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Novel Industry 4.0 Technologies and Applications

Nikolaos Papakostas
Carmen Constantinescu
2 and
Dimitris Mourtzis
Laboratory for Advanced Manufacturing Simulation and Robotics, School of Mechanical and Materials Engineering, University College Dublin, D04 V1W8 Dublin, Ireland
Fraunhofer IAO, Nobelstraße 12, 70569 Stuttgart, Germany
Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(18), 6498;
Submission received: 13 September 2020 / Accepted: 16 September 2020 / Published: 17 September 2020
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
The Industry 4.0 paradigm has led to the creation of new opportunities for taking advantage of a series of diverse technologies in the manufacturing domain, including Internet of Things, Augmented and Virtual Reality, Machine Learning, Advanced Robotics, Additive Manufacturing, System and Process Simulation, Computer-Aided Design/Engineering/Manufacturing/Process Planning systems as well as Product Lifecycle Management platforms.
The integration of such technologies, employing information that is generated during different phases of a product lifecycle, may lead to the better utilization and optimization of existing resources, such as labor, materials, energy, and equipment, as well as to the development of products of higher quality and performance in a sustainable manner.
Considering the continuous growth of available computational power, the proliferation of cloud-based platforms, the cost-efficient development and utilization of once prohibitively expensive equipment, such as robotic systems (stationary, mobile, collaborative, and wearable), advanced sensors, and 3D printers, there will be a time when engineers will be able to transform the requirements pertaining to a new product to detailed production, supply chain, and product lifecycle management configurations in a very accurate manner, exploring diverse demand and production scenarios. Engineers would at some point be capable of identifying very fast, perhaps in a fully automated and intuitive way, what the product design would look like, which resources would be needed for developing the product and how they should be configured, who would be supplying parts, equipment, and services, how the product could be repaired and updated, and how it could be recycled when reaching its end of life.
Although products and manufacturing processes are typically quite complex and are often associated with a high degree of uncertainty, it is expected that the availability of more information will lead to the generation of structured product development knowledge and models, which will make their way in tightly integrated digital manufacturing platforms, thus enabling the faster and overall more efficient development of products and services.
However, the first demonstrations of Industry 4.0 principles and technologies are already here and will pave the way towards further developments in manufacturing. This book includes 13 papers that discuss how the Industry 4.0 paradigm may be applied in real engineering and manufacturing cases. The topics covered span a series of diverse areas related to: product design and development [1,2,3], manufacturing systems and operations [4,5,6,7,8], process engineering [9,10], and Industry 4.0 technologies review and realization [11,12,13].

Author Contributions

All authors contributed equally to the preparation of this manuscript. All authors have read and agreed to the published version of the manuscript.


The partial financial support from a research grant from Science Foundation Ireland (SFI) under Grant Number 16/RC/3872, through the I-Form Advanced Manufacturing Research Centre, is gratefully appreciated.


This publication was only possible with the invaluable contributions from the authors, reviewers, and the editorial team of Applied Sciences. We would particularly like to thank our Managing Editor Melon Zhang.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Papakostas, N.; Newell, A.; George, A. An agent-based decision support platform for additive manufacturing applications. Appl. Sci. 2020, 10, 4953. [Google Scholar] [CrossRef]
  2. He, C.; Zhang, S.-Y.; Qiu, L.-M.; Liu, X.; Wang, Z. Assembly tolerance design based on skin model shapes considering processing feature degradation. Appl. Sci. 2019, 9, 3216. [Google Scholar] [CrossRef] [Green Version]
  3. Saorín, J.L.; De La Torre-Cantero, J.; Melian-Diaz, D.; López-Chao, V. Cloud-based collaborative 3D modeling to train engineers for the industry 4.0. Appl. Sci. 2019, 9, 4559. [Google Scholar] [CrossRef] [Green Version]
  4. Papacharalampopoulos, A.; Giannoulis, C.; Stavropoulos, P.; Mourtzis, D. A digital twin for automated root-cause search of production alarms based on KPIs aggregated from IoT. Appl. Sci. 2020, 10, 2377. [Google Scholar] [CrossRef] [Green Version]
  5. Zan, T.; Liu, Z.; Su, Z.; Wang, M.; Gao, X.; Chen, D. Statistical process control with intelligence based on the deep learning model. Appl. Sci. 2019, 10, 308. [Google Scholar] [CrossRef] [Green Version]
  6. Dahmen, C.; Constantinescu, C. Methodology of employing exoskeleton technology in manufacturing by considering time-related and ergonomics influences. Appl. Sci. 2020, 10, 1591. [Google Scholar] [CrossRef] [Green Version]
  7. Wang, X.; Luo, X.; Han, B.; Chen, Y.; Liang, G.; Zheng, K. Collision-free path planning method for robots based on an improved rapidly-exploring random tree algorithm. Appl. Sci. 2020, 10, 1381. [Google Scholar] [CrossRef] [Green Version]
  8. De Miranda, S.S.-F.; Gonzalez, F.A.; Salguero, J.; Gutiérrez, M.J. Ávila life cycle engineering 4.0: A proposal to conceive manufacturing systems for industry 4.0 centred on the human factor (DfHFinI4.0). Appl. Sci. 2020, 10, 4442. [Google Scholar] [CrossRef]
  9. Mourtzis, D.; Siatras, V.; Angelopoulos, J. Real-time remote maintenance support based on Augmented Reality (AR). Appl. Sci. 2020, 10, 1855. [Google Scholar] [CrossRef] [Green Version]
  10. Li, J.; Liu, Y.; Xie, J.; Wang, X.; Ge, X. Cutting path planning technology of shearer based on virtual reality. Appl. Sci. 2020, 10, 771. [Google Scholar] [CrossRef] [Green Version]
  11. Pech, M.; Vrchota, J. Classification of small and medium-sized enterprises based on the level of industry 4.0 implementation. Appl. Sci. 2020, 10, 5150. [Google Scholar] [CrossRef]
  12. Vrchota, J.; Pech, M. Readiness of enterprises in Czech Republic to implement industry 4.0: Index of industry 4.0. Appl. Sci. 2019, 9, 5405. [Google Scholar] [CrossRef] [Green Version]
  13. Zhang, Q.; Kong, X.-D.; Yu, B.; Ba, K.-X.; Jin, Z.-G.; Kang, Y. Review and development trend of digital hydraulic technology. Appl. Sci. 2020, 10, 579. [Google Scholar] [CrossRef] [Green Version]

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MDPI and ACS Style

Papakostas, N.; Constantinescu, C.; Mourtzis, D. Novel Industry 4.0 Technologies and Applications. Appl. Sci. 2020, 10, 6498.

AMA Style

Papakostas N, Constantinescu C, Mourtzis D. Novel Industry 4.0 Technologies and Applications. Applied Sciences. 2020; 10(18):6498.

Chicago/Turabian Style

Papakostas, Nikolaos, Carmen Constantinescu, and Dimitris Mourtzis. 2020. "Novel Industry 4.0 Technologies and Applications" Applied Sciences 10, no. 18: 6498.

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