## 4 Application and Illustrations
### Illustrations of the Benefits  
OWL DL can provide the strongest expressive capability under the premise of computational completeness and computational decidability, which enables reasoning mechanisms like consistency checking of the OWL DL ontology, knowledge reasoning on the ontology and semantic query from the ontology.  
Consistency checking is performed to determine whether an instantiation of a concept satisfies the definition of the concept or would create an inconsistency. Using the HermiT reasoner in Protege, the consistency of the developed ontology for LPBF process planning can be checked automatically. For example, if _surfaceRoughness_ is further asserted as an instance of _BuildRequirement_ in the ontology, then an inconsistency will be detected automatically, as shown in Figure 14, because _BuildRequirement_ has been defined to be disjoint with _QualityRequirement_ and the instance _surfaceRoughness_ does not satisfy this definition. Once there are no inconsistencies in the developed ontology, knowledge reasoning can be performed on it.  
Figure 12: Results of model slicing for the LPBF part in the ontology.  
Figure 13: Results of path planning for the LPBF part in the ontology.  
Knowledge reasoning is carried out to reach new conclusions in a context based on the explicit knowledge in this context. In a DL-based ontology with SWRL rules, new knowledge can be inferred via either a DL reasoner or an SWRL reasoner. For example, the previous subsection has described the details of using the Drools reasoner (an SWRL reasoner integrated in Protege) to reason out a recommended part orientation method, a recommended model slicing strategy, and a recommended laser scanning path in the developed ontology for LPBF process planning. After performing knowledge reasoning on the ontology, its an enriched version will become available for semantic query.  
Semantic query is performed to search specific knowledge from an ontology. Three commonly used semantic query techniques for an OWL DL ontology with SWRL rules are DL query, SPARQL query, and SQWRL query, which respectively uses the reasoning mechanism of DL, SPARQL, and SQWRL to implement semantic query. For example, assume the following terminological axioms are defined in the developed ontology:  
\[\begin{array}{rcl}\mathit{SelectiveLaserSintering}\equiv\mathit{AmProcess}&\sqcap& \exists\mathit{hasEnergySource.LaserBeam}\\ &\sqcap&\exists\mathit{hasBuildMaterial.PowderMaterial}\\ &\sqcap&\exists\mathit{hasBuildPlatform.PowderBed}\\ &\sqcap&\exists\mathit{hasBuildMechanism.Sintering}\\ &\mathit{SelectiveHeatSintering}\equiv\mathit{AmProcess}&\sqcap&\exists\mathit{hasEnergySource.ThermalEnergy}\\ &\sqcap&\exists\mathit{hasBuildMaterial.PowderMaterial}\\ &\sqcap&\exists\mathit{hasBuildPlatform.PowderBed}\\ &\sqcap&\exists\mathit{hasBuildMechanism.Sintering}\\ &\mathit{ElectronBeamMelting}\equiv\mathit{AmProcess}&\sqcap&\exists\mathit{hasEnergySource.ElectronBeam}\\ &\sqcap&\exists\mathit{hasBuildMaterial.PowderMaterial}\\ &\sqcap&\exists\mathit{hasBuildPlatform.PowderBed}\\ &\sqcap&\exists\mathit{hasBuildMechanism.Melting}\\ \end{array} \tag{23}\]  
There is an AM process whose build material is a powder material, build platform is a powder bed, and build mechanism is sintering or melting. Someone needs to determine which specific AM processes it might belong to. If a common text search is adopted, no results will be obtained. However, if a DL query is carried out, the situation will be completely different. As shown in Figure 15, the description of the AM process is taken as the input of a DL query, and it can be inferred from the query results that the process might belong to selective laser sintering, selective heat sintering, electron beam melting, or LPBF.  
Figure 14: Example of consistency checking of the ontology.