verified by a case study of the turning process with a CNC Lathe machine while machining of Aluminium 5052 workpiece using Titanium Nitride coated tool inserts. The sensor’s data are acquired and fed to the microcontroller for real-time data acquisition. The real-time dataset is processed in the DT model for monitoring and predicting the tool conditions. The tool wear classification using the DT model is achieved. Developing the Digital Twin model in machining increases productivity and assists in predictive maintenance.

How to Cite
Ganeshkumar, S., Singh, B. K., Suresh Kumar, R., & Haldorai, A. (2023). Digital Twin Framework for Lathe Tool Condition Monitoring in Machining of Aluminium 5052. Defence Science Journal, 73(3), 341-350.
Materials Science & Metallurgy