Prof. Andrea Matta
Department of Mechanical Engineering, Politecnico di Milano, Italy
Andrea Matta is Full Professor in Manufacturing and Production Systems at Department of Mechanical Engineering of Politecnico di Milano. He graduated in Industrial Engineering at Politecnico di Milano where he develops his teaching and research activities since 1998. He was Distinguished Professor at the School of Mechanical Engineering of Shanghai Jiao Tong University from 2014 to 2016 and Guest Professor between 2017-2019. He has been visiting scholar at Ecole Centrale Paris (France), University of California at Berkeley (USA), and Tongji University (China). He is member of the technical committee of MADE Competence Center. His research area includes analysis, design and management of manufacturing and health care systems. He has published 210 scientific papers on international and national journals/conference proceedings. He is Editor in Chief of Flexible Services and Manufacturing Journal since 2017, past member of editorial board of OR Spectrum journal, and IEEE Robotics and Automation Letters journal. He was awarded with the Shanghai One Thousand Talent and Eastern Scholar in 2013. President of the Italian National Committee for Professor Qualification in Manufacturing Engineering.
Talk title: Generation of Graph-based Models for Digital Twins of Discrete Event Systems
With the coming of the Industry 4.0 wave, digital representations of production systems have been promoted from marginal to central. Digital twins are not simply conceived as simulation models of their physical counterparts for offline what-if analysis, differently they are developed as self-adaptable and empowered decision-makers timely aligned with the dynamics of the real system. Enriched by these new features, digital twins are widely recognized as the key enablers for the implementation of optimal control of smart manufacturing systems. Graphs are well recognized as the mathematical structures representing the common denominator of engineering activities in manufacturing. They are the unifying language in industrial engineering, underpinning, for example, toolpaths, process plans, workflows, and system topologies. This talk will present methods for automatically generating graphs from real process gathered data to represent physical entities in a digital twin scenario. Further, generative approaches based on discrete diffusion will be discussed in relation to model tuning, control, and optimization.