Rockwell Automation's AI-Powered Digital Twin Solution for Industrial Automation Processes

[Cengiz Özemli]

Rockwell Automation has developed a new AI-powered engineering framework for industrial automation lifecycle management. This system enables automatic controller code generation and system validation by synchronizing digital twin simulations with controller engineering platforms.

### Digital Twin and Automated Programming Integration
In traditional industrial engineering, the disconnect between simulation and implementation is a significant challenge. Engineers often use separate environments for mechanical simulation and PLC development. Rockwell Automation, by combining Emulate3D digital twin software with FactoryTalk Design Studio, makes it possible to directly convert simulation parameters into controller code within a cloud-based environment.

This system uses Large Language Models (LLMs) and autonomous AI agents to enable the automatic generation of structured text and ladder logic code from natural language inputs. This significantly reduces the time and effort required for manual PLC configuration.

### Reliability Through Closed-Loop Validation
Before physical hardware is commissioned, the AI-generated controller project is tested via the digital twin. This closed-loop simulation provides measurable validation of system logic, timings, and safety protocols. Jordan Reynolds, Vice President of AI and Autonomy at Rockwell Automation, states that this method allows engineers to transition from a validated model to a fully tested controller project.

### Impacts on Industrial Efficiency and Safety
This AI-powered design model offers significant benefits in engineering processes:

• Reduced Commissioning Time: Thanks to virtual validation, codes are tested to be functional before going to the factory.
• Design Improvements: Engineers can quickly and frequently optimize factory models using natural language.
• Standardization: AI-generated codes comply with company standards, reducing variation across different teams and locations.

Furthermore, this approach contributes to worker safety and sustainability goals by optimizing machine movements and energy consumption. These software-based technologies are seen as a significant step towards autonomous industrial systems.