Laser Powder Bed Fusion Ontology (LPBFO)¶
The Laser Powder Bed Fusion Ontology (LPBFO) is a domain ontology developed to describe knowledge related to Laser Powder Bed Fusion (LPBF) additive manufacturing [1] [#lpbfo-matportal]*. LPBFO provides a structured vocabulary for representing LPBF manufacturing knowledge, including process information, materials, equipment, component-related data, and quality-relevant concepts [#lpbfo-gitlab]* [2].
The ontology supports semantic annotation of LPBF manufacturing workflows, enabling data integration, knowledge sharing, semantic search, and interoperability across additive manufacturing research and industrial platforms [1] [#lpbfo-matportal]*. By providing a standardized semantic framework, LPBFO supports the reuse and comparison of LPBF manufacturing knowledge [#lpbfo-gitlab]* [2].
Example Usage: Annotate an LPBF manufacturing workflow with LPBFO terms to specify process parameters such as laser power, scan speed, layer thickness, material type, equipment information, component geometry, and quality-related attributes, enabling semantic search and integration with digital manufacturing platforms [1] [2].
Metrics & Statistics¶
Total Nodes |
1835 |
Total Edges |
3548 |
Root Nodes |
129 |
Leaf Nodes |
1056 |
Classes |
508 |
Individuals |
0 |
Properties |
38 |
Maximum Depth |
90 |
Minimum Depth |
0 |
Average Depth |
13.97 |
Depth Variance |
590.11 |
Maximum Breadth |
276 |
Minimum Breadth |
1 |
Average Breadth |
10.16 |
Breadth Variance |
1618.27 |
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