Template-as-Ontology: Configurable Synthetic Data for Manufacturing AI Validation
A recent study presents the Template-as-Ontology principle aimed at creating synthetic manufacturing data for the validation of AI agents based on LLMs. This method utilizes a single Python configuration module, comprising 700-770 lines and 45 validated exports, functioning as both a specification for a time-stepped manufacturing simulator and the runtime domain schema for AI analytics. This design ensures structural alignment through construction rather than integration. The paper defines the domain template as a typed relational configuration schema, demonstrating that single-source consumption ensures alignment between simulation and tool layers. A five-layer pipeline—simulation, PostgreSQL, CDC/Iceberg lakehouse, star schema, and 12 parameterized AI tools—produces causally coherent, MES-shaped data across 66 entity types in four operations, tackling the issue of proprietary, privacy-laden, and vendor-specific production MES data that complicates AI validation in manufacturing settings.
Key facts
- Template-as-Ontology principle introduced for synthetic manufacturing data generation
- Single Python configuration module of 700-770 lines with 45 validated exports
- Module serves as both simulator specification and runtime domain schema
- Structural alignment guaranteed by single-source consumption
- Five-layer pipeline: simulation, PostgreSQL, CDC/Iceberg lakehouse, star schema, 12 AI tools
- Data spans 66 entity types across four operations
- Addresses proprietary and privacy-encumbered nature of production MES data
- Paper available on arXiv with ID 2605.11259
Entities
Institutions
- arXiv