Topic Editors

Department of Mechanical Engineering and Aeronautics, University of Patras, Rio Patras 26504, Greece
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
Prof. Dr. Baicun Wang
School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
Grenoble Institute of Technology (Grenoble INP), Grenoble, France
Dr. Sihan Huang
1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
2. Key Laboratory of Industry Knowledge & Data Fusion Technology and Application, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, 6928 Manno, Switzerland
Department of Chemical, Materials and Industrial Production Engineering, Piazzale Tecchio 80, 80125 Naples, Italy

Smart Manufacturing and Industry 5.0

Abstract submission deadline
closed (30 September 2023)
Manuscript submission deadline
closed (30 November 2023)
Viewed by
39941

Topic Information

Dear Colleagues,

Manufacturing and Production Industries are currently being reshaped to integrate the new Information and Communication Technologies (ICT) in the existing workplaces. Industry 5.0 is a value-driven approach and is based on three interconnected core pillars: 1) human-centricity, 2) sustainability, and 3) resilience. However, it is necessary to fully utilize the technologies and techniques developed under the framework of Industry 4.0 to implement a successful transition to Industry 5.0, and by extension to further facilitate the realization of Society 5.0. Therefore, authors are invited to participate in this topic and submit interesting research works, either research manuscripts or review manuscripts, in order to highlight the key results of research in areas relevant to the upcoming Industry 5.0 in the framework of Society 5.0.

Prof. Dr. Dimitris Mourtzis
Prof. Dr. Fei Tao
Prof. Dr. Baicun Wang
Dr. Andreas Riel
Dr. Sihan Huang
Prof. Dr. Emanuele Carpanzano
Prof. Dr. Doriana Marilena D'Addona
Topic Editors

Keywords

  • artificial intelligence (AI)
  • augmented reality (AR)
  • big data analytics (BDA)
  • digital twins (DT)
  • extended reality (XR)
  • global manufacturing and production networks
  • human-centric systems
  • human cyber-physical systems (HCPS)
  • human-robot collaboration (HRC)
  • Internet of Things (IoT)
  • mixed reality (MR)
  • predictive analytics
  • resilient manufacturing networks
  • simulation
  • sustainable manufacturing networks
  • virtual reality (VR)

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 5.3 2011 16.9 Days CHF 2400
Automation
automation
- 2.9 2020 26.3 Days CHF 1000
Electronics
electronics
2.9 5.3 2012 15.6 Days CHF 2400
Energies
energies
3.2 6.2 2008 16.1 Days CHF 2600
Machines
machines
2.6 3.0 2013 15.6 Days CHF 2400
Technologies
technologies
3.6 6.7 2013 19.7 Days CHF 1600
Inventions
inventions
3.4 4.8 2016 17.4 Days CHF 1800

Preprints.org is a multidiscipline platform providing preprint service that is dedicated to sharing your research from the start and empowering your research journey.

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

  1. Immediately share your ideas ahead of publication and establish your research priority;
  2. Protect your idea from being stolen with this time-stamped preprint article;
  3. Enhance the exposure and impact of your research;
  4. Receive feedback from your peers in advance;
  5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (12 papers)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
14 pages, 2244 KiB  
Article
A Kinetic Study of a Photo-Oxidation Reaction between α-Terpinene and Singlet Oxygen in a Novel Oscillatory Baffled Photo Reactor
by Jianhan Chen, Rohen Prinsloo and Xiongwei Ni
Technologies 2024, 12(3), 29; https://doi.org/10.3390/technologies12030029 - 21 Feb 2024
Viewed by 1523
Abstract
By planting LEDs on the surfaces of orifice baffles, a novel batch oscillatory baffled photoreactor (OBPR) together with polymer-supported Rose Bengal (Ps-RB) beads are here used to investigate the reaction kinetics of a photo-oxidation reaction between α-terpinene and singlet oxygen (1O [...] Read more.
By planting LEDs on the surfaces of orifice baffles, a novel batch oscillatory baffled photoreactor (OBPR) together with polymer-supported Rose Bengal (Ps-RB) beads are here used to investigate the reaction kinetics of a photo-oxidation reaction between α-terpinene and singlet oxygen (1O2). In the mode of NMR data analysis that is widely used for this reaction, α-terpinene and ascaridole are treated as a reaction pair, assuming kinetically singlet oxygen is in excess or constant. We have, for the first time, here examined the validity of the method, discovered that increasing α-terpinene initially leads to an increase in ascaridole, indicating that the supply of singlet oxygen is in excess. Applying a kinetic analysis, a pseudo-first-order reaction kinetics is confirmed, supporting this assumption. We have subsequently initiated a methodology of estimating the 1O2 concentrations based on the proportionality of ascaridole concentrations with respect to its maximum under these conditions. With the help of the estimated singlet oxygen data, the efficiency of 1O2 utilization and the photo efficiency of converting molecular oxygen to 1O2 are further proposed and evaluated. We have also identified conditions under which a further increase in α-terpinene has caused decreases in ascaridole, implying kinetically that 1O2 has now become a limiting reagent, and the method of treating α-terpinene and ascaridole as a reaction pair in the data analysis would no longer be valid under those conditions. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
Show Figures

Figure 1

12 pages, 1131 KiB  
Article
Virtual Commissioning of Linked Cells Using Digital Models in an Industrial Metaverse
by Marco Ullrich, Rashik Thalappully, Frieder Heieck and Bernd Lüdemann-Ravit
Automation 2024, 5(1), 1-12; https://doi.org/10.3390/automation5010001 - 2 Feb 2024
Viewed by 1445
Abstract
Various software environments have been developed in the past to create digital twins of single cells or a digital twin of a factory. Each environment has its own strengths and weaknesses and has been designed with a specific focus. The environments that are [...] Read more.
Various software environments have been developed in the past to create digital twins of single cells or a digital twin of a factory. Each environment has its own strengths and weaknesses and has been designed with a specific focus. The environments that are able to holistically simulate complete factories are limited in terms of the modelling details required for the analysis of single manufacturing cells (e.g., manufacturer-independence of the individual digital twins) and their ability for virtual commissioning. This paper presents three options for realising a virtual commissioning of linked cells using a 3D integration platform with NVIDIA Omniverse, consisting of two different digital models fused into a combined model, also representing material flow. First, with a source/sink solution and unidirectional connector controlled by OPC UA; secondly, with a bidirectional connector, developed in the course of this elaboration, and an extension of the 3D integration platform controlled by Apache Kafka; thirdly, with a bidirectional connector and using only an extension of the 3D integration platform. The research demonstrates that virtually commissioning multiple linked digital twins from different manufacturers in a 3D platform with material flow makes a significant contribution to the industrial metaverse. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
Show Figures

Figure 1

18 pages, 3209 KiB  
Article
Application of Task-Aligned Model Based on Defect Detection
by Ming-Hung Hung, Chao-Hsun Ku and Kai-Ying Chen
Automation 2023, 4(4), 327-344; https://doi.org/10.3390/automation4040019 - 27 Oct 2023
Viewed by 1455
Abstract
In recent years, with the rise of the automation wave, reducing manual judgment, especially in defect detection in factories, has become crucial. The automation of image recognition has emerged as a significant challenge. However, the problem of how to effectively improve the classification [...] Read more.
In recent years, with the rise of the automation wave, reducing manual judgment, especially in defect detection in factories, has become crucial. The automation of image recognition has emerged as a significant challenge. However, the problem of how to effectively improve the classification of defect detection and the accuracy of the mean average precision (mAP) is a continuous process of improvement and has evolved from the original visual inspection of defects to the present deep learning detection system. This paper presents an application of deep learning, and the task-aligned approach is firstly used on metal defects, and the anchor and bounding box of objects and categories are continuously optimized by mutual correction. We used the task-aligned one-stage object detection (TOOD) model, then improved and optimized it, followed by deformable ConvNets v2 (DCNv2) to adjust the deformable convolution, and finally used soft efficient non-maximum suppression (Soft-NMS) to optimize intersection over union (IoU) and adjust the IoU threshold and many other experiments. In the Northeastern University surface defect detection dataset (NEU-DET) for surface defect detection, mAP increased from 75.4% to 77.9%, a 2.5% increase in mAP, and mAP was also improved compared to existing advanced models, which has potential for future use. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
Show Figures

Figure 1

26 pages, 11465 KiB  
Article
Smart Platform for Monitoring and Control of Discrete Event System in Industry 4.0 Concept
by Filip Žemla, Ján Cigánek, Danica Rosinová, Erik Kučera and Oto Haffner
Appl. Sci. 2023, 13(19), 10697; https://doi.org/10.3390/app131910697 - 26 Sep 2023
Cited by 1 | Viewed by 1155
Abstract
We are now living in a time when the fourth industrial revolution is bringing new technologies with intensive digitalization of all levels of production. This paper presents a complex solution for the monitoring, diagnosis, and control of the production process, in our case, [...] Read more.
We are now living in a time when the fourth industrial revolution is bringing new technologies with intensive digitalization of all levels of production. This paper presents a complex solution for the monitoring, diagnosis, and control of the production process, in our case, discrete event systems. The proposed solution—a mechatronic platform—further develops and extends our recent results where the overall concept was outlined and some partial tasks were studied. The present paper provides the complete structure of the proposed platform together with implementation details for all functionalities. The developed platform uses the latest technologies, such as OPC UA; industrial communication and visualization protocols and tools; and augmented reality, which enables comfortable interaction with real processes. The laboratory-scaled case study shows the qualities of the proposed solution. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
Show Figures

Figure 1

19 pages, 2442 KiB  
Article
Solution Space Management to Enable Data Farming in Strategic Network Design
by Sebastian Kroeger, Marc Wegmann, Christoph Soellner and Michael F. Zaeh
Appl. Sci. 2023, 13(15), 8604; https://doi.org/10.3390/app13158604 - 26 Jul 2023
Cited by 1 | Viewed by 821
Abstract
During strategic network design, not only strategic but also operational decisions must be made long before a production network is put into operation. These include determining the location and size of inventories within the production network and setting operational parameters for production lines, [...] Read more.
During strategic network design, not only strategic but also operational decisions must be made long before a production network is put into operation. These include determining the location and size of inventories within the production network and setting operational parameters for production lines, such as the shift model. However, the large solution space comprising a high number of highly uncertain design parameters makes these decisions challenging without decision support. Therefore, data farming offers a potential solution, as synthetic data can be generated via the execution of multiple simulation experiments spanning the solution space and then analyzed using data mining techniques to provide data-based decision support. However, data farming has not yet been applied to strategic network design due to the lack of adequate solution space management. To address this shortcoming, this paper presents a structured solution space management approach that integrates production network-specific requirements and Design of Experiment (DoE) methods. The approach enables converting the solution space in strategic network design into individual input data sets for simulation experiments, generating a comprehensive database that can be mined for data-based decision support. The applicability and validity of the comprehensive approach are ensured via a case study in the automotive industry. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
Show Figures

Figure 1

35 pages, 5159 KiB  
Perspective
Towards Customer Outcome Management in Smart Manufacturing
by Paul Grefen, Irene Vanderfeesten, Anna Wilbik, Marco Comuzzi, Heiko Ludwig, Estefania Serral, Frank Kuitems, Menno Blanken and Marcin Pietrasik
Machines 2023, 11(6), 636; https://doi.org/10.3390/machines11060636 - 7 Jun 2023
Viewed by 1648
Abstract
The outcome economy is a relatively new economic and business paradigm that promotes focusing on the effects that the use of provided products and services create for customers in their markets, rather than focusing on these products or services themselves from the providers’ [...] Read more.
The outcome economy is a relatively new economic and business paradigm that promotes focusing on the effects that the use of provided products and services create for customers in their markets, rather than focusing on these products or services themselves from the providers’ perspective. This paradigm has been embraced in various fields of business but has not yet been fully integrated with the concept of smart industry. To fill this gap, in this vision paper we provide a framework that does make this integration, showing the full structure of customer outcome management in smart manufacturing, from both business and digital technology perspectives. In applying this structure, a feedback loop is created that spans the markets of provider and customer and supports data-driven product evolution, manufacturing, and delivery. We propose a business reference framework that can be used as a blueprint for designing practical scenarios. We show how integrated digital support for such a scenario can be realized using a well-structured combination of technologies from the fields of the internet of things, business intelligence and federated learning, blockchain, and business process management. We illustrate all of this with a visionary case study inspired by industrial practice in the automotive domain. In doing so, we provide both an academic basis for the integration of several currently dispersed research fields that need to be integrated to further smart manufacturing towards outcome management and a practical basis for the well-structured design and implementation of customer outcome management business cases in smart manufacturing. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
Show Figures

Figure 1

28 pages, 8846 KiB  
Article
Optimization of 3D Tolerance Design Based on Cost–Quality–Sensitivity Analysis to the Deviation Domain
by Kaili Yang, Yi Gan, Yanlong Cao, Jiangxin Yang and Zijian Wu
Automation 2023, 4(2), 123-150; https://doi.org/10.3390/automation4020009 - 21 Apr 2023
Cited by 1 | Viewed by 2021
Abstract
Under the new geometric product specification (GPS), a two-dimensional chain cannot completely guarantee quality of the product. To optimize the allocation of three-dimensional tolerances in the conceptual design stage, the geometric variations of the tolerance zone to the deviation domain will be mapped [...] Read more.
Under the new geometric product specification (GPS), a two-dimensional chain cannot completely guarantee quality of the product. To optimize the allocation of three-dimensional tolerances in the conceptual design stage, the geometric variations of the tolerance zone to the deviation domain will be mapped in this paper. The deviation-processing cost, deviation-quality loss cost, and deviation-sensitivity cost function relationships combined with the tolerance zone described by the small displacement torsor theory are discussed. Then, synchronous constraint of the function structure and tolerance is realized. Finally, an improved bat algorithm is used to solve the established three-dimensional tolerance mathematical model. A case study in the optimization of three-part tolerance design is used to illustrate the proposed model and algorithms. The performance and advantage of the proposed method are discussed in the end. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
Show Figures

Figure 1

11 pages, 5968 KiB  
Communication
Examination of Polymer Blends by AFM Phase Images
by Enrico Werner, Uwe Güth, Bennet Brockhagen, Christoph Döpke and Andrea Ehrmann
Technologies 2023, 11(2), 56; https://doi.org/10.3390/technologies11020056 - 12 Apr 2023
Cited by 4 | Viewed by 3200
Abstract
Atomic force microscopy (AFM) belongs to the high-resolution surface morphology investigation methods. Since it can, in many cases, be applied in air, samples can more easily be inspected than by a scanning electron microscope (SEM). In addition, several special modes exist which enable [...] Read more.
Atomic force microscopy (AFM) belongs to the high-resolution surface morphology investigation methods. Since it can, in many cases, be applied in air, samples can more easily be inspected than by a scanning electron microscope (SEM). In addition, several special modes exist which enable examination of the mechanical and other physical parameters of the specimen, such as friction, adhesion between tip and sample, elastic modulus, etc. In tapping mode, e.g., phase imaging can be used to qualitatively distinguish between different materials on the surface. This is especially interesting for polymers, for which the evaluation by energy-dispersive X-ray spectroscopy (EDS) is mostly irrelevant. Here we give an overview of phase imaging experiments on different filaments used for 3D printing by fused deposition modeling (FDM). Furthermore, the acrylonitrile butadiene styrene (ABS), especially different poly(lactide acids) (PLAs) with special features, such as thermochromic or photochromic properties, are investigated and compared with SEM images. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
Show Figures

Figure 1

25 pages, 7049 KiB  
Article
Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
by Antonio Maffei, Michela Dalle Mura, Fabio Marco Monetti and Eleonora Boffa
Appl. Sci. 2023, 13(3), 1760; https://doi.org/10.3390/app13031760 - 30 Jan 2023
Cited by 2 | Viewed by 2368
Abstract
Augmented (AR) and Mixed Reality (MR) technologies are enablers of the Industry 4.0 paradigm and are spreading at high speed in production. Main applications include design, training, and assembly guidance. The latter is a pressing concern, because assembly is the process that accounts [...] Read more.
Augmented (AR) and Mixed Reality (MR) technologies are enablers of the Industry 4.0 paradigm and are spreading at high speed in production. Main applications include design, training, and assembly guidance. The latter is a pressing concern, because assembly is the process that accounts for the biggest portion of total cost within production. Teaching and guiding operators to assemble with minimal effort and error rates is pivotal. This work presents the development of a comprehensive MR application for guiding novice operators in following simple assembly instructions. The app follows innovative programming logic and component tracking in a dynamic environment, providing an immersive experience that includes different guidance aids. The application was tested by experienced and novice users, data were drawn from the performed experiments, and a questionnaire was submitted to collect the users’ perception. Results indicate that the MR application was easy to follow and even gave confidence to inexperienced subjects. The guidance support was perceived as useful by the users, though at times invasive in the field of view. Further development effort is required to draw from this work a complete and usable architecture for MR application in assembly, but this research forms the basis to achieve better, more consistent instructions for assembly guidance based on component tracking. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
Show Figures

Figure 1

19 pages, 1881 KiB  
Article
Research on a Visual Servoing Control Method Based on Perspective Transformation under Spatial Constraint
by Chenguang Cao
Machines 2022, 10(11), 1090; https://doi.org/10.3390/machines10111090 - 18 Nov 2022
Cited by 5 | Viewed by 1802
Abstract
Visual servoing has been widely employed in robotic control to increase the flexibility and precision of a robotic arm. When the end-effector of the robotic arm needs to be moved to a spatial point without a coordinate, the conventional visual servoing control method [...] Read more.
Visual servoing has been widely employed in robotic control to increase the flexibility and precision of a robotic arm. When the end-effector of the robotic arm needs to be moved to a spatial point without a coordinate, the conventional visual servoing control method has difficulty performing the task. The present work describes space constraint challenges in a visual servoing system by introducing an assembly node and then presents a two-stage visual servoing control approach based on perspective transformation. A virtual image plane is constructed using a calibration-derived homography matrix. The assembly node, as well as other objects, are projected into the plane after that. Second, the controller drives the robotic arm by tracking the projections in the virtual image plane and adjusting the position and attitude of the workpiece accordingly. Three simple image features are combined into a composite image feature, and an active disturbance rejection controller (ADRC) is established to improve the robotic arm’s motion sensitivity. Real-time simulations and experiments employing a robotic vision system with an eye-to-hand configuration are used to validate the effectiveness of the presented method. The results show that the robotic arm can move the workpiece to the desired position without using coordinates. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
Show Figures

Figure 1

16 pages, 10733 KiB  
Article
Retrieval of a 3D CAD Model of a Transformer Substation Based on Point Cloud Data
by Lijuan Long, Yonghua Xia, Minglong Yang, Bin Wang and Yirong Pan
Automation 2022, 3(4), 563-578; https://doi.org/10.3390/automation3040028 - 28 Sep 2022
Cited by 3 | Viewed by 2321
Abstract
When constructing a three-dimensional model of a transformer substation, it is critical to quickly find the 3D CAD model corresponding to the current point cloud data from a large number of transformer substation model libraries (due to the complexity and variety of models [...] Read more.
When constructing a three-dimensional model of a transformer substation, it is critical to quickly find the 3D CAD model corresponding to the current point cloud data from a large number of transformer substation model libraries (due to the complexity and variety of models in the model base). In response to this problem, this paper proposes a method to quickly retrieve a 3D CAD model. Firstly, a 3D CAD model that shares the same size as the current point cloud model bounding box is extracted from the model library by the double-layer bounding box screening method. Then, the selected 3D CAD model is finely compared with the point cloud model by the multi-view method. The 3D CAD model that has the highest degree of corresponding to the point cloud data is acquired. The proposed algorithm, compared to other similar methods, has the advantages of retrieval accuracy and high efficiency. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
Show Figures

Figure 1

29 pages, 8042 KiB  
Review
A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0
by Dimitris Mourtzis, John Angelopoulos and Nikos Panopoulos
Energies 2022, 15(17), 6276; https://doi.org/10.3390/en15176276 - 28 Aug 2022
Cited by 145 | Viewed by 14307
Abstract
In the era of Industry 4.0, manufacturing and production systems were revolutionized by increasing operational efficiency and developing and implementing new business models, services, and products. Concretely, the milestone set for Industry 4.0 was to improve the sustainability and efficiency of production systems. [...] Read more.
In the era of Industry 4.0, manufacturing and production systems were revolutionized by increasing operational efficiency and developing and implementing new business models, services, and products. Concretely, the milestone set for Industry 4.0 was to improve the sustainability and efficiency of production systems. By extension, the emphasis was focused on both the digitization and the digitalization of systems, providing room for further improvement. However, the current technological evolution is more system/machine-oriented, rather than human-oriented. Thus, several countries have begun orchestrating initiatives towards the design and development of the human-centric aspect of technologies, systems, and services, which has been coined as Industry 5.0. The impact of Industry 5.0 will extend to societal transformation, which eventually leads to the generation of a new society, the Society 5.0. The developments will be focused on the social and human-centric aspect of the tools and technologies introduced under the framework of Industry 4.0. Therefore, sustainability and human well-being will be at the heart of what comes next, the Industry 5.0, as a subset of Society 5.0. Industry 5.0 will build on the foundations laid during Industry 4.0 by emphasizing human-centered, resilient, and sustainable design. Consequently, the authors in this research work, through a critical literature review, aim to provide adequate reasoning for considering Industry 5.0 as a framework for enabling the coexistence of industry and emerging societal trends and needs. The contribution of this research work extends to the provision of a framework to facilitate the transition from Industry 4.0 to Society 5.0. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
Show Figures

Figure 1

Back to TopTop