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

S. Ganeshkumar; Bipin Kumar Singh; R. Suresh Kumar; Anandakumar Haldorai

doi:10.14429/dsj.73.18650

S. Ganeshkumar Department of Mechanical Engineering, Sri Eshwar College of Engineering, Coimbatore, India https://orcid.org/0000-0003-0737-3937
Bipin Kumar Singh Department of Mechanical Engineering, Sri Eshwar College of Engineering, Coimbatore, India https://orcid.org/0000-0001-8019-3866
R. Suresh Kumar Department of Mechanical Engineering, Sri Eshwar College of Engineering, Coimbatore, India https://orcid.org/0000-0003-4783-8874
Anandakumar Haldorai Department of Computer Science & Engineering, Sri Eshwar College of Engineering, Coimbatore, India https://orcid.org/0000-0001-9975-6462

DOI: https://doi.org/10.14429/dsj.73.18650

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

Abstract

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.