Systematic knowledge modeling and extraction methods for manufacturing process planning based on knowledge graph

Wen, Peihan; Ma, Yan; Wang, Ruiquan

doi:10.1016/j.aei.2023.102172

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…Zhang et al [40] devised a method to construct a metallic materials knowledge graph (MMKG) using semantic algorithms and ontology, leveraging DBpedia and Wikipedia to demonstrate excellence in performance. Wen et al [41] introduced a process knowledge graph construction method, applying ontology and a pattern-guided bootstrapping framework with a two-tiered filtering mechanism to prevent overfitting.…”

Section: Knowledge Acquisition For Manufacturing Processes Based On S...mentioning

confidence: 99%

A Review and Prospects of Manufacturing Process Knowledge Acquisition, Representation, and Application

Wu,

Liang

2024

Machines

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The evolution of knowledge acquisition and representation in manufacturing technologies is vital for translating complex manufacturing data into actionable insights and advancing a comprehensive knowledge framework. This framework is pivotal in driving innovation and efficiency in intelligent manufacturing. This review aggregates recent research on knowledge acquisition and representation within the manufacturing process, addressing existing challenges and mapping potential future developments. It includes an analysis of 123 papers that focus on harnessing advanced intelligent analytics to extract operationally relevant knowledge from the extensive datasets typical in manufacturing environments. The narrative then examines the methodologies for constructing models of knowledge in manufacturing processes and explores their applications in manufacturing principles, design, management, and decision-making. This paper highlights the limitations of current technologies and projects emerging research avenues in the acquisition and representation of process knowledge within intelligent manufacturing systems, with the objective of informing future technological breakthroughs.

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Section: Knowledge Acquisition For Manufacturing Processes Based On S...mentioning

confidence: 99%

A Review and Prospects of Manufacturing Process Knowledge Acquisition, Representation, and Application

Wu,

Liang

2024

Machines

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“…A knowledge graph (KG) serves as a structured representation of information using nodes and edges to illustrate relationships among entities, thus forming a comprehensive knowledge network. In the cases of chemical engineering, KGs depict process details such as chemical substances, equipment, and workflows, along with their interconnections. , Building upon this foundation, Daoutidis et al , abstracted dynamic system processes into network graphs and further decomposed them into several modules using community detection, elucidating the process knowledge and structural relationships within dynamic systems. Analogous to the KG, the causal relationship graph emphasizes causal connections among key process variables .…”

Section: Introductionmentioning

confidence: 99%

Causality Analysis of the Green Ammonia Synthesis Process Using the Convergent Cross Mapping Algorithm

Yang,

Li,

Zhang

et al. 2024

Ind. Eng. Chem. Res.

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Ammonia synthesis has been gradually altered from the gray process (using fossil fuels as hydrogen sources) to the green process (directly or indirectly using water electrolytic cells as hydrogen sources powered by renewable energy), with the motivation of sustainable development and carbon neutrality. The fluctuating nature of renewable energy and the location mismatch between power plants and the production complex make gray ammonia production roadmaps likely to fail or embrace the change. Establishing knowledge graphs in the form of causal relationship network diagrams will help enterprise decision-makers and engineers better understand the process and generate correct production operations and scheduling. In this study, a chemical engineeringinformed method is introduced to generate causal networks of multiple load conditions for green ammonia production. Expert knowledge of chemical engineering is embedded in the determination of the existence and corresponding time delay of the causal relationships of variable pairs. In an industrial case study, the skeletal knowledge graphs and evolution of the control mechanisms were identified in a comparison of the derived causal networks. Extended applications are expected with further integration of controlling theory and algorithms.

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Hierarchical construction and application of machining domain knowledge graph based on as-fabricated information model

Bao,

Zheng,

Dai

2024

Advanced Engineering Informatics

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Systematic knowledge modeling and extraction methods for manufacturing process planning based on knowledge graph

Cited by 4 publications

References 38 publications

A Review and Prospects of Manufacturing Process Knowledge Acquisition, Representation, and Application

A Review and Prospects of Manufacturing Process Knowledge Acquisition, Representation, and Application

Causality Analysis of the Green Ammonia Synthesis Process Using the Convergent Cross Mapping Algorithm

Hierarchical construction and application of machining domain knowledge graph based on as-fabricated information model

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