Digital Twins in Additive Manufacturing: A Systematic Review

• URL: http://arxiv.org/abs/2409.00877v2
• Date: Fri, 1 Nov 2024 15:41:56 GMT
• Title: Digital Twins in Additive Manufacturing: A Systematic Review
• Authors: Md Manjurul Ahsan, Yingtao Liu, Shivakumar Raman, Zahed Siddique,
• Abstract summary: Digital Twins (DTs) are becoming popular in Additive Manufacturing (AM) Advanced techniques such as Machine Learning (ML), Augmented Reality (AR), and simulation-based models play key roles in developing DTs.
• Score: 0.4218593777811082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital Twins (DTs) are becoming popular in Additive Manufacturing (AM) due to their ability to create virtual replicas of physical components of AM machines, which helps in real-time production monitoring. Advanced techniques such as Machine Learning (ML), Augmented Reality (AR), and simulation-based models play key roles in developing intelligent and adaptable DTs in manufacturing processes. However, questions remain regarding scalability, the integration of high-quality data, and the computational power required for real-time applications in developing DTs. Understanding the current state of DTs in AM is essential to address these challenges and fully utilize their potential in advancing AM processes. Considering this opportunity, this work aims to provide a comprehensive overview of DTs in AM by addressing the following four research questions: (1) What are the key types of DTs used in AM and their specific applications? (2) What are the recent developments and implementations of DTs? (3) How are DTs employed in process improvement and hybrid manufacturing? (4) How are DTs integrated with Industry 4.0 technologies? By discussing current applications and techniques, we aim to offer a better understanding and potential future research directions for researchers and practitioners in AM and DTs.

Related papers

• Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
  Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout the product life cycle, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. mechanisms that leverage sensing Industrial Internet of Things (IIoT) devices to share data for the construction of DTs are susceptible to adverse selection problems.
  arXiv  Detail & Related papers  (2024-08-02T10:47:10Z)
• Constructing and Evaluating Digital Twins: An Intelligent Framework for DT Development [11.40908718824589]
  Development of Digital Twins (DTs) represents a transformative advance for simulating and optimizing complex systems in a controlled digital space. This paper introduces an intelligent framework for the construction and evaluation of DTs, specifically designed to enhance the accuracy and utility of DTs in testing algorithmic performance. We propose a novel construction methodology that integrates deep learning-based policy gradient techniques to dynamically tune the DT parameters, ensuring high fidelity in the digital replication of physical systems.
  arXiv  Detail & Related papers  (2024-06-19T01:45:18Z)
• MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild [81.32127423981426]
  Multimodal emotion recognition based on audio and video data is important for real-world applications. Recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multimodal encoders. We propose a different perspective on the problem and investigate the advancement of multimodal DFER performance by adapting SSL-pre-trained disjoint unimodal encoders.
  arXiv  Detail & Related papers  (2024-04-13T13:39:26Z)
• Multi-Tier Computing-Enabled Digital Twin in 6G Networks [50.236861239246835]
  In Industry 4.0, industries such as manufacturing, automotive, and healthcare are rapidly adopting DT-based development. The main challenges to date have been the high demands on communication and computing resources, as well as privacy and security concerns. To achieve low latency and high security services in the emerging DT, multi-tier computing has been proposed by combining edge/fog computing and cloud computing.
  arXiv  Detail & Related papers  (2023-12-28T13:02:53Z)
• Enabling Automated Integration Testing of Smart Farming Applications via Digital Twin Prototypes [49.44419860570116]
  Industry 4.0 and smart farming are closely related, as many of the technologies used in smart farming are also used in Industry 4.0. Digital twins have the potential for cost-effective software development of such applications. We present a case study for employing our Digital Twin Prototype approach to automated testing of software.
  arXiv  Detail & Related papers  (2023-11-09T21:24:12Z)
• Enabling Spatial Digital Twins: Technologies, Challenges, and Future Research Directions [13.210510790794006]
  A Digital Twin (DT) is a virtual replica of a physical object or system, created to monitor, analyze, and optimize its behavior and characteristics. A Spatial Digital Twin (SDT) is a specific type of digital twin that emphasizes the geospatial aspects of the physical entity. We are the first to systematically analyze different spatial technologies relevant to building an SDT in layered approach.
  arXiv  Detail & Related papers  (2023-06-11T06:28:44Z)
• A New Era of Mobility: Exploring Digital Twin Applications in Autonomous Vehicular Systems [0.0]
  Digital twins (DTs) are virtual representations of physical objects or processes that can collect information from the real environment to represent, validate, and replicate the physical twin's present and future behavior. DTs are becoming increasingly prevalent in a variety of fields, including manufacturing, automobiles, medicine, smart cities, and other related areas. We addressed DTs and their essential characteristics, emphasized on accurate data collection, real-time analytics, and efficient simulation capabilities, while highlighting their role in enhancing performance and reliability.
  arXiv  Detail & Related papers  (2023-05-09T06:39:57Z)
• Digital Twin: From Concept to Practice [1.3633989508250934]
  This paper proposes a framework to help practitioners select an appropriate level of sophistication in a Digital Twin. Three real-life case studies illustrate the application and usefulness of the framework.
  arXiv  Detail & Related papers  (2022-01-14T17:41:26Z)
• Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions [62.67593386796497]
  This work explores the various DT features and current approaches, the shortcomings and reasons behind the delay in the implementation and adoption of digital twin. The major reasons for this delay are the lack of a universal reference framework, domain dependence, security concerns of shared data, reliance of digital twin on other technologies, and lack of quantitative metrics.
  arXiv  Detail & Related papers  (2020-11-02T19:08:49Z)
• AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes [56.65379135797867]
  We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes. We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
  arXiv  Detail & Related papers  (2020-08-29T14:57:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.