Artificial Intelligence for Digitising Industry - Applications

This book provides in-depth insights into use cases implementing artificial intelligence (AI) applications at the edge. It covers new ideas, concepts, research, and innovation to enable the development and deployment of AI, the industrial internet of things (IIoT), edge computing, and digital twin technologies in industrial environments. The work is based on the research results and activities of the AI4DI (ECSEL JU) project, including an overview of industrial use cases, research, technological innovation, validation, and deployment.

This book's sections build on the research, development, and innovative ideas elaborated for applications in five industries: automotive, semiconductor, industrial machinery, food and beverage, and transportation.

The articles included under each of these five industrial sectors discuss AI-based methods, techniques, models, algorithms, and supporting technologies, such as IIoT, edge computing, digital twins, collaborative robots, silicon-born AI circuit concepts, neuromorphic architectures, and augmented intelligence, that are anticipating the development of Industry 5.0.

Automotive applications cover use cases addressing AI-based solutions for inbound logistics and assembly process optimisation, autonomous reconfigurable battery systems, virtual AI training platforms for robot learning, autonomous mobile robotic agents, and predictive maintenance for machines on the level of a digital twin.

AI-based technologies and applications in the semiconductor manufacturing industry address use cases related to AI-based failure modes and effects analysis assistants, neural networks for predicting critical 3D dimensions in MEMS inertial sensors, machine vision systems developed in the wafer inspection production line, semiconductor wafer fault classifications, automatic inspection of scanning electron microscope cross-section images for technology verification, anomaly detection on wire bond process trace data, and optical inspection.

The use cases presented for machinery and industrial equipment industry applications cover topics related to wood machinery, with the perception of the surrounding environment and intelligent robot applications.

AI, IIoT, and robotics solutions are highlighted for the food and beverage industry, presenting use cases addressing novel AI-based environmental monitoring; autonomous environment-aware, quality control systems for Champagne production; and production process optimisation and predictive maintenance for soybeans manufacturing.

For the transportation sector, the use cases presented cover the mobility-as-a-service development of AI-based fleet management for supporting multimodal transport.

This book highlights the significant technological challenges that AI application developments in industrial sectors are facing, presenting several research challenges and open issues that should guide future development for evolution towards an environment-friendly Industry 5.0.

The challenges presented for AI-based applications in industrial environments include issues related to complexity, multidisciplinary and heterogeneity, convergence of AI with other technologies, energy consumption and efficiency, knowledge acquisition, reasoning with limited data, fusion of heterogeneous data, availability of reliable data sets, verification, validation, and testing for decision-making processes.

Dr. Ovidiu Vermesan holds a PhD degree in microelectronics and a Master of International Business (MIB) degree. He is Chief Scientist at SINTEF Digital, Oslo, Norway. His research interests are in the area of smart systems integration, mixed-signal embedded electronics, analogue neural networks, artificial intelligence and cognitive communication systems. Dr. Vermesan received SINTEF's 2003 award for research excellence for his work on the implementation of a biometric sensor system. He is currently working on projects addressing nanoelectronics, integrated sensor/actuator systems, communication, cyber-physical systems (CPSs) and Industrial Internet of Things (IIoT), with applications in green mobility, energy, autonomous systems, and smart cities. He has authored or co-authored over 85 technical articles and conference papers. He is actively involved in the activities of the Electronic Components and Systems for European Leadership Joint Undertaking (ECSEL JU) and involved in technical activities to define the priorities for the new European partnership for Key Digital Technologies (KDT). He has coordinated and managed various national, EU and other international projects related to smart sensor systems, integrated electronics, electromobility and intelligent autonomous systems such as E3Car, POLLUX, CASTOR, IoE, MIRANDELA, IoF2020, AUTOPILOT, AutoDrive, ArchitectECA2030, AI4DI, AI4CSM. Dr. Vermesan actively participates in national, H2020 EU and other international initiatives by coordinating and managing various projects. He is the coordinator of the IoT European Research Cluster (IERC) and a member of the board of the Alliance for Internet of Things Innovation (AIOTI). He is currently the technical co-coordinator of the Artificial Intelligence for Digitising Industry (AI4DI) project.

Reiner JOHN received his degree in Electrical Engineering from the Fachhochschule des Saarlandes (Germany) in collaboration with the University of Metz / Perpignan (France). In 1984 he started his career with the Siemens Semiconductor Group in Munich, where he worked in automatic test system development. In 1989 he was responsible for the consultation and application of embedded control development tools in the Siemens Automation Group. After joining Siemens Corporate Research and Development in 1991, Reiner John researched knowledge-based embedded systems within the Fuzzy group. Moving to Regensburg to work for the Siemens Automotive Division three years later, he developed concepts and implementations for a real-time operating system to manage and control the engine and transmission system. In 1996 joined Siemens Semiconductors, the later IPO of Infineon Technologies, where he served in several management positions in the Quality and Production Department of the company. In 2000, he further pursued his career in Taiwan, where he set up and managed the Infineon Silicon Foundry Taiwan Office as the Head of Department for seven years. At present, Reiner John is working in AVL List GmbH, Austria, where he oversees the coordination of public-funded R&D projects in the area of trustable AI for industrial and electromobility applications.

Dr. Cristina De Luca received the Laurea degree in statistical and economic science from, University of Padova (Italy) and the PhD degree in mathematics from, University of Klagenfurt (Austria), 2003. She joined Infineon Technologies Austria AG in 2002. She has worked on a wide range of R2R control applications for lithography, CMP and CVD semiconductor production processes and rollout in Regensburg (Germany), Kulim (Malaysia) and Villach (Austria) and contributed to research on R2R for the epitaxy process. Her research interests included advanced process control, automation and statistical data analysis, production automation, predictive maintenance, virtual metrology and industry4.0 automation, model predictive control for semiconductor manufacturing. She was an external professor for statistical quality control at the "Fachhochschule Kärnten" for 2004-2008 in cooperation with Infineon Technologies Austria AG. She is certified in Project Management since 2008. In 2009, she became project manager for European projects, first ENIAC and then ECSEL JU. She followed projects at different levels and contributed to their preparation, implementation, and coordination. To cite some of the projects: IMPROVE, EPPL, EPT300, SemI40, PRODUCTIVE4.0, Arrowhead Tools, AI4CSM. She is currently the coordinator of the ArchitectECA2030 project (Automotive) and AI4DI project (artificial intelligence). In 2019 she joined Infineon Technologies AG, Munich (Germany), where she is Senior Manager Funding Projects and Coordination.