Springer 3rd Mexican-Hungarian Workshop on Factory Automation and Material Sciences: Select Proceedings (Mechanisms and Machine Science, 209)

This book provides an in-depth exploration of cutting-edge research at the intersection of robotics, autonomous manufacturing, and intelligent production systems. It integrates theoretical foundations with applied case studies, offering readers both a conceptual framework and practical approaches to advancing the next generation of industrial technologies. The central focus lies in the convergence of smart sensor systems, predictive maintenance algorithms, fault detection methodologies, and advanced control systems engineering. These elements are examined not as isolated disciplines, but as interconnected pillars driving the transformation of modern manufacturing into highly adaptive, efficient, and resilient ecosystems. Key chapters include pioneering studies such as the effect of fluid-structure interaction on the instability of porous gas bearings, novel fault detection and localization methods in photovoltaic systems under dark conditions, and the prediction and control of burr formation in milling and drilling. Readers will also encounter innovative approaches like thermographic image preprocessing for fault diagnosis in electromechanical systems, predictive hysteresis modeling in discrete dynamic systems, and AI-based predictive modeling of tool life in Industry 4.0 contexts. Practical applications are highlighted through case studies on production process simulation, along with strategies to improve the productivity and sustainability of CNC machining via process monitoring. These contributions underline the importance of predictive and adaptive intelligence in addressing pressing industrial challenges such as minimizing downtime, optimizing energy consumption, ensuring process reliability, and extending equipment life cycles. The relevance of these topics lies in their direct response to the urgent demands of global manufacturing: sustainability, efficiency, and resilience in an era defined by Industry 4.0 and beyond. By bridging advanced theory with applied methodologies, the book not only contributes to academic research but also offers insights with real-world industrial impact. Ultimately, this book seeks to solve the challenge of how to design, monitor, and control intelligent manufacturing systems that are capable of adapting to uncertainty, predicting failures before they occur, and ensuring seamless integration of robotics and autonomous technologies.