Quality Control – Artificial Intelligence, Big Data, and New Trends provides a timely and comprehensive examination of how digital transformation is revolutionizing quality management across various industries. As artificial intelligence (AI), machine learning (ML), and Big Data analytics become central to engineering and manufacturing systems, this book examines their profound impact on quality assurance practices—from predictive maintenance and intelligent diagnostics to smart process control and dynamic optimization. The book begins by framing the transformation of traditional quality control into a data-driven, AI-enabled discipline, setting the stage for deeper technical explorations. Readers are then guided through a diverse set of case studies and innovations, including AI analysis of emotional development in fictional datasets, paradigm-shifting applications in software development, and comparative assessments of deep learning models for arrhythmia detection. Unique frameworks, such as “pipes and puddles” for process visualization and intelligent strategies for defect reduction in automotive paint shops, demonstrate how quality engineering is being reimagined. Vibration signal analysis for gearboxes, QoS-aware IoT frameworks, and enterprise architecture powered by ML and IoT further enrich the discussion with practical insights and future-ready methodologies. Later chapters critically assess the challenges and opportunities posed by remote operations, digital workflows, and adaptive manufacturing, providing a forward-looking perspective on quality control in evolving production ecosystems.

Whether you are a researcher, quality engineer, industrial technologist, academic or student in the QA-QC area, this book provides essential knowledge and innovative perspectives on the intersection of quality control, AI, and Big Data. It is not just a reflection of current capabilities but a roadmap to future excellence in intelligent quality management.