About this book:

This book provides a comprehensive blueprint for deploying Artificial Intelligence within the framework of Industry 4.0, moving beyond theoretical "pilot purgatory" to scalable, plantwide operations. It establishes a foundational hierarchy for industrial data, categorized into raw sensor signals, operational context (from PLC, MES, and ERP systems), and the vital "ground truth" required to train supervised models. By detailing technical infrastructure—such as OPC UA and MQTT protocols, edge-to-cloud computing architectures, and MLOps for regulated environments—the text bridges the gap between traditional Operational Technology (OT) and modern Information Technology (IT).

Central to the book's applied strategies are predictive maintenance and quality optimization. It explores how sensor fusion and digital twins allow AI to detect subtle anomalies in rotating machinery and continuous processes, shifting maintenance from reactive or scheduled intervals to precise, condition-based interventions. In quality control, the book details the integration of Vision AI and augmented Statistical Process Control (SPC), enabling real-time defect detection and root-cause analysis that links downstream failures to upstream process deviations. These technologies are presented not as human replacements, but as decision-support tools that enhance operator capability through intuitive interfaces and augmented reality.

Beyond the factory floor, the book addresses the broader industrial ecosystem, including autonomous material handling via AMRs and the use of AI to bolster supply chain resilience. It emphasizes that the success of AI is tethered to rigorous change management and economic justification. By quantifying ROI through reduced unplanned downtime, increased first-pass yield, and optimized energy consumption, the text provides a framework for building sustainable business cases. Special attention is given to the ethical and safety implications of closed-loop control and the necessity of "security by design" in cyber-physical systems.

The concluding chapters ground these high-level concepts in diverse case studies across discrete and process industries, from automotive assembly and semiconductor fabrication to chemical refining and pharmaceutical production. These real-world examples highlight critical lessons, such as the importance of explainability in regulated environments and the necessity of multi-objective optimization to balance throughput with sustainability. Ultimately, the book serves as an end-to-end guide for transforming legacy manufacturing into an adaptive, self-optimizing, and data-driven enterprise.

What You'll Find Inside:

• Practical predictive maintenance architectures combining sensor fusion, anomaly detection, and physics-informed models to reduce unplanned downtime and optimize maintenance scheduling.
• Critical data foundations: strategies for sensor deployment, time-series feature engineering, data governance, labeling, and quality assurance to ensure trustworthy AI models in industrial settings.
• Vision AI implementation for real-time inline and end-of-line quality inspection, including defect detection/classification, integration with SPC, and edge computing for high-speed production lines.
• Distributed computing strategies across edge, fog, and cloud layers for latency-sensitive control, scalable analytics, and secure data pipelines using OPC UA, MQTT, and IIoT protocols.
• Human-in-the-loop design principles for operator interfaces, decision support systems, and explainable AI to build trust, enable feedback loops, and augment rather than replace human expertise.

Who's It For:

This book is for manufacturing engineers, plant managers, automation specialists, and industrial data scientists seeking to deploy AI solutions on real production lines. It targets professionals responsible for implementing predictive maintenance, quality control systems, or process optimization initiatives who need practical, battle-tested strategies beyond theoretical concepts. Readers will benefit most if they are evaluating or scaling AI pilots to plant-wide adoption in discrete or process manufacturing environments and require guidance on data infrastructure, model deployment, change management, and ROI validation.