## 3 DL-Based Ontology
### Entities for Path Planning  
Path planning is a process planning task that aims to design a laser scanning path and a set of process parameters that best meets the build and quality requirements to build a part [6; 7]. The main inputs of this task include an LPBF material, an LPBF machine, a set \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Data Relation** & **Domain** & **Range** & **Meaning** \\ \hline _hasContour_ & _Slice_ & _SliceContour_ & A slice that has contour of \\ _hasThickness-mm_ & _Slice_ & _xsd:double_ & A slice that has thickness of \\ _hasRecommendedStrategy_ & _ModelSlicing_ & _SlicingStrategy_ & A model slicing task that has recommended strategy of \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Domain, range, and meaning of three relations in model slicing. of slices for a part to be built, and specific build and quality requirements on the part. Its outputs are a laser scanning path and a set of process parameters. An important step in path planning is to select or develop a proper laser scanning path. This step is affected by the adopted model slicing strategy. If a planar slicing strategy is adopted, alternative path patterns mainly include raster, zigzag, multi-direction, grid, spiral, contour, and hybrid paths. Each path has its specific strengths and shortcomings. In general, the path patterns are selected according to specific objectives [6]:  
* If the objective of path planning is high build efficiency, then raster path is recommended;
* If the objectives of path planning are high build efficiency and high mechanical performance, then zigzag path or grid path is recommended;
* If the objectives of path planning are high geometric accuracy and high mechanical performance, then multi-direction path or contour path is recommended;
* If the objectives of path planning are high geometric accuracy, less path passes, less path elements, and high mechanical performance, then spiral path is recommended;
* If the objectives of path planning are high build efficiency and high geometric accuracy, then hybrid path is recommended. Another important step in path planning is to design a set of optimal process parameters. According to the study in [79], controllable process parameters include layer thickness, laser power, scanning speed, hatch spacing, recoating time, recoating speed, dosing per layer, bulk temperature, oxygen level, chamber pressure, gas flow speed, and ambient temperature. Among them, layer thickness, laser power, scanning speed, and hatch spacing are four critical process parameters. The volumetric energy density calculated from them is an important indicator to measure the effect of process parameters on part quality [80]. For design of process parameters, most of existing methods first generate a set of combinations of process parameters, then predict certain part quality indicators under each combination, and finally determine the optimal process parameters on the basis of the prediction results [8]. To represent the knowledge in path planning, an object relation named _hasRecommendedPath_, thirteen data relations named _hasLayerThickness-mm_, _hasLayerPower-W_, _hasScanningSpeed-mm/s_, _hasHatchSpacing-mm_, _hasLaserSpotDiameter-mm_, _hasRecoatingTime-s_, _hasRecoatingSpeed-mm/s_, _hasDosingPerLayer-Pct_, _hasBulkTemperature-DegC_, _hasOxygenLevel-Pct_, _hasChamberPressure-kPa_, _hasGasFlowSpeed-m3/s_, and _hasAmbientTemperature-DegC_, and seven instances named _rasterPath_, _zigzagPath_, _multiDirectionPath_, _gridPath_, _spiralPath_, _contourPath_, and _HybridPath_ (instances of _LaserScanningPath_) were created. The domain and range of the object relation are respectively _PathPlanning_ and _ScanningPath_. This means that a path planning task has designed laser scanning path of (a specific scanning path). The domain and range of each data relation are respectively _PathPlanning_ and _xsd:double. This means that a path planning task has designed process parameter value of _xsd:double_.