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Workshop 1: Industrial Big Data and Industrial Digitization
Title 1: Industrial Big Data and Industrial Digitization
Keywords: Industrial Big Data, Intelligent Scheduling, Industrial Digitization
Summary:
Big data-driven smart manufacturing makes factory operation transparent, workshop management accurate, product quality consistent, production line efficiency optimized, and equipment running smoothly, and it promotes collaborative optimization of the whole production life cycle. Big data-driven manufacturing model is the current research hotspot of intelligent manufacturing system. However, the main challenges are: the theoretical system of "big data-driven" scientific research paradigm is not yet complete, the enabling technologies of industrial big data are far from mature, and the industrial application scenarios on the ground are rare. The new generation of artificial intelligence technology will promote the analysis and application of industrial big data, comprehensively portray the system operation law, change the production operation mode, and create a new generation of intelligent manufacturing mode driven by big data.
This workshop aims to show the latest research results in the field of Industrial Big Data and Industrial Digitization. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Wuhan University of Technology, School of Mechanical and Electrical Engineering, China
Hongtao Tang was mainly engaged in digital design, digital manufacturing, intelligent manufacturing, intelligent optimization algorithm and application in manufacturing industry (automobile, hydraulic cylinder, mold, casting and other industries). He has developed the digital design software suitable for automobile, hydraulic cylinder, mold, casting and other industries, and developed the intelligent manufacturing PLM/ERP/MES/SCADA intelligent manufacturing system, which has been applied in many companies in different industries and achieved great economic benefits. He has presided over 2 national natural science general and youth funds, with more than 20 horizontal funds, and the total fund was over 8 million yuan. He has published more than 60 academic papers, including 20 SCI papers, 1 English monograph, 20 soft books, 6 invention patents, and has been reviewers of more than 20 international journals such as IJPR, JCLP, CAIE, JIM, ASOC and COR.
Workshop 2: Cloud-native Edge Computing for Intelligent Manufacturing
Title 1: Cloud Native Edge Computing for Intelligent Manufacturing
Keywords: Cloud-native Computing, Edge Computing, Intelligent Manufacturing
Summary:
With the vigorous development of industrial digitalization, the organic combination of cloud native and edge computing brings more innovation into enterprises, and becomes a key force to promote digitalization and intelligence, which has also attracted extensive attention from academia and industry. Cloud Native Edge Computing is committed to providing enterprises with a unified management and control platform for multidimensional workloads, realizing flexible definition of infrastructure and application platforms, and helping enterprises create greater business value. Recently, many advanced technologies have been explored to enable cloud native edge computing for intelligent manufacturing. These technologies mainly include container technology, edge-cloud collaborative orchestration, microservices, service grid, edge intelligence, etc.
This workshop aims to show the latest research results in the field of cloud native edge computing for intelligent manufacturing. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Jinan University, China
Saiqin Long received the PhD degree in computer applications technology from the South China University of Technology, Guangzhou, China, in 2014. She is currently a professor with the College of Information Science and Technology, Jinan University, China. Her research interests include cloud computing, edge computing, parallel and distributed systems, and Internet of things. She has published 20+ refereed papers in these areas, most of which are published in premium conferences and journals, including IEEE TSC, IEEE TPDS, IEEE TMC, etc. She has been a Publicity Chair or Technical Program Conference Member of many international conferences. She also serves as a reviewer for many international journals, such as IEEE JSAC, JSA, and JPDC. She is a member of Chinese Computer Federation (CCF).
Workshop 3: Artificial Intelligence and Machine Learning
Title 1: The Eye of Artificial Intelligence: Machine Vision Technology
Keywords: Artificial Intelligence, Machine Learning, Machine Vision
Summary:
With the development of artificial intelligence technology, human beings have gradually entered the era of artificial intelligence. As the key technology to realize industrial automation and intellectualization, machine vision is becoming the fastest developing branch of artificial intelligence. The significance of machine vision to artificial intelligence is as important as the value of eyes to human beings. With the development of deep learning, three-dimensional vision technology, high-precision imaging technology and machine vision interconnection technology, the performance advantage of machine vision has been further improved, and its application field has also expanded to multi-dimensional, such as industrial detection, medical auxiliary diagnosis, traffic monitoring, bridge detection, etc, which greatly liberating the human labor force, and improving the level of automation and intelligence. Therefore, it has a broad application prospect and brings a new technological revolution to the development of society.
This workshop aims to show the latest research results in the field of enabling artificial intelligence and machine learning technology, especially machine vision technology. We encourage prospective authors to submit related distinguished research papers on the subject of both theoretical approaches and practical case reviews.
Guilin University of Electronic Technology, China
Haijian Wang received his Ph.D. degree in Mechanical Engineering from the Liaoning Technical University, China, in 2017. From July 2017 to November 2020, he was a Lecturer with the Guilin University of Electronic Technology, China, where he has been an Associate Professor (Master Supervisor) and the Assistant Dean since December 2020. Dr. Wang is currently serving as the contributing editor of Journal of Intelligent Mine. His research interest is machine vision and artificial intelligence. He has authored/co-authored over 40 journal/conference papers. Meanwhile, he presided over a National Natural Science Foundation, a provincial key R & D projects, 3 provincial Natural Science Foundation and four Key Laboratory open Foundation. Moreover, he obtained 16 provincial and ministerial progress awards in science and technology, 9 national invention patents, 22 practical new-type patents, 19 national software copyrights. In particular, he also edited 2 group standards and published a monograph.
Workshop 4: Artificial Intelligence in Modern Industry
Title 1: Artificial Intelligence in Modern Industry
Keywords: Variable Granular Access to Industrial Data, Federated Learning, Multimodal learning
Summary:
Artificial intelligence accelerates the realization of intelligent manufacturing, which has drawn great attention to the technologies of intelligent fault diagnosis, remaining useful life prediction and quality traceability. Meanwhile, the process of digital transformation of manufacturing enterprises generates massive data, but due to the concerns of different enterprises and departments on data security, these data exist in the form of "data island", which hinders data sharing. Advanced AI methods are suitable to improve the industrial data mining effectiveness and enhance the reliability with respect to various industrial systems, including deep learning, federated learning, transfer learning, few/zero-shot learning, reinforcement learning, cross-modal information fusion, interpretability and explainable AI etc.
This forum aims to present the latest research results in the field of enabling technologies of artificial intelligence methods. We encourage authors to submit related distinguished research paper on the subjects of both: theoretical and practical cases.
Shanghai University of Engineering Science, China
Xiaoli Zhao received the PhD degree from Shanghai University in 2018. She was a visiting scholar with the School of Computer Science, University of California, Santa Barbara in 2015. She currently is an Associate Professor with the School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, China. Her research interests include federated learning, video and image processing, pattern recognition, computer vision, and intelligent computing. She also serves as a reviewer for journals such as Signal Processing, Signal Processing- Image Communication etc.
Xi'an University of Posts and Telecommunications, China
Yao Liu received his Ph.D. degree in Mechanical Engineering from Xi'an Jiaotong University, Xi'an, China, in 2016. From January 2017 to May 2020, he was a Lecturer in the School of Mechano-Electronic Engineering, Xidian University, Xi'an, China. Currently, he is an associate professor in the School of Communications and Information Engineering & School of Artificial Intelligence, Xi'an University of Posts and Telecommunications in China. His research interest includes industrial big data analysis and intelligent manufacturing, specifically concerning mechanical system intelligent maintenance and production process monitoring and optimization. Dr. Liu has presided one National Natural Science Foundation, one provincial Natural Science Foundation, one sub-project of Key State Science and Technology Project, and participated several provincial key R&D projects. He has been a Technical Program Conference Member of many international conferences. He also serves as a reviewer for several international journals, such as AMT, PROCEEDINGS OF THE IMECHE et. al.
Tianjin University, China
Ruonan Liu received the B.S., M.S. and PhD degrees from Xi'an Jiaotong University, Xi'an, China, in 2013, 2015 and 2019, respectively. She was a postdoctoral researcher with the School of Computer Science, Carnegie Mellon University in 2019. She currently is an associate professor in the College of Intelligence and Computing, Tianjin University, Tianjin, China. Her research interests include artificial intelligence and machine vision systems. Dr. Liu is currently serving as the Associate Editor of Sustainable Energy Technologies and Assessments, Shock and Vibration, and Frontiers in Artificial Intelligence. She has been a Session Chair or Technical Program Conference Member of many international conferences. She also serves as a reviewer for many international journals, such as IEEE TNNLS, IEEE TIE, IEEE TII et. al.
Workshop 5: Digital Twin Technologies for Discrete Manufacturing Scenarios
Title 1: Digital Twin Technologies for Discrete Manufacturing Scenarios
Keywords: Digital Twin, Discrete Manufacturing, Virtual Workshop
Summary:
Digital twin technology provides a new solution for the physical fusion of information in the manufacturing process. The digital twin workshop system technologies synchronizes the virtual model with the real state of the physical equipment by acquiring the dynamic and static information of the actual equipment or products in the physical workshop and mapping them to the corresponding digital twin virtual model. The digital twin workshop system achieves the goal of improving enterprise productivity, reducing production cost and improving product quality. Digital twin technologies for discrete manufacturing scenarios include virtual model construction, information model, self-mapping, data acquisition, big data, information fusion, cloud computing, edge computing, communication and sensing, artificial intelligence, unreal engine, cloud platform, man-machine interactive, computational service, etc.
This workshop aims to show the latest research results in the field of digital twin technologies for discrete manufacturing scenarios. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Beijing University of Technology, China
Cong-bin Yang received his Ph.D. degree in School of Mechanical Engineering from Beijing Institute of Technology, Beijing, China, in 2015. He is currently a Full Professor at Faculty of Materials and Manufacturing, Beijing University of Technology, China. Prior to this position, he held lecturer in 2015 and associate professor in 2019. His current research interests include machine tool precision design, digital design and manufacture, advanced manufacturing technology, and automation. He has authored/co-authored over 30 journal/conference papers. Prof. Yang is currently serving as a guest editor of the journal METAL. He also serves as a reviewer for many international journals, such as MECHANISM AND MACHINE THEORY, SURFACE AND COATINGS TECHNOLOGY, and PROCEEDINGS OF THE IMECHE. He has won the first prize of the Beijing Science and Technology Progress Award and the first prize of the Ministry of Education's excellent achievements in scientific research of University.
Workshop 6: Cloud-edge-Collaboration-based Cyber-physical Systems in Smart Manufacturing
Title 1: Cloud-edge-Collaboration-based Cyber-physical Systems in Smart Manufacturing
Keywords: Smart Manufacturing, Cyber-physical Systems, Cloud-edge Collaboration
Summary:
The increased product diversity imposed on manufacturers by the market requires a high degree of adaptability in manufacturing systems, which can be achieved through the introduction of reconfigurable manufacturing systems consisting of interoperable devices with changing architectures. The control and management of such a complex system requires fast and reliable real-time virtualization of real-world applications, as well as real-time feedback from virtual models to the real world. Cyber-Physical Systems (CPS) enable the interaction with the physical process through the human-machine interaction interface, and use the network space to control a physical entity in methods of remote, reliable, real-time, safe and cooperative. Facing the requirements of real-time sensing and high-performance computing, traditional cloud computing can not meet the demand for timely and efficient task processing anymore. Edge computing enables tasks to be processed close to edge nodes, in order to reduce the load on the cloud, and enhances the overall responsiveness of cloud manufacturing systems. Therefore, relevant technologies are introduced to address these issues, such as Edge-Computing, Cyber-physical Systems, Modeling and Simulation, Cloud-edge Collaboration, Algorithm Hardware-acceleration, etc.
This workshop aims to show the latest research results in the field of cloud-edge collaborative cyber-physical systems in smart manufacturing. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Beijing Information Science and Technology University (BISTU), China
Zhao Chun is an Associate Professor at Beijing Information Science and Technology University in China. He is working in the School of Computer. In 2017, he received his Ph.D degree from Beihang University, China. Prof. Zhao focuses on the Smart Manufacturing and Cloud Manufacturing, Modeling & Simulation of complex system, and FPGA based cloud-edge systems. He has published more than 30 papers. In addition to the research work, he is actively engaged in various reviews of international journals, such as the Guest Editor of International Journal of Modeling, Simulation, and Scientific Computing, the Review Editor of Frontiers in Software Technologies, and the Reviewer of Cogent, Journal of Manufacturing Systems, Transactions on Network Science and Engineering. Furthermore, he often devoted himself to science popularization activities.
Workshop 7: Intelligent Manufacturing and Innovative Design
Title 1: Intelligent Manufacturing and Innovative Design
Keywords: Advanced Manufacturing Technology, Innovative Design, Industrial Artificial Intelligence
Summary:
Advanced manufacturing technologies involve the automation in the traditional and emerging manufacturing processes with the help of smart manufacturing, additive manufacturing, innovative design, intelligent manufacturing systems, robotics, Internet of Things (IoT), big data analytics, artificial intelligence, and autonomous systems. It can improve manufacturing capability and efficiency, enhance the value of innovative products and has shown the great potential on the various aspects of the intelligent manufacturing. The main purpose of this workshop is to present the new methods and advanced manufacturing technologies on the creative design, additive manufacturing, industrial artificial intelligence and the advanced technologies in intelligent manufacturing and innovative design. The topics are as follows (but not limited to):
1) advanced manufacturing technologies (for example, smart manufacturing, additive manufacturing, laser processing, precision cutting, micro/nano manufacturing, etc.);
2) innovative design (for example, design creativity & optimization, service design, intelligent design, ecological design, customized design, design for cost & sustainability, CAD/CAM, etc.);
3) industrial artificial intelligence (for example, online monitoring, in-situ monitoring, intelligent perception, machine learning, big data technology, intelligent interaction, etc.).
This workshop aims to show the latest research results in the field of Intelligent Manufacturing and Innovative Design. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
China University of Geosciences (Wuhan)
Liang Hao received his Ph.D. degree in Mechanical Engineering from Nanyang Technological University, Singapore, in 2005. From September 2006 to August 2009, he was a Lecturer at University of Exeter, UK, where he had been a senior lecturer since September 2009. Prof. Liang Hao is the founding director of Advanced Manufacturing Research Centre as well as the Vice Dean of Gemological Institute at China University of Geosciences (Wuhan). With a 5-year and over-10-million-RMB internal and external investment plan, the center has performed cross-disciplinary research on innovative materials and additive manufacturing process. Since 2015, he had been awarded with three NSFC grants (a PI project on slurry deposition of dual materials, No.51675496, No.51671091, No. 5191101701), and a CI project on selective laser melting). Between 2015 and 2006, he worked at University of Exeter and obtained 2 EPSRC (EP/I006885/1, CASE/CAN/07/86), 2 TSB (TP14/BA036D, TP11/AB183A) and 2 EU-funded projects and 3 industrially-sponsored PhD projects. His current research interest is additive manufacturing and innovative design. He has established well-recognized scientific achievements with 100 high quality journal papers (over 10000 citation, and with H-index 51), 1 first-authored English book and 4 English book chapters, invited presentations in several international conferences.
China University of Geosciences (Wuhan)
Long Wen is currently a professor of the School of Mechanical Engineering and Electronic Information, China University of Geosciences (CUG), Wuhan, China. He received his BS and PhD from Huazhong University of Science and Technology (HUST), Wuhan, China in 2010 and 2014. Then, he worked as the postdoctoral researcher in HUST from 2016 to 2019. His main research interests include deep learning, intelligent fault diagnosis, intelligent algorithm, and intelligent manufacturing system. He has published more than 40 papers, including three ESI hot papers and five ESI highly-cited papers. He also got several funding from National Natural Science Foundation of China, China Postdoctoral Science Foundation, etc.
China University of Geosciences (Wuhan)
Yan Li had received her Ph.D. degree in Materials Science from Queen Mary University of London, UK, in 2017. From July 2017 to October 2022, she was an associate professor at China University of Geosciences, Wuhan, where she has been a professor since November 2022. Her research interest has been in the field of multi-material composites and micro-nano structure additive manufacturing over the past 11 years. Yan Li is Principal Investigator/Co-Investigator of a portfolio of projects with a total budget in excess of 2 million RMB, which are funded by two NSFC grants (a PI project on DLP of graphene reinforced composites with No. 51902295, and a CI project with No. 5191101701), Hubei Natural Science Funding (PI), Wuhan Science and Technology Bureau Funding (PI), etc. She has developed research collaborations with national and international institutions such as University of Exeter, University of Birmingham, Queen Mary University of London, etc. These collaborations have been typically consolidated into grant applications and academic visits. She has established scientific achievements with 70 high-quality journal papers, three patents, three English book chapters and two Chinese book chapters, invited presentations in several national and international conferences. She also serves as a reviewer for many international journals, such as Advanced Materials, Composites Part A, Additive Manufacturing, Advanced Material Technologies, Polymer.