Soldatos J. Artificial Intelligence in Manufacturing...2024

Textbook in PDF format

This book presents a rich set of innovative solutions for Artificial Intelligence (AI) in manufacturing. The various chapters of the book provide a broad coverage of AI systems for state of the art flexible production lines including both cyber-physical production systems (Industry 4.0) and emerging trustworthy and human-centered manufacturing systems (Industry 5.0). From a technology perspective, the book addresses a wide range of AI paradigms such as Deep Learning, reinforcement learning, active learning, agent-based systems, explainable AI, industrial robots, and AI-based digital twins. Emphasis is put on system architectures and technologies that foster human-AI collaboration based on trusted interactions between workers and AI systems. From a manufacturing applications perspective, the book illustrates the deployment of these AI paradigms in a variety of use cases spanning production planning, quality control, anomaly detection, metrology, workers’ training, supply chain management, as well as various production optimization scenarios.
Industry 4.0 applications are usually developed based on advanced digital technologies such as Big Data, Internet of Things (IoT), and Artificial Intelligence (AI), which are integrated with CPPS systems in the manufacturing shopfloor and across the manufacturing value chain. In cases of nontrivial Industry 4.0 systems, this integration can be challenging, given the number and the complexity of the systems and technology involved. For instance, sophisticated Industry 4.0 use cases are likely to comprise multiple sensors and automation devices, along with various data analytics and AI modules that are integrated in digital twins (DTs) systems and applications. To facilitate such challenging integration tasks, industrial automation solution providers are nowadays offered with access to various reference architecture models for Industry 4.0 applications. These models illustrate the functionalities and technological building blocks of Industry 4.0 applications, while at the same time documenting structuring principles that facilitate their integration and deployment in complete systems and applications. Some of these reference architecture models focus on specific aspects of Industry 4.0 such as data collection, data processing, and analytics, while others take a more holistic view that addresses multiple industrial functionalities. Moreover, several architecture models address nonfunctional requirements as well, such as the cybersecurity and safety of industrial systems