Digital twin technology has recently been introduced as one of the emerging technologies within the Industry 4.0 framework. Digital twin technology opens a new prospective approach for a more prognostic tool wear monitoring system since the dynamics of tool conditions can be monitored and predicted by running the digital twin. Nevertheless, more research is needed on utilizing digital twin technology for monitoring tool wear. In the past, many systems used primarily data-driven methods to provide wear monitoring services within the digital twin application. The framework for representing the physical object consisted of ‘black-box’ data-driven models that described the relationship between sensor data and wear value. Implementing the digital twin principle, which entails a dynamic representation and up-to-date information of physical entities, has been limited. This paper presents a micro-milling digital twin that simulates the micro-milling process dynamics based on physics-based models, such as spindle motor, spindle controller, and cutting torque models. The proposed digital twin is an alternative to prior tool wear monitoring systems, primarily based on indirect data-driven or direct visual approaches. The proposed digital twin has shown potential benefits and a new direction for the tool wear monitoring field by adapting emerging technology within the Industry 4.0 ecosystem. © 2025 Informa UK Limited, trading as Taylor & Francis Group.