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Applying Knowledge Bases to Make Factories Smarter at – Automatisierungstechnik 2019; 67(6): 504–517 Survey Felix Ocker*, Christiaan J. J. Paredis and Birgit Vogel-Heuser Applying knowledge bases to make factories smarter Anwendung von Wissensbasen zur Steigerung der Intelligenz von Fabriken https://doi.org/10.1515/auto-2018-0138 und Produktion sowie generische Top-Level-Ontologien. Received November 19, 2018; accepted February 7, 2019 Die Anwendung solcher generischen Ontologien ermög- licht die Gewinnung neuer Erkenntnisse durch die Inte- Abstract: Knowledge Bases (KBs) enable engineers to cap- gration des Wissens verschiedener Domänen, Interessens- ture knowledge in a formalized way. This formalization gruppen und Unternehmen. Um die Lücke zwischen Top- allows us to combine knowledge, thus creating the ba- Level-Ontologien und bestehenden domänenspezifschen sis for smart factories while also supporting product and KBs zu schließen, führen wir eine zwischengelagerte Ebe- production system design. Building comprehensive and ne, die “Intermediate Engineering Ontology” (IEO), ein. reusable KBs is still a challenge, though, especially for knowledge-intensive domains like engineering and pro- Schlagwörter: Intelligente Fabriken, Intelligentes Engi- duction. To cope with the sheer amount of knowledge, en- neering, Wissensbasen gineers should reuse existing KBs. This paper presents a comprehensive overview of domain-specifc KBs for pro- duction and engineering, as well as generic top-level on- 1 Introduction tologies. The application of such top-level ontologies of- fers new insights by integrating knowledge from various In a world that continues to become more interlinked, we domains, stakeholders, and companies. To bridge the gap collect an ever increasing amount of information [1]. This between top-level ontologies and existing domain KBs, we impacts complex and knowledge-intensive professions es- introduce an Intermediate Engineering Ontology (IEO). pecially, such as engineering and production. By lever- Keywords: Smart factories, smart engineering, knowledge aging the available information, unprecedented synergies bases can be achieved that help to build smart factories and sup- port smarter engineering, too. Zusammenfassung: Wissensbasen (KBs) ermöglichen es More specifcally, Knowledge-Based Systems (KBSs) Ingenieuren ihr Wissen zu formalisieren. Diese Formali- support engineers by making information about previous sierung wiederum ermöglicht es Wissen zu bündeln und designs available, and by providing automatic feasibility damit die Grundlage für intelligente Produktionssysteme feedback for new designs. Such feasibility feedback can zu schafen. Gleichzeitig können die KBs auch die Ent- help to integrate: diverse disciplines, e. g., mechanical and wicklung von Produkten und Produktionssystemen unter- software engineering; diferent viewpoints along the prod- stützen. Der Schafung umfassender und wiederverwend- uct development process, e. g., product and process de- barer KBs ist jedoch eine Herausforderung, insbesondere sign; and companies and their suppliers. Additionally, the für wissensintensive Bereiche wie Entwicklung und Pro- Knowledge Base (KB) can also serve as a basis for opti- duktion. Um den enormen Wissensbestand dieser Berei- mization, if several feasible designs exist. The accumu- che nutzen zu können, sollten Ingenieure auf bestehende lated knowledge can also be used later on in the develop- KBs zurückgreifen. Dieser Beitrag gibt einen umfassenden ment process. Parts of the developed KBs can be reused Überblick über domänenspezifsche KBs für Entwicklung in agent based production systems, and KBSs provide the means to efciently reconfgure entire plants. *Corresponding author: Felix Ocker, Technical University of Munich, However, the beneft from the information gathered is Munich, Germany, e-mail: [email protected] limited unless it is formalized and combined. Combining Christiaan J. J. Paredis, Clemson University, Clemson, USA, e-mail: [email protected] heterogeneous information from diferent domains, dif- Birgit Vogel-Heuser, Technical University of Munich, Munich, ferent stakeholders, and diferent companies still poses Germany, e-mail: [email protected] a major challenge. To address this challenge within the Open Access. © 2019 Ocker et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 Public License. Bereitgestellt von | Technische Universität München Angemeldet Heruntergeladen am | 19.03.20 10:14 F. Ocker et al., Applying knowledge bases to make factories smarter | 505 Figure 1: myJoghurt demonstrator (left) and functional process description of the yogurt production process (right). engineering and production domain, we compare various glasses with liquid and solid parts. The detailed process of established knowledge bases such as MAnufacturing Se- the yogurt production and flling is described graphically mantic’s ONtology (MASON) [2] and Ontology for Com- via a functional process description in Figure 1. puter Aided Process Engineering (OntoCAPE) [3] and an- First, we want to support engineers throughout the alyze their compatibility. Also, we assess various generic, design process by providing feasibility feedback and au- so-called top-level, ontologies regarding their suitability tomating the production planning. This is expressed by for the engineering and production domains. Based on this the two questions “CQ1: Is this product feasible?” and analysis, we derive an Intermediate Engineering Ontology “CQ2: In which order should the given resources execute (IEO) that allows us to combine domain-specifc knowl- the process steps?”. In the case of the myJoghurt demon- edge bases. Additionally, we align the IEO with the top- strator, the designer might want to check whether a cer- level Descriptive Ontology for Linguistic and Cognitive En- tain chocolate ball fts through the dispenser. There might gineering (DOLCE) [4] to ensure that the knowledge base also be a restriction that the glass should always be flled is well-formed and can be reused easily. With this paper, with yogurt frst. Additionally, interdisciplinary informa- we contribute to the recent research that supports mov- tion should be checked for inconsistencies (“CQ3: Which ing away from separate data silos to integrated knowledge inconsistencies exist within the product’s specifcation?”). bases. This can be as simple as checking whether the combined volume of the yogurt and, e. g., chocolate balls fts into the desired glass. Second, the KB considers supply chain plan- 2 Applications ning, so that the capabilities of suppliers can be leveraged. Hence, with the KB, designers should be able to answer the questions “CQ4: Which supplier can provide this sub- The reusability of KBs can be increased by clarifying their product?” and “CQ5: Which supplier has sufcient capac- purpose. This can be achieved by formulating competency questions [5, 6]. Competency questions are those ques- ity to deliver in time?”. In the case of the yogurt produc- tions that a KB should be able to answer. tion process, these two questions could be asked for choco- The KB presented in this paper is designed for four ap- late balls, which are procured from a supplier. Third, the plications, each refned by competency questions. These KB should also be usable for agent-based Cyber-Phyiscal competency questions are made more accessible through Production Systems. Such autonomous but cooperative the example of the myJoghurt demonstrator,1 presented in agents require individual KBs, which should be compati- Figure 1. The demonstrator consists of a logistics part and ble. Only then can they answer questions like “CQ6: Which a procedural part. The logistics part comprises a storage resource is available to process this product?”. That way, unit, a robot, and a conveyor system, while the procedural the myJoghurt demonstrator can autonomously decide at part includes the equipment necessary to produce yogurt which flling station the glass will be flled with yogurt. and two flling stations. Each of the flling stations can fll Also, in case of a local conveyor malfunction, the demon- strator could also fnd a new route through the logistics system. Fourth, the KB serves as information storage. This 1 http://i40d.ais.mw.tum.de/ last retrieved: February 12, 2019. is expressed in the competency question “CQ7: How did we Bereitgestellt von | Technische Universität München Angemeldet Heruntergeladen am | 19.03.20 10:14 506 | F. Ocker et al., Applying knowledge bases to make factories smarter Figure 2: Competency questions throughout the product lifecycle, adapted from [7]. design previous plants?”. That information can be used in 3.1 Domain-specifc knowledge bases two ways. On the one hand, the status of a plant can be rep- resented, making it easier to maintain and possibly retroft Knowledge bases in engineering and production can be it. E. g., designers can more easily check in advance if a grouped into reference KBs and application KBs. While new sensor model can be used to replace an older one that reference KBs serve as textbook-like collections of knowl- is no longer available. Additionally, knowledge of previous edge, application KBs are tailored to specifc use cases. designs can be reused when a similar plant is designed. Four reference KBs from the manufacturing domain The competency questions show that the desired KB are MAnufacturing Semantic’s ONtology (MASON), Man- is relevant and usable throughout the entire lifecycle of ufacturing Core Concepts Ontology (MCCO), Semantis- consumer
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