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Journal ArticleDOI

Digital twin-driven product design, manufacturing and service with big data

01 Feb 2018-The International Journal of Advanced Manufacturing Technology (Springer Science and Business Media LLC)-Vol. 94, Iss: 9, pp 3563-3576

Abstract: Nowadays, along with the application of new-generation information technologies in industry and manufacturing, the big data-driven manufacturing era is coming. However, although various big data in the entire product lifecycle, including product design, manufacturing, and service, can be obtained, it can be found that the current research on product lifecycle data mainly focuses on physical products rather than virtual models. Besides, due to the lack of convergence between product physical and virtual space, the data in product lifecycle is isolated, fragmented, and stagnant, which is useless for manufacturing enterprises. These problems lead to low level of efficiency, intelligence, sustainability in product design, manufacturing, and service phases. However, physical product data, virtual product data, and connected data that tie physical and virtual product are needed to support product design, manufacturing, and service. Therefore, how to generate and use converged cyber-physical data to better serve product lifecycle, so as to drive product design, manufacturing, and service to be more efficient, smart, and sustainable, is emphasized and investigated based on our previous study on big data in product lifecycle management. In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed. The detailed application methods and frameworks of digital twin-driven product design, manufacturing, and service are investigated. Furthermore, three cases are given to illustrate the future applications of digital twin in the three phases of a product respectively.
Topics: Product management (75%), Product engineering (73%), Product design specification (73%), Product lifecycle (72%), Product design (68%)
Citations
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Journal ArticleDOI
TL;DR: The findings show that Industry 4.0 is related to a systemic adoption of the front-end technologies, in which Smart Manufacturing plays a central role, and the implementation of the base technologies is challenging companies, since big data and analytics are still low implemented in the sample studied.
Abstract: Industry 4.0 has been considered a new industrial stage in which several emerging technologies are converging to provide digital solutions. However, there is a lack of understanding of how companies implement these technologies. Thus, we aim to understand the adoption patterns of Industry 4.0 technologies in manufacturing firms. We propose a conceptual framework for these technologies, which we divided into front-end and base technologies. Front-end technologies consider four dimensions: Smart Manufacturing, Smart Products, Smart Supply Chain and Smart Working, while base technologies consider four elements: internet of things, cloud services, big data and analytics. We performed a survey in 92 manufacturing companies to study the implementation of these technologies. Our findings show that Industry 4.0 is related to a systemic adoption of the front-end technologies, in which Smart Manufacturing plays a central role. Our results also show that the implementation of the base technologies is challenging companies, since big data and analytics are still low implemented in the sample studied. We propose a structure of Industry 4.0 technology layers and we show levels of adoption of these technologies and their implication for manufacturing companies.

581 citations


Journal ArticleDOI
Fei Tao1, Qinglin Qi1, Ang Liu2, Andrew Kusiak3Institutions (3)
TL;DR: The role of big data in supporting smart manufacturing is discussed, a historical perspective to data lifecycle in manufacturing is overviewed, and a conceptual framework proposed in the paper is proposed.
Abstract: The advances in the internet technology, internet of things, cloud computing, big data, and artificial intelligence have profoundly impacted manufacturing. The volume of data collected in manufacturing is growing. Big data offers a tremendous opportunity in the transformation of today’s manufacturing paradigm to smart manufacturing. Big data empowers companies to adopt data-driven strategies to become more competitive. In this paper, the role of big data in supporting smart manufacturing is discussed. A historical perspective to data lifecycle in manufacturing is overviewed. The big data perspective is supported by a conceptual framework proposed in the paper. Typical application scenarios of the proposed framework are outlined.

544 citations


Journal ArticleDOI
01 Jan 2018-IFAC-PapersOnLine
TL;DR: It is shown, that literature concerning the highest development stage, the DT, is scarce, whilst there is more literature about DM and DS.
Abstract: The Digital Twin (DT) is commonly known as a key enabler for the digital transformation, however, in literature is no common understanding concerning this term. It is used slightly different over the disparate disciplines. The aim of this paper is to provide a categorical literature review of the DT in manufacturing and to classify existing publication according to their level of integration of the DT. Therefore, it is distinct between Digital Model (DM), Digital Shadow (DS) and Digital Twin. The results are showing, that literature concerning the highest development stage, the DT, is scarce, whilst there is more literature about DM and DS.

514 citations


Cites background from "Digital twin-driven product design,..."

  • ...(Tao et al. 2017; Lee et al. 2013a; Rosen et al. 2015) To get a more common understanding of the Digital Twin, the level of integration are discussed in the following section....

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  • ...(Tao et al. 2017) The Digital Twin in its origin describes mirroring a product, while the state of the art allows processes (manufacturing, power generation etc.) to be as well subjects of virtual space reproduction (“twinning”) in order to gain the very same benefits....

    [...]

  • ...…PPC MTConnect;MQTT;Database;NOSQL;Middleware Söderberg et al. (2017) concept DS PPC simulation Stark et al. (2017) review DS product lifecycle simulation Tao et al. (2017) concept DM PPC Semantic Web Terkaj, Urgo (2015) concept undefined PPC simulation, CPS Terkaj et al. (2015) concept DM process…...

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  • ...(Tao et al. 2017) It is the virtual and computerized counterpart of a physical system....

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Journal ArticleDOI
Abstract: Industry 4.0 is considered a new industrial stage in which vertical and horizontal manufacturing processes integration and product connectivity can help companies to achieve higher industrial performance. However, little is known about how industries see the potential contribution of the Industry 4.0 related technologies for industrial performance, especially in emerging countries. Based on the use of secondary data from a large-scale survey of 27 industrial sectors representing 2225 companies of the Brazilian industry, we studied how the adoption of different Industry 4.0 technologies is associated with expected benefits for product, operations and side-effects aspects. Using regression analysis, we show that some of the Industry 4.0 technologies are seen as promising for industrial performance while some of the emerging technologies are not, which contraries the conventional wisdom. We discuss the contextual conditions of the Brazilian industry that may require a partial implementation of the Industry 4.0 concepts created in developed countries. We summarize our findings in a framework, that shows the perception of Brazilian industries of Industry 4.0 technologies and their relations with the expected benefits. Thus, this work contributes by discussing the real expectations on the future performance of the industry when implementing new technologies, providing a background to advance in the research on real benefits of the Industry 4.0.

466 citations


Journal ArticleDOI
Qinglin Qi1, Fei Tao1Institutions (1)
15 Jan 2018-IEEE Access
TL;DR: The similarities and differences between big data and digital twin are compared from the general and data perspectives and how they can be integrated to promote smart manufacturing are discussed.
Abstract: With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.

461 citations


Cites background or methods from "Digital twin-driven product design,..."

  • ...Through the cyber-physical closed loop, digital twin could achieve the optimization of the whole manufacturing process [9]....

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  • ...After the simulation and optimization of product design,manufacturing and maintenance process, it guides the physical process to perform the optimized solution [9]....

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References
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Journal ArticleDOI
Li Da Xu1, Wu He2, Shancang Li3Institutions (3)
TL;DR: This review paper summarizes the current state-of-the-art IoT in industries systematically and identifies research trends and challenges.
Abstract: Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of radio-frequency identification (RFID), and wireless, mobile, and sensor devices. A wide range of industrial IoT applications have been developed and deployed in recent years. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art IoT in industries systematically.

3,348 citations


"Digital twin-driven product design,..." refers background in this paper

  • ..., internet of things technology and devices are employed to collect various data generated in the entire produce lifecycle [2], cloud technology is used to realize the data management and processing [3], and artificial intelligence is used for data mining and realizing added-value [4], the big data-driven manufacturing era is coming....

    [...]


01 Jan 1965-

768 citations


"Digital twin-driven product design,..." refers methods in this paper

  • ...The concept of product lifecycle was proposed by Dean [12] in 1950 and was used in product marketing strategy research by Levitt [13]....

    [...]


Proceedings ArticleDOI
Edward H. Glaessgen1, D. S. StargelInstitutions (1)
16 Apr 2012-
Abstract: Future generations of NASA and U.S. Air Force vehicles will require lighter mass while being subjected to higher loads and more extreme service conditions over longer time periods than the present generation. Current approaches for certification, fleet management and sustainment are largely based on statistical distributions of material properties, heuristic design philosophies, physical testing and assumed similitude between testing and operational conditions and will likely be unable to address these extreme requirements. To address the shortcomings of conventional approaches, a fundamental paradigm shift is needed. This paradigm shift, the Digital Twin, integrates ultra-high fidelity simulation with the vehicle s on-board integrated vehicle health management system, maintenance history and all available historical and fleet data to mirror the life of its flying twin and enable unprecedented levels of safety and reliability.

601 citations


Book ChapterDOI
Stefan Boschert1, Roland Rosen1Institutions (1)
01 Jan 2016-
TL;DR: This chapter focuses on the simulation aspects of the Digital Twin, where simulation merges the physical and virtual world in all life cycle phases and enables the users to master the complexity of mechatronic systems.
Abstract: The vision of the Digital Twin itself refers to a comprehensive physical and functional description of a component, product or system, which includes more or less all information which could be useful in all—the current and subsequent—lifecycle phases. In this chapter we focus on the simulation aspects of the Digital Twin. Today, modelling and simulation is a standard process in system development, e.g. to support design tasks or to validate system properties. During operation and for service first simulation-based solutions are realized for optimized operations and failure prediction. In this sense, simulation merges the physical and virtual world in all life cycle phases. Current practice already enables the users (designer, SW/HW developers, test engineers, operators, maintenance personnel, etc) to master the complexity of mechatronic systems.

394 citations


Journal ArticleDOI
TL;DR: A conceptual model of how the Digital Twin can be used for predicting the life of aircraft structure and assuring its structural integrity is presented and the technical challenges to developing and deploying a Digital Twin are discussed.
Abstract: Reengineering of the aircraft structural life prediction process to fully exploit advances in very high performance digital computing is proposed. The proposed process utilizes an ultrahigh fidelity model of individual aircraft by tail number, a Digital Twin, to integrate computation of structural deflections and temperatures in response to flight conditions, with resulting local damage and material state evolution. A conceptual model of how the Digital Twin can be used for predicting the life of aircraft structure and assuring its structural integrity is presented. The technical challenges to developing and deploying a Digital Twin are discussed in detail.

394 citations


"Digital twin-driven product design,..." refers background or methods in this paper

  • ...Structural Sciences Center at US Air Force Research Laboratory employed digital twin to build a realistic highfidelity flight model and combine virtual model data with physical data to make a more accurate fatigue life prediction [24]....

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  • ...Digital twin can update data in real time, so that virtual models can undergo continuous improvement through comparing virtual space with physical space in parallel [24]....

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Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
20228
2021383
2020295
2019198
201874
20172