###### Abstract  
Laser powder bed fusion (LPBF) provides a rapid and cost-effective solution for fabricating metallic parts with near full density and high precision, strength, and stiffness directly from metallic powders. In LPBF, process variables are widely recognised as fundamental factors that have important effect on the quality of the built parts. However, activity of designing process variables for LPBF, i.e., process planning for LPBF, still heavily depends on knowledge from domain experts. This necessitates a knowledge base that enables the capture, representation, inference, and reuse of existing knowledge. In this paper, a description logic (DL) based ontology for knowledge representation in process planning for LPBF is presented. Firstly, a set of top-level DL entities and specific DL entities and semantic web rule language (SWRL) rules for part orientation, support generation, model slicing, and path planning are created to construct the ontology. The application of the ontology is then illustrated via process planning on an LPBF part. Finally, the benefits of the ontology are demonstrated through a few examples. The demonstration results show that the ontology has rigorous computer-interpretable semantics, which provides a semantic enrichment model for LPBF process planning knowledge and enables automatic consistency checking of the ontology, knowledge reasoning on the ontology, and semantic query from the ontology. This would lay solid foundation for development of a process planning tool with autonomous decision-making capability. l 202  
LPBF has advantages in providing high flexibility for geometric design and achieving geometric complexity without additional cost, which are the common advantages of AM technologies over traditional manufacturing technologies. More importantly, LPBF enables rapid fabrication of different metallic parts in a mixed batch with near full density and high precision, strength, and stiffness directly from metallic powders at no extra cost [2]. This feature makes it an attractive technology for producing functional components in a number of key industrial sectors, such as the aerospace, automotive, and biomedical sectors [3]. In general, the process of using LPBF to realise a part meeting certain quality requirements includes a set of activities, where process planning is an important one. In conventional manufacturing, process planning is an activity of determining appropriate sequence of operations and process variables for converting a workpiece from an engineering drawing to its final form. In LPBF, the engineering drawing is replaced by a 3D model. The aim of process planning remains the same, namely to determine appropriate sequence of LPBF operations or process variables to enable efficient and accurate build of an LPBF part from its 3D model. In process planning for LPBF, the process variables to be designed include build orientation, support structure, slices, laser scanning path, and process parameters. These variables are widely recognised as fundamental factors that have important influence on the quality of the built part [4; 5; 6; 7; 8]. However, process planning for LPBF is still considered as a challenging activity [9; 10] because of the following reasons. Firstly, the physical and chemical processes of LPBF are complex. The lack of a complete understanding of these underlying processes brings great difficulty to the development of optimal process planning methods to build high-quality components [2]. Secondly, most of the existing process planning methods for LPBF focus on specific variables, materials, or structures rather than general strategies. They are difficult to be transferred directly to a build with new variables, materials, or structures. Thirdly, most of the current process planning tools for LPBF are configured by users manually without a standard procedure. There is not yet a tool with autonomous decision-making capability [4; 5; 6; 7; 8].