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Artificial Intelligence for Digitising Industry - Applications (Hardcover)
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Artificial Intelligence for Digitising Industry - Applications (Hardcover)
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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.
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