Using augmented reality to build digital twin for reconfigurable additive manufacturing system

Cai, Yi; Wang, Yi; Burnett, Morice

doi:10.1016/j.jmsy.2020.04.005

Cited by 81 publications

(18 citation statements)

References 21 publications

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“…This study used augmented reality with robotic arms in additive manufacturing for rapidly reconfigurable additive manufacturing systems [107]. The developed methodology helps to apply changes in layout and toolpath quickly.…”

Section: Reconfigurable Additive Manufacturing Systems Using Robotic Armsmentioning

confidence: 99%

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

et al. 2021

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The Fourth Industrial Revolution drives industries from traditional manufacturing to the smart manufacturing approach. In this transformation, existing equipment, processes, or devices are retrofitted with some sensors and other cyber-physical systems (CPS), and adapted towards digital production, which is a blend of critical enabling technologies. In the current scenario of Industry 4.0, industries are shaping themselves towards the development of customized and cost-effective processes to satisfy customer needs with the aid of a digital twin framework, which enables the user to monitor, simulate, control, optimize, and identify defects and trends within, ongoing process, and reduces the chances of human prone errors. This paper intends to make an appraisal of the literature on the digital twin (DT) framework in the domain of smart manufacturing with the aid of critical enabling technologies such as data-driven systems, machine learning and artificial intelligence, and deep learning. This paper also focuses on the concept, evolution, and background of digital twin and the benefits and challenges involved in its implementation. The Scopus and Web of Science databases from 2016 to 2021 were considered for the bibliometric analysis and used to study and analyze the articles that fall within the research theme. For the systematic bibliometric analysis, a novel approach known as Proknow-C was employed, including a series of procedures for article selection and filtration from the existing databases to get the most appropriate articles aligned with the research theme. Additionally, the authors performed statistical and network analyses on the articles within the research theme to identify the most prominent research areas, journal/conference, and authors in the field of a digital twin. This study identifies the current scenarios, possible research gaps, challenges in implementing DT, case studies and future research goals within the research theme.

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Section: Reconfigurable Additive Manufacturing Systems Using Robotic Armsmentioning

confidence: 99%

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

et al. 2021

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“…One example is the application of smart glasses in advanced logistics and production environments, creating opportunities to develop and evaluate innovative solutions for work assistance, efficiency, satisfaction, and safety [9]. Other examples cited in the literature show how, combined, AR and AM may impact SCs: (a) AR can improve the fabrication speed in a SAM process, through the communication of the layout information between a reconfigurable AM system made of robotic arms and its corresponding digital twin for toolpath planning and simulation, allowing the introduce of multiple independent actuators for concurrent deposition of materials without collision among them [10]. (b) AR can also support the detection of problems in an AM process caused by inaccessible measurements or parts movements that are difficult to anticipate, through an AR-based application that allows evaluating individually engineered parts based on virtual three-dimensional (3D)-computer-aided design models (CAD) projected to the intended installation-site [11].…”

Section: Digital Manufacturing Digital Supply Chains and Digital Value Chainsmentioning

confidence: 99%

Smart Additive Manufacturing: The Path to the Digital Value Chain

2021

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The aim of this article is to characterize the impacts of Smart Additive Manufacturing (SAM) on industrial production, digital supply chains (DSCs) and corresponding digital value chains (DVCs), logistics and inventory management. The method used consists of a critical review of the literature, enriched by the authors’ field experience. The results show that digital transformation of manufacturing is affecting business models, from resource acquisition to the end user. Smart manufacturing is considered a successful improvement introduced by Industry 4.0. Additive Manufacturing (AM) plays a crucial role in this digital transformation, changing the way manufacturers think about the entire lifecycle of a product. SAM combines AM in a smart factory environment. SAM reduces the complexity of DSCs and contributes to a more flexible approach to logistics and inventory management. It has also spurred the growth and popularization of customized mass production as well as decentralized manufacturing, rapid prototyping, unprecedented flexibility in product design, production and delivery, and resource efficiency and sustainability. SAM technology impacts all five Fletcher’s stages in DVCs. However, the need for clear definitions and regulations on 3D printing of digital files and their reproduction, as well as product health, safety, and integrity issues, cannot be ignored. Furthermore, investment in this technology is still expensive and can be prohibitive for many companies, namely SMEs.

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“…Liu et al [51] proposed a collaborative data management framework for metal additive manufacturing systems. Cai et al [52] used the augmented reality technique to communicate the layout information between a reconfigurable additive manufacturing system made of robotic arms and its corresponding digital Transactions of the ASME twin for toolpath planning and simulation. Mandolla et al [53] introduced a blockchain-based model that supports the creation of a digital twin for additive manufacturing in the aircraft industry.…”

Section: Digital Twins Of Additive Manufacturing Processesmentioning

confidence: 99%

Physics-Based Compressive Sensing to Enable Digital Twins of Additive Manufacturing Processes

Shevtshenko

Wang

2021

Journal of Computing and Information Science in Engineering

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Sensors play an important role in monitoring manufacturing processes and update their digital twins. However, the data transmission bandwidth and sensor placement limitations in the physical systems may not allow us to collect the amount or the type of data that we wish. Recently, a physics based compressive sensing (PBCS) approach was proposed to monitor manufacturing processes and obtain high-fidelity information with the reduced number of sensors by incorporating physical models of processes in compressed sensing. It can recover and reconstruct complete three-dimensional temperature distributions based on some limited measurements. In this paper, a constrained orthogonal matching pursuit algorithm is developed for PBCS, where coherence exists between the measurement matrix and basis matrix. The efficiency of recovery is improved by introducing a boundary-domain reduction approach, which reduces the size of PBCS model matrices during the inverse operations. The improved PBCS method is demonstrated with the measurement of temperature distributions in the cooling and real-time printing processes of fused filament fabrication.

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Using augmented reality to build digital twin for reconfigurable additive manufacturing system

Cited by 81 publications

References 21 publications

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

Smart Additive Manufacturing: The Path to the Digital Value Chain

Physics-Based Compressive Sensing to Enable Digital Twins of Additive Manufacturing Processes

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