A core reference ontology for steelmaking process knowledge modelling and information management

Cao, Qiushi; Beden, Sadeer; Beckmann, Arnold

doi:10.1016/j.compind.2021.103574

Cited by 16 publications

(7 citation statements)

References 27 publications

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“…Numerous research studies have been carried out to develop ontological models for the steel industry. These inquiries cover a range of topics including knowledge modeling and information management in the steelmaking process [20], planning and scheduling in primary steel production [21], scheduling and control decision frameworks for process industries [22], and supply chain decision support for steel manufacturers [23]. However, the focus of the majority of these studies is primarily on supply chain and scheduling issues, with limited attention given to normal production tasks.…”

Section: The Application Of Ikgs In the Bfipmentioning

confidence: 99%

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Huang,

Yang

2024

Electronics

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Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry.

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Section: The Application Of Ikgs In the Bfipmentioning

confidence: 99%

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Huang,

Yang

2024

Electronics

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“…For example, Dinar et al [10] proposed an ontology for Design for Additive Manufacturing (DFAM) to model 3D printed components. In the steel manufacturing industry, Common Reference Ontology for Steelmaking (CROS) was proposed as a solution to the problem of semantic interoperability [11]. One study presented an OWL-based manufacturing resource capability ontology (MaRCO) [12], which aims to describe the capabilities of manufacturing resources in a common formal resource model.…”

Section: A Ontologies In Manufacturingmentioning

confidence: 99%

Ontology-Based Feedback to Improve Runtime Control for Multi-Agent Manufacturing Systems

Lim,

Pfeiffer,

Ocker

et al. 2023

2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)

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Improving the overall equipment effectiveness (OEE) of machines on the shop floor is crucial to ensure the productivity and efficiency of manufacturing systems. To achieve the goal of increased OEE, there is a need to develop flexible runtime control strategies for the system. Decentralized strategies, such as multi-agent systems, have proven effective in improving system flexibility. However, runtime multi-agent control of complex manufacturing systems can be challenging as the agents require extensive communication and computational efforts to coordinate agent activities. One way to improve communication speed and cooperation capabilities between system agents is by providing a common language between these agents to represent knowledge about system behavior. The integration of ontology into multi-agent systems in manufacturing provides agents with the capability to continuously update and refine their knowledge in a global context. This paper contributes to the design of an ontology for multi-agent systems in manufacturing, introducing an extendable knowledge base and a methodology for continuously updating the production data by agents during runtime. To demonstrate the effectiveness of the proposed framework, a case study is conducted in a simulated environment, which shows improvements in OEE during runtime.

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“…Ontology provides a shared conceptualization to capture the knowledge and meaning [6], which are essential as a common base between the information requester and provider [7] for domain understanding, data understanding, data integration, and further analytics applications such as data inspection, information summary, diagnostics. It has been widely applied in various industrial areas, such as steel-making process [8], robot collaboration [9], 4D printing [10], additive manufacturing [11], etc.…”

Section: Introductionmentioning

confidence: 99%

Semantic Modeling, Development and Evaluation for the Resistance Spot Welding Industry

et al. 2023

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The ongoing industrial revolution termed Industry 4.0 (I4.0) has borne witness to a series of profound changes towards increasing smart automation, particularly in the industrial sectors of automotive, aerospace, manufacturing, etc. Automatic welding, a widely applied manufacturing process in these domains, is not an exception of these changes. One type of automatic welding, Resistance Spot Welding (RSW), lies in the center of this work. Large volumes and varieties of RSW data are being generated, thanks to the technologies behind I4.0. To address the associated data challenges, ontologies are essential in various aspects: integrating data sources, enhancing interoperability, and unifying knowledge etc. However, there have been limited studies around the semantic modelling of Resistance Spot Welding: Existing ontologies have overlooked some crucial concepts, such as an operation-centric view, welding software, and welding electrodes, which are essential for the monitoring of sensor measurements as well as the status of machine components (e.g., electrode wear). Additionally, current ontologies are not publicly available (to the best of our knowledge), and therefore cannot be accessed by other users. Such a lack of availability often requires that users build their own ontologies from scratch. In this paper, we propose our RSW ontology (RSWO) a to formalize knowledge in the RSW domain. It combines three sources of knowledge: extensive discussions with Bosch welding experts; reusing terminologies following ISO-14327 and ISO-14373 standards; and existing established ontologies. We have evaluated RSWO on real-world data from monitoring welding quality at Bosch in Germany, using Competency Questions, FAIR principles, OOPS!, and OntoMetrics.

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A core reference ontology for steelmaking process knowledge modelling and information management

Cited by 16 publications

References 27 publications

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Ontology-Based Feedback to Improve Runtime Control for Multi-Agent Manufacturing Systems

Semantic Modeling, Development and Evaluation for the Resistance Spot Welding Industry

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