scholarly journals Towards Integrated Digital Twins for Industrial Products: Case Study on an Overhead Crane

2021 ◽  
Vol 11 (2) ◽  
pp. 683
Author(s):  
Juuso Autiosalo ◽  
Riku Ala-Laurinaho ◽  
Joel Mattila ◽  
Miika Valtonen ◽  
Valtteri Peltoranta ◽  
...  

Industrial Internet of Things practitioners are adopting the concept of digital twins at an accelerating pace. The features of digital twins range from simulation and analysis to real-time sensor data and system integration. Implementation examples of modeling-oriented twins are becoming commonplace in academic literature, but information management-focused twins that combine multiple systems are scarce. This study presents, analyzes, and draws recommendations from building a multi-component digital twin as an industry-university collaboration project and related smaller works. The objective of the studied project was to create a prototype implementation of an industrial digital twin for an overhead crane called “Ilmatar”, serving machine designers and maintainers in their daily tasks. Additionally, related cases focus on enhancing operation. This paper describes two tools, three frameworks, and eight proof-of-concept prototypes related to digital twin development. The experiences show that good-quality Application Programming Interfaces (APIs) are significant enablers for the development of digital twins. Hence, we recommend that traditional industrial companies start building their API portfolios. The experiences in digital twin application development led to the discovery of a novel API-based business network framework that helps organize digital twin data supply chains.

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8194
Author(s):  
Mehdi Kherbache ◽  
Moufida Maimour ◽  
Eric Rondeau

The Industrial Internet of Things (IIoT) is known to be a complex system because of its severe constraints as it controls critical applications. It is difficult to manage such networks and keep control of all the variables impacting their operation during their whole lifecycle. Meanwhile, Digital Twinning technology has been increasingly used to optimize the performances of industrial systems and has been ranked as one of the top ten most promising technological trends in the next decade. Many Digital Twins of industrial systems exist nowadays but only few are destined to networks. In this paper, we propose a holistic digital twinning architecture for the IIoT where the network is integrated along with the other industrial components of the system. To do so, the concept of Network Digital Twin is introduced. The main motivation is to permit a closed-loop network management across the whole network lifecycle, from the design to the service phase. Our architecture leverages the Software Defined Networking (SDN) paradigm as an expression of network softwarization. Mainly, the SDN controller allows for setting up the connection between each Digital Twin of the industrial system and its physical counterpart. We validate the feasibility of the proposed architecture in the process of choosing the most suitable communication mechanism that satisfies the real-time requirements of a Flexible Production System.


2020 ◽  
Vol 46 (4) ◽  
pp. 547-573
Author(s):  
Bernd Ketzler ◽  
Vasilis Naserentin ◽  
Fabio Latino ◽  
Christopher Zangelidis ◽  
Liane Thuvander ◽  
...  

During the last decades, a variety of digital tools have been developed to support both the planning and management of cities, as well as the inclusion of civic society. Here, the concept of a Digital Twin – which is rapidly emerging throughout many disciplines due to advances in technology, computational capacities and availability of large amounts of data – plays an important role. In short, a digital twin is a living virtual model, a connected digital representation of a physical system and has been a central concept in the manufacturing industry for the past decades. In this article, we review the terminology of digital twins for cities and identify commonalities and relations to the more established term 3D city models. Our findings indicate an increasing use of the term digital twin in academic literature, both in general and in the context of cities and the built environment. We find that while there is as yet no consensus on the exact definition of what constitutes a digital twin, it is increasingly being used to describe something that is more than a 3D city model (including, e.g. semantic data, real-time sensor data, physical models, and simulations). At the same time, the term has not yet replaced the term 3D city model as the most dominant term in the 3D GIS domain. By looking at grey literature we discuss how digital twins for cities are implemented in practice and present examples of digital twins in a global perspective. Further, we discuss some of the application areas and potential challenges for future development and implementation of digital twins for cities. We conclude that there are significant opportunities for up-scaling digital twins, with the potential to bring benefits to the city and its citizens and clients.


2021 ◽  
Vol 4 (S2) ◽  
Author(s):  
Daniel Anthony Howard ◽  
Zheng Ma ◽  
Christian Veje ◽  
Anders Clausen ◽  
Jesper Mazanti Aaslyng ◽  
...  

AbstractThe project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability. Applying the Industry 4.0 Digital Twin concept to the Danish horticulture greenhouse industry provides digital models for simulating and evaluating the physical greenhouse facility’s performance. A Digital Twin combines modeling, AI, and Big Data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under varying operational conditions. The Digital Twins support the co-optimization of the production schedule, energy consumption, and labor cost by considering influential factors, including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. The ecosystem of digital twins extends the state-of-the-art by adopting a scalable distributed approach of “system of systems” that interconnects Digital Twins in a production facility. A collection of specialized Digital Twins are linked together to describe and simulate all aspects of the production chain, such as overall production capacity, energy consumption, delivery dates, and supply processes. The contribution of this project is to develop an ecosystem of digital twins that collectively capture the behavior of an industrial greenhouse facility. The ecosystem will enable the industrial greenhouse facilities to become increasingly active participants in the electricity grid.


2021 ◽  
Author(s):  
Sabri Deniz ◽  
Ulf Christian Müller ◽  
Ivo Steiner ◽  
Thomas Sergi

Abstract The Covid-19 pandemic has changed the university education, with most teaching moved off campus and students learning online or remote at home, but a cornerstone of undergraduate engineering education has been a big challenge, namely the laboratory classes. As the engineering and education communities continue to adapt to the realities of a global pandemic, it is important to recognize the importance of the laboratory-based courses. In order to address to this situation, an ambitious approach is taken to recreate the laboratory experience entirely online with the help of the digital twins of the fluid mechanics, thermodynamics, and turbomachinery laboratory experiments. Laboratory based undergraduate courses such as EFPLAB1, EFPLAB2 (Energy; Fluid and Process Laboratory 1 & 2) and EFPENG (Energy; Fluid and Process Engineering) are important parts of the “mechanical engineering” and “energy systems engineering” curricula of the Lucerne University of Applied Sciences (HSLU) in Switzerland. Each course mentioned above include six different laboratory experiments about fluid mechanics, thermodynamics, turbomachinery, energy efficiency, and energy systems, including mass- and energy flow balances in energy systems. During the Covid-19 pandemic, it was necessary to adapt to the new environment of remote learning courses and modify the laboratory experiments so that they can be carried out online. The approach was developing digital twins of each laboratory experiment with web applications and providing an environment together with supporting videos and interactive problems so that the laboratory experiments can be carried out remotely. A digital twin is a digital representation of a physical system, e.g., the test rig. It may contain a collection of various digital models with related physical equations and solutions, information related to the operation of the test rig, including 2D or 3D models, process models, sensor data records, and documentation. Ideally, all quantities and attributes that could be measured or observed from the real experiment should be accessible from its digital twin. The digital twin not only reproduces the experimental setup in the laboratory but also helps to improve the knowledge related to the theory and concepts of the laboratory experiments. One major advantage of the digital twin is that the number and range of the parameters, which can be manipulated or varied, is larger in comparison to the actual testing in the laboratory. This paper explains the development of the digital twins (web applications) of the laboratory experiments and provides information about the selected experiments such as potential vortex, linear momentum equation, diffuser flow, radial compressor, fuel cell, and pump test rig with the measurement of pump characteristics. A remote or distance learning has many hurdles, one of the largest being how to teach hands-on laboratory courses outside of an actual laboratory. The experience at the Lucerne University of Applied Sciences showed that teaching online labs using the digital twins of the laboratory experiments can work and the students can take part in remote laboratories that meet the learning outcomes and objectives as well as engage in scientific inquiry from a distance.


2021 ◽  
pp. 1-28
Author(s):  
Shuo Wang ◽  
Xiaonan Lai ◽  
Xiwang He ◽  
Yiming Qiu ◽  
Xueguan Song

Abstract Digital twin has the potential for increasing production, achieving real-time monitor, and realizing predictive maintenance by establishing a real-time high-fidelity mapping between the physical entity and its digital model. However, the high accuracy and instantaneousness requirements of digital twins have hindered their applications in practical engineering. This paper presents a universal framework to fulfill the requirements and to build an accurate and trustworthy digital twin by integrating numerical simulations, sensor data, multi-fidelity surrogate (MFS) models, and visualization techniques. In practical engineering, the number of sensors available to measure quantities of interest is often limited, complementary simulations are necessary to compute these quantities. The simulation results are generally more comprehensive but not as accurate as the sensor data. Therefore, the proposed framework combines the benefits of both simulation results and sensor data by using an MFS model based on moving least squares, named MFS-MLS. The MFS-MLS was developed as an essential part to calibrate the continuous field of the simulation by limited sensor data to obtain accurate results for the digital twin. Then single-fidelity surrogate models are built on the whole domain using the calibrated results of the MFS-MLS as training samples and sensor data as inputs to predict and visualize the quantities of interest in real-time. In addition, the framework was validated by a truss test case, and the results demonstrate that the proposed framework has the potential to be an effective tool to build accurate and trustworthy digital twins.


Author(s):  
Carlo Cortese ◽  
Marco A. Calamari ◽  
Paolo Spagli

This paper aims to discuss 30 years of evolution of technical design tools (software and architecture) in GE Oil&Gas. Most important changes are highlighted, as well as some promising evolutionary paths. Legacy codes are the heritage of industrial companies from 70s and 80s. FORTRAN was used in order to automate the calculation the engineers had to perform to design turbines or compressors. The results of legacy codes were files that contain several information’s, relevant to stage geometry and performances, which could be used to generate drawings or to evaluate machines operability. However this large amount of data was spread on different computer and each designer was keeping track manually of the files modification. In order to better archive those data in 2000 most of the companies started to use databases and created modern user interfaces: in this way the users can dialog with a friendly interface and retrieve the data in a more organized format. The discussion on how to link the legacy codes and the database is still on going. Some GUIs are installed on different computer and interact on a centralized database, but in 2010 a more robust architecture started to be used transforming the GUI and the calculation in a centralized system based on web application. This allowed creating a solid and scalable environment since the legacy code and DB can be installed in servers reachable through the net by each user, simplifying the installation and maintenance issue. With INDUSTRIAL INTERNET advent more interaction between tools is required and Application Programming Interfaces (API) permit to have a direct interaction among tools without human interface, and the applications can directly interact with other programs.


2021 ◽  
Vol 93 ◽  
pp. 01024
Author(s):  
Evgeniy Starikov ◽  
Marina Evseeva ◽  
Irina Tkachenko

The article analyzes the possibility of using such digital technologies as the Industrial Internet of Things (IIoT), Big Data and the creation of models of digital twins in the formation of intelligent management systems for "smart" production within the framework of the concept of digital transformation of the manufacturing sector Industry 4.0. The essence and features of these technologies, problematic aspects of their implementation in real production enterprises are considered. The concept of the functional structure of the digital production management system of a "smart" enterprise based on the digital twin model is proposed. The conclusion is made about the integrating role of technologies for the development and application of digital twin models in the construction of intelligent control systems for "smart" production.


Author(s):  
Maja Bärring ◽  
Björn Johansson ◽  
Goudong Shao

Abstract The manufacturing sector is experiencing a technological paradigm shift, where new information technology (IT) concepts can help digitize product design, production systems, and manufacturing processes. One of such concepts is Digital Twin and researchers have made some advancement on both its conceptual development and technological implementations. However, in practice, there are many different definitions of the digital-twin concept. These different definitions have created a lot of confusion for practitioners, especially small- and medium-sized enterprises (SMEs). Therefore, the adoption and implementation of the digital-twin concept in manufacturing have been difficult and slow. In this paper, we report our findings from a survey of companies (both large and small) regarding their understanding and acceptance of the digital-twin concept. Five supply-chain companies from discrete manufacturing and one trade organization representing suppliers in the automotive business were interviewed. Their operations have been studied to understand their current digital maturity levels and articulate their needs for digital solutions to stay competitive. This paper presents the results of the research including the viewpoints of these companies in terms of opportunities and challenges for implementing digital twins.


2021 ◽  
pp. 1-7
Author(s):  
Nick Petro ◽  
Felipe Lopez

Abstract Aeroderivative gas turbines have their combustion set points adjusted periodically in a process known as remapping. Even turbines that perform well after remapping may produce unacceptable behavior when external conditions change. This article introduces a digital twin that uses real-time measurements of combustor acoustics and emissions in a machine learning model that tracks recent operating conditions. The digital twin is leveraged by an optimizer that select adjustments that allow the unit to maintain combustor dynamics and emissions in compliance without seasonal remapping. Results from a pilot site demonstrate that the proposed approach can allow a GE LM6000PD unit to operate for ten months without seasonal remapping while adjusting to changes in ambient temperature (4 - 38 °C) and to different fuel compositions.


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