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Meena, A., Andersson Lassila, A., Lönn, D., Salomonsson, K., Wang, W., Nielsen, C. V. & Bayat, M. (2024). Numerical and experimental study of the variation of keyhole depth with an aluminum alloy (AA1050). Journal of Advanced Joining Processes, 9, Article ID 100196.
Open this publication in new window or tab >>Numerical and experimental study of the variation of keyhole depth with an aluminum alloy (AA1050)
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2024 (English)In: Journal of Advanced Joining Processes, E-ISSN 2666-3309, Vol. 9, article id 100196Article in journal (Refereed) Published
Abstract [en]

The keyhole depth is a key measurement characteristic in the laser welding of busbar to battery tabs in battery packs for electric vehicles (EV), as it directly affects the quality of the weld. In this work, experiments are carried out with controlled and adjusted laser power and feed rate parameters to investigate the influence on the keyhole width, keyhole depth and porosities. A 3D numerical model of laser keyhole welding of an aluminum alloy (A1050) has been developed to describe the porosity formation and the keyhole depth variation. A new integration model of the recoil pressure and the rate of evaporation model is implemented which is closer to the natural phenomena as compared to the conventional methods. Additionally, major physical forces are employed including plume formation, upward vapor pressure and multiple reflection in the keyhole. The results show that keyhole depth is lower at higher feed rate, while lower feed rates result in increased keyhole depth. This study reveals that low energy densities result in an unstable keyhole with high spattering, exacerbated by increased laser power. Mitigating incomplete fusion is achieved by elevating laser energy density. The findings emphasize the critical role of keyhole depth in optimizing laser welding processes for applications like busbar-to-battery tab welding.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Multiphysics simulation, Laser welding, Incident angle, Melt pool, Keyhole depth and width
National Category
Applied Mechanics Fluid Mechanics and Acoustics Manufacturing, Surface and Joining Technology
Research subject
Virtual Manufacturing Processes
Identifiers
urn:nbn:se:his:diva-23611 (URN)10.1016/j.jajp.2024.100196 (DOI)
Funder
Vinnova, 2022-01257
Note

CC BY-NC-ND 4.0 DEED

Corresponding author. E-mail address: akmee@dtu.dk (A. Meena).

The authors would like to acknowledge the financial support by the European M-ERA.NET 3 call (project9468 LaserBATMAN), Innovation Fund Denmark (grant number 1139-00001), and the Swedish Governmental Agency for Innovation Systems (Vinnova grant number 2022-01257). ASSAR Innovation Arena in Skövde, Sweden is also acknowledged for the experimental activities.

Available from: 2024-02-19 Created: 2024-02-19 Last updated: 2024-02-19Bibliographically approved
Andersson Lassila, A., Svensson, D., Wang, W. & Andersson, T. (2024). Numerical evaluation of cutting strategies for thin-walled parts. Scientific Reports, 14(1), Article ID 1459.
Open this publication in new window or tab >>Numerical evaluation of cutting strategies for thin-walled parts
2024 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 14, no 1, article id 1459Article in journal (Refereed) Published
Abstract [en]

Static form errors due to in-process deflections is a major concern in flank milling of thin-walled parts. To increase both productivity and part geometric accuracy, there is a need to predict and control these form errors. In this work, a modelling framework for prediction of the cutting force-induced form errors, or thickness errors, during flank milling of a thin-walled workpiece is proposed. The modelled workpiece geometry is continuously updated to account for material removal and the reduced stiffness matrix is calculated for nodes in the engagement zone. The proposed modelling framework is able to predict the resulting thickness errors for a thin-walled plate which is cut on both sides. Several cutting strategies and cut patterns using constant z-level finishing are studied. The modelling framework is used to investigate the effect of different cut patterns, machining allowance, cutting tools and cutting parameters on the resulting thickness errors. The framework is experimentally validated for various cutting sequences and cutting parameters. The predicted thickness errors closely correspond to the experimental results. It is shown from numerical evaluations that the selection of an appropriate cut pattern is crucial in order to reduce the thickness error. Furthermore, it is shown that an increased machining allowance gives a decreased thickness error for thin-walled plates.

Place, publisher, year, edition, pages
Springer Nature, 2024
National Category
Applied Mechanics Control Engineering Manufacturing, Surface and Joining Technology
Research subject
Virtual Manufacturing Processes; Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23541 (URN)10.1038/s41598-024-51883-1 (DOI)2-s2.0-85182423435 (Scopus ID)
Funder
University of SkövdeKnowledge Foundation, 20180168
Note

CC BY 4.0 DEED

School of Engineering Science, University of Skövde, Kaplansgatan 11, SE‑541 34 Skövde, Sweden. *email: daniel.svensson@his.se

Open access funding provided by University of Skövde. This work was supported financially by the Swedish Knowledge Foundation through the project SIMPLE (dnr: 20180168).

Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-01-25Bibliographically approved
Lu, X., Li, X., Wang, W., Chao, K.-M., Xu, L., De Vrieze, P. & Jing, Y. (2022). A generic and modularized Digital twin enabled human-robot collaboration. In: Proceedings 2022 IEEE International Conference on e-Business Engineering ICEBE 2022: 14–16 October 2022 Bournemouth, United Kingdom. Paper presented at IEEE International Conference on E-Business Engineering (ICEBE), 14–16 October 2022 Bournemouth, United Kingdom (pp. 66-73). IEEE
Open this publication in new window or tab >>A generic and modularized Digital twin enabled human-robot collaboration
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2022 (English)In: Proceedings 2022 IEEE International Conference on e-Business Engineering ICEBE 2022: 14–16 October 2022 Bournemouth, United Kingdom, IEEE, 2022, p. 66-73Conference paper, Published paper (Refereed)
Abstract [en]

Recently, the manufacturing paradigm shifts from mass production to mass customization, which results in urgently demands for the development of intelligent, flexible and automatic manufacturing systems for handling complex manufacturing tasks with high efficiency. The use of collaborative robots, an essential enabling technology for developing human-robot collaboration (HRC), is on the rise for human-centric intelligent automation design. An effective virtual simulation platform, which can continuously simulate and evaluate HRC performance in different working scenarios, is lacking in developing an HRC system in a sophisticated industrial arena. This paper presents a generic and modularized digital twin enabled HRC framework based on the synergy effect of human, robotic and environment-related factors to provide a flexible, compatible, re-configurable solution to ease the implementation of HRC in the real world. The feasibility of the proposed framework is validated through the practical implementation of a food packaging job, which involves a human operator and an ABB robotic arm collaboratively working together, on an industrial shop.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Collaborative robots, Intelligent robots, Machine design, Automatic manufacturing systems, Complex manufacturing, Human-robot collaboration, Intelligent manufacturing system, Manufacturing paradigm, Mass customization, Mass production, Modularized, Paradigm shifts, Simulation platform, collaborative robot, Digital Twin
National Category
Production Engineering, Human Work Science and Ergonomics Robotics
Research subject
Virtual Manufacturing Processes; VF-KDO
Identifiers
urn:nbn:se:his:diva-22311 (URN)10.1109/ICEBE55470.2022.00021 (DOI)2-s2.0-85148656253 (Scopus ID)978-1-6654-9244-7 (ISBN)978-1-6654-9245-4 (ISBN)
Conference
IEEE International Conference on E-Business Engineering (ICEBE), 14–16 October 2022 Bournemouth, United Kingdom
Funder
Knowledge Foundation
Note

© 2022 IEEE

This research was supported by the Knowledge Foundation (KKS, Sweden, through virtual Factory with Knowledge Driven Optimization (VF-KDO) project, EU FoF-06-2014 SYMBIO-TIC project (No.637107) and Natural Science Foundation of China (grant no. 61803169) and the Fundamental Research Funds for the Central Universities (grant no. 2662018JC029).

Available from: 2023-03-02 Created: 2023-03-02 Last updated: 2023-09-01Bibliographically approved
Lu, X., Wang, W., Li, W., Jing, Y. & Li, X. (2022). A Generic Digital Twin Framework for Collaborative Supply Chain Development. In: 2022 5th International Conference on Computing and Big Data (ICCBD 2022): . Paper presented at 2022 5th International Conference on Computing and Big Data, ICCBD 2022, December 16-18, 2022 Shanghai, China (pp. 177-181). IEEE
Open this publication in new window or tab >>A Generic Digital Twin Framework for Collaborative Supply Chain Development
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2022 (English)In: 2022 5th International Conference on Computing and Big Data (ICCBD 2022), IEEE, 2022, p. 177-181Conference paper, Published paper (Refereed)
Abstract [en]

Current Supply Chains (SCs) are complex and diverse along with fragile to SC disruptions. This leads urgently needs to develop an intelligent, transparent, collaborative and resilient SC system to cope with unexpected SC disruptions. Digital twin (DT) is one of the most promising solutions to develop smart SCs that has been extensively studied recent years. However, SCDT paradigm is still at an early stage. This paper presents a generic and modularized five layers DT framework to provide a flexible and collaborative solution, which can be compatible with different DT systems in various SCs. The feasibility of the proposed framework is validated through a practical implementation in a distributed eyewear industry. 

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Digital twin, Supply Chain, Supply chain risk assessment, Supply chains, Collaborative supply chains, Current supplies, Eyewear, Modularized, Risks assessments, Supply chain systems, Supply-chain disruptions, Supply-chain risks, Risk assessment
National Category
Transport Systems and Logistics Production Engineering, Human Work Science and Ergonomics Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Sciences
Research subject
Virtual Manufacturing Processes
Identifiers
urn:nbn:se:his:diva-22474 (URN)10.1109/ICCBD56965.2022.10080555 (DOI)2-s2.0-85152411561 (Scopus ID)978-1-6654-5716-3 (ISBN)978-1-6654-5715-6 (ISBN)978-1-6654-5717-0 (ISBN)
Conference
2022 5th International Conference on Computing and Big Data, ICCBD 2022, December 16-18, 2022 Shanghai, China
Note

© 2022 IEEE

This research was performed within the project sustainable and resilient supply chain system based on AI and Big data analytics sponsored by Bournemouth University and Natural Science Foundation of China (grant no. 61803169) and the Fundamental Research Funds for the Central Universities (grant no. 2662018JC029). The authors would acknowledge the support from the experimental factory and engineers. 

Available from: 2023-04-27 Created: 2023-04-27 Last updated: 2023-07-14Bibliographically approved
Shao, B., Hou, Y., Huang, N., Wang, W., Lu, X. & Jing, Y. (2022). Deep Learning based Coffee Beans Quality Screening. In: Proceedings 2022 IEEE International Conference on e-Business Engineering ICEBE 2022: 14–16 October 2022 Bournemouth, United Kingdom. Paper presented at 2022 IEEE International Conference on e-Business Engineering, ICEBE 2022, 14-16 October 2022 Bournemouth, United Kingdom (pp. 271-275). IEEE
Open this publication in new window or tab >>Deep Learning based Coffee Beans Quality Screening
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2022 (English)In: Proceedings 2022 IEEE International Conference on e-Business Engineering ICEBE 2022: 14–16 October 2022 Bournemouth, United Kingdom, IEEE, 2022, p. 271-275Conference paper, Published paper (Refereed)
Abstract [en]

Coffee bean quality screening is a time-consuming work, and its workload increases abruptly with the rapid development of coffee beverage consumer market. In this work, a CNN-based classifier is developed to categorizing the coffee beans into sour, black, broken, moldy, shell, insect damage and good beans. The screening test results show that the screening accuracy could reach more than 90% for all other beans except for shell beans (88%). Therefore, the proposed method is feasible and promising. Moreover, a cost-effective automatic coffee bean screening system using the developed classifier is manufactured and implemented for a local company. 

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Cost effectiveness, Deep learning, Coffee bean screening, Coffee beans, Coffee beverages, Consumer market, Convolutional neural network, Cost effective, Insect damage, Screening system, Screening tests, Convolutional neural networks, coffee beans screening
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-22314 (URN)10.1109/ICEBE55470.2022.00054 (DOI)2-s2.0-85148621439 (Scopus ID)978-1-6654-9244-7 (ISBN)978-1-6654-9245-4 (ISBN)
Conference
2022 IEEE International Conference on e-Business Engineering, ICEBE 2022, 14-16 October 2022 Bournemouth, United Kingdom
Note

© 2022 IEEE

Available from: 2023-03-02 Created: 2023-03-02 Last updated: 2023-05-03Bibliographically approved
Xu, L., de Vrieze, P., Lu, X. & Wang, W. (2022). Digital Twins Approach for Sustainable Industry. In: Jennifer Horkoff; Estefania Serral; Jelena Zdravkovic (Ed.), Advanced Information Systems Engineering Workshops: CAiSE 2022 International Workshops, Leuven, Belgium, June 6–10, 2022, Proceedings. Paper presented at International Conference on Advanced Information Systems Engineering (CAiSE 2022), Advanced Information Systems Engineering Workshops, CAiSE 2022 International Workshops, Leuven, Belgium, June 6–10, 2022 (pp. 126-134). Cham: Springer, 451
Open this publication in new window or tab >>Digital Twins Approach for Sustainable Industry
2022 (English)In: Advanced Information Systems Engineering Workshops: CAiSE 2022 International Workshops, Leuven, Belgium, June 6–10, 2022, Proceedings / [ed] Jennifer Horkoff; Estefania Serral; Jelena Zdravkovic, Cham: Springer, 2022, Vol. 451, p. 126-134Conference paper, Published paper (Refereed)
Abstract [en]

Sustainable industry is a part of The European Green Deal, which aims to achieve the EU’s climate and environmental goals based on the circular economy. Digital twins are important technologies for realizing industry 4.0 and related sectors. In this paper, we looked at building the DTs for manufacturing, healthcare and construction industrial sectors in Industry 4.0 architecture to realize a sustainable industry.

Place, publisher, year, edition, pages
Cham: Springer, 2022
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 451
Keywords
Digital Twins, Industry 4.0, Sustainable industry
National Category
Engineering and Technology Computer and Information Sciences
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-21236 (URN)10.1007/978-3-031-07478-3_11 (DOI)000871752700010 ()2-s2.0-85131296285 (Scopus ID)978-3-031-07477-6 (ISBN)978-3-031-07478-3 (ISBN)
Conference
International Conference on Advanced Information Systems Engineering (CAiSE 2022), Advanced Information Systems Engineering Workshops, CAiSE 2022 International Workshops, Leuven, Belgium, June 6–10, 2022
Note

Springer Cham

© 2022 Springer Nature Switzerland AG. Part of Springer Nature.

Available from: 2022-06-16 Created: 2022-06-16 Last updated: 2022-11-10Bibliographically approved
Li, X., Lu, X., Wang, W. & Jing, Y. (2022). Review on Learning-based Methods for shop Scheduling problems. In: Proceedings 2022 IEEE International Conference on e-Business Engineering ICEBE 2022: 14–16 October 2022 Bournemouth, United Kingdom. Paper presented at IEEE International Conference on E-Business Engineering (ICEBE), 14–16 October 2022 Bournemouth, United Kingdom (pp. 294-298). IEEE
Open this publication in new window or tab >>Review on Learning-based Methods for shop Scheduling problems
2022 (English)In: Proceedings 2022 IEEE International Conference on e-Business Engineering ICEBE 2022: 14–16 October 2022 Bournemouth, United Kingdom, IEEE, 2022, p. 294-298Conference paper, Published paper (Refereed)
Abstract [en]

Shop scheduling is an effective way for manufacturers to improve their manufacturing performances. However, due to its complexity, it is difficult to deal with shop scheduling problems (SSP). Thus, SSP has received a lot of attention from industry and academia. Various kinds of methods have been proposed to solve SSP. Learning-based method is just one of the most representative methods for SSP. This paper focuses on reviewing the learning-based methods for SSP. Firstly, the methods for SSP are briefly introduced. Then, its description and model are provided and its classification is discussed. Next, the learning-based methods for SSP are classified according to the machine learning technique used in the methods. Based on the classification, the related work on each type of learning-based methods for SSP is summarized and further analyzed and compared with other traditional methods. Finally, the future research opportunities and challenges of the learning-based methods for SSP are summarized. 

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Learning systems, Reinforcement learning, Classifieds, Learning-based methods, Machine learning techniques, Manufacturing performance, Reinforcement learnings, Related works, Research opportunities, Scheduling problem, Shop scheduling, Shop scheduling problem, Neural networks, artificial neural networks, learning-based method, shop scheduling problems
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Production Engineering, Human Work Science and Ergonomics Communication Systems
Research subject
Virtual Manufacturing Processes; VF-KDO
Identifiers
urn:nbn:se:his:diva-22313 (URN)10.1109/ICEBE55470.2022.00058 (DOI)2-s2.0-85148646349 (Scopus ID)978-1-6654-9244-7 (ISBN)978-1-6654-9245-4 (ISBN)
Conference
IEEE International Conference on E-Business Engineering (ICEBE), 14–16 October 2022 Bournemouth, United Kingdom
Note

© 2022 IEEE

The work is supported by the Knowledge Foundation (KKS), Sweden, through Virtual Factory with Knowledge-Driven Optimization (VF-KDO) project and Natural Science Foundation of China (grant no. 61803169). The paper reflects only the authors’ views.

Available from: 2023-03-02 Created: 2023-03-02 Last updated: 2023-09-08Bibliographically approved
Wilhelmsson, U., Wang, W., Zhang, R. & Toftedahl, M. (2022). Shift from game-as-a-product to game-as-a-service research trends. Service Oriented Computing and Applications, 16(2), 79-81
Open this publication in new window or tab >>Shift from game-as-a-product to game-as-a-service research trends
2022 (English)In: Service Oriented Computing and Applications, ISSN 1863-2386, E-ISSN 1863-2394, Vol. 16, no 2, p. 79-81Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
Springer Nature Switzerland AG, 2022
National Category
Computer and Information Sciences Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
GAME Research Group; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-21234 (URN)10.1007/s11761-022-00335-7 (DOI)000811413100001 ()2-s2.0-85132124155 (Scopus ID)
Note

© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022

© 2022 Springer Nature Switzerland AG. Part of Springer Nature.

Published online: 15 june 2022

Available from: 2022-06-16 Created: 2022-06-16 Last updated: 2022-09-01Bibliographically approved
Wang, W. & Zhang, R. (2021). Improved Game Units Balancing In Game Design Through Combinatorial Optimization. In: Proceedings 2021 IEEE International Conference on e-Business Engineering ICEBE 2021: 12-14 November 2021 Guangzhou, China. Paper presented at 2021 IEEE International Conference on e-Business Engineering, ICEBE 2021, 12-14 November 2021, Guangzhou, China (pp. 64-69). IEEE
Open this publication in new window or tab >>Improved Game Units Balancing In Game Design Through Combinatorial Optimization
2021 (English)In: Proceedings 2021 IEEE International Conference on e-Business Engineering ICEBE 2021: 12-14 November 2021 Guangzhou, China, IEEE, 2021, p. 64-69Conference paper, Published paper (Refereed)
Abstract [en]

Game balancing is an essential part of game design, and it plays a vital role as the balancing results directly affect the players' experiences. At present, there is no well-admitted definition of game balance. Generally, the game designers hold their own envisions of “balancing” in specific contextualized environments. Accordingly, it is a great challenge for game designers/developers to understand and establish. Since game balance is a multifaceted concept, there is no prescriptive guideline either. In this work, numerical balancing is selected to investigate the possibility of applying engineering optimization techniques to the game unit balancing in a quantitative manner. Currently, although the definite rules of game mechanics could be expressed mathematically, the game developers tune different attributes manually to evaluate the changes in an iterative way, which is tedious and time-consuming. Moreover, the balancing quality and efficiency strongly rely on the game developers' personal skills. To address this issue, we modeled the balancing process as a combinatorial optimization problem, in which the assessment metrics of game units are optimization objectives, and the attributes of game units are variables. The “balancing” of game units is interpreted as minimizing the standard deviations of the assessment metrics, and the evolutionary algorithm such as NSGA-II is used to find the optimization solutions. A typical case study is employed to demonstrate the proposed idea, and the optimization results show that the proposed method can simultaneously improve the numerical balancing in terms of quality and efficiency. At last, we summarize the contributions of the work and discuss how the proposed method can be further improved for game balancing in future work.

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
game balance, game units balance, numerical balancing, combinatorial optimization, evolutionary algorithm, game design
National Category
Computer Sciences
Research subject
Production and Automation Engineering; Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-21075 (URN)10.1109/ICEBE52470.2021.00022 (DOI)000942017800011 ()2-s2.0-85128722628 (Scopus ID)978-1-6654-4418-7 (ISBN)978-1-6654-4419-4 (ISBN)
Conference
2021 IEEE International Conference on e-Business Engineering, ICEBE 2021, 12-14 November 2021, Guangzhou, China
Note

©2021 IEEE

IEEE Computer Society

Available from: 2022-04-22 Created: 2022-04-22 Last updated: 2023-08-23Bibliographically approved
Holm, M., Senington, R., Wang, W. & Lindblom, J. (2021). Real-World Industrial Demonstrators on Human–Robot Collaborative Assembly (1ed.). In: Lihui Wang; Xi Vincent Wang; József Váncza; Zsolt Kemény (Ed.), Advanced Human-Robot Collaboration in Manufacturing: (pp. 413-438). Cham: Springer
Open this publication in new window or tab >>Real-World Industrial Demonstrators on Human–Robot Collaborative Assembly
2021 (English)In: Advanced Human-Robot Collaboration in Manufacturing / [ed] Lihui Wang; Xi Vincent Wang; József Váncza; Zsolt Kemény, Cham: Springer, 2021, 1, p. 413-438Chapter in book (Refereed)
Abstract [en]

The development of human–robot collaboration (HRC) is expected to have increasing importance in Industry 4.0 for a growing number of companies. The purpose of this chapter is to address the role and relevance of jointly designed, developed and implemented industrial demonstrators of HRC systems in projects, resulting in an increased knowledge—both for academia and industrial partners—of how to successfully present the obtained research results in an industrial environment. In particular, the chapter focuses on the role of demonstrators and presents three perspectives related to the use of demonstrators in bridging the gap between current knowledge and the work practice on the shop floor. One perspective is the joint process of developing three industrial demonstrators of HRC within the SYMBIO-TIC project, in order to provide the envisioned benefits for the addressed industrial requirements from the companies. Another perspective is how to evaluate the intended operators’ perceptions and experiences of these HRC systems from a human’s perspective as well as presenting the results obtained from such an evaluation. The last perspective is the voices raised from the industrial project partners’ views about jointly building industrial demonstrators as well as the benefits of participating in the research project. The chapter ends with conclusions, an identified research challenge and future work. It also addresses the societal impact of using collaborative robots in industry, and their contributions to society.

Place, publisher, year, edition, pages
Cham: Springer, 2021 Edition: 1
National Category
Robotics
Research subject
Production and Automation Engineering; Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-19943 (URN)10.1007/978-3-030-69178-3_17 (DOI)978-3-030-69177-6 (ISBN)978-3-030-69180-6 (ISBN)978-3-030-69178-3 (ISBN)
Projects
SYMBIO-TIC
Available from: 2021-06-22 Created: 2021-06-22 Last updated: 2021-09-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1781-2753

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