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Keynote Lectures

Digital Twins and Generative AI for 3D Printing
Dimitris Mourtzis, University of Patras, Greece

Available Soon
Michele Dassisti, Politecnico di Bari, Italy

Towards True Explainable Artificial Intelligence for Real-World Applications
Hani Hagras, University of Essex, United Kingdom

Digital Twins and Generative AI for 3D Printing

Dimitris Mourtzis
University of Patras
Greece

Brief Bio
Professor in the Department of Mechanical Engineering and Aeronautics. He is Vice President of Research and Development Council of the University of Patras, Founding Member at the CLEAN AVIATION JU, Director of the Laboratory for Manufacturing Systems and Automation. He is Fellow of the CIRP, of the IFAC 5.2, of the IFIP WG 5.7, of the SPEL, and IALF Scientific Committee Member. He is Member of the EFFRA, of the ASME, of the SME, of the IEEE, of the SAE, of the EMIRACLE, and Founding Member of the EASN. His scientific interests are focus on the Advanced Simulation, Design, Planning and Control of Manufacturing Systems and Networks, Robotic Systems, Automation, eXtended Reality, Maintenance, Blockchain and Metaverse. He authored one Elsevier Book in 2022 (Link) and is included in the 2% of global top scientists. He has won Best Paper Awards (APMS 2012, IFAC MIM 2022, MDPI Applied Sciences (Link)) and the Best presentation Award in 2nd Digital Twin Conference. He is the STC Dn Section Editor in the CIRP CNTPE board and Editorial Board Member and Guest Editor of several Scopus Indexed International Journals. He authored the Book “Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology” (Link) published in 2022 by Elsevier and he is Authoring a second Elsevier Book entitled “Advances in Manufacturing from Industry 4.0 to Industry 5.0”to be published in June 2024. He participates as Editorial Board ?ember at thirteen (13) International Scientific Journals, as Editor / Guest Editor in eight (8) International Scientific Journals with high impact factor and as co-Editor in the MDPI Topic: Smart Manufacturing and Industry 5.0 (Link). More specifically, he is Associate Section Editor-in-Chief of Robotics and Automation in Applied Sciences (Link), Area Editor in Computers & Industrial Engineering (Link) and Specialty Chief Editor in Digital Manufacturing Section in Frontiers in Manufacturing Technology (Link). Additionally, he is Editorial Board Member in International Journal of Production Research – IJPR (Link), in Journal of Manufacturing Systems - JMS (Link), in International Journal of Computer Integrated Manufacturing – IJCIM, (Link), in Journal of Engineering Design (Advisory Board Member) (Link), in Machines (Link), in Advances in Manufacturing (Link), in Mathematical Problems in Engineering of Hindawi (Link), in Journal of Green Manufacturing Open (Link), in Digital Twin (Link), and in Digital Engineering and Digital Twin (Link). He has chaired and organized four Webinars in cooperation with MDPI Applied Sciences and OAE (Link 1, Link 2, Link 3, Link 4) in the area of Human Centric Systems Towards Industry 5.0 and advances in Human Machine Interfaces (HMI) towards Industry 5.0. He has also been interviewed by MDPI Encyclopedia (Link). He has authored monographs, book chapters, papers, white papers, and research project reports, has published numerous articles in International scientific journals (peer-reviewed). He has published more than three hundred and twenty (327) scientific papers. The unique citations of his published work exceed 13,500 (h-index 60/i-10 index: 199) according to the databases: Google Scholar, Scopus, and Research Gate.

Abstract
In the context of Industry 4.0, the integration of digital technologies in manufacturing systems, networks, and processes is continuously advancing, targeting at the improvement system reliability, process monitoring, and optimization. Therefore, in order to address the abovementioned challenges, advanced simulation techniques, such as Digital Twins (DT), are becoming increasingly important. Further to that, modern manufacturing systems incorporate non-conventional processes like Additive Manufacturing (AM) or 3D printing for small-scale production of personalized products. Furthermore, by leveraging generative AI techniques within the Digital Twin framework, the integration of non-conventional processes and optimization of 3D printing in real-time can be achieved, thus further enhancing the efficiency and effectiveness of additive manufacturing systems. Therefore, this keynote speech is focused on the discussion of the concurrent challenges and opportunities for the integration of Digital Twins and Generative AI in AM.

Keynote Lecture

Michele Dassisti
Politecnico di Bari
Italy

Brief Bio
Michele Dassisti is full professor of “Systems and Production Technologies” in the Polytechnic University of Bari, he has been Rector’s delegate since 2003 for Sustainable Development Strategies. Professor Dassisti has extensive academic and industrial research experience nurtured along several national and international research cooperation with multidisciplinary groups dating from 1988 on the following subjects: sustainable manufacturing, continuous process improvement, advanced material for manufacturing applications, smart manufacturing and additive processes, interoperability and integration of manufacturing systems, sustainable de-manufacturing processes, manufacture of storage energy-systems from renewable sources, systems for quality management and for statistical control.Michele Dassisti successfully managed several research projects for the continuous improvement of sustainability of industrial processes with more than 20 Italian and international companies, being local scientific coordinator of Italian research funded as well as international projects.As scientific dissemination, he has published over 200 scientific works and books. Holder of two patents deposited on the recycling of electronic waste and sustainable innovative buildings. Michele Dassisti is strongly involved in public engagement activities, organizing dissemination events to promote the culture of continuous improvement and sustainability, running a large number of dissemination and innovation projects for sustainable development of territories.Former founder of the Italian Pole of INTEROP-VLab.It , he is currently responsible for the first public interuniversity laboratory in Apulia for designing and managing territorial and industrial resilience. He is also a regular member of the Technical Committee IFAC 5.3 “Interoperability and enterprise network” as well as ENBIS association.

Abstract
Available Soon

Towards True Explainable Artificial Intelligence for Real-World Applications

Hani Hagras
University of Essex
United Kingdom
http://cswww.essex.ac.uk/staff/hagras.htm

Brief Bio
Hani Hagras is a Professor of Artificial Intelligence, Director of Impact, Director of the Computational Intelligence Centre and Head of the Artificial Intelligence Research Group, in the School of Computer Science and Electronic Engineering, University of Essex, UK. He is a Fellow of Institute of Electrical and Electronics Engineers (IEEE), a Fellow of the Institution of Engineering and Technology (IET), Principal Fellow of the UK Higher Education Academy (PFHEA) and Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) His main research interests are in Explainable Artificial Intelligence (XAI) and Data Science with applications to Finance, Cyber Physical Systems, Neuroscience, Life Sciences, Uncertainty Management, Intelligent Robotics and Intelligent Control of Industrial Processes. He has authored more than 400 papers in international journals, conferences and books. He is amongst the top 2% of the most-cited scientists in the World (Scopus August 2021). His work received funding from major research councils and industry. He holds eleven industrial patents in the field of Explainable AI. His research has won numerous prestigious international awards where he was awarded by the IEEE Computational Intelligence Society (CIS), the 2010 Outstanding Paper Award in the IEEE Transactions on Fuzzy Systems and the 2004 Outstanding Paper Award in the IEEE Transactions on Fuzzy Systems. He was also awarded the 2015 and 2017 Global Telecommunications Business award for his joint project with British Telecom. In 2016, he was elected as Distinguished Lecturer by the IEEE Computational Intelligence Society. His work has also won best paper awards in several leading international conferences including the 2014 and 2006 IEEE International Conference on Fuzzy Systems, the 2012 UK Workshop on Computational Intelligence and the 2016 International Conference of the BCS SGAI International Conference on Artificial Intelligence. He was awarded by the IEEE Computational Intelligence Society (CIS) the 2011 IEEE CIS Outstanding Chapter Award. In 2017, he was awarded by the University of Essex, the 2017 best Research impact award for his work with British Telecom. He acted as the Principal Investigator for a project which was awarded by the UK Technology Strategy Board, the 2011 UK Best Knowledge Transfer Partnership for London and the East Region. He also acted as the Principal Investigator for a project which was awarded the 2009 Lord Stafford Achievement in Innovation Award for East of England. In 2010, he Led a Research Students team to win the First place in the RoboCup 2010. In 2007, he was Shortlisted by the Times Higher Education supplement (THES) for the UK Young researcher of the year award. He is Associate Editor of many journals including IEEE Transactions on Fuzzy Systems, IEEE Transactions on Artificial Intelligence, Knowledge Based Systems, Cognitive Computations and others. He served as the General and Programme Chair of numerous major international conferences where he served as the General co-Chair of the 2007 IEEE International Conference on Fuzzy Systems, and Programme Chair of the 2021 and 2017 IEEE International Conference on Fuzzy Systems as well as many other conferences

Abstract
The recent advances in computing power coupled with the rapid increases in the quantity of available data has led to a resurgence in the theory and applications of Artificial Intelligence (AI). However, the use of complex AI algorithms could result in a lack of transparency to users which is termed as black/opaque box models. Thus, for AI to be trusted and widely used by governments and industries, there is a need for greater transparency through the creation of human friendly explainable AI (XAI) systems. XAI aims to make machines understand the context and environment in which they operate, and over time build underlying explanatory models that allow them to characterize real-world phenomena. The XAI concept provides an explanation of individual decisions, enables understanding of overall strengths and weaknesses, and conveys an understanding of how the system will behave in the future and how to correct the system’s mistakes. In this keynote speech, Hani Hagras introduce the concepts of XAI by moving towards “explainable AI” (XAI) to achieve a significantly positive impact on communities and industries all over the world and will present novel techniques enabling to deliver human friendly XAI systems which could be easily understood, analysed and augmented by humans. This will allow to the wider deployment of AI systems which are trusted in various real world applications.

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