When, in 1956, Artificial Intelligence (AI) was officially declared a research field, no one would have ever predicted the huge influence and impact its description, prediction, and prescription capabilities were going to have on our daily lives. In parallel to continuous advances in AI, the past decade has seen the spread of broadband and ubiquitous connectivity, (embedded) sensors collecting descriptive high dimensional data, and improvements in big data processing techniques and cloud computing. The joint usage of such technologies has led to the creation of digital twins, artificial intelligent virtual replicas of physical systems. Digital Twin (DT) technology is nowadays being developed and commercialized to optimize several manufacturing and aviation processes, while in the healthcare and medicine fields this technology is still at its early development stage. This paper presents the results of a study focused on the analysis of the state-of-the-art definitions of DT, the investigation of the main characteristics that a DT should possess, and the exploration of the domains in which DT applications are currently being developed. The design implications derived from the study are then presented: they focus on socio-technical design aspects and DT lifecycle. Open issues and challenges that require to be addressed in the future are finally discussed.

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A survey on digital twin : definitions, characteristics, applications, and design Implications

Industry 5.0: A survey on enabling technologies and potential applications

With the rapid advancement of cyber-physical systems, Digital Twin (DT) is gaining ever-increasing attention owing to its great capabilities to realize Industry 4.0. Enterprises from different fields are taking advantage of its ability to simulate real-time working conditions and perform intelligent decision-making, where a cost-effective solution can be readily delivered to meet individual stakeholder demands. As a hot topic, many approaches have been designed and implemented to date. However, most approaches today lack a comprehensive review to examine DT benefits by considering both engineering product lifecycle management and business innovation as a whole. To fill this gap, this work conducts a state-of-the art survey of DT by selecting 123 representative items together with 22 supplementary works to address those two perspectives, while considering technical aspects as a fundamental. The systematic review further identifies eight future perspectives for DT, including modular DT, modeling consistency and accuracy, incorporation of Big Data analytics in DT models, DT simulation improvements, VR integration into DT, expansion of DT domains, efficient mapping of cyber-physical data and cloud/edge computing integration. This work sets out to be a guide to the status of DT development and application in today’s academic and industrial environment.

A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives

Digital Twin as a Service (DTaaS) in Industry 4.0: An Architecture Reference Model

Digital Twin for maintenance: A literature review

This paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the context of Industry 4.0. To understand Digital Twin and its future potential in manufacturing, we summarized the definition and state-of-the-art development outcomes of Digital Twin. Existing technologies for developing a Digital Twin for smart manufacturing are reviewed under a Digital Twin reference model to systematize the development methodology for Digital Twin. Representative applications are reviewed with a focus on the alignment with the proposed reference model. Outstanding research issues of developing Digital Twins for smart manufacturing are identified at the end of the paper.

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Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

Digital Twin-driven online anomaly detection for an automation system based on edge intelligence

There is a recognized need for mass personalization for sustainability at scale. Mass personalization is becoming a leading research trend in the latest Industrial Revolution, whereas substantial research has been undertaken on the role of Industry 4.0 enabling technologies. The world is moving beyond mass customization, while manufacturing has led to mass personalization ahead of other industries. However, most studies have not treated human capabilities, machines, and technologies as sustainable collaboration. This research investigates mass personalization as a common goal under the latest Industrial revolutions. Also, it proposes a Reference Architecture Model for achieving mass personalization that contributes to understanding how Industry 5.0 enhances Industry 4.0 for higher resilience and sustainability through a human-centric approach. The study implies that Human Capital 5.0 leads collaboration with machines and technologies, bringing more value-added and sustainable products.

/pdf/toward-sustainability-and-resilience-with-industry-4-0-and-21ssqth8.pdf

Toward sustainability and resilience with Industry 4.0 and Industry 5.0

Manufacturing is becoming smart with capabilities of self-awareness, autonomous decision-making, and adaptive excitation and collaboration. Standardization is a crucial enabler for achieving the required intelligence for smart manufacturing. Though a large number of efforts have been made to the development of manufacturing standards, there is still a significant research gap to be fulfilled. This paper reviews the landscape of existing standards in the context of smart manufacturing and offers guidance on the selection of the standards for different smart manufacturing applications.

/pdf/standards-for-smart-manufacturing-a-review-38tluaq05c.pdf

Standards for Smart Manufacturing: A review

The fourth industrial revolution holds the promise of increased flexibility in manufacturing, along with mass personalization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead time to market and higher quality. Future manufacturing systems will become smart manufacturing systems that can respond in real time to dynamic changes via ubiquitous sensing, reconfigurable manufacturing processes, artificial intelligence-based decision-making, and end-to-end visibility. Digital Twin (DT), as an emerging technology that replicates physical assets in the digital space, plays a significant role in enabling smart manufacturing systems and operations. This chapter elaborates the vision of DT-based smart manufacturing, together with representative DT-enabled smart manufacturing scenarios and case studies in product design, machining, industrial robot, and process optimization.

Huiyue Huang

Papers

Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

Digital Twin-driven online anomaly detection for an automation system based on edge intelligence

Toward sustainability and resilience with Industry 4.0 and Industry 5.0

Standards for Smart Manufacturing: A review

Smart manufacturing based on Digital Twin technologies