PBF-AMP-Onto: an ontology for powder bed fusion additive manufacturing processes Mina Abd Nikooie Pour1,2 , Prithwish Tarafder3 , Anton Wiberg3 , Huanyu Li1 , Johan Moverare3,4 and Patrick Lambrix1,2,5,∗ 1 Department of Computer and Information Science, Linköping University, Linköping, Sweden 2 Swedish e-Science Research Centre, Linköping University, Linköping, Sweden 3 Department of Management and Engineering, Linköping University, Linköping, Sweden 4 Wallenberg Initiative Materials Science for Sustainability, Linköping University, Linköping, Sweden 5 Wallenberg AI, Autonomous Systems and Software Program, Linköping University, Linköping, Sweden Abstract Additive manufacturing is an innovative production approach aimed at creating products that traditional techniques cannot produce with the desired quality and requirements. Throughout the additive manu- facturing process, data is either used (such as materials properties, printer characteristics and settings) or generated (such as monitoring data during printing, slicing strategies setting parameters). However, managing such data with complex relationships remains a significant challenge in both research and industry in the additive manufacturing field. To address this issue, we developed a modular ontology that can be used as the basis for a framework that supports decision-making systems, facilitate semantics- aware data management, and enhance the understanding and optimization of additive manufacturing processes. In this paper we focus on one of the state-of-the-art additive manufacturing approaches, i.e., powder bed fusion. To show the use and the feasibility of our approach, we created a knowledge graph for an actual additive manufacturing experiment based on our ontology, and show how queries relevant to domain experts can be answered using this knowledge graph. Keywords Ontology, Additive Manufacturing Process, Powder Bed Fusion, Electron Beam Powder Bed Fusion 1. Introduction Additive Manufacturing (AM), also known as 3D printing, is a production method to create three-dimensional objects based on respective 3D models in an automatic way. There are several benefits to use AM for manufacturing [1]. The 3D model design can be easily modified based SeMatS 2024: The 1st International Workshop on Semantic Materials Science co-located with the 20th International Conference on Semantic Systems (SEMANTiCS), September 17-19, Amsterdam, The Netherlands. ∗ Corresponding author. Envelope-Open mina.abd.nikooie.pour@liu.se (M. Abd Nikooie Pour); prithwish.tarafder@liu.se (P. Tarafder); anton.wiberg@liu.se (A. Wiberg); huanyu.li@liu.se (H. Li); johan.moverare@liu.se (J. Moverare); patrick.lambrix@liu.se (P. Lambrix) GLOBE https://liu.se/en/employee/minab62 (M. Abd Nikooie Pour); https://liu.se/en/employee/prita53 (P. Tarafder); https://liu.se/en/employee/antwi87 (A. Wiberg); http://huanyuli.se/ (H. Li); https://liu.se/en/employee/johmo31 (J. Moverare); https://www.ida.liu.se/~patla00/ (P. Lambrix) Orcid 0000-0002-4936-0889 (M. Abd Nikooie Pour); 0000-0002-7210-0209 (A. Wiberg); 0000-0003-1881-3969 (H. Li); 0000-0002-8304-0221 (J. Moverare); 0000-0002-9084-0470 (P. Lambrix) © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR ceur-ws.org Workshop ISSN 1613-0073 Proceedings on requirements, manufacturing constraints and be shared as a digital model. Furthermore, it is reasonable to create a limited number of product samples using AM (e.g. for research purposes), whereas establishing an entire production line with dedicated tools using traditional manufacturing techniques may not be practical. Also, it may be easier to meet sustainability goals. To print 3D models with high quality and resolutions, different AM methods were developed, such as Fused Deposition Modeling (FDM), Powder Bed Fusion (PBF), Inkjet printing and contour crafting, and Stereolithography (SLA) [2]. The choice of method for AM depends on several factors, including the material used for printing, the desired quality, and any constraints during production (such as whether the model can be printed with or without adding a support part based on the printing angle). In this paper, our focus lies on Electron Beam Powder Bed Fusion (EB-PBF). EB-PBF represents a state-of-the-art technology that leverages a focused electron beam to melt and fuse metal powder layers [3]. This method offers several advantages, including the capability to fabricate intricate geometries with high precision and exceptional material properties. Notably, the electron beam technique is well-suited for processing high-temperature metals and alloys (e.g., stainless steel), rendering it indispensable in industries such as aerospace, automotive, and medicine. Typically, AM processes follow a number of steps, which may be different for different AM techniques. Some common steps are: (1) designing digital models using Computer-Aided Design (CAD) software, (2) configuring parameters such as printing angle and speed using slicing software, (3) the actual printing using 3D printing machines, and (4) inspecting and testing the printed objects. During each step, different types of data are used and generated [4, 5, 6]. For instance, data representing a 3D model is generated by CAD software in the design step and used for configuring printing parameters by slicing software. However, there is no standardized way to store and format such data. Also, data in materials science and engineering is frequently sparse or incomplete [7]. For instance, metadata and provenance information is often lacking when storing information about AM experiments. Therefore, standardized formats may guide how to store data as well as provide information about what data is lacking. Further, it would allow for the development of design and analysis tools that would alleviate the design and quality assurance of AM products. Further, there is no formal model based on domain knowledge to represent and interpret such data. These issues bring challenges in semantics-aware data search and data analytics applications. They also relate to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles [8, 9] which aim to enable machines to automatically find and use the data, and help individuals easily reuse the data. Ontologies can address these issues by formally representing domain knowledge in AM. Therefore, in this paper we propose a first version of a modular ontology, PBF-AMP-Onto, for PBF processes. We describe the ontology and its development in Section 3. We created two modules: one with core concepts for PBF and a specialized module for EB-PBF. Further, in Section 4.1 we show how a knowledge graph (KG) based on the ontology can be used to describe a particular 3D printing experiment for printing screws. We also exemplify how this KG can be queried to find information about, e.g., the sub-processes and materials in this experiment which are relevant to the experiment developers. We describe related work in Section 2, and conclude the paper Section 5. The ontology, KG and queries that are described in this paper are available at https://github. com/LiUSemWeb/PBF-AMP-Onto. 2. Related work Although much research has been performed regarding the modeling of processes in general, for the sake of brevity, we focus here on related work in the AM domain. In [10] AM ontologies are categorized based on whether they contain information on products, processes, resources and parameters. The authors also define the upper levels of an AM ontology based on these concepts. The Platform Material Digital Core Ontology (PMDco) [7] is a mid-level ontology that aims to bridge semantic gaps between high-level materials science and engineering-specific, and other science domain semantics. It defines processes, processing nodes which execute processes, and objects which are inputs and outputs to processes. These core concepts can then be further specialized for different types of processes. The Additive Manufacturing Ontology (AMOntology) attempts to model the knowledge and terminology within Metal AM [11]. As different types of laser beam models produce different heat distribution and flux, and different thermal models use different analyses, AMOntology represents relationships between AM modeling parameters regarding different laser, thermal, microstructural, and mechanical property models for metal-based AM. The ontology can be used for process control and predicting effects of changes. In [12], the Innovative Capabilities of Additive Manufacturing (ICAM) ontology is presented. It is based on a review of AM manufacturers and machines that identified the capabilities that they possess. These capabilities can relate to such things as fabrication method, manufacturing scale and shapes. This knowledge can be used to find, e.g., machines that allow for printing products with specific properties. The Design for Additive Manufacturing Ontology (DFAM Ontology) [13] aims to represent knowledge needed in a general fabrication scenario. A process is represented by an AM event. Different parameters for things such as builds, design and processes are concepts on their own. Similarly, parts are represented as concepts. We make other choices as these are better represented as relations and there seem to be confusions between is-a and part-of [14, 10]. For instance, an object becomes a part only in relation to another object. It is often not an inherent property of the object. Another DFAM ontology is presented in [15] with processes, capabilities, features and parameters. Our work aims to represent knowledge about PBF printing processes to guide the storage and analysis of AM process data. The closest works to this paper are [7] and [10]. We specialize the process concepts in [7] by focusing on PBF printing. Further, our ontology has a larger focus on the sub-processes of the AM process than [10]. 3. Ontology development We employed the NeOn ontology engineering methodology [16] to develop our modular ontol- ogy. This approach allows for the flexible extension of the ontology to accommodate various future scenarios. A team of knowledge engineers and domain experts collaborated to gather requirements and gain insights into the specific needs of the AM field. There are many AM techniques with different sub-processes and strategies. Therefore, we decided to model the AM domain knowledge in a modular way. We chose to start with PBF and in particular EB-PBF, as it is a state-of-the-art technique and our results will be directly applicable in the development of databases and KGs in ongoing research in EB-PBF. We used Protégé1 as ontology development tool. We reused some concepts from the PROV-O Ontology2 . We note that in future versions we will reuse more ontologies. For instance, in this first version we have used strings to represent the values and units of quantities. A natural next step is to reuse an ontology such as Quantities, units, dimensions and data types ontologies (QUDT)3 for representing these. 3.1. Competency questions Conform to the methodology, we formulate competency questions that our developed ontology should be capable to answer: • CQ1: What is the material used for each printed build in an EB-PBF printing process? • CQ2: Who is the manufacturer of the metal powder used in an EB-PBF printing process? • CQ3: What are different sub-processes in an EB-PBF process? • CQ4: What are the inputs and outputs of each sub-process in an EB-PBF process? • CQ5: What are the properties of the layer melting strategy used in an EB-PBF slicing sub-process? • CQ6: Which 3D printing machine has been used for an EB-PBF printing process? • CQ7: What types of sensors are utilized in an EB-PBF 3D printing machine? • CQ8: What is the total number of layers used in an EB-PBF printing process? • CQ9: What is the layer thickness used in an EB-PBF printing process? • CQ10: What is the start and end date and time for an PBF-AM process? • CQ11: What is the typical beam power for the energy source used in an EB-BPF printing process? 3.2. PBF-AMP-Onto_Core The first module, PBF-AMP-Onto_Core, models the core concepts and relationships in PBF processes. A visualization of PBF-AMP-Onto_Core is presented in orange in Figure 1. The Powder_Bed_Fusion_Additive_Manufacturing_Process is modeled as a sub-concept of Additive_Manufacturing_Process which in its turn is a sub-concept of Process , and inherits temporal information from that concept. A Process is supervised by at least one Prov:Agent and has a start and an end date and time. In general, a Powder_Bed_Fusion_Additive_Manufacturing_Process has sub-processes which are performed in a specific order: 3D_Model_Design_Process where a 3D model is created, the Slicing_Process where the 3D model is sliced to layers and a digital twin is 1 https://protege.stanford.edu/ 2 https://www.w3.org/TR/prov-o/ 3 https://qudt.org/ created for each layer, the Printing_Process where the actual physical objects are printed, and the Post_Printing_Process where the physical objects undergo various post-processing methods such as cleaning off excess powder and detaching the printed objects from the build plate. Further, the Monitoring_Process is carried out during the printing process to monitor and document each layer, collecting data for future decisions or potential adjustments. The Inspection_And_Quality_Management_Process investigates the printed build using various analysis methods, such as microstructural analysis. In some specific PBF processes some of the sub-processes may be missing. For the first steps in the PBF workflow (part of) File s in different formats are used as input and output for the sub-processes. The printing sub-process has a physical object (Printed_Build ) as output. 3.3. PBF-AMP-Onto_EB PBF-AMP-Onto_EB focuses on a specific kind of PBF, namely EB-PBF. The concepts that are specific for PBF-AMP-Onto_EB are presented in blue in Figure 1. For EB-PBF, the sub-processes xsd:dateTime xsd:dateTime Prov:Activity has start date time has end date time rdfs:subClassOf Process rdfs:subClassOf rdfs:subClassOf rdfs:subClassOf rdfs:subClassOf rdfs:subClassOf rdfs:subClassOf rdfs:subClassOf AM Process Prov:Agent rdfs:subClassOf is supervised by PBF-AM Process is sub-process of is sub-process of is sub-process of is sub-process of is sub-process of is sub-process of Inspection And Quality 3D Model Design Process is predecessor process of Slicing Process is predecessor process of Printing Process is predecessor process of Post-Printing Process is predecessor process of Measurement Process monitors File Format Monitoring Process has output xsd:string For Strategy File Format has output has output printed build inspects printed build has height For Layer has input has output printed build File Format has printing medium uses quality measurement method 3D Model Build rdfs:subClassOf For 3D Model has input printed build rdfs:subClassOf rdfs:subClassOf Prov:Entity rdfs:subClassOf Quality Measurement File Format is used to print Method uses post printing method Layer Digital Twin Printed Build rdfs:subClassOf has file format is represented by rdfs:subClassOf is composed of Post-Printing Method is represented by rdfs:subClassOf File has output Layer Of Printed Build PBF-AMP-Onto_Core EB-PBF rdfs:subClassOf xsd:string xsd:string rdfs:subClassOf EB-PBF is predecessor process of EB-PBF rdfs:subClassOf 3D Model Design Process has status Quality Measurement Method Slicing Process rdfs:subClassOf has particle size distribution xsd:string is predecessor EB-PBF has point distance Infill Scan Strategy has output process of monitors EB-PBF EB-PBF has input EB-PBF has scan speed Monitoring Process Post-Printing Method Printing Process has printing medium EB-PBF xsd:string has strategy name has beam power has slicing strategy has input Metal Powder has beam spot size has scan strategy is represeted by receives data from has dwell time is manufactured from xsd:string xsd:string xsd:string EB-PBF xsd:string xsd:string contributes to Layer Digital Twin is manufactured by xsd:string has infill scan strategy EB-PBF has sensor type has recorded data xsd:string Material xsd:string Slicing Strategy Manufacturer EB-PBF is sub-slicing strategy of has strategy name Scan Strategy xsd:string xsd:string has build plate EB-PBF has beam spot size has address has contour scan strategy is operated by has name 3D Printing Machine Sensor has timeout has target temperature rdfs:subClassOf has scan strategy is composed of xsd:string EB-PBF EB-PBF xsd:string has sensor rdfs:subClassOf Contour Scan Strategy Start Heating Strategy EB-PBF rdfs:subClassOf has beam power xsd:integer has layer thickness Build Cleaning Method has number of repetitions EB-PBF has dwell time rdfs:subClassOf xsd:string 3D Printing Machine xsd:string EB-PBF EB-PBF rdfs:subClassOf Layer Pre-Heating Strategy Support Removal Method has point distance xsd:string rdfs:subClassOf EB-PBF xsd:integer has scan speed Layer Of Printed Build has number of repetitions rdfs:subClassOf xsd:string EB-PBF EB-PBF is manufactured from EB-PBF Freemelt is used to print Build Separation From Build Plate Method xsd:string Layer Post-Heating Strategy Printing Machine is composed of has start heating strategy is composed of EB-PBF EB-PBF Layer Melting Strategy xsd:string has shape EB-PBF Heat Treatment Method xsd:string xsd:string Build Plate EB-PBF is composed of has scaled height has size has rotation angle Geometry Layer Printed Build xsd:string EB-PBF has surface texture Geometry Layer Melting Strategy has output printed build has energy source has thickness is composed of has offset margin has output xsd:string has uniform point distance xsd:string EB-PBF xsd:string Geometry Layer Digital Twin xsd:string EB-PBF is composed of EB-PBF xsd:string is composed of Printed Build Geometry Printed Build has 3D printing method has electron beam wavelength xsd:string has name EB-PBF xsd:string has size Geometry Digital Twin has position xsd:string EB-PBF EB-PBF Energy Source has energy source 3D Printing Method PBF-AMP-Onto_EB Figure 1: PBF-AMP-Onto including the Core and EB modules. of PBF are specialized. We describe here the two most complex sub-processes. An EB-PBF_Slicing_Process has a slicing strategy (EB-PBF_Slicing_Strategy ) and strat- egy for scanning (EB-PBF_Scan_Strategy ). At the beginning of the printing process, the EB-PBF_Start_Heating_Strategy is applied once and defines how to heat the build plate. Each layer is prepared before melting the metal powder as defined in the EB-PBF_Layer_Pre- Heating_Strategy . Further, the EB-PBF_Layer_Melting_Strategy defines the strategy for melting the metal powder spread on the previous layer. Finally, the EB-PBF_Layer_Post- Heating_Strategy guides the heating of the melted layer in different repetitions. Each of these strategies are represented in (part of) File s in different formats. They use an EB- PBF_Energy_Source (an electron beam). Further, they have an EB-PBF_Scan_Strategy which is composed of an EB-PBF_Infill_Scan_Strategy and an EB-PBF_Contour_Scan_Strategy where infill strategies focus on the interior part of a layer, while contour strategies deal with the outer part of a layer. All these strategies are part of the EB-PBF_Layer_Digital_Twin . As different geometries (e.g., different screws) can be printed at the same time and we like to store information and reason about these, we define an EB-PBF_Geometry_Digital_Twin which is composed of EB-PBF_Geometry_Layer_Digital_Twin s which are produced by an EB-PBF_Geometry_Layer_Melting_Strategy . The EB-PBF_Printing_Process uses an EB-PBF_Printing_Machine that allows for cer- tain EB-PBF_Printing_Method s. It uses an EB-PBF_Build_Plate that is heated using the EB- PBF_Start_Heating_Strategy . The process uses EB-PBF_Metal_Powder . The actual printing is performed based on the information in the EB-PBF_Layer_Digital_Twin . The output is a Printed_Build . 4. Use case and evaluation In this section, we describe an example use case where we construct a KG for an EB-PBF experiment and demonstrate how competency questions in Section 3 can be answered using SPARQL queries. 4.1. Use case We used the data from an EB-PBF printing experiment where 13 screws were printed. As printing medium, stainless steel was used. Also the build plate was manufactured from stainless steel. Figure 2a shows a Python file where the first line reads the 3D model in .stl format of a single screw. Then, the locations of the 13 different copies (part1 to part13) on the build plate are defined. Figure 2b shows the 3D model of the 13 screws on the build plate. Once the geometries are located on the build plate, they are sliced into layers using various slicing strategies. Figure 3a shows part of the Python code for the layer melting strategies used to slice each geometry in the experiment. For instance, all 13 parts have a spot size, i.e., the size of the electron beam after passing through the gun, of 1 µm. However, the beam power of part7 is set to 720 kW, while for the other parts it is set to 660 kW. Additionally, each part has a different dwell time, representing the duration the beam stays on a point. There are other parameter settings as well that reflect various settings for the beam power and its movements such as the scan speed (speed of the beam), and point distance (distance between adjacent spots). (a) Parts placement on the build plate. (b) 3D model of the experiment. Figure 2: A build with 13 geometries (parts). (a) Layer melting strategies. (b) Input to printing sub-process. Figure 3: Files used in the EB-PBF process. The layers may have different layer thicknesses. The rotation angle represents the angle which a geometry is rotated. The infill strategies and contour strategies represent the methods for scanning the surface, focusing on the interior part of a layer, and the outer part of the layer, respectively. If no contour strategy is specified, then the infill strategy is also used for the outer part. The number of layers is computed from the layer thickness and the height of the 3D_Model_Build . In addition to the layer melting strategy, each layer needs a pre-heating and post-heating strategy. In our experiment, there is one pre-heating strategy and one post-heating strategy that is used for all layers, respectively. The different strategies contribute to the layer digital twins which are represented in .obp files. All these strategies are combined in Python by domain experts4 . While printing, sensors in the printing machine record data that can be used to generate images from the electrons scattered off the surface which is used to monitor the printing process. We created a KG by instantiating PBF-AMP-Onto_EB with the collected data from the experiment. Figure 4 shows part of this KG. It shows, for example, that build_2024_04_16_Ex- periment is an instance of the PBF-AM_Process concept in PBF-AMP-Onto_Core and has build_2024_04_16_Experiment_SlicingProcess and build_2024_04_16_Experiment_PrintingPro- cess as sub-processes. The build_2024_04_16_Experiment started on 2024-05-31T14:30:00Z and finished on 2024-05-31T23:30:00Z. One of the geometry layer melting strategies (build_2024_04_16_Geometry_layer_melting_Strategy_geometry1) has energy source Electron- 4 https://github.com/wiberganton/obpcreator/tree/main BeamEnergySource1. Moreover, build_2024_04_16_Geometry_layer_melting_Strategy_geom- etry1 has build_2024_04_16_Scan_Strategy_geometry1 as the EB-PBF_Scan_Strategy that has Infill_Strategy_2 and Contour_Strategy_2 as EB-PBF_Infill_Scan_Strategy and EB- PBF_Contour_Scan_Strategy , respectively. Infill_Strategy_2 has a beam power of 660 kW, and a beam scan speed of 1700000 µm/s with dwell time 570000 ns. build_2024_04_16_Geometry_layer_melting_Strategy_geometry7 EB-PBF Energy Source EB-PBF Scan Strategy "1µm"^^xsd:string "660kw"^^xsd:string EB-PBF Infill Scan Strategy has beam power build_2024_04_16_Geometry_layer_melting_Strategy_geometry11 rdf:type rdf:type has beam spot size "point_random"^^xsd:string "0°"^^xsd:string rdf:type rdf:type ElectronBeamEnergySource1 build_2024_04_16_Scan_Strategy_geometry1 rdf:type has infill scan strategy has strategy name has energy source Infill_Strategy_2 has rotation angle has scan strategy has scan speed has dwell time EB-PBF Geometry Layer Melting Strategy rdf:type build_2024_04_16_Geometry_layer_melting_Strategy_geometry1 "1700000µm/s"^^xsd:string "570000ns"^^xsd:string "1µm"^^xsd:string "660kw"^^xsd:string "45mm"^^xsd:string has scaled height "1700000µm/s"^^xsd:string has contour scan strategy has beam power EB-PBF has uniform point distance has beam spot size Slicing Process rdfs:subClassOf is composed of "570000ns"^^xsd:string Slicing Process has scan speed "True"^^xsd:string has dwell time has scan strategy EB-PBF Contour Scan Strategy rdf:type EB-PBF 3D Model Build rdf:type Prov:Agent Slicing Strategy Contour_Strategy_2 "point_random"^^xsd:string rdf:type rdf:type has strategy name "2024-05-31T14:30:00Z"^^xsd:dateTime EB-PBF swemac_screw_stl stl rdf:type Print_2024_04_16_Slicing_Srategy Layer Melting Strategy "2024-05-31T23:30:00Z"^^xsd:dateTime is represented by has file format Anton-Wiberg has height is sub slicing strategy of swemac_screw.stl has start date time has slicing strategy rdf:type has input has end date time "45mm"^^xsd:string "0.07mm"^^xsd:string rdf:type is supervised by build_2024_04_16_Experiment has layer thickness PBF-AM Process rdf:type build_2024_04_16_layer_melting_Strategy is sub process of File build_2024_04_16_Experiment_SlicingProcess contributes to build_2024_04_16/layer0_obp Printing Process is sub process of rdf:type Material is represented by rdfs:subClassOf has_input rdf:type "new"^^xsd:string EB-PBF Printing Process rdf:type build_2024_04_16_Experiment_PrintingProcess rdf:type has output printed build Stainless_Steel layer0.obp is operated by has status EB-PBF Printed Build Print_2024_04_16_Printed_Build has printing medium is manufactured from Layer Digital Twin Manufacturer has file format rdfs:subClassOf Print_2024_04_16_Metal_Powder IEI_Freemelt_Printing_Machine rdf:type rdf:type is manufactured by obp rdf:type rdfs:subClassOf EB-PBF Printed Build rdf:type Manufacturer1 has name "Manufacturer1"^^xsd:string Layer Digital Twin EB-PBF 3D Printing Machine rdfs:subClassOf EB-PBF Freemelt Printing Machine EB-PBF Metal Powder rdfs:subClassOf Prov:Entity Figure 4: Part of the KG for the printing experiment. 4.2. SPARQL query examples To show the use and the feasibility of our approach, we implemented SPARQL queries based on competency questions (see Section 3.1). To execute these queries, we used blazegraph5 which is an ultra high-performance graph database supporting RDF/SPARQL APIs. As examples, we show the SPARQL queries for the competency questions CQ1, CQ7, CQ8, and CQ10 in Tables 1, 2, 3, and 4 respectively. The retrieved results for each query are pre- sented in Table 5. For example, executing the SPARQL query for CQ1 in Table 1 returns the result that the printed build in build_2024_04_16_Experiment_PrintingProcess has used Stain- less_Steel as the metal powder. The result of the SPARQL query CQ7 (Table 2) indicates that the IEI_Freemelt_Printing_Machine is equipped with four temperature sensors (Temp_Sensor_1 to Temp_Sensor_4). The SPARQL query for CQ8 (Table 3) returns that there are five layers for the EB-PBF printing process in the KG. The SPARQL query for CQ10 (Table 4) reveals the start and end date and time of build_2024_04_16_Experiment. We note that all CQs could be formulated using PBF-AMP-Onto_EB. Table 6 shows the concepts and relationships used for each CQ. 5 https://github.com/blazegraph/database/releases/tag/BLAZEGRAPH_2_1_6_RC Table 1 An example SPARQL query CQ1 (What is the material used for each printed build in an EB-PBF printing process?). 1 PREFIX pbfampocore: 2 PREFIX pbfampoeb: 3 PREFIX rdf: 4 SELECT ?printing_process ?printed_build ?material 5 WHERE { 6 ?printed_build rdf:type pbfampoeb:Electron_Beam_Powder_Bed_Fusion_Printed_Build. 7 ?printing_process rdf:type pbfampoeb:Electron_Beam_Powder_Bed_Fusion_Printing_Process. 8 ?printing_process pbfampoeb:has_output_printed_build ?printed_build. 9 ?metal_powder rdf:type pbfampoeb:Electron_Beam_Powder_Bed_Fusion_Metal_Powder. 10 ?printing_process pbfampocore:has_printing_medium ?metal_powder. 11 ?metal_powder pbfampoeb:is_manufactured_from ?material. } Table 2 An example SPARQL query for CQ7 (What types of sensors are utilized in an EB-PBF 3D printing machine?). 1 PREFIX pbfampocore: 2 PREFIX pbfampoeb: 3 PREFIX rdf: 4 PREFIX rdfs: 5 SELECT ?printing_machine ?sensor ?sensor_type 6 WHERE { 7 ?printing_machine_subclass rdfs:subClassOf 8 pbfampoeb:Electron_Beam_Powder_Bed_Fusion_3D_Printing_Machine. 9 ?printing_machine rdf:type ?printing_machine_subclass. 10 ?printing_machine pbfampoeb:has_sensor ?sensor. 11 ?sensor pbfampoeb:has_sensor_type ?sensor_type. } 5. Conclusion In this paper we developed a modular ontology for PBF with a specialized module for EB-PBF. We showed the use of the ontology for describing and querying information on EB-PBF processes. In the future we will propose a standardized way to (store and) integrate information from different sources regarding PBF processes to enable semantic and integrated access to these different sources based on our ontology. We will take inspiration from our previous work on integrating materials computation databases [17, 18]. This will be the basis for advanced design and analysis tools that guide the design and provide quality assurance of AM products. Another important task will be to align our ontology with other ontologies. For instance, we will investigate the connection between our process concept and PMDco’s [7] process concept, and our material concept with, e.g., the material concept in EMMO (Elementary Multiperspective Material Ontology)6 . In [10] several attributes are defined that can be connected to our ontology. 6 https://github.com/emmo-repo/EMMO Table 3 An example SPARQL query for CQ8 (What is the total number of layers used in an EB-PBF printing process?). 1 PREFIX pbfampocore: 2 PREFIX pbfampoeb: 3 PREFIX rdf: 4 SELECT ?printing_process (COUNT(?layer_digital_twin) AS ?numberofLayers) 5 WHERE { 6 ?printing_process rdf:type pbfampoeb:Electron_Beam_Powder_Bed_Fusion_Printing_Process. 7 ?printing_process pbfampocore:has_input ?layer_digital_twin. 8 ?layer_melting_strategy rdf:type pbfampoeb:Electron_Beam_Powder_Bed_Fusion_Layer_Melting_Strategy. 9 ?layer_melting_strategy pbfampoeb:contributes_to ?layer_digital_twin. } 10 GROUP BY ?printing_process Table 4 An example SPARQL query for CQ10 (What is the start and end date and time for an PBF-AM process?). 1 PREFIX pbfampocore: 2 PREFIX pbfampoeb: 3 PREFIX rdf: 4 SELECT ?process ?startTime ?endTime 5 WHERE { 6 ?process rdf:type pbfampocore:Powder_Bed_Fusion_Additive_Manufacturing_Process. 7 ?process pbfampocore:has_start_date_time ?startTime. 8 ?process pbfampocore:has_end_date_time ?endTime. } For this alignment, we will need to investigate possible ontological commitments. We will also reuse an ontologies for quantities. Further, we will investigate other AM processes and extend the ontology with new modules accordingly. Acknowledgments This work has been financially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) and the Wallenberg Initiative Materials Science for Sustainability (WISE) Joint Call for pre-projects, the EU Horizon project Onto-DESIDE (Grant Agreement 101058682), the Swedish e-Science Research Centre (SeRC), and the Swedish National Graduate School in Computer Science (CUGS). Table 5 Results of the SPARQL queries for CQ1, CQ7, CQ8, and CQ10 presented in Tables 1, 2, 3, and 4. CQ Result of the SPARQL query CQ1 CQ7 CQ8 CQ10 Table 6 Coverage of concepts and relationships in CQs from Section 3.1. CQ Relevant Concepts Relevant Relationships CQ1 EB_PBF_Printed_Build, EB_PBF_Printing_Process, has_output_printed_build, has_printing_medium, EB_PBF_Metal_Powder,Material is_manufactured_from CQ2 EB_PBF_Printing_Process, EB_PBF_Metal_Powder, has_printing_medium, is_manufactured_by Manufacturer CQ3 PBF_AM_Process is_sub_process_of CQ4 PBF_AM_Process is_sub_process_of, has_input,has_output, has_in- put_printed_build, has_output_printed_build CQ5 EB_PBF_Slicing_Process, EB_PBF_Slicing_Strategy, has_slicing_strategy, is_sub_slicing_strategy_of, con- EB_PBF_Layer_Melting_Strategy, EB_PBF_In- tributes_to, has_layer_thickness, has_scan_strategy, fill_Scan_Strategy, EB_PBF_Contour_Scan_Strategy, has_infill_scan_strategy, has_contour_scan_strategy, EB_PBF_Energy_Source has_energy_source CQ6 EB_PBF_3D_Printing_Machine, EB_PBF_Print- is_operated_by ing_Process CQ7 EB_PBF_3D_Printing_Machine,EB_PBF_3D_Print- rdfs:subClassOf, has_sensor, has_sensor_type ing_Machine_Sensor CQ8 EB_PBF_Printing_Process, EB_PBF_Layer_Digi- has_input,contributes_to tal_Twin, EB_PBF_Layer_Melting_Strategy CQ9 EB_PBF_Printing_Process, EB_PBF_Layer_Digi- has_input,contributes_to, has_layer_thickness tal_Twin CQ10 EB_PBF_Process has_start_date_time, has_end_date_time CQ11 EB_PBF_Printing_Process, EB_PBF_Slicing_Pro- is_sub_process_of, has_scan_strategy, has_in- cess, EB_PBF_Process,EB_PBF_Scan_Strategy, fill_scan_strategy, has_contour_scan_strategy, EB_PBF_Infill_Scan_Strategy, EB_PBF_Con- has_beam_power tour_Scan_Strategy References [1] S. 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