Digital Twin Series · Level 4 · Control
Closed-Loop Digital Twin and Autonomous Control
How does a twin move from showing what is happening to recommending, and then to controlling the machine, and how do I keep that safe?
In a closed-loop twin the insight goes back to the machine. It can stop at an alert, propose an action for the operator to approve, or send a setpoint to the PLC by itself within safe limits. This page compares the three levels, shows how long each step of the loop takes, lets you test a setpoint against a safety layer, and runs a CNC tool-wear loop that you can run open loop, with an operator in the loop, or autonomously.
| Feature | Monitoring-only twin | Recommendation system | Autonomous control (closed loop) |
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The twin works at the supervisory level: it changes setpoints such as spindle speed, feed rate or a route, and the PLC and drives keep running the fast control loops (current, speed, position) on their own. Add up the steps of your loop and compare the total with the time scale of the thing you want to control. The step times are typical orders of magnitude; measure your own network and software.
Every command from the AI passes four layers before it reaches the PLC: limits and constraints, rule-based checks, human oversight and a fallback to a safe state. The limits here are example values. Real machine safety (guards, emergency stop, safe torque off) stays in certified safety hardware and is never handed to the twin.
| Mode | Cycle time (s) | Tools used | Scrap parts | Tool breakages | Cost per good part (Rs) | Total time (h) |
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The simulator machines 300 parts. Each tool wears faster at higher speed, higher feed and in harder material, and the surface roughness grows with feed per revolution and with tool wear. Open loop uses fixed settings and a fixed tool-change interval. Monitoring-only uses fixed settings and changes the tool when the twin alerts, after the operator reaction time. Recommendation proposes new settings every part but applies them at the approval interval. Autonomous applies the best setting at once, within limits. Table values are the average of 10 runs with different random material variation. The wear and roughness relations are assumptions of this teaching model; use your own machine data.
Figures are indicative and the simulators are simplified teaching models. Test any autonomous control in a safe scope, on your own machine, with your own safety review.