Multi-Agent Framework Leveraging Knowledge Graphs for Virtual Commissioning Models

2026-06-02

AI Summary

This work addresses the challenge in virtual commissioning model (VCM) development where PLC engineering and mechatronic simulation data are fragmented across heterogeneous tools, lacking a unified representation and thus heavily relying on manual effort for system comprehension, component generation, and signal mapping. To overcome this, the study proposes the first framework integrating knowledge graphs with multi-agent systems to extract data from Siemens TIA Portal and NX Mechatronics Concept Designer (MCD), constructing a unified graph-based representation. Through hierarchical multi-agent collaboration, the framework enables semantic understanding of the system, template-driven automatic generation of NX Open scripts, and intelligent recommendation of mappings between PLC variables and simulation objects. Experimental results demonstrate that the approach significantly reduces cross-domain manual intervention and enhances the automation level and executability of repetitive VCM tasks.

Abstract

Virtual commissioning models (VCMs) of discrete manufacturing systems are used to validate automation behavior before physical deployment, but creating and maintaining them remains labor-intensive. Relevant engineering information is distributed across programmable logic controller (PLC) engineering projects, such as Siemens TIA Portal, and kinematic simulation models, such as Siemens NX Mechatronics Concept Designer (NX MCD), where it is stored in incompatible, tool-specific data structures. In practice, IEC 61131-3-based PLC programs and variables are engineered separately from rigid-body and kinematic simulation objects such as parts, joints, sensors, and actuators. As a result, understanding system behavior, generating simulation components, and mapping PLC variables to corresponding simulation objects require cross-domain expertise and remain largely manual. This paper presents a knowledge-graph-grounded multi-agent framework for semi-automated VCM development. A deterministic setup process extracts structured data from Siemens TIA Portal and Siemens NX MCD and transforms both sources into graph-based representations within a shared graph database. The framework uses a hierarchical multi-agent architecture to support three task classes in early-stage VCM development: system understanding, simulation component generation, and cross-domain signal mapping. It provides grounded natural-language access to engineering knowledge, template-guided generation of executable NX Open journal scripts, and ranked mapping suggestions between PLC variables and NX MCD simulation objects. Evaluation on a laboratory-scale discrete manufacturing system shows that the approach reduces manual cross-domain interpretation effort and makes recurring VCM engineering tasks more actionable.

Problem

• Virtual Commissioning
• Knowledge Graph
• Multi-Agent System
• Cross-domain Mapping
• Discrete Manufacturing

Innovation

• knowledge graph
• multi-agent system
• virtual commissioning
• cross-domain integration
• automated simulation