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Engineering Application Ontology - Coggle Diagram
Engineering Application Ontology
Purpose of Engineering Application Ontology
Facilitates data sharing across different engineering disciplines.
Standardizes terminology in engineering applications.
Supports Artificial Intelligence (AI) and Machine Learning (ML) applications in engineering.
Enhances collaboration between different engineering tools and systems.
Core Components
Engineering Entities: Materials, Components, Systems.
Disciplines: Mechanical, Electrical, Civil, Aerospace, Software, etc.
Activities: Design (CAD), Simulation (FEA, CFD), Manufacturing, Testing.
Relationships:
is-a (Bridge is-a Structure)
part-of (Engine is part-of Vehicle)
uses (Circuit uses Resistors)
Applications
Supports Lifecycle Assessment (LCA) for eco-friendly materials.
Smart Manufacturing (IoT, Digital Twins)
AI-Assisted Design & Simulation
Predictive Maintenance
Engineering Software Integration
Key Differences from General Engineering Ontology
Integration with Emerging Technologies → Strong linkage with AI, IoT, Digital Twins, and Industry 4.0.
Focus on Practical Implementation → Unlike general engineering ontologies, AEO is more application-driven, emphasizing real-world use in design, manufacturing, and automation.
Interoperability & Data Exchange → Designed for seamless communication between CAD, CAE, PLM, ERP, and IoT platforms.
Disadvantages of Application Engineering Ontology
High Learning Curve → Requires expertise in ontology modeling (OWL, RDF, SPARQL).
Difficult to Maintain → Frequent updates needed due to evolving engineering technologies.
Integration Issues → Different engineering software (CAD, CAE, PLM) may lack standard support for ontology-based systems.