A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang, Ziqi; Shen, Yang; Li, Jiayi; Fey, Marcel; Brecher, Christian

doi:10.3390/s21196340

Cited by 189 publications

(62 citation statements)

References 289 publications

(309 reference statements)

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“…Smart manufacturing deployments involve integrating IoT devices into manufacturing machinery to collect operational status and performance data. In addition, many technologies are being used to help enable smart manufacturing, including data streams processing, edge and fog computing, artificial intelligence, robotics, driverless vehicles, blockchain, and digital twins [30,31].…”

Section: Smart Manufacturingmentioning

confidence: 99%

Edge AI and Blockchain for Smart Sustainable Cities: Promise and Potential

Badidi

2022

Sustainability

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Modern cities worldwide are undergoing radical changes to foster a clean, sustainable and secure environment, install smart infrastructures, deliver intelligent services to residents, and facilitate access for vulnerable groups. The adoption of new technologies is at the heart of implementing many initiatives to address critical concerns in urban mobility, healthcare, water management, clean energy production and consumption, energy saving, housing, safety, and accessibility. Given the advancements in sensing and communication technologies over the past few decades, exploring the adoption of recent and innovative technologies is critical to addressing these concerns and making cities more innovative, sustainable, and safer. This article provides a broad understanding of the current urban challenges faced by smart cities. It highlights two new technological advances, edge artificial intelligence (edge AI) and Blockchain, and analyzes their transformative potential to make our cities smarter. In addition, it explores the multiple uses of edge AI and Blockchain technologies in the fields of smart mobility and smart energy and reviews relevant research efforts in these two critical areas of modern smart cities. It highlights the various algorithms to handle vehicle detection, counting, speed identification to address the problem of traffic congestion and the different use-cases of Blockchain in terms of trustworthy communications and trading between vehicles and smart energy trading. This review paper is expected to serve as a guideline for future research on adopting edge AI and Blockchain in other smart city domains.

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Section: Smart Manufacturingmentioning

confidence: 99%

Edge AI and Blockchain for Smart Sustainable Cities: Promise and Potential

Badidi

2022

Sustainability

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“…DTs are hence used for simulation and assessment of system or process behavior, based on which it is possible to take appropriate actions. Note also that, within the Industry 4.0 concept, AI is very often used together with DT [8]. The use of DTs for manufacturing systems can be aimed at the support of technical condition analysis for the purpose of improvement and predictive planning of maintenance activities, management and optimization of performance of devices during their lifecycle [9].…”

Section: Digital Twinmentioning

confidence: 99%

“…with DT [8]. The use of DTs for manufacturing systems can be aimed at the support of 37 technical condition analysis for the purpose of improvement and predictive planning of 38 maintenance activities, management and optimization of properties of devices during 39 their lifecycle [9].…”

Section: Digital Twinmentioning

confidence: 99%

Hybrid Virtual Commissioning of a Robotic Manipulator with Machine Vision Using a Single Controller

Noga

Juhás

Gulan

2022

Sensors

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Digital twin (DT) is an emerging key technology that enables sophisticated interaction between physical objects and their virtual replicas, with applications in almost all engineering fields. Although it has recently gained significant attraction in both industry and academia, so far it has no unanimously adopted and established definition. One may therefore come across many definitions of what DT is and how to create it. DT can be designed for an existing process and help us to improve it. Another possible approach is to create the DT for a brand new device. In this case, it can reveal how the system would behave in given conditions or when controlled. One of purposes of a DT is to support the commissioning of devices. So far, recognized and used techniques to make the commissioning more effective are virtual commissioning and hybrid commissioning. In this article, we present a concept of hybrid virtual commissioning. This concept aims to point out the possibility to use real devices already at the stage of virtual commissioning. It is introduced in a practical case study of a robotic manipulator with machine vision controlled with a programmable logic controller in a pick-and-place application. This study presents the benefits that stem from the proposed approach and also details when it is convenient to use it.

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“…Along with artificial intelligence, the use of digital twins has also grown rapidly in recent years, and are considered a key supporting factor for Industry 4.0 [27]. In the manufacturing process, all state changes of physical objects are detected by sensors and used to analyze the manufacturing process.…”

Section: Ai For Manufacturingmentioning

confidence: 99%

A Novel Embedding Model Based on a Transition System for Building Industry-Collaborative Digital Twin

et al. 2022

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Recently, the production environment has been rapidly changing, and accordingly, correct mid term and short term decision-making for production is considered more important. Reliable indicators are required for correct decision-making, and the manufacturing cycle time plays an important role in manufacturing. A method using digital twin technology is being studied to implement accurate prediction, and an approach utilizing process discovery was recently proposed. This paper proposes a digital twin discovery framework using process transition technology. The generated digital twin will unearth its characteristics in the event log. The proposed method was applied to actual manufacturing data, and the experimental results demonstrate that the proposed method is effective at discovering digital twins.

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A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Cited by 189 publications

References 289 publications

Edge AI and Blockchain for Smart Sustainable Cities: Promise and Potential

Edge AI and Blockchain for Smart Sustainable Cities: Promise and Potential

Hybrid Virtual Commissioning of a Robotic Manipulator with Machine Vision Using a Single Controller

A Novel Embedding Model Based on a Transition System for Building Industry-Collaborative Digital Twin

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