With the data logic and dashboard in place, the final stage of the Node-RED integration was testing. This involved running the full simulation, including Siemens NX Mechatronics Concept Designer, PLC SIM Advanced, TIA Portal, and Node-RED, all together in real-time to validate signal integrity, responsiveness, and stability.

The goal wasn’t just to verify that values appeared correctly, but to ensure that the entire feedback loop, from virtual sensors in NX, through PLC logic in TIA Portal, to live dashboard updates in Node-RED, functioned seamlessly under simulated operating conditions.

End-to-End Signal Flow Validation

To begin testing, I launched the Siemens NX simulation and initiated part flow through the system. As the simulation progressed through the distribution, pick-and-place, sorting, and dismantling stations, various sensor and actuator states changed rapidly. Each of these changes triggered signal updates, which flowed into PLCSIM Advanced, processed through the control logic in TIA Portal, and were then exposed via the OPC UA server.

In Node-RED, the dashboard instantly reflected these changes:

• Workpiece counters updated live

• LEDs toggled in sync with sensor triggers

• Classification results appeared within a fraction of a second of part arrival

• System status indicators, such as gripper position, remained accurate throughout

This confirmed that the polling frequency (every 0.5 second) was fast enough to feel responsive, without overloading the OPC UA server or the Node-RED runtime.

Simulating Faults

To stress-test the system, I deliberately introduced conditions that might occur in a real production line:

• A jammed part that triggered no classification sensors

• An empty magazine in the distribution station

• A disconnection of PLCSIM Advanced mid-run

Each of these cases was caught by Node-RED logic nodes and displayed meaningful status messages on the dashboard. For example, if the PLC connection were lost, the OPC UA client would return an error, which would be picked up by a status node and passed to a function node.

Similarly, a lack of lid detection during sorting correctly triggered the bypass logic, sending the incomplete part to the dismantling station, with the result immediately logged on-screen.

Performance Observations

Even when monitoring more than a dozen signals, Node-RED remained stable. CPU usage on my test machine stayed under 40%, and network latency had no noticeable effect on dashboard updates. Given that the PLC was virtual and running locally via PLCSIM Advanced, response times were effectively real-time for all practical purposes.

One crucial performance benefit came from my earlier design decision: keeping Node-RED in read-only mode. Since no writes or control commands were sent back to the PLC, the risk of interference or accidental changes was zero. This also ensured consistent simulation results when testing automation logic.

This final phase highlighted just how powerful lightweight IIoT tools can be, even when used in conjunction with complex industrial software such as TIA Portal or Siemens NX. Node-RED proved to be:

• Easy to integrate

• Quick to adapt to new tag structures or process changes

• Capable of creating a responsive, user-friendly interface with minimal coding

More importantly, the testing process revealed the importance of observability in digital twin environments. Visualising data is not just for presentation; it directly supports debugging, validation, and even learning. Watching values change in real-time clarified timing issues, logic conflicts, and mechanical interactions that would have been harder to detect otherwise.

Conclusion

With Node-RED fully configured, tested, and operational, I now have a complete digital twin ecosystem, spanning mechanical simulation and PLC control, OPC UA communication, and real-time visual monitoring in the browser. This setup doesn’t just simulate automation, it actively demonstrates how Industry 4.0 principles can be applied using accessible, modular tools.

It also lays the groundwork for future enhancements like:

• Data logging and analytics

• Event-based alarms and notifications

• Remote monitoring in cloud-hosted environments

This experience has proven invaluable, not just in technical terms, but in understanding the challenges of cross-platform integration, system validation, and real-world constraints in smart manufacturing systems.