The future of machining lies in the fully autonomous machine tool. New technologies must be developed that predict, sense and action intelligent decisions autonomously. Digital twins are one component on this journey and are already having significant impact in the manufacturing industries. Despite this, the implementation of machining Digital Twins has been slow due to the computational burden of simulating cutting forces online resulting in no commercially available Digital Twin that can automatically control the machining process in real time. Addressing this problem, this research presents a machining Digital Twin capable of real-time adaptive control of intelligent machining operations. The computational bottleneck of calculating cutter workpiece engagements online has been overcome using a novel method which combines a priori calculation with real-time tool centre point position data. For the first time, a novel online machine-induced residual stress control system is presented which integrates real-time model-based simulations with online feedback for closed loop residual stress control. Autonomous Digital Twin technologies presented also include chatter prediction and control and adaptive feed rate control. The proposed machining Digital Twin system has been implemented on a large-scale CNC machine tool designed for high-speed machining of aerostructure parts. Validation case studies have been conducted and are presented for each of the machining Digital Twin applications.