Realizing an RDF-Based Information Model for a Manufacturing Company – A Case Study

Petersen, Niklas; Halilaj, Lavdim; Grangel-González, Irlán; Lohmann, Steffen; Lange, Christoph; Auer, Sören

doi:10.1007/978-3-319-68204-4_31

Cited by 21 publications

(10 citation statements)

References 19 publications

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“…However, the digitization of manufacturing systems requires the efficient connection of all assets within the production network and the seamless exchange of data between assets. It also requires the development of information models that can accurately describe all assets and information sources to enable semantic integration and interoperable exchange of data between all assets [ 221 ]. The development of robust data/information models is critical to realizing some of the more creative goals of the Factory of the Future, such as plug-and-play automation of production modules, easy reconfigurability of production systems to cater to small batch production of customized products, and self-organization of the production line.…”

Section: Realizing the Promise Of Industry 40 Through The Digitizatio...mentioning

confidence: 99%

A Primer on the Factories of the Future

Anumbe

Saidy

Harik

2022

Sensors

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In a dynamic and rapidly changing world, customers’ often conflicting demands have continued to evolve, outstripping the ability of the traditional factory to address modern-day production challenges. To fix these challenges, several manufacturing paradigms have been proposed. Some of these have monikers such as the smart factory, intelligent factory, digital factory, and cloud-based factory. Due to a lack of consensus on general nomenclature, the term Factory of the Future (or Future Factory) has been used in this paper as a collective euphemism for these paradigms. The Factory of the Future constitutes a creative convergence of multiple technologies, techniques, and capabilities that represent a significant change in current production capabilities, models, and practices. Using the semi-narrative research methodology in concert with the snowballing approach, the authors reviewed the open literature to understand the organizing principles behind the most common smart manufacturing paradigms with a view to developing a creative reference that articulates their shared characteristics and features under a collective lingua franca, viz., Factory of the Future. Serving as a review article and a reference monograph, the paper details the meanings, characteristics, technological framework, and applications of the modern factory and its various connotations. Amongst other objectives, it characterizes the next-generation factory and provides an overview of reference architectures/models that guide their structured development and deployment. Three advanced communication technologies capable of advancing the goals of the Factory of the Future and rapidly scaling advancements in the field are discussed. It was established that next-generation factories would be data rich environments. The realization of their ultimate value would depend on the ability of stakeholders to develop the appropriate infrastructure to extract, store, and process data to support decision making and process optimization.

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Section: Realizing the Promise Of Industry 40 Through The Digitizatio...mentioning

confidence: 99%

A Primer on the Factories of the Future

Anumbe

Saidy

Harik

2022

Sensors

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“…As indicated earlier, several industry scenarios require populating models from a wide variety of unstructured to semiand structured documents; in many of these scenarios, both domain modeling and model population stages use such sources of information. This activity is also referred to as information modeling especially, in the manufacturing domain, and in cyber-physical systems, and IoT systems and smart digital factories created for any business domain [37,40,57,58].…”

Section: Ai-driven Model Populationmentioning

confidence: 99%

AI-driven streamlined modeling: experiences and lessons learned from multiple domains

et al. 2022

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Model-driven technologies (MD*), considered beneficial through abstraction and automation, have not enjoyed widespread adoption in the industry. In keeping with the recent trends, using AI techniques might help the benefits of MD* outweigh their costs. Although the modeling community has started using AI techniques, it is, in our opinion, quite limited and requires a change in perspective. We provide such a perspective through five industrial case studies where we use AI techniques in different modeling activities. We discuss our experiences and lessons learned, in some cases evolving purely modeling solutions with AI techniques, and in others considering the AI aids from the beginning. We believe that these case studies can help the researchers and practitioners make sense of various artifacts and data available to them and use applicable AI techniques to enhance suitable modeling activities.

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“…Chhim et al presented a practical product design and manufacturing process based ontology for manufacturing knowledge reuse [35]. Petersen et al established an RDF-based information model for manufacturing enterprises to describe production resources centered on processing equipment and realized the data exchange and sharing among factories [36].…”

Section: B Manufacturing Knowledge Managementmentioning

confidence: 99%

Social Production System: A Three-Layer Smart Framework for Implementing Autonomous Human-Machine Collaborations in a Shop Floor

2021

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The continuous fast-changing of production requirements and the development of advanced information technology have promoted the development of the production system in a more knowledgeintensive, resources-decentralized, and autonomous collaborative manner. In order to provide guidelines for the smart transformation of the production shop floor, this paper proposes a smart production system with a three-layer framework defined as the social production system (Social-PS) by integrating cyber-physical system (CPS), knowledge graph technology, and production-event driven model. The concept of smart resources (include smart operator, smart machine, smart workpiece, and smart gateway), social sensor, the unified information model is clarified to construct the physical carrier of Social-PS. The unified knowledge repository's overall construction method based on the knowledge graph is elaborated to provide taskresource-associated heterogeneous production data sharing and management for human-machine collaboration. Subsequently, to support the production dynamic monitoring and interaction, a productionevent and active knowledge indexing model is proposed. Furthermore, to validate the proposed Social-PS framework, a software and hardware integrated prototype system is implemented to demonstrate the knowledge-driven human-machine collaboration of the production process, which provides a basis for realizing smart and autonomous production collaboration.

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Realizing an RDF-Based Information Model for a Manufacturing Company – A Case Study

Cited by 21 publications

References 19 publications

A Primer on the Factories of the Future

A Primer on the Factories of the Future

AI-driven streamlined modeling: experiences and lessons learned from multiple domains

Social Production System: A Three-Layer Smart Framework for Implementing Autonomous Human-Machine Collaborations in a Shop Floor

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