scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Digital-twin-driven geometric optimization of centrifugal impeller with free-form blades for five-axis flank milling

TL;DR: This paper breaks traditional procedures and presents a DT-based optimization strategy on the consideration of both machining efficiency and aerodynamic performance, as well as builds a reified 5-dimensional DT model.
About: This article is published in Journal of Manufacturing Systems.The article was published on 2021-01-01. It has received 51 citations till now. The article focuses on the topics: Machining.
Citations
More filters
Journal ArticleDOI
23 Sep 2021-Sensors
TL;DR: In this paper, a survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics.
Abstract: Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

54 citations

Journal ArticleDOI
TL;DR: A solution to existing challenging issues is proposed by introducing the digital twin (DT) technology into the OWT support structures and this new DT framework will enable real-time monitoring, fault diagnosis and operation optimization of the O WT support structures, which may provide a useful application prospect in the reliability analysis in the future.

51 citations

Journal ArticleDOI
TL;DR: Shortening product development cycles and fully customisable products pose major challenges for production systems as mentioned in this paper, and these not only have to cope with an increased product diversity but also enable h.....
Abstract: Shortening product development cycles and fully customisable products pose major challenges for production systems. These not only have to cope with an increased product diversity but also enable h...

44 citations

Journal ArticleDOI
TL;DR: A multi-level cloud computing enabled digital twin system for the real-time monitor, decision and control of a synchronized production logistics system and the PLS optimization model of production and storage is presented with an industrial case, and the effectiveness is demonstrated and analyzed.

41 citations

Journal ArticleDOI
TL;DR: A technical system that embeds machine learning modules into digital twins and a combination of the digital twin in maintenance with machine learning in predictive maintenance of diesel locomotives is presented.
Abstract: The full life cycle management of complex equipment is considered fundamental to the intelligent transformation and upgrading of the modern manufacturing industry. Digital twin technology and machine learning have been emerging technologies in recent years. The application of these two technologies in the full life cycle management of complex equipment can make each stage of the life cycle more responsive, predictable, and adaptable. This paper first proposes a technical system that embeds machine learning modules into digital twins. Next, on this basis, a full life cycle digital twin for complex equipment is constructed, and joint application of sub-models and machine learning is explored. Then, the application of a combination of the digital twin in maintenance with machine learning in predictive maintenance of diesel locomotives is presented. The effectiveness of the proposed management method is verified by experiments. The abnormal axle temperature can be alarmed about one week in advance. Lastly, possible application advantages of the combination of digital twin and machine learning in addressing future research direction in this field are introduced.

26 citations

References
More filters
Proceedings Article
21 Jun 2014
TL;DR: This paper introduces an off-policy actor-critic algorithm that learns a deterministic target policy from an exploratory behaviour policy and demonstrates that deterministic policy gradient algorithms can significantly outperform their stochastic counterparts in high-dimensional action spaces.
Abstract: In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions. The deterministic policy gradient has a particularly appealing form: it is the expected gradient of the action-value function. This simple form means that the deterministic policy gradient can be estimated much more efficiently than the usual stochastic policy gradient. To ensure adequate exploration, we introduce an off-policy actor-critic algorithm that learns a deterministic target policy from an exploratory behaviour policy. We demonstrate that deterministic policy gradient algorithms can significantly outperform their stochastic counterparts in high-dimensional action spaces.

2,174 citations

Journal ArticleDOI
Fei Tao1, Jiangfeng Cheng1, Qinglin Qi1, Meng Zhang1, He Zhang1, Fangyuan Sui1 
TL;DR: In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed, and three cases are given to illustrate the future applications of digital twin in three phases of a product respectively.
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.

1,571 citations

Journal ArticleDOI
TL;DR: This paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development ofDTs, and the major DT applications in industry and outlines the current challenges and some possible directions for future work.
Abstract: 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.

1,467 citations

Journal ArticleDOI
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.

937 citations

Journal ArticleDOI
Fei Tao1, Meng Zhang1
TL;DR: A novel concept of digital twin shop-floor (DTS) based on digital twin is explored and its four key components are discussed, including physicalShop-floor, virtual shop- Floor, shop- floor service system, and shop-ground digital twin data.
Abstract: With the developments and applications of the new information technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, a smart manufacturing era is coming. At the same time, various national manufacturing development strategies have been put forward, such as Industry 4.0 , Industrial Internet , manufacturing based on Cyber-Physical System , and Made in China 2025 . However, one of specific challenges to achieve smart manufacturing with these strategies is how to converge the manufacturing physical world and the virtual world, so as to realize a series of smart operations in the manufacturing process, including smart interconnection, smart interaction, smart control and management, etc. In this context, as a basic unit of manufacturing, shop-floor is required to reach the interaction and convergence between physical and virtual spaces, which is not only the imperative demand of smart manufacturing, but also the evolving trend of itself. Accordingly, a novel concept of digital twin shop-floor (DTS) based on digital twin is explored and its four key components are discussed, including physical shop-floor, virtual shop-floor, shop-floor service system, and shop-floor digital twin data. What is more, the operation mechanisms and implementing methods for DTS are studied and key technologies as well as challenges ahead are investigated, respectively.

741 citations