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Machines
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Social Manufacturing on Industrial Internet
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Social Manufacturing on Industrial Internet

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 34233

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Pingyu Jiang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: networked digital manufacturing; service-oriented manufacturing systems engineering; RFID/ IOT; logistics engineering; product family design theory; collaborative engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gang Xiong

E-Mail Website
Co-Guest Editor
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, China
Interests: social manufacturing; industrial internet; cyber physical social system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Timo R. Nyberg

E-Mail Website
Co-Guest Editor
Aalto Advisors, Finland
Interests: social manufacturing; industrial Internet; cyber physical social system
Prof. Dr. Zhen Shen

E-Mail Website
Co-Guest Editor
State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Interests: social manufacturing; industrial Internet; cyber physical social system
Dr. Maolin Yang

E-Mail Website
Co-Guest Editor
State Key Laboratory of Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710054, China
Interests: social manufacturing; industrial Internet; cyber physical social system
Prof. Dr. Guangyu Xiong

E-Mail Website
Co-Guest Editor
1# Laboratory-Operations Development, Ningbo Intelligent Manufacturing Institute, Ningbo 315151, China
Interests: social manufacturing; industrial Internet; cyber physical social system

Special Issue Information

Dear Colleagues,

The fast development of the industrial Internet is boosting the evolution of the manufacturing industry to a new stage of socialization, servitization, universal interaction and connection, and platformization. Under this background, social manufacturing has emerged as a new kind of manufacturing paradigm established based on the self-driven, self-organized, self-adaptive, and cyber–physical–social interaction among huge numbers of socialized manufacturing resource providers. The most prominent advantage of social manufacturing is its capability to complete production/service orders with the limited internal manufacturing resources of an enterprise by utilizing socialized manufacturing resources from the outside, and this can be applied in both large and small enterprises and trigger value co-creation for both resource providers and demanders. To date, social manufacturing has drawn the attention of both the academic and industrial field due to its promising research and application values. However, social manufacturing is still in its infant stage as the fast development of the industrial Internet, artificial intelligence, collective intelligence, cloud/edge/fog computing, and the new generation of information and communication technologies are changing the interaction/configuration/operational mechanisms of social manufacturing every day.

In this regard, this Special Issue aims at exploring a wide range of topics related to social manufacturing on/over the industrial Internet, from the debating of its conation and concept architecture to its key enabling technologies and application verification. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • The debating of the conation and concept architecture of social manufacturing on/over the industrial Internet.
  • The interaction/configuration/operation architecture of social manufacturing from multiple manufacturing resource dimensions such as equipment level, production line level, factory level, and cross factory level.
  • The intelligent and interconnected equipment that supports machine–machine and human–machine interaction in the industrial Internet environment of social manufacturing.
  • The application of advanced technologies that support interaction, configuration, and operation in the context of social manufacturing, such as CPSS, big data analysis, industrial Internet of Things, block chain, artificial intelligence, and digital twins.
  • Novel industrial Internet oriented distributed software models for social manufacturing.
  • Case studies of social manufacturing in both testbeds and real industrial scenarios, especially in large and complex manufacturing systems.

We look forward to hearing from you.

Prof. Dr. Pingyu Jiang
Guest Editor

Prof. Dr. Gang Xiong
Prof. Dr. Timo R. Nyberg
Prof. Dr. Zhen Shen
Dr. Maolin Yang
Prof. Dr. Guangyu Xiong
Co-Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • social manufacturing
  • industrial Internet
  • manufacturing system
  • intelligent and interconnected equipment
  • cyber physical social system
  • artificial intelligence
  • industrial Internet of Things
  • digital twins

Published Papers (15 papers)

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Editorial

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3 pages, 169 KiB  
Editorial
Editorial: Social Manufacturing on Industrial Internet
by Pingyu Jiang, Gang Xiong, Timo R. Nyberg, Zhen Shen, Maolin Yang and Guangyu Xiong
Machines 2023, 11(3), 383; https://doi.org/10.3390/machines11030383 - 14 Mar 2023
Viewed by 1098
Abstract
The fast development of the industrial internet is boosting the evolution of the manufacturing industry to a new stage of socialization, servitization, universal interaction and connection, and platformization [...] Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)

Research

Jump to: Editorial, Review

16 pages, 15747 KiB  
Article
An Efficient Product-Customization Framework Based on Multimodal Data under the Social Manufacturing Paradigm
by Yanpeng Li, Huaiyu Wu, Tariku Sinshaw Tamir, Zhen Shen, Sheng Liu, Bin Hu and Gang Xiong
Machines 2023, 11(2), 170; https://doi.org/10.3390/machines11020170 - 26 Jan 2023
Cited by 2 | Viewed by 1531
Abstract
With improvements in social productivity and technology, along with the popularity of the Internet, consumer demands are becoming increasingly personalized and diversified, promoting the transformation from mass customization to social manufacturing (SM). How to achieve efficient product customization remains a challenge. Massive multi-modal [...] Read more.
With improvements in social productivity and technology, along with the popularity of the Internet, consumer demands are becoming increasingly personalized and diversified, promoting the transformation from mass customization to social manufacturing (SM). How to achieve efficient product customization remains a challenge. Massive multi-modal data, such as text and images, are generated during the manufacturing process. Based on the data, we can use large-scale pre-trained deep learning models and neural radiation field (NeRF) techniques to generate user-friendly 3D contents for 3D Printing. Furthermore, by the cloud computing technology, we can achieve more efficient SM operations. In this paper, we propose an efficient product-customization framework that can provide new ideas for the design, implementation, and optimization of collaborative production, and can provide insights for the upgrading of manufacturing industries. Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)
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18 pages, 4340 KiB  
Article
Research on the High Precision Synchronous Control Method of the Fieldbus Control System
by Lingyu Chen, Jieji Zheng, Dapeng Fan and Ning Chen
Machines 2023, 11(1), 98; https://doi.org/10.3390/machines11010098 - 11 Jan 2023
Cited by 1 | Viewed by 1549
Abstract
The synchronization control performance of the Fieldbus control system (FCS) is an important guarantee for the completion of multi-axis collaborative machining tasks, and its synchronization control accuracy is one of the decisive factors for the machining quality. To improve the synchronization control accuracy [...] Read more.
The synchronization control performance of the Fieldbus control system (FCS) is an important guarantee for the completion of multi-axis collaborative machining tasks, and its synchronization control accuracy is one of the decisive factors for the machining quality. To improve the synchronization control accuracy of FCS, this paper first makes a comprehensive analysis of the factors affecting synchronization in FCS. Secondly, by analyzing the communication model of linear Ethernet, a distributed clock compensation method based on timestamps is proposed to solve the asynchronous problem of communication data transmission in the linear ethernet bus topology. Then, based on the CANopen application layer protocol, the FCS communication and device control task collaboration method is proposed to ensure the synchronous control of multiple devices by FCS. Finally, an experimental platform is built for functional verification and performance testing of the proposed synchronization method. The results show that the proposed synchronization method can achieve a communication synchronization accuracy of 50 ns and a device control synchronization accuracy of 150 ns. Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)
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19 pages, 4617 KiB  
Article
A Novel Method for LCD Module Alignment and Particle Detection in Anisotropic Conductive Film Bonding
by Tengyang Li, Feng Zhang, Huabin Yang, Huiyuan Luo and Zhengtao Zhang
Machines 2023, 11(1), 49; https://doi.org/10.3390/machines11010049 - 01 Jan 2023
Cited by 1 | Viewed by 1515
Abstract
In this paper, we propose a misalignment correct method and a particle detection algorithm to improve the accuracy in the quality inspection of the LCD module after the anisotropic conductive film (ACF) bonding. We use only one camera to acquire images of multiple [...] Read more.
In this paper, we propose a misalignment correct method and a particle detection algorithm to improve the accuracy in the quality inspection of the LCD module after the anisotropic conductive film (ACF) bonding. We use only one camera to acquire images of multiple positions in order to establish the transformation from the image space to the world coordinate. Our method can accurately determine the center of rotation of the carrier table and calculate the deviation of position and angle of the tested module. Compared to traditional ways that rely on multiple cameras to align the large-sized product, our method has the advantages of simple structure, low cost, and fast calibration process. The particle detection is performed after positioning all bumps of the bonded module. The gray morphology-based algorithm is developed to detect the extreme point of every particle and refine the particle result through blob analysis. This method reduces the over-checking rate and performs better on the detection precision for dense particles. We verify the effectiveness of our proposed methods in our experiments. The alignment error can be less than 0.05 mm, and the accuracy of the particle detection is 93% while the recall rate is 92.4%. Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)
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21 pages, 4563 KiB  
Article
Dimensional Error Prediction of Grinding Process Based on Bagging–GA–ELM with Robust Analysis
by Lei Yang, Yibo Jiang, Hua Liu and Xianna Yang
Machines 2023, 11(1), 32; https://doi.org/10.3390/machines11010032 - 27 Dec 2022
Cited by 3 | Viewed by 1213
Abstract
Grinding, which determines the final dimension of parts, is an important process in manufacturing companies. In praxis, in order to avoid quality problems on the customer’s side, an online dimension check is normally used after the grinding process to ensure the product dimensions; [...] Read more.
Grinding, which determines the final dimension of parts, is an important process in manufacturing companies. In praxis, in order to avoid quality problems on the customer’s side, an online dimension check is normally used after the grinding process to ensure the product dimensions; however, it is always hysteretic and needs extra space and machine investment. To deal with the issue, dimensional error prediction of the grinding process is highly needed, and does not require extra space or machinery, as well as having better real-time performance. In this paper, a dimensional error prediction algorithm using principal component analysis (PCA), extreme learning machine (ELM), genetic algorithm (GA), and ensemble strategy (bagging algorithm) is designed. Specifically, PCA is used as a pre-treatment method to extract the main relevant components, then a bagging–GA–ELM model is constructed to predict the final product dimensional error after the grinding process, in which extreme learning machine (ELM) is utilized as a basic framework because of its fast calculation speed. GA, with its excellent global optimization capability, is implemented to search optimal input weights and thresholds of ELM, enabling ELM to obtain a better prediction performance. In addition, considering the complex environment of the industrial field, the bagging algorithm is employed to enhance the anti-noise ability of the proposed algorithm. Finally, the proposed algorithm is verified by a case from a bearing company. Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)
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17 pages, 9091 KiB  
Article
Mask-Guided Generation Method for Industrial Defect Images with Non-uniform Structures
by Jing Wei, Zhengtao Zhang, Fei Shen and Chengkan Lv
Machines 2022, 10(12), 1239; https://doi.org/10.3390/machines10121239 - 18 Dec 2022
Cited by 1 | Viewed by 2446
Abstract
Defect generation is a crucial method for solving data problems in industrial defect detection. However, the current defect generation methods suffer from the problems of background information loss, insufficient consideration of complex defects, and lack of accurate annotations, which limits their application in [...] Read more.
Defect generation is a crucial method for solving data problems in industrial defect detection. However, the current defect generation methods suffer from the problems of background information loss, insufficient consideration of complex defects, and lack of accurate annotations, which limits their application in defect segmentation tasks. To tackle these problems, we proposed a mask-guided background-preserving defect generation method, MDGAN (mask-guided defect generation adversarial networks). First, to preserve the normal background and provide accurate annotations for the generated defect samples, we proposed a background replacement module (BRM), to add real background information to the generator and guide the generator to only focus on the generation of defect content in specified regions. Second, to guarantee the quality of the generated complex texture defects, we proposed a double discrimination module (DDM), to assist the discriminator in measuring the realism of the input image and distinguishing whether or not the defects were distributed at specified locations. The experimental results on metal, fabric, and plastic products showed that MDGAN could generate diversified and high-quality defect samples, demonstrating an improvement in detection over the traditional augmented samples. In addition, MDGAN can transfer defects between datasets with similar defect contents, thus achieving zero-shot defect detection. Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)
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43 pages, 10079 KiB  
Article
Application of Industrial Internet for Equipment Asset Management in Social Digitalization Platform Based on System Engineering Using Fuzzy DEMATEL-TOPSIS
by Yuguang Bao, Xianyu Zhang, Tongtong Zhou, Zhihua Chen and Xinguo Ming
Machines 2022, 10(12), 1137; https://doi.org/10.3390/machines10121137 - 29 Nov 2022
Cited by 7 | Viewed by 3303
Abstract
In any industry, Equipment Asset Management (EAM) is at the core of the production activities. With the rapid development of Industrial Internet technologies and platforms, the EAM based on the Industrial Internet has become an important development trend. Meanwhile, the paradigm of EAM [...] Read more.
In any industry, Equipment Asset Management (EAM) is at the core of the production activities. With the rapid development of Industrial Internet technologies and platforms, the EAM based on the Industrial Internet has become an important development trend. Meanwhile, the paradigm of EAM is changing, from a single machine to integrated systems, from the phase of using them to the end of their lifecycle, from breakdown maintenance to predictive maintenance, and from local decision-making to collaborative optimization. However, because of the lack of a unified understanding of the Industrial Internet platforms (IIPs) and the lack of a comprehensive reference architecture and detailed implementation framework, the implementation of EAM projects will face greater risks according to special needs in different industries. Based on the method of system engineering, this study proposes a general reference model and a reference architecture of implementation for the Industrial Internet Solution for Industrial Equipment Asset Management (I3EAM). Further, to help enterprise to evaluate and select their best-fit I3EAM scheme and platform partner, we proposed a set of performance indicators of I3EAM schemes and a quantitative decision-making method based on fuzzy DEMATEL-TOPSIS. Finally, a case study for an I3EAM in automated container terminals was conducted. In the multi-criteria decision environment with complex uncertainty, the project group identified the I3EAM metrics priorities and social digitalization platforms that were more in line with the actual needs of the automated container terminal and firms. The complexity and time of the decision-making process were dramatically reduced. In terms of feasibility and validity, the decision result was positively verified by the feedback from the enterprise implementation. The given model, architecture, and method in this study can create a certain reference value for various industrial enterprises to carry out the analysis and top-level planning of their I3EAM needs and choose the partner for co-implementation. In addition, the research results of this study have the potential to support the construction of standard systems and the planning and optimization of the cross-domain social platform, etc. Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)
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22 pages, 5836 KiB  
Article
Industry 4.0-Oriented Turnkey Project: Rapid Configuration and Intelligent Operation of Manufacturing Systems
by Shulian Xie, Weimin Zhang, Feng Xue, Dongdong Li, Yangbokun Liu, Jürgen Fleischer and Christopher Ehrmann
Machines 2022, 10(11), 983; https://doi.org/10.3390/machines10110983 - 27 Oct 2022
Cited by 2 | Viewed by 2190
Abstract
More extensive personalized product requirements and shorter product life cycles have put forward higher requirements for the rapid establishment, commissioning, and operation of corresponding manufacturing systems. However, the traditional manufacturing system development process is complicated, resulting in a longer delivery time. Many manufacturing [...] Read more.
More extensive personalized product requirements and shorter product life cycles have put forward higher requirements for the rapid establishment, commissioning, and operation of corresponding manufacturing systems. However, the traditional manufacturing system development process is complicated, resulting in a longer delivery time. Many manufacturing enterprises, especially small and micro enterprises, may not have the necessary manufacturing knowledge or capabilities to meet these requirements. Therefore, it is essential to promote the construction of turnkey projects under the paradigm of Industry 4.0, parallelizing and integrating the existing manufacturing system development process based on mass manufacturing equipment to quickly provide turnkey solutions for manufacturing systems’ configuration and implementation for these enterprises. This paper aims to extract and refine the configuration and operation key views of the Industry 4.0-oriented Turnkey Project (I4TP) from Reference Architecture Model Industrie 4.0 (RAMI4.0) and use it to guide the development of key functional processes of turnkey projects to achieve rapid configuration and efficient operation management of manufacturing systems. The turnkey project platform in the Advanced Manufacturing Technology Center (AMTC) is taken as a demonstration case to provide a reference idea for the rapid configuration and intelligent operation of the turnkey manufacturing system. Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)
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23 pages, 1426 KiB  
Article
A Case Study in Social Manufacturing: From Social Manufacturing to Social Value Chain
by Guang-Yu Xiong, Petri Helo, Steve Ekstrom and Tariku Sinshaw Tamir
Machines 2022, 10(11), 978; https://doi.org/10.3390/machines10110978 - 26 Oct 2022
Cited by 4 | Viewed by 1249
Abstract
A new manufacturing mode, called social manufacturing, has been developing widely, and employed in many enterprises across the business value chain in recent years. Faced with this increasing dynamic, both enterprises and customers have to be more aware of the potential opportunity and [...] Read more.
A new manufacturing mode, called social manufacturing, has been developing widely, and employed in many enterprises across the business value chain in recent years. Faced with this increasing dynamic, both enterprises and customers have to be more aware of the potential opportunity and benefit to be derived from this new manufacturing mode. One benefit is more value-adding potential for both enterprises upstream and customers downstream across the business value chain, compared with the normal mode. This research extends the application of social manufacturing to the entire business value chain system to bring new opportunities and value-adding potential for enterprises. This paper proposes a social value chain system that applies the social manufacturing mode to the entire value chain and contributes to three areas: (1) a new way of thinking for enterprises to create new opportunities to add value throughout the value chain by employing the social manufacturing mode; (2) establishing the social value chain system for all participants/enterprises across the chain in order to gain a win–win situation for all participants; and (3) suggesting some idea of a suitable performance measurement to monitor and evaluate the proposed social value chain system. Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)
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16 pages, 2592 KiB  
Article
Data Acquisition Network Configuration and Real-Time Energy Consumption Characteristic Analysis in Intelligent Workshops for Social Manufacturing
by Chaoyang Zhang, Juchen Zhang, Weixi Ji and Wei Peng
Machines 2022, 10(10), 923; https://doi.org/10.3390/machines10100923 - 10 Oct 2022
Cited by 1 | Viewed by 1296
Abstract
To achieve energy-saving production, one critical step is to calculate and analyze the energy consumption and energy efficiency of machining processes. However, considering the complexity and uncertainty of discrete manufacturing job shops, it is a significant challenge to conduct data acquisition and energy [...] Read more.
To achieve energy-saving production, one critical step is to calculate and analyze the energy consumption and energy efficiency of machining processes. However, considering the complexity and uncertainty of discrete manufacturing job shops, it is a significant challenge to conduct data acquisition and energy consumption data processing of manufacturing systems. Meanwhile, under the growing trend of personalization, social manufacturing is an emerging technical practice that allows prosumers to build individualized services with their partners, which produces new requirements for energy data processing. Thus, a real-time energy consumption characteristic analysis method in intelligent workshops for social manufacturing is established to realize data processing and energy efficiency evaluation automatically. First, an energy-conservation production architecture for intelligent manufacturing processes is introduced, and the configuration of a data acquisition network is described to create a ubiquitous manufacturing environment. Then, an energy consumption characteristic analysis method is proposed based on the process time window. Finally, a case study of coupling-part manufacturing verifies the feasibility and applicability of the proposed method. This method realizes a combination of social manufacturing and real-time energy characteristic analysis. Meanwhile, the energy consumption characteristics provide a decision basis for the energy-saving control of intelligent manufacturing workshops. Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)
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20 pages, 26200 KiB  
Article
An Efficient IIoT Gateway for Cloud–Edge Collaboration in Cloud Manufacturing
by Yi Zhang, Dunbing Tang, Haihua Zhu, Shihui Zhou and Zhen Zhao
Machines 2022, 10(10), 850; https://doi.org/10.3390/machines10100850 - 23 Sep 2022
Cited by 6 | Viewed by 2232
Abstract
The cloud manufacturing system can provide consumers with on-demand manufacturing services, which significantly improve the utilization rate of distributed manufacturing resources and the response speed of personalized product needs. In the cloud manufacturing platform, the successful implementation of various industrial applications relies on [...] Read more.
The cloud manufacturing system can provide consumers with on-demand manufacturing services, which significantly improve the utilization rate of distributed manufacturing resources and the response speed of personalized product needs. In the cloud manufacturing platform, the successful implementation of various industrial applications relies on the uploading and streaming of related field-level manufacturing data. For example, the realization of manufacturing service composition application should match the manufacturing tasks with distributed manufacturing resources according to their working state data and performance measurement data. Therefore, this paper proposes a data integration and analysis framework of a cloud manufacturing system based on cloud–edge collaboration and the Industrial Internet of Things (IIoT). A service-oriented information model is established to uniformly describe the related operational data and functional attributes of heterogeneous manufacturing resources. Secondly, a real-time transmission and integration method of high-volume operational field and sensor data based on message middleware is proposed to realize the remote monitoring of distributed manufacturing resources and efficient distribution of related data. Finally, a cloud–edge collaboration mechanism is put forward to train and update the parameters of various artificial intelligence models deployed at edge gateways. In the experiment, taking the computer numerical control (CNC) lathe as an example, the effectiveness of the proposed manufacturing resource access method is verified. Taking the fault diagnosis model of the CNC lathe as an example, the efficiency of the proposed cloud–edge collaboration mechanism for model updating is verified. Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)
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15 pages, 2034 KiB  
Article
A Graph Matching Model for Designer Team Selection for Collaborative Design Crowdsourcing Tasks in Social Manufacturing
by Dianting Liu, Danling Wu and Shan Wu
Machines 2022, 10(9), 776; https://doi.org/10.3390/machines10090776 - 06 Sep 2022
Cited by 2 | Viewed by 1342
Abstract
In order to find a suitable designer team for the collaborative design crowdsourcing task of a product, we consider the matching problem between collaborative design crowdsourcing task network graph and the designer network graph. Due to the difference in the nodes and edges [...] Read more.
In order to find a suitable designer team for the collaborative design crowdsourcing task of a product, we consider the matching problem between collaborative design crowdsourcing task network graph and the designer network graph. Due to the difference in the nodes and edges of the two types of graphs, we propose a graph matching model based on a similar structure. The model first uses the Graph Convolutional Network to extract features of the graph structure to obtain the node-level embeddings. Secondly, an attention mechanism considering the differences in the importance of different nodes in the graph assigns different weights to different nodes to aggregate node-level embeddings into graph-level embeddings. Finally, the graph-level embeddings of the two graphs to be matched are input into a multi-layer fully connected neural network to obtain the similarity score of the graph pair after they are obtained from the concat operation. We compare our model with the basic model based on four evaluation metrics in two datasets. The experimental results show that our model can more accurately find graph pairs based on a similar structure. The crankshaft linkage mechanism produced by the enterprise is taken as an example to verify the practicality and applicability of our model and method. Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)
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18 pages, 3513 KiB  
Article
A Multi-Part Production Planning System for a Distributed Network of 3D Printers under the Context of Social Manufacturing
by Inno Lorren Désir Makanda, Maolin Yang, Haoliang Shi, Wei Guo and Pingyu Jiang
Machines 2022, 10(8), 605; https://doi.org/10.3390/machines10080605 - 24 Jul 2022
Cited by 7 | Viewed by 1805
Abstract
Additive manufacturing (AM) systems are currently evolving into network-based models, where the distributed manufacturing resources from multiple enterprises are coordinated to complete product orders. The layer-by-layer approach of AM technologies gives manufacturers unprecedented freedom to create complex parts tailored to customer needs, but [...] Read more.
Additive manufacturing (AM) systems are currently evolving into network-based models, where the distributed manufacturing resources from multiple enterprises are coordinated to complete product orders. The layer-by-layer approach of AM technologies gives manufacturers unprecedented freedom to create complex parts tailored to customer needs, but this comes at slow build rates. Consequently, for AM to become mainstream in the industry, challenges in production planning remain to be addressed to increase AM system productivity. This paper considers two practical problems encountered in AM systems, namely, production planning and part-to-printer assignment, and a series of heuristic algorithms are proposed to solve these problems. In particular, an approach for automatically determining part orientation, part-to-printer allocation, and nesting of multiple parts for a distributed network of fused filament fabrication three-dimensional printers is described to reduce the total production cost and time regarding the context of social manufacturing. The proposed method is implemented through a web application. The case study, using real-world parts and comparative analysis findings, indicated that the proposed method produces high-performance results. Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)
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Review

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27 pages, 4054 KiB  
Review
Remote Monitoring and Maintenance for Equipment and Production Lines on Industrial Internet: A Literature Review
by Qingzong Li, Yuqian Yang and Pingyu Jiang
Machines 2023, 11(1), 12; https://doi.org/10.3390/machines11010012 - 22 Dec 2022
Cited by 5 | Viewed by 4530
Abstract
Monitoring and maintaining equipment and production lines ensure stable production by detecting and resolving abnormalities immediately. In the Industrial Internet, operational technology and advanced information technology are fused to improve the digitalization and intelligence of monitoring and maintenance. This paper provides a comprehensive [...] Read more.
Monitoring and maintaining equipment and production lines ensure stable production by detecting and resolving abnormalities immediately. In the Industrial Internet, operational technology and advanced information technology are fused to improve the digitalization and intelligence of monitoring and maintenance. This paper provides a comprehensive survey of monitoring and maintenance of equipment and production lines on the Industrial Internet. Firstly, a brief review of its architecture is given, and a reference architecture is summarized accordingly, clarifying the key enabling technologies involved. These key technologies are data collection technologies, edge computing, advanced communication technologies, fog computing, big data, artificial intelligence, and digital twins. For each of the key technologies, we provide a detailed literature review of their state-of-the-art advances. Finally, we discuss the challenges that it currently faces and give some suggestions for future research directions. Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)
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32 pages, 2590 KiB  
Review
Secure Blockchain Middleware for Decentralized IIoT towards Industry 5.0: A Review of Architecture, Enablers, Challenges, and Directions
by Jiewu Leng, Ziying Chen, Zhiqiang Huang, Xiaofeng Zhu, Hongye Su, Zisheng Lin and Ding Zhang
Machines 2022, 10(10), 858; https://doi.org/10.3390/machines10100858 - 26 Sep 2022
Cited by 24 | Viewed by 3870
Abstract
Resilient manufacturing is a vision in the Industry 5.0 blueprint for satisfying sustainable development goals under pandemics or the rising individualized product needs. A resilient manufacturing strategy based on the Industrial Internet of Things (IIoT) networks plays an essential role in facilitating production [...] Read more.
Resilient manufacturing is a vision in the Industry 5.0 blueprint for satisfying sustainable development goals under pandemics or the rising individualized product needs. A resilient manufacturing strategy based on the Industrial Internet of Things (IIoT) networks plays an essential role in facilitating production and supply chain recovery. IIoT contains confidential data and private information, and many security issues arise through vulnerabilities in the infrastructure. The traditional centralized IIoT framework is not only of high cost for system configuration but also vulnerable to cyber-attacks and single-point failure, which is not suitable for achieving the resilient manufacturing vision in Industry 5.0. Recently, researchers are seeking a secure solution of middleware based on blockchain technology integration for decentralized IIoT, which can effectively protect the consistency, integrity, and availability of IIoT data by utilizing the auditing and tamper-proof features of the blockchain. This paper presented a review of secure blockchain middleware for decentralized IIoT towards Industry 5.0. Firstly, the security issues of conventional IIoT solutions and the advantages of blockchain middleware are analyzed. Secondly, an architecture of secure blockchain middleware for decentralized IIoT is proposed. Finally, enabling technologies, challenges, and future directions are reviewed. The innovation of this paper is to study and discuss the distributed blockchain middleware, investigating its ability to eliminate the risk of a single point of failure via a distributed feature in the context of resilient manufacturing in Industry 5.0 and to solve the security issues from traditional centralized IIoT. Also, the four-layer architecture of blockchain middleware presented based on the IIoT application framework is a novel aspect of this review. It is expected that the paper lays a solid foundation for making IIoT blockchain middleware a new venue for Industry 5.0 research. Full article
(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)
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