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Number of results: 6
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Abstract

Digital twin (DT) is a solution for presenting reality in a virtual world. DTs have been discussed in the literature only recently. The aim of this work is to review and analyse literature connected to DTs. Under a systematic literature review the authors searched databases for the information how DTs can support organization operations and how they can support sustainability of companies. A literature review was performed according to a developed research methodology, which covers research questions and keywords identification, selection criteria and results analysis. Databases, such as Web of Science, Scopus and Science Direct, were searched. The titles, abstracts and keywords were searched for works related to digital twins, sustainable development and manufacturing processes. Moreover, the search was focused on real-time monitoring, data, decision-making etc. The keywords used in the searching process are specified in the methodology. Afterwards, quantitate and qualitative analysis were performed taking into account number of publication, year of publications, type of publication, based on keywords and available information concerning the papers. Deeper analysis was performed on available full texts of the papers. The main goal of this paper was to assess how much the specified problem is discussed in literature in the context of production organizations and real-time and what kind of topics are present in publications to indicate future research needs.
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Authors and Affiliations

Jerzy Pater
Dorota Stadnicka
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Abstract

The intelligent automated store warehouse (iZMS) research and development project was created to meet the expectations of a modern automatic store. The project concerns the development of the concept and pilot implementation of an automated store warehouse adapted to the autonomous and automatic sales of goods selected by retail chains. One of the aims of the iZMS project is to develop a scalable solution that allows for the simple adaptation of the iZMS to the needs of a potential customer, taking into account their requirements in terms of the quantity and variety of assortment offered within the iZMS. An important requirement in the use of the iZMS system is minimizing the customer waiting time for purchased products. This problem is related, among others, to the placement of products on the shelves of racks and will be solved in the optimizing process. Running optimization tasks requires a simulator that will mimic the features of a physical device faster than in real time in order to generate many proposals of the allocation of goods on storage racks in the shortest possible time and choose the best one, guaranteeing the shortest picking time of a representative basket of goods. A numerical simulator was developed to model the physical structures of food storage equipment and then simulate the sales process. Among the results obtained, the most important are the time parameters of individual operations, which will ultimately be used to optimize the placement of goods on storage racks. After analyzing the needs resulting from the usage of the iZMS system, we decided to develop a dynamic, deterministic simulator with discrete objects and perform the simulation with a controlled time increment and, in some cases, to utilize elements of event-driven simulation, in which the flow of goods is simulated with first-in, first-out (FIFO) queues. Finally,verification of the numerical simulator with a physical model confirmed that it can be employed in optimization processes.
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Authors and Affiliations

Sebastian Rzydzik
Piotr Kroczek
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Abstract

To improve the dynamic adaptability and flexibility of process route during manufacturing, a dynamic optimization method of multi-process route based on improved ant colony algorithm driven by digital twin is proposed. Firstly, on the basis of part manufacturing features analysis, the machining methods of each process are selected, and the fuzzy precedence constraint relationship between machining metas and processes is constructed by intuitionistic fuzzy information. Then, the multi-objective optimization function driven by digital twin is established with the optimization objectives of least manufacturing cost and lowest carbon emission, also the ranking of processing methods is optimized by an improved adaptive ant colony algorithm to seek the optimal processing sequence. Finally, the transmission shaft of some equipment is taken as an engineering example forverification analysis, which shows that this method can obtain a process route that gets closer to practical production.
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Authors and Affiliations

Zhaoming Chen
Jinsong Zou
Wei Wang
ORCID: ORCID
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Abstract

The study examines various approaches oriented towards conceptual and numerical reduction of first-principle models, data-driven methodologies for surrogate (black box) and hybrid (grey box) modeling, and addresses the prospect of using digital twins in chemical and process engineering. In the case of numerical reduction of mechanistic models, special attention is paid to methodologies in which simulation data are used to construct light but robust numerical models while preserving all the physics of the problem, yielding reduced-order datadriven but still white-box models. In addition to reviewing various methodologies and identifying their applications in chemical engineering, including industrial process engineering, as well as fundamental research, the study outlines associated problems and challenges, as well as the risks posed by the era of big data.
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Authors and Affiliations

Katarzyna Bizon
1
ORCID: ORCID

  1. Cracow University of Technology, Faculty of Chemical Engineering and Technology,Warszawska 24, 31-155 Kraków, Poland
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Abstract

Structural health monitoring (SHM) of bridges is constantly upgraded by researchers and bridge engineers as it directly deals with bridge performance and its safety over a certain time period. This article addresses some issues in the traditional SHM systems and the reason for moving towards an automated monitoring system. In order to automate the bridge assessment and monitoring process, a mechanism for the linkage of Digital Twins (DT) and Machine Learning (ML), namely the Support Vector Machine (SVM) algorithm, is discussed in detail. The basis of this mechanism lies in the collection of data from the real bridge using sensors and is providing the basis for the establishment and calibration of the digital twin. Then, data analysis and decision-making processes are to be carried out through regression-based ML algorithms. So, in this study, both ML brain and a DT model are merged to support the decision-making of the bridge management system and predict or even prevent further damage or collapse of the bridge. In this way, the SHM system cannot only be automated but calibrated from time to time to ensure the safety of the bridge against the associated damages.
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Authors and Affiliations

Asseel Za'al Ode Al-Hijazeen
1
ORCID: ORCID
Muhammad Fawad
1 2
ORCID: ORCID
Michael Gerges
3
ORCID: ORCID
Kalman Koris
1
ORCID: ORCID
Marek Salamak
2
ORCID: ORCID

  1. Budapest University of Technology and Economics, Faculty of Civil Engineering, Muegyetem rkp. 3, 1111 Budapest, Hungary
  2. Silesian University of Technology, Faculty of Civil Engineering, ul. Akademicka 2A, 44-100 Gliwice, Poland
  3. University of Wolverhampton, Wulfruna St, Wolverhampton WV1 1LY, the United Kingdom
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Abstract

Virtual digital representation of a physical object or system, created with precision through computer simulations, data analysis, and various digital technologies can be used as training set for real life situations. The principal aim behind creating a virtual representation is to furnish a dynamic, data-fueled, and digital doppelgänger of the physical asset. This digital counterpart serves multifaceted purposes, including the optimization of performance, the continuous monitoring of its well-being, and the augmentation of informed decision-making processes. Main advantage of employing a digital twin is its capacity to facilitate experimentation and assessment of diverse scenarios and conditions, all without impinging upon the actual physical entity. This capability translates into substantial cost savings and superior outcomes, as it allows for the early identification and mitigation of issues before they escalate into significant problems in the tangible world. Within our research endeavors, we've meticulously constructed a digital twin utilizing the Unity3D software. This digital replica faithfully mimics vehicles, complete with functioning headlamp toggles. Our lighting system employs polygons and normal vectors, strategically harnessed to generate an array of dispersed and reflected light effects. To ensure realism, we've meticulously prepared the scene to emulate authentic road conditions. For validation and testing, we integrated our model with the YOLO (You Only Look Once) neural network. A specifically trained compact YOLO model demonstrated impressive capabilities by accurately discerning the status of real vehicle headlamps. On average, it achieved an impressive recognition probability of 80%, affirming the robustness of our digital twin.
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Authors and Affiliations

Aleksander Dawid
1
Paweł Buchwald
1

  1. WSB University, Department of Transport and Computer Science, Poland

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