Book

Description

Manufacturing is entering a new era driven by Artificial Intelligence, Industry 4.0, and the transition toward Industry 5.0. Intelligent monitoring systems are becoming essential for improving product quality, optimizing industrial processes, reducing operational costs, and supporting reliable decision-making. This book presents a practical and progressive introduction to Artificial Intelligence for industrial process monitoring. Beginning with the foundations of Statistical Process Control and Control Chart Pattern Recognition, it explores the evolution toward Artificial Neural Networks, Neuro-Fuzzy Systems (ANFIS), Deep Learning, Explainable Artificial Intelligence, and modern hybrid CNN–ANFIS architectures. Unlike many books that focus exclusively on algorithms, this work emphasizes the industrial application of Artificial Intelligence through real manufacturing examples, practical methodologies, and an industrial case study based on the Tennessee Eastman Process. Readers will discover how modern AI techniques can transform industrial data into actionable knowledge for fault diagnosis, predictive monitoring, intelligent quality control, and smart manufacturing. Written for graduate students, researchers, engineers, and industrial practitioners, this book provides a bridge between academic research and real industrial implementation while offering a clear vision of the future of Explainable Artificial Intelligence and Industry 5.0.