Synthetic Data: Revolutionizing the Industrial Metaverse - Paperback

by Jimmy Nassif (Author), Joe Tekli (Author), Marc Kamradt (Author)

The book concentrates on the impact of digitalization and digital transformation technologies on the Industry 4.0 and smart factories, how the factory of tomorrow can be designed, built, and run virtually as a digital twin likeness of its real-world counterpart, before the physical structure is actually erected.

It highlights the main digitalization technologies that have stimulated the Industry 4.0, how these technologies work and integrate with each other, and how they are shaping the industry of the future.

It examines how multimedia data and digital images in particular are being leveraged to create fully virtualized worlds in the form of digital twin factories and fully virtualized industrial assets. It uses BMW Group's latest SORDI dataset (Synthetic Object Recognition Dataset for Industry), i.e., the largest industrial images dataset to-date and its applications at BMW Group and Idealworks, as one of the main explanatory scenarios throughout the book.

It discusses the need of synthetic data to train advanced deep learning computer vision models, and how such datasets will help create the "robot gym" of the future: training robots on synthetic images to prepare them to function in the real world.

Back Jacket

The book concentrates on the impact of digitalization and digital transformation technologies on the Industry 4.0 and smart factories, how the factory of tomorrow can be designed, built, and run virtually as a digital twin likeness of its real-world counterpart, before the physical structure is actually erected.

It highlights the main digitalization technologies that have stimulated the Industry 4.0, how these technologies work and integrate with each other, and how they are shaping the industry of the future.

It examines how multimedia data and digital images in particular are being leveraged to create fully virtualized worlds in the form of digital twin factories and fully virtualized industrial assets. It uses BMW Group's latest SORDI dataset (Synthetic Object Recognition Dataset for Industry), i.e., the largest industrial images dataset to-date and its applications at BMW Group and Idealworks, as one of the main explanatory scenarios throughout the book.

It discusses the need of synthetic data to train advanced deep learning computer vision models, and how such datasets will help create the "robot gym" of the future: training robots on synthetic images to prepare them to function in the real world.

Author Biography

Jimmy Nassif is currently serving as Chief Technology Officer (CTO) at Idealworks GmbH (since 2020), a wholly owned subsidiary of BMW Group. The mission of Idealworks is to develop solutions for autonomous logistics, building intelligent, flexible, and collaborative autonomous mobile robots. From hardware to software, to the smart factory cloud platform, the activities of Idealworks are validated in BMW Group production environments, producing solutions which are centered on the customers' needs to reliably improve safety and efficiency across their facilities. J. Nassif has more than 15 years of experience in automotive engineering. He previously served as Head of Information Technology (IT) Planning and Product Owner Logistics Planning at BMW Group (2018-2022). He also served as Innovation Manager and Head of Logistics Virtual Reality (VR), Mixed Reality (VR/MR), Cognitive Computing (CC) and Artificial Intelligence (AI) within BMW Group's Logistics division (2014-2020). His activities revolve around leveraging and integrating innovative solutions in the above mention fields to solve real-world problems in logistics, distribution, and manufacturing. J. Nassif obtained his M.Sc. degree in Mechatronics, Robotics, and Automation from Technical University of Munich (TUM, 2007). He pursued his Ph.D. as part of the BMW Ph.D. program from 2008 to 2011. His recent research activities focus on understanding the impact of digitalization and simulation on logistics planning, and finding concrete value, pushing towards accelerated rollout and development cycles within the industry.

Joe Tekli is an Associate Professor in Computer Engineering in the Lebanese American University (LAU). He was appointed as Assistant Provost for Strategic Planning and Academic Initiatives & Partnerships in September 2023. He obtained his M.Sc. and Ph.D. degrees from the University of Bourgogne (UB), LE2I CNRS, France (2009), both awarded with Highest Honors. He has completed various post-docs and visiting scholar research missions: University of Michigan (UMich), USA (Summer 2018), University of Sao Paulo (USP- ICMC), Brazil (2010-2011), University of Shizuoka, Japan (Spring 2010), and University of Milan, Italy (Fall 2009). He was awarded various prestigious fellowships: Fulbright (USA), FAPESP (Brazil), JSPS (Japan), Fondazione Cariplo (Italy), French Ministry of Education (France), and Association of Francophone Universities (AUF, Canada). His research covers semi-structured, semantic, and multimedia data processing, data-mining, and information retrieval. He has coordinated various international research projects and has more than 65 publications in prestigious journals and conferences. J. Tekli has initiated a collaboration between LAU and BMW Group (since 2018), offering engineering students internships at the company's seat in Munich, and promoting collaborative projects between both institutions. He co-founded and is currently serving as Director of the InMind Academy (since 2022), a professional training program in collaboration with BMW Group, Idealworks, and InMind .ai. J. Tekli is a senior member of IEEE, a member of ACM, and is currently serving as the vice-chair of ACM SIGAPP French

chapter (since 2018), Associate Editor of Springer KAIS journal, and member of the US-Atlantic Council AI Connect initiative (since 2022).

Marc Kamradt is currently serving as Head of BMW Group TechOffice, Munich (since 2021). The work of the TechOffice mainly revolves around the development and integration of digitalization technologies to help build next generation smart factories, including Ominverse Artificial Intelligence (AI) pipeline, synthetic data generation, and BMW Green physics AI. M. Kamradt was previously appointed to different leadership positions within BMW Group. He served as Senior Expert Innovation, Lead BMW Innovation Lab (2016-2020), leading projects around visual object recognition, knowledge graph enabled manufacturing assistance, and chat bot production at BMW Group. He also served as Innovations Manager (2013-2016), leading projects around cognitive production control and error analysis, and knowledge graph enabled manufacturing assistance. Prior to his leadership roles, he had an extensive technical experience as Enterprise Information Technology (IT) Architect at BMW Group (2011-2012), Product and Process Planning Electonics at BMW Group (2007-2010), IT Specialist at BMW Group (2002-2007), and IT Specialist at T-Systems International GmbH (2000-2002). M. Kamradt obtained his Computer Science diploma from Carl von Ossietzky University of Oldenburg, Germany (1999).