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IoT, Big Data and Artificial Intelligence in Smart Manufacturing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 3807

Special Issue Editor

Special Issue Information

Dear Colleagues,

Smart manufacturing is used to describe the use of advanced technologies, such as artificial intelligence (AI), Internet of Things (IoT), and automation, to optimize manufacturing processes. Big data play a crucial role in smart manufacturing by providing valuable insights into production processes, enabling predictive maintenance, and optimizing product design and performance. In smart manufacturing, big data are generated from various sources, such as sensors, machines, or complete production lines. These data are collected and processed in real time to gain insights into manufacturing processes, identify bottlenecks, and optimize production operations.

The aim of this Special Issue is to highlight innovative developments with respect to the current challenges and opportunities centered around “IoT, Big Data and Artificial Intelligence in Smart Manufacturing”.

Dr. Haipeng Dai
Guest Editor

Manuscript Submission Information

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Keywords

  • Internet of Things
  • smart manufacturing
  • artificial intelligence
  • big data
  • cloud computing

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Published Papers (1 paper)

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Research

19 pages, 3966 KiB  
Article
Unity and ROS as a Digital and Communication Layer for Digital Twin Application: Case Study of Robotic Arm in a Smart Manufacturing Cell
by Maulshree Singh, Jayasekara Kapukotuwa, Eber Lawrence Souza Gouveia, Evert Fuenmayor, Yuansong Qiao, Niall Murry and Declan Devine
Sensors 2024, 24(17), 5680; https://doi.org/10.3390/s24175680 - 31 Aug 2024
Cited by 2 | Viewed by 3258
Abstract
A digital twin (DT) is a virtual/digital model of any physical object (physical twin), interconnected through data exchange. In the context of Industry 4.0, DTs are integral to intelligent automation driving innovation at scale by providing significant improvements in precision, flexibility, and real-time [...] Read more.
A digital twin (DT) is a virtual/digital model of any physical object (physical twin), interconnected through data exchange. In the context of Industry 4.0, DTs are integral to intelligent automation driving innovation at scale by providing significant improvements in precision, flexibility, and real-time responsiveness. A critical challenge in developing DTs is achieving a model that reflects real-time conditions with precision and flexibility. This paper focuses on evaluating latency and accuracy, key metrics for assessing the efficacy of a DT, which often hinder scalability and adaptability in robotic applications. This article presents a comprehensive framework for developing DTs using Unity and Robot Operating System (ROS) as the main layers of digitalization and communication. The MoveIt package was used for motion planning and execution for the robotic arm, showcasing the framework’s versatility independent of proprietary constraints. Leveraging the versatility and open-source nature of these tools, the framework ensures interoperability, adaptability, and scalability, crucial for modern smart manufacturing applications. Our approach was validated by conducting extensive accuracy and latency tests. We measured latency by timestamping messages exchanged between the physical and digital twin, achieving a latency of 77.67 ms. Accuracy was assessed by comparing the joint positions of the DT and the physical robotic arm over multiple cycles, resulting in an accuracy rate of 99.99%. The results highlight the potential of DTs in enhancing operational efficiency and decision-making in manufacturing environments. Full article
(This article belongs to the Special Issue IoT, Big Data and Artificial Intelligence in Smart Manufacturing)
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