Data-driven engineering design: A systematic review using scientometric approach

Vlah, Daria; Kastrin, Andrej; Povh, Janez; Vukašinović, Nikola

doi:10.1016/j.aei.2022.101774

Cited by 8 publications

(1 citation statement)

References 133 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the extensive volume of research, only notable reviews are included here to offer a comprehensive perspective on recent DT advancements and their benefits across the three targeted phases. In the product design domain, DT-enabled product design approaches are outlined [271], with emphasis on data-driven engineering design for rapid prototyping and product management [272], involving various schools of design theory to support complexity [273]. In the manufacturing realm, which covers areas from layout optimization to maintenance, studies include the impact of DT technology in the context of Industry 5.0 [274], unit level DT for production equipment [275], and process-specific operations such as predictive maintenance [276].…”

Section: Engineering Plm Stages Descriptionmentioning

confidence: 99%

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

Lim

View full text Add to dashboard Cite

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...

show abstract