Laser Powder Bed Fusion Ontology (LPBFO)

The Laser Powder Bed Fusion Ontology (LPBFO) is a domain ontology for describing additive manufacturing processes, specifically Laser Powder Bed Fusion (LPBF) and Selective Laser Melting (SLM). LPBFO provides a structured vocabulary for representing process parameters, materials, equipment, component geometries, and quality attributes relevant to LPBF manufacturing. The ontology builds on BFO2.0 and BWMD_mid, and incorporates terminology from ISO/ASTM 52900:2015 to ensure alignment with industry standards. LPBFO supports semantic annotation of digital manufacturing workflows, enabling data integration, process optimization, and sustainability assessment through Life Cycle Analysis (LCA) classes. By providing a standardized framework, LPBFO facilitates interoperability between digital manufacturing systems, quality management, and research databases. The ontology is actively maintained and extended to support new developments in additive manufacturing and sustainability assessment.

Example Usage: Annotate an LPBF manufacturing workflow with LPBFO terms to specify process parameters (e.g., laser power, scan speed), material types, component geometry, and LCA attributes, enabling semantic search and integration with digital manufacturing platforms.

Metrics & Statistics

Graph Statistics

Total Nodes

1835

Total Edges

3548

Root Nodes

129

Leaf Nodes

1056
Knowledge Coverage Statistics

Classes

508

Individuals

0

Properties

38
Hierarchical Metrics

Maximum Depth

90

Minimum Depth

0

Average Depth

13.97

Depth Variance

590.11
Breadth Metrics

Maximum Breadth

276

Minimum Breadth

1

Average Breadth

10.16

Breadth Variance

1618.27
LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

507

Non-taxonomic Relations

22

Average Terms per Type

0.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import LPBFO

ontology = LPBFO()
ontology.load("path/to/LPBFO-ontology.owl")

# Extract datasets
data = ontology.extract()

# Access specific relations
term_types = data.term_typings
taxonomic_relations = data.type_taxonomies
non_taxonomic_relations = data.type_non_taxonomic_relations