A metrics-based meta-learning model with meta-pretraining for industrial knowledge graph construction

Liu, Yangshengyan; Gu, Fu; Wu, Yicheng; Gu, Xiaolei; Guo, Jianfeng

doi:10.1016/j.compind.2022.103753

Cited by 10 publications

(2 citation statements)

References 68 publications

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“…Song et al [361] presented an enterprise KG that comprises supply chain relations to satisfy semantic information retrieval. Liu et al [362] emphasized the use of the proposed meta-learning model for iKG construction which comprises various few-shot data from supply chain networks and production lines. However, constructing and maintaining multifaceted supply chains can be challenging, especially since sharing data within KGs can pose risks to data privacy and security across multi-echelon SC networks.…”

Section: Industrial Knowledge Graph-enabled Value Chain Analysismentioning

confidence: 99%

“…As an emerging technology, industrial knowledge graphs (iKGs) are utilized to integrate and represent domain-specific knowledge and conduct graph reasoning to infer and learn new relations, correlations, and topology [362]. Here, graph autoencoder (GAE) is well suited to tackle the above-mentioned challenges due to its capabilities to learn the content and topology information and reconstruct complex network data.…”

Section: Overviewmentioning

confidence: 99%

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Graph-enabled digital twins for intelligent product lifecycle management: a multi-dimensional approach to design, manufacturing, and supply chain transformation

Lim

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In today's increasingly dynamic business landscape, industries are faced with global headwinds, intensified competition, and increased volatility driven by factors such as increased demand for tailored products and solutions as well as geopolitical, economic, and climate disruptions.To address these challenges and remain competitive, the product lifecycle management (PLM) paradigm, which allows companies to be in control of their products and services across the lifecycle, has gained attention from industries. This cross-functional approach, enabled by Industry 4.0 information and communication technology (ICT) enablers such as digital twins (DT) and knowledge graphs (KGs), can elevate existing capabilities for customizability and manufacturing resilience. This thesis explores the role of DT and KG in enhancing PLM capabilities, integrating multiple lifecycle phases with emphasis on product design, manufacturing, and supply chain management (SCM). The adoption of these transformative technologies has the potential to enhance existing mass customization strategies such as the product family (PF) approach and Smart Product-Service Systems (Smart PSS), paving the way for intelligent decision support systems. Applicable to a wide range of industries, the use of DT systems with KG as a computational driver can support stakeholders in improving operational efficiencies, and responsiveness for disruption management. The main contributions of the thesis can be categorized into five research areas highlighted below:1) Establishing an environment-based context-aware DT system to enhance PF design and optimization. To overcome the challenges of identifying optimal product configurations in the planning phase and overcoming user requirement changes in the usage phase, a context-aware Abstract 10 DT system incorporating real-world environment aspects is proposed to aid PF reconfiguration and redesign. A novel benchmark and interacting mechanism automatically identifies feasible PF modules, reducing the need for costly and bias-prone expert recommendations, as demonstrated by a case study on tower crane planning and deployment.2) Developing DT-as-a-Service to advance service-oriented digital manufacturing. Based on the flexibility and versatility of DT in manufacturing processes, these systems are pivotal towards customized production systems. A four-tier technology stack is proposed to construct DT systems using a modular approach and leverages advanced computational methodologies and tools such as extended reality (XR) to support a wider range of shop floor manufacturing processes. Here, DT systems are used as a fundamental to enable Smart PSS and circular economy paradigms to drive sustainability and customizability efforts. These multi-faceted capabilities are featured in two use cases, additive manufacturing, and gearbox assembly line. 3) Enabling causal inference for maintenance operations using cognitive digital twins (CDTs).The intricate nature of large production systems results in challenges pertaining to the root ca...

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Section: Industrial Knowledge Graph-enabled Value Chain Analysismentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Graph-enabled digital twins for intelligent product lifecycle management: a multi-dimensional approach to design, manufacturing, and supply chain transformation

Lim

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Cognitive manufacturing: definition and current trends

El Kalach,

Yousif,

Wuest

et al. 2024

J Intell Manuf

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Manufacturing systems have recently witnessed a shift from the widely adopted automated systems seen throughout industry. The evolution of Industry 4.0 or Smart Manufacturing has led to the introduction of more autonomous systems focused on fault tolerant and customized production. These systems are required to utilize multimodal data such as machine status, sensory data, and domain knowledge for complex decision making processes. This level of intelligence can allow manufacturing systems to keep up with the ever-changing markets and intricate supply chain. Current manufacturing lines lack these capabilities and fall short of utilizing all generated data. This paper delves into the literature aiming at achieving this level of complexity. Firstly, it introduces cognitive manufacturing as a distinct research domain and proposes a definition by drawing upon various preexisting themes. Secondly, it outlines the capabilities brought forth by cognitive manufacturing, accompanied by an exploration of the associated trends and technologies. This contributes to establishing the foundation for future research in this promising field.

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An improved sequential recommendation model based on spatial self-attention mechanism and meta learning

Ni,

Shen,

Tang

et al. 2024

Multimed Tools Appl

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A metrics-based meta-learning model with meta-pretraining for industrial knowledge graph construction

Cited by 10 publications

References 68 publications

Graph-enabled digital twins for intelligent product lifecycle management: a multi-dimensional approach to design, manufacturing, and supply chain transformation

Graph-enabled digital twins for intelligent product lifecycle management: a multi-dimensional approach to design, manufacturing, and supply chain transformation

Cognitive manufacturing: definition and current trends

An improved sequential recommendation model based on spatial self-attention mechanism and meta learning

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