Loading systems are among the applications where many technical dependencies must function reliably at the same time. Moving system components, interlocks, sensors, drive technology, and process logic interact and react to real boundary conditions that need to be precisely coordinated during commissioning.

In practice, however, testing on the real system is often only possible at a late stage or can only be carried out with restrictions. Components may not yet be fully available, mechanical processes can only be checked to a limited extent, or ongoing operations do not permit structured testing. This presents a challenging starting point for training, diagnosis, and initial functional tests.

It is precisely in such situations that it becomes clear: The real problem is not a lack of know-how, but rather the lack of an environment in which processes can be trained and prepared safely and reproducibly.

A training twin shifts precisely these tasks into a controlled digital environment. Typical processes, malfunctions, and conditions can be comprehensibly built, tested, and analyzed without blocking or endangering real hardware.

This creates a framework in which not only training, but also diagnostics and virtual commissioning can be effectively prepared. Users can check signal flows, understand interlocks, analyze step sequences, and systematically narrow down errors. At the same time, training and testing processes become plannable because they can take place independently of the availability of the real system.

This is particularly relevant for complex loading systems: the quality of onboarding increases, and technical correlations can be conveyed much earlier and in a more structured manner.

Together with Alztec, we have developed another NexaTwin training and preview twin for a loading station. The focus is on an application that can be used to practically train, test, and prepare the automatic loading of a freight train for virtual commissioning.

The twin digitally maps the central processes and technical correlations of the loading station, thus creating a realistic environment for qualification and testing. The PLC connection also allows typical PLC-related tasks to be understood under practical conditions.
The solution is available in two variants in the NexaSwift Marketplace. The free preview version is suitable for a quick technology check as well as for performance and system tests. The training twin with PLC connection is designed for practical training, diagnostics, and virtual commissioning.

The solution is aimed at students of automation technology, technicians and electricians in the PLC environment, companies with junior or young talent, and training centers that require practical training environments for industrial automation.

In practical use, the twin enables structured PLC training based on typical Siemens environments. By coupling S7-1500, PLCSIM Advanced, and NexaTwin, signal flows, variable logic, and process sequences can be tested and understood under reproducible conditions.

Another focus is on drive technology and higher-level process logic. In the model, SEW Movimot and Movitrac B are represented as positioners and speed-controlled drives. This allows loading processes with releases, interlocks, stop logic, and release chains to be systematically built up and tested. Step sequences and state models can also be developed and debugged in a comprehensible way in this context.

In addition, the handling of sensors and analog values is covered. Analog feedback via displacement sensors can be scaled, evaluated, and used for process decisions. At the same time, binary sensors for status and end position monitoring can be safely integrated into the overall logic. This supports a better understanding of the interaction between field signals, PLC logic, and process behavior.

The twin is particularly valuable for diagnosis and troubleshooting. Typical fault patterns such as missing releases, unreached end positions, telegram or drive errors, and sensor errors can be specifically generated, detected, and analyzed. Variable lists, watch and trace functions, and status displays help to systematically narrow down errors and specifically optimize processes.

For companies, the primary benefit lies in improved planning. Training, diagnostics, and preparatory tests no longer have to be conducted exclusively on the real system. This relieves the burden on ongoing operations, reduces the risk of downtime, and creates room for structured onboarding.

At the same time, the likelihood that critical learning and testing phases will be pushed into critical project stages due to time pressure decreases.
Knowledge can be built up earlier, conveyed in a standardized manner, and secured in reproducible scenarios. This shortens onboarding times, reduces rework, and improves preparation for real-world commissioning.

This advantage is particularly noticeable in complex loading systems, where mechanical interaction, drive technology, and logic are closely intertwined.

For realistic tests, it is often not enough to have only the real plant available. It also requires available components, suitable boundary conditions, and a mechanical interplay that can be fully replicated. In real projects, this is often only possible late in the process or to a limited extent.

The training twin creates a reliable alternative here. It shifts training, diagnosis, and virtual commissioning to a safe, reproducible environment, thereby improving both qualification and technical preparation. This reduces risk, creates more security during the project, and makes complex processes much easier to manage.