Digital twin (DT) is one of the most promising enabling technologies for realizing smart manufacturing and Industry 4.0. DTs are characterized by the seamless integration between the cyber and physical spaces. The importance of DTs is increasingly recognized by both academia and industry. It has been almost 15 years since the concept of the DT was initially proposed. To date, many DT applications have been successfully implemented in different industries, including product design, production, prognostics and health management, and some other fields. However, at present, no paper has focused on the review of DT applications in industry. In an effort to understand the development and application of DTs in industry, this paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development of DTs, and the major DT applications in industry. This paper also outlines the current challenges and some possible directions for future work.

Digital Twin in Industry: State-of-the-Art

Digital Twin in manufacturing: A categorical literature review and classification

Industry 4.0 technologies: Implementation patterns in manufacturing companies

The expected contribution of Industry 4.0 technologies for industrial performance

Data-driven smart manufacturing

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.

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

In this paper, the interplay and relationship between digital twin and Industrial Internet are discussed at first. The sensing/transmission network capability, which is one of the main characteristics of Industrial Internet, can be a carrier for providing digital twin with a means of data acquisition and transmission. Conversely, with the capability of high-fidelity virtual modeling and simulation computing/analysis, digital twin evolving from lifecycle management for a single product to application in production/manufacturing in the shop-floor/enterprise, can further greatly enhance the simulation computing and analysis of Industrial Internet. This paper proposes a digital twin enhanced Industrial Internet (DT-II) reference framework towards smart manufacturing. To further illustrate the reference framework, the implementation and operation mechanism of DT-II is discussed from three perspectives, including product lifecycle level, intra-enterprise level and inter-enterprise level. Finally, steam turbine is taken as an example to illustrate the application scenes from above three perspectives under the circumstance of DT-II. The differences between with and without DT-II for design and development of steam turbine are also presented.

DT-II:Digital twin enhanced Industrial Internet reference framework towards smart manufacturing

A consistency evaluation method for digital twin models

Digital twin, as an effective means to realize the fusion between physical and virtual spaces, has attracted more and more attention in the past few years. Based on ultra-fidelity models, more accurate service, e.g. real-time monitoring and failure prediction, can be reached. Against the background, some scholars studied the related theories and methods on modeling to depict various features of physical objects. Some scholars studied how to use Internet of Things to realize the connections and interactions, thereby keeping the consistency between the virtual and physical spaces. During this process, a new question arises that how to update the models once digital twin models are inconsistent with the practical situations. To solve the problem, this paper proposed a general digital twin model update framework at first. Then, the update methods for multi-dimension models are further explored. The cutting tool is the core component of machine tools which are the key equipment in industry. The precise cutting tool models are essential for realizing the digitalization and servitization of machine tools. Therefore, this paper takes a cutting tool as the application object to discuss how to conduct physics model update based on the proposed framework and methods. Through model update, a more accurate and updated tool wear model could be obtained, which contributes to the prognostics and health management for machine tools. 

He Zhang

Papers

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

Digital Twin in Industry: State-of-the-Art

DT-II:Digital twin enhanced Industrial Internet reference framework towards smart manufacturing

A consistency evaluation method for digital twin models

An update method for digital twin multi-dimension models