Speakers
Chen Yun
Professor Yun Chen holds a PhD in Information Management System in manufacturing from the Shanghai Jiao Tong University (SJTU), China. She is a Professor at Shanghai University of Finance and Economicsthe, Executive Deputy Director of Shanghai Key Laboratory of Financial Information Technology Research, and Deputy Director of Shanghai International Financial Center Research Institute. She is also an active researcher and serves as a technical adviser in Several companies. She was presided over more than 30 projects, including the National Natural Science Foundation of China, the Shanghai Natural Science Foundation, the National 863 Project, the Shanghai Science and Technology Commission's major projects, and the Shanghai Science and Technology Innovation Project. She published 70 technical peer-reviewed papers in international journals. Professor Chen’s current areas of interest are financial market risk management, intelligent financial decision support, management information systems, intelligent manufacturing systems, and Industry 4.0.
Speech Title: A new model for cultivating intelligent manufacturing talents based on the industry 4.0 learning factory
Speech Abstract: The presentation will describe the design of a learning factory for Industry 4.0 that addresses the growing demand for future skills of production staff. Existing learning factories often focus on the technical skills whereas this learning factory also trains decision making, group work and performance monitoring skills. The paper refers to the existing categorizes of learning factories and unveils its numerous features. The conceptual design includes theoretical and practical parts, which prove to be successful in a China learning factory that was realized by the authors. Especially, for the industry 4.0 environment, the layout consists of three stages of a production system, from manual to automatized manufacturing. The practical tasks cover the introduction of smart devices, connection of information flows as well as monitoring of performance. The learning factory is a part of a whole research institute for intelligent manufacturing in China including consultancy and application support. One of the underlying goals of the learning factory is to enable production staff for change management, decision making and innovation.
Henry X.G. Ming
Henry X.G. Ming, a professor at School of Mechanical Engineering, Shanghai Jiao Tong University. He received his Ph.D. degree in Mechanical Engineering from Shanghai Jiao Tong University, China in 1995. He was a Research Scientist in Singapore Institute of Manufacturing Technology and Visiting Faculty Fellow in MIT. His research interests include Industrial Artificial Intelligence, Industrial Internet, Smart Manufacturing Systems, Product Innovation Engineering, Service-oriented Manufacturing (Smart Product Service Ecosystem), Green Design and Supply Chain, Lean Enterprise and Management, etc. Prof. Ming has published over 100 scientific papers and 5 books. He was a member of the editorial board of Concurrent Engineering: Research and Applications, Business Process Management Journal, etc. He undertakes and participates in several Industrial-Academic-Research cooperative projects funded by the National and Shanghai government.
Speech Title: Innovation and Transformation of China Manufacturing in New Era
Speech Abstract: In this speech, recent progress of digital transformation in new era is summarized, and the program of China manufacturing 2025 is introduced. The on-going trend of China manufacturing transformation is described, including product innovation, lean enterprise, digital workshop and factory for smart manufacturing, and service transformation, etc. The future trend of smart product service ecosystem (SPSE) together with recent example in China will be expected.
Jin Yuan
Professor Jin Yuan, Ph.D. in Engineering, is a Doctoral Supervisor at Shandong Agricultural University, and serves as an Adjunct Professor at Shanghai University of Engineering Science. He completed postdoctoral research at Shanghai Jiao Tong University and the Norwegian University of Science and Technology. Dr. Yuan is the Deputy Director of the Artificial Intelligence Committee of the Chinese Society for Agricultural Machinery, a member of the Embedded Systems Committee of the Chinese Society of Instrumentation, and the Deputy Director of the Agricultural Informatization and Intelligent Equipment Committee of the Shandong Agricultural Engineering Society. He is also an expert member of the Tai’an Intelligent Manufacturing Industry Expert Committee and has served as an editorial board member for the Transactions of the Chinese Society for Agricultural Machinery (11th and 12th terms). He is on the editorial board of an SCI Q2 international journal and serves as a peer reviewer for the National Natural Science Foundation of China (NSFC) and an evaluation expert for major national R&D programs under China’s 13th and 14th Five-Year Plans. He is also a science and technology service expert for the "Sci-Tech China" initiative of the Chinese Society for Agricultural Machinery, a core R&D member at the Collaborative Innovation Center for Efficient Wheat and Maize Production in Shandong Province, and a designated Sci-Tech Commissioner of Shandong Province. Dr. Yuan has long been engaged in the research of intelligent agricultural machinery theory and equipment technology. His research interests include intelligent agricultural equipment, agricultural robotics, electromechanical control, machine learning, and artificial intelligence. He has received six national and provincial-level awards, including the Second Prize of the National Science and Technology Progress Award and the Young Scientist Award from the Chinese Society for Agricultural Machinery. He has led more than 20 major research projects, including two NSFC grants, key national R&D programs, the China Postdoctoral Science Foundation, major agricultural innovation projects in Shandong Province, and provincial key R&D programs. Dr. Yuan has published over 90 high-quality SCI/EI papers in the field of agricultural engineering, with more than 1,200 citations. He is the chief editor of two English academic monographs, has filed 138 patent applications (87 granted invention patents, 105 utility models), and holds 26 software copyrights. He has made significant original contributions to intelligent harvesting equipment, particularly in key technologies for low-loss and high-efficiency harvesting and intelligent agricultural machinery control.
Speech Title: Fruit and Vegetable Selective Harvesting Robot Key Technology Development and Research Progress Analysis
Speech Abstract: The maturity of fresh fruits and vegetables is inconsistent, requiring selective harvesting based on indicators such as color and size, which consumes the most labor and becomes a bottleneck affecting the development of the fruit and vegetable industry. Selective harvesting technology (SHT) is an important research field of agricultural robots, which can reduce labor costs and increase fruit and vegetable profits, and has become an important direction for the development of fruit and vegetable harvesting technology in the world. The SHT of fresh fruits and vegetables, including the representative underground parts such as white asparagus and the representative aerial parts such as apples, strawberries, tomatoes, etc., has accelerated iteratively and has become a research hotspot of agricultural robots in recent years. Our agricultural robotics team focuses on the development of SHT with market-oriented prospects in the past years, and sorts out the implementation path, application objects and development context of technology research and development. The common key issues of end effector and harvesting mechanism, harvesting target recognition and positioning technology, and selective harvesting collaborative control technology, and summarizes the open problem of technical research in this field is discussed. Aiming at the application scenarios of dehumanized or unmanned fruit and vegetable production, it points out that the future development of the industry and the implementation of technology need to be balanced.
Hirpa Lemu
Hirpa Lemu is a Professor of Mechanical Design Engineering at Dept. of Mechanical & Structural Engineering and Materials Science, University of Stavanger (UiS), Norway. He earned his PhD within Computational Intelligence Systems to Integrated CAD/CAM Systems from the Norwegian University of Science and Technology (NTNU) in 2002. Dr. Lemu’s current research focuses on additive manufacturing technologies and applications, design for additive manufacturing, modelling and simulation of mechanical systems and material behaviour, composite materials and related topics. He has supervised and graduated over 250 BSc, 60 MSc and 10 PhD candidates, while 8 PhD candidates are currently conducting their research. He has published over 300 peer reviewed articles in reputed journals and international conferences. Dr. Lemu is director of the 3D Printing laboratory that he started to build in 2008 and currently this lab is equipped with diverse advanced 3D printing and 3D scanning tools. In the period 2020 to 2022, he served as steering committee member of AM energy and involved in the establishment of Norwegian AM cluster. He has been initiator and leader of several collaborative projects with many universities in Africa, Europe and North America. Dr. Lemu has been serving as a leading chair of COTech conference that he initiated in 2017, which is organized biannually at University of Stavanger.
Speech Title: Additive Fabrication of Natural Fibers for Environmentally Sustainable manufacturing
Speech Abstract: The current demand on environmentally sustainable manufacturing technology heavily lies on both sustainable materials and advanced manufacturing methods. While sustainable materials are those that can be, among others, biodegradable and can be hybridized with other materials, additive fabrication of materials leads to low waste manufacturing of optimized products. Recent research focused on transforming natural fibers into useful and sustainable composites by blending the fibers with thermoplastic polymer matrix that serve as binder materials for the fibres. Generally, natural fibers are obtained from animals, plants and vegetable sources. This keynote speech highlights the works done in our research group at University of Stavanger on study of natural fiber based composite materials with particular focus on investigating the additively fabricated composites. The goal is both to reduce the environmental harmful effect of polymer composites and to make input materials for additive manufacturing from locally available plant fibers. The studies investigated different plant fibers such as enset, sisal and teff straw to identify their mechanical and thermal properties aligned with their potential applications demanding strength and lightweight such as in aerospace, automotive and medical sector.
Vishal S Sharma, Sr
Vishal Santosh Sharma is an accomplished Senior Lecturer and Research Coordinator at the Engineering Institute of Technology, Melbourne, Australia, specializing in Mechanical Engineering. He holds a Doctorate degree from Kurukshetra University in India, followed by a post-doctoral fellowship at École Nationale Supérieure d'Arts et Métiers in France, one of the renowned Grandes Écoles. Subsequently, he actively participated in various projects related to wind turbines and machining at NTNU in Norway. With extensive experience of over 25 years in teaching and research, Vishal has made significant contributions to the field of advanced manufacturing. He has previously worked at the University of the Witwatersrand in Johannesburg, South Africa, as well as at Dr. B R Ambedkar NIT Jalandhar, India. Prior to his academic career, he spent three years working in the industry, and even while pursuing teaching and research, he maintained strong ties with the industrial sector, successfully completing multiple collaborative projects. Vishal’s research primarily focuses on advanced manufacturing, resulting in the publication of over 110 scientific articles. His work has garnered more than 4,500 citations, establishing an impressive H-index of 38 on Scopus. His expertise has been acknowledged globally, as he was recognized among the top 2% of researchers for three consecutive years worldwide, according to a study conducted by Stanford University. Furthermore, Vishal has demonstrated his leadership abilities by organizing seven international conferences, editing special journal issues, and publishing three books in collaboration with Springer publishers. As a sought-after supervisor, Vishal has successfully guided thirteen PhD and over 30 Masters students in their research journeys, fostering their academic growth. He has also forged productive collaborations with international faculty members and industry partners, further enriching his contributions to the field. Additionally, Vishal is serving as an Associate Editor for esteemed journals such as the Journal of Intelligent Manufacturing, Alexandria Engineering Journal, and International Journal of Mechatronics and Manufacturing Systems, further highlighting his influential contributions in academia.
Speech Title: Machining and Additive Manufacturing: A Research Perspective Overview
Speech Abstract: The talk will cover ongoing research in Machining and Additive Manufacturing (AM). The focus will be on the application of Minimum Quantity Lubrication (MQL), including the use of bio-fluids, and efforts to enhance machining accuracy and efficiency. In AM, the discussion will extend to the performance of bio-inspired 3D printed structures and the modeling of multi-jet printed parts. Future research will explore machining newly developed Metal Matrix Composites with advanced methods, producing hydrophobic surfaces through 3D printing, and studying the durability and post-processing effects on 3D printed parts. Additionally, the talk will address metal 3D printing for medical implants, use of Cold Spray technology, and surface texturing. The speaker will also share insights into making manufacturing processes more sustainable, emphasizing the importance of reducing environmental impacts.
Zongfeng Zou
Professor Zongfeng Zou holds a PhD in Mechanical Manufacture and Automation from Shanghai University (SHU), CHINA. Since 2018 he has been appointed Professor at the Department of Information Management , SHU. He has published 2 books, more than 50 papers on published journals and conferences. Professor Zou’s current areas of interest are intelligent manufacturing systems, applied computational intelligence, data mining, deep learning and knowledge discovery, big data, blockchain, RFID applications and Industry 4.0.
Speech Title: Prediction of Sailing Yacht Residual Resistance Based on Bayesian Optimization and Explainable Deep Learning
Speech Abstract: Accurately predicting Sailing Yacht Residual Resistance is a pivotal aspect of the initial design process for Sailing Yachts, vital for calculating the necessary propulsion power and ensuring high-speed performance. The ongoing study aims to tackle the intricate challenge of predicting this residual resistance by leveraging a deep learning algorithm to establish a predictive model for residual resistance per unit. This model takes the hull geometry and Froude number as its input variables. To enhance prediction accuracy, the Bayesian optimization algorithm is utilized to optimize the hyperparameters of the deep learning model. Additionally, the Shapley Additive Explanation (SHAP) model is employed to identify the crucial factors influencing Sailing Yacht Residual Resistance. The results of the study show that the Froude number and length-displacement ratio have substantial positive impacts on Sailing Yacht Residual Resistance, while the length-beam ratio exerts a notable negative influence. This approach offers invaluable insights into the design parameters that must be optimized to enhance yacht performance.
Jinghui Yang
As a distinctive light industrial sector, the vacuum flask industry is currently in a phase of rapid development, characterized by a continuously expanding market size and an increasingly competitive landscape. Particularly against the backdrop of consumption upgrading, market demands for product quality are persistently rising. Traditional quality inspection models can no longer support the industry's high-quality development requirements, suffering long-standing pain points such as low manual inspection efficiency, high defect escape rates, and insufficient data value extraction. Under conventional development paradigms, enterprises often exhibit low levels of production automation and digital management, resulting in unstable product quality, low operational efficiency, and opaque production data. Amidst the rapid advancement of large artificial intelligence (AI) model technology, the world has entered a new phase of "AI industrialization." Currently, the most dynamically developing segment primarily comprises general-purpose large models. However, compared to these, the true future value of large models will be manifested in their widespread application and implementation across diverse industries and enterprises. Focusing on industrial scenarios, large model applications exhibit a "bimodal" characteristic: penetration rates are relatively high in sectors like power and automotive, yet the majority of light industrial products currently lack large model applications within their manufacturing processes. By deeply integrating AI vision technology with industrial scenarios, it seeks to infuse intelligent features into traditional manufacturing.
Speech Title: Intelligent Detection System for Vacuum Flasks Using Multimodal Data Fusion Technology
Speech Abstract: Professor Yang Jinghui, female, holds a Ph.D. and serves as a Professor at Shanghai Polytechnic University, where she concurrently holds the position of Dean at the Wuyi Intelligent Manufacturing Industry Technology Research Institute. She was awarded an Honorary Doctorate by Ruse University, Bulgaria, and earned her Doctorate in Management from Dalian University of Technology in 2005. Professor Yang has long been engaged in research encompassing intelligent manufacturing, enterprise informatization, scenario planning, system scheduling and optimization, and decision-making and management theory and methods. She has conducted in-depth investigations specifically in the fields of enterprise information system integration and enterprise scenario planning and scheduling. Her scholarly output includes over 50 published papers, comprising 5 SCI-indexed papers, 6 EI-indexed papers, and multiple papers in Chinese core journals. She holds 26 patents, including 5 invention patents, possesses 4 software copyrights, and has contributed to the development of 1 group standard. Furthermore, Professor Yang has served as Principal Investigator for two European Union Erasmus+ projects.
Lizhen Huang
Professor Lizhen Huang is renowned for her leadership and contributions in Civil Engineering and Geomatics. Leading the Civil Engineering and Geomatics Group at the Department of Manufacturing and Civil Engineering. In the current evaluation from the Research council for Norway, The group is reported “has grown organically over the last 5 years through successful grant capture activities (under the leadership of the group leader) that have shaped its current wide portfolio of research applications. The volume of research activities of the group is comparable to leading research groups in Europe and can be classed as internationally leading.” Professor Huang serves as the Innovation Head at the departmet and the IV faculty's innovation committee member to promote "research-innovation-education integration". She is also the leader for the strategic research on the "digital twin for sustainable built environment." Over the past decade, she's been credited with 50+ high-impact journal publications, and one notable piece has achieved over 900 citations since 2018 at Google Scholar. A passionate advocate for sustainability, she's initiated several projects, both at the EU and national levels, targeting a carbon-neutral future. She is joint initiating and leading the ongoing 3 EU projects and 2 National projects on circular and digital built environment, with over €30 million budget. Beyond research, her mentorship has shaped the careers of 10+ PhD students, 5+ post-doctoral researchers, and 2+ two faculty members. She has significant involvement in scientific evaluations spanning PhD defenses to faculty recruitments. Professor Huang's collaboration network is large, marked by her involvement in European innovation platforms like ECTP-DBE and AIOTI. She is the executive committee member for ECTP-DBE and leads the Task force on Digital twin & AI, which will shape the B4P research program at Horizon Europe.
Speech Title: Decarbonization of built environment through the digitalized and smart circular construction
Speech Abstract: The built environment is responsible for nearly 40% of global carbon emissions, and play critical role to meet UN SDGs. Our research addresses how digitalization can reshape our future buildings method and stock. Through Digital twins, AI- technologies, and intelligent material traceability, the construction sector can shift from a linear resource-intensive model to a green circular and low-carbon paradigm. We will explore how the BIM (Building information modelling) based digital solutions enable circular construction with focusing on the upcycling of construction and demolition waste. It will also highlight the role of digital platforms in facilitating reuse and upcycling. Drawing on insights from leading European research initiatives and real-world pilots, we will provide a vision for how stakeholders across the value chain can collaborate to accelerate the circularity and decarbonization of our buildings and cities