**Contents**


#### **Lingyu Chen, Jieji Zheng, Dapeng Fan and Ning Chen**


## **About the Editors**

## **Pingyu Jiang**

Pingyu Jiang is a professor of the state key laboratory for manufacturing systems engineering at Xi'an Jiatong University (XJTU), China. He received his B.E. degree in Mechanical Engineering from Nanjing Institute of Technology (now Southeast University), China, in 1983, and M.E. and Ph.D. degrees in Mechanical Engineering from XJTU in 1988 and 1991. Dr. Jiang held Humboldt and JSPS international research fellowships from 1995 to 1999, respectively, in WZL of RWTH Aachen, IPK of TU Berlin, Germany, and the Department of Production Engineering of Tokyo Metropolitan University, Japan. He was also an ASTAR visiting staff of Nanyang Technological University in 2022, Singapore, JSPS, and a Humboldt short-term visiting professor, respectively, in the Department of Production Engineering of Tokyo Metropolitan University in 2003, Japan, and LPS of TU Dortmund and IWB of TU Munich in 2011, Germany. Dr. Jiang has been a faculty member at XJTU since 1991 and was promoted to Full Professor in 1999. He also served as the Vice Dean of the School of Mechanical Engineering, XJTU, from 2003 to 2006. He is both an active trustee and a deputy head in several divisions of nation-wide academic societies such as China Mechanical Engineering Society, China Artificial Intelligence Society, China Automation Society, etc. He also serves as an Associate Editor of Journal of Xi'an Jiaotong University and is the Editorial Board Member of several journals, such as the Journal of Engineering Design, Machines, Systems, etc. Dr. Jiang is author or co-author of more than 300 papers and 6 monographs. His current research interest is systems engineering for manufacturing and services, including social manufacturing, cyber-physical systems, MES/smart factory, product design with artificial intelligence, product service systems, open collaborative product development, etc.

## **Gang Xiong**

Gang Xiong is a professor at the Institute of Automation, Chinese Academy of Sciences (CAS), China. He received B.Eng. and M.Eng. degrees from Xi'an University of Science and Technology, China, in 1991 and 1994, respectively, and a Ph.D. degree from Shanghai Jiao Tong University, China. From 1996 to 1998, he was a Postdoctoral Scientist and an Associate Professor for Zhejiang University, China. From 1998 to 2001, he was a Senior Research Fellow with the Tampere University of Technology, Finland. From 2001 to 2007, he was a Specialist and the Project Manager with Nokia Corporation, Finland. In 2007, he was a Senior Consultant and the Team Leader with Accenture and Chevron, USA. In 2008, he was the Deputy Director of the Informatization Office, CAS, China. In 2011, he became the Deputy Director of Cloud Computing Center, CAS, China. Between January 2015 and December 2017, he worked as FinDiPro Professor for Aalto University, Finland. He has successfully finished more than 50 projects funded by Chinese NSFC/MOST/MIIT, Finnish TEKES, the Academy of Finland etc. He is the author or co-author of about 450 refereed journal and conference papers, including 70 SCI papers, and 320 EI papers. He is editor or co-editor of 2 academic books, the author of 10 book chapters. He is author or co-author of about 60 Chinese patents, 6 PCT, and 60 Chinese Software Copyrights. He received 5 awards from the IEEE and the government. His research interests include artificial intelligence, intelligent control and management, 3D printing and social manufacturing.

## **Timo R. Nyberg**

Timo R. Nyberg received M.Sc. (Tech) degrees in Mechanical Engineering from the Helsinki University of Technology, Finland, in 1988, and the Dr. (Tech) degrees in Automation from Helsinki University of Technology, Finland, in 1993. From 1996 to 2002, he was Professor of Automation and Director of Graduate School, Tampere University of Technology, Finland. From 2001 to 2002, he was Senior Vice President, Novo Astra Oy, Finland. From 2002 to 2005, he was Laboratory Director and Management Board member, Helsinki University of Applied Sciences, Finland. Since 2010, he has worked as a Senior Research Fellow and Research Director of the School of Science at Aalto University, Finland. Since 2012, he has also been a Visiting Professor of the Cloud Computing Center, CAS, China. His research covers enterprise automation systems, business strategies in software, and social manufacturing. He has finished more than 30 research projects funded by Finnish TEKES, the Academy of Finland, the European Union, and international companies. He is the author or co-author of about 100 refereed journal and conference proceeding papers, and 35 EU/US/Finnish patents. He has experience from both the academic world and industries (Valmet, Nokia). He has over 15 years of experience from R&D cooperation with Chinese and American universities (Tsinghua University, Tongji University, MIT, and Stanford University). He is a board member in Finland–China Trade association and Finnish Foundation for Inventions.

## **Zhen Shen**

Zhen Shen received his B. E. and Ph.D. in 2004 and 2009, respectively, both from the Department of Automation, Tsinghua University. Currently, he is an Associate Professor at the Institute of Automation, Chinese Academy of Sciences. His research interests are intelligent manufacturing and complex systems. He has authored about 100 referred journal and conference papers, and holds more than 30 national invention patents and 5 US patents. He is a recipient of the 2005 "Outstanding Achievement Award" from the United Technology Research Center, a recipient of the Second Class Award of China Industry-University-Research Cooperation Innovation Achievement in 2018 and the Wu Wenjun Second Class Award of Artificial Intelligence Science and Technology in 2019. He is a member of the IEEE, and was selected into the CAS Key Technical Talents program in 2019.

## **Maolin Yang**

Maolin Yang obtained the Bachelor's degree in Mechanical Engineering from Tongji University in 2012, Master's degree in Industrial Engineering from Shanghai University of Engineering Science in 2015, and Ph.D. degree in Mechanical Engineering From Xi'an Jiaotong University in 2021. Currently, he is an Assistant Professor in the School of Mechanical Engineering at Xi'an Jiaotong University (XJTU), China. His research interests include social manufacturing, innovation product development, deep learning-based data-driven intelligent product design, etc.

## **Guangyu Xiong**

Guangyu Xiong is a SCOR Professional (SCOR-P) and SOCR-DS specialist certified by ASCM, and an ASCM (Association for Supply Chain Management) instructor. She has worked for one of the top 500 enterprises, the ABB group, for many years as Scientist, Senior specialist, Chief Specialist and Quality and OpX manager. She has led improvement projects to meet customers' expectation and business targets in previous years. Meanwhile, she has had more than 40 publications, including academic papers and books. Dr. Xiong is skilled and knowledgeable at SCM and OpX (Operations Excellence) with Lean/TPS, 6-sigma and TOC (theory of constraints), both in implementation and training/coaching. Her work is focused on process development or improvement through business supply chain/value chain. She is extending her subject on certain innovation areas, such as lean cleaner production and lean energy efficiency. Her new interests are industrial 4.0, intelligent technology in SCM and industrial management. She is experienced in supply chain/logistics and operations. Her subjects cover SCM/VCM (Supply Chain Management/Value Chain Management), Supplier Development, Quality Improvement, Change Management, and OpX (Operational Excellence) with related methodologies and tools.

## *Editorial* **Editorial: Social Manufacturing on Industrial Internet**

**Pingyu Jiang 1,\*, Gang Xiong 2, Timo R. Nyberg 3, Zhen Shen 2, Maolin Yang 1 and Guangyu Xiong 4**


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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 such a background, social manufacturing has emerged as a new kind of manufacturing paradigm characterized by self-driven, selforganization, 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 of completing production/service orders with the limited internal manufacturing resource 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. In this regard, this Special Issue is established to explore how exactly the newly emerged social manufacturing paradigm influences the trends of mass customization and the configuration/operation patterns during order delivering, and how advanced information technologies such as industrial internet, cloud computing, blockchain, etc., can boost the development and application of social manufacturing. In total, 14 papers, including 2 review articles, have been collected in this Special Issue, and the key topics explored in these papers are briefly introduced below.

The paper in [1] introduced the issue of product customization under the context of social manufacturing. It introduced a framework that utilizes advanced deep learning models and cloud computing technologies to transfer the text/image data generated during manufacturing process into customized 3D contents that can be directly used for 3D printing. Supported by the framework, more effective product customization and more efficient social manufacturing operation and optimization can be realized.

The papers in [2–5] provided insights from the perspective of production configuration under the context of social manufacturing. Specifically, a graph convolutional networkbased method for socialized designer team configuration was proposed in [2]. By utilizing the graph convolution network embedded with the graph matching algorithm, it can identify the socialized designers with the suitable design resources for a certain socialized design project, and thus provide decision making support for designer team configuration. In [3], a fast manufacturing system configuration model was established under the context of industrial 4.0 and social manufacturing, and the model could be particularly useful for small enterprises to improve their existing manufacturing system to meet the new requirements of wider customized product varieties, a shorter response time to new orders, a faster manufacturing system configuration process, etc., from the new market environment. Aiming at the situation that one large 3D printing order could be collaboratively carried out by multiple factories under the context of social manufacturing, a multi-part production planning system particularly for 3D printing orders was established

**Citation:** Jiang, P.; Xiong, G.; Nyberg, T.R.; Shen, Z.; Yang, M.; Xiong, G. Editorial: Social Manufacturing on Industrial Internet. *Machines* **2023**, *11*, 383. https://doi.org/10.3390/ machines11030383

Received: 9 March 2023 Accepted: 11 March 2023 Published: 14 March 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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in [4]. The system utilizes multiple types of intelligent algorithms to support printing order separation, printing part orientation, and nesting during order collaboration. A novel method for configurating the data acquisition network for socialized intelligent factory was proposed in [5]. The data acquisition network can realize real-time data collection and pre-processing for energy consumption analysis, and thus support further intelligent optimization on energy consumption.

The papers in [6–9] provided ideas from the perspective of supporting production operations with advanced techniques such as industrial internet, cloud-edge collaboration technologies, etc., which are fundamental for enabling the social manufacturing paradigm. For example, production data managemen<sup>t</sup> and application during cloud manufacturing is considered in [6]. In this paper, a kind of cloud manufacturing system enhanced with an industrial internet of things and cloud-edge collaboration was established. The system can describe the characteristics of heterogeneous manufacturing resources, the operational data of the resources, and their relations with the service-oriented manufacturing system. In addition, a middleware and AI edge gate way model was established, and it utilizes real-time sensor data to realize the remote monitoring and controlling of cloud manufacturing resources. Supported by the system, companies can better utilize the distributed cloud manufacturing resources and improve their response speed to personalized orders. In [7], a kind of equipment asset managemen<sup>t</sup> model was established based on industrial internet platform techniques and a fuzzy DEMATEL-TOPSIS algorithm under the general framework of system engineering. By collecting the required data for asset managemen<sup>t</sup> from the industrial internet platform established for the target industrial, the fuzzy DEMATEL-TOPSIS algorithm is then applied to identify the relations among the requirements from customers and the characteristics of the assets registered in the platform. In this way, the model helps to establish the solutions for asset managemen<sup>t</sup> for the entire product lifecycle. The one review article in [8] reviewed the related research on utilizing advanced information technologies (e.g., edge/cloud/fog computing, big data collecting and processing, artificial intelligence, digital twin, etc.) for equipment or production line maintenance. The other review paper in [9] established a blockchain-based method to support a trustworthy operation environment for manufacturing activities under the context of industrial 5.0 and social manufacturing. By applying an industrial Internet of Things network enhanced with blockchain networks, the method can protect the confidential and private data of stakeholders, and thus support the realization of resilient and trustworthy manufacturing operation.

The papers in [10–13] provided technical road maps from the perspective of a few fundamental information and data techniques that can support social and intelligent manufacturing realization. For example, a kind of high precision synchronous control method for a fieldbus control system is established in [10], and it can improve the control accuracy of multi-axis collaborative machining tasks. A novel method for liquid crystal display module alignment and particle detection in anisotropic conductive film bonding was established in [11]. By applying only one camera instead of multiple to obtain images of multiple locations, the method can realize the transformation from image space to world coordinates. Compared with the traditional methods which apply multiple cameras, the method can accurately identify the rotation center, the position, and angle deviation of the target object with a relatively lower economic and time cost. A kind of deep learningdriven defect image generation method was established in [12]. The method was for solving data enhancement problems in industrial defect detection. By applying a masked defect image generation adversarial network, the method can solve the problems of a loss of background information, an insufficient consideration of complex defects, and a lack of accurate annotation image data which usually occurred during data-driven defect detection. A kind of online dimensional error prediction method to predict the errors occurred during grinding process was proposed in [13]. The method was driven by principal component analysis, extreme learning machine, genetic algorithm, and ensemble strategy (bagging algorithm). By applying the method, grinding errors can be detected in a real-time manner with our extra devices and space occupation.

Finally, a case study of social manufacturing from the entry point of the value chain was proposed in [14]. First, a kind of value chain model under the context of the social manufacturing paradigm was established; it then utilized the cases from the crane industry to demonstrate the advantages of the established model.

All the papers above provide a reference for academic research and the industrial application of social manufacturing. However, as a newly emerged research topic, there are still unexplored issues for more optimal interaction/configuration/operation under the context of social manufacturing such as the implementation of collective and social 3D printing factories, realizing collective intelligence during social manufacturing operation, applying advanced cross-modal data processing techniques for extracting and utilizing production data for social manufacturing optimization, etc.

**Author Contributions:** Conceptualization, P.J., G.X. (Gang Xiong), T.R.N., Z.S., M.Y. and G.X. (Guangyu Xiong); writing—original draft preparation, M.Y.; writing—review and editing, P.J. and M.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** We express thanks to the editors of MDPI for their excellent support for this Special Issue; it would have been impossible without their persistence and grea<sup>t</sup> effort. Many thanks to all our colleagues who have made contributions and thank you especially to all the reviewers for their professional and valuable comments, helping us finalize these high-quality manuscripts.

**Conflicts of Interest:** The authors declare no conflict of interest.
