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.
