Outline

Industrial data sources (Sensors, PLCs, SCADA) • IoT-enabled systems and data acquisition frameworks • Data integration and communication frameworks • Challenges in industrial data quality and real-time monitoring

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Fundamentals of AI, ML, and Deep Learning in industry • Supervised, unsupervised, and reinforcement learning • Classification, regression, and anomaly detection • Model performance evaluation in manufacturing

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Principles of predictive maintenance and asset health • Sensor-driven fault detection and failure prediction • AI models for maintenance planning and downtime reduction • Applications in machinery and equipment systems

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Computer vision in inspection and defect detection • Visual quality assurance and deep learning analysis • Automated quality control in production environments

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AI-driven process optimization and production planning • Resource allocation, scheduling, and throughput enhancement • Intelligent robotics and human-machine collaboration (Cobots)

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Introduction to digital twins and edge computing • Real-time industrial AI and cloud-edge integration • Challenges: Scalability, latency, and cybersecurity

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Case studies in predictive maintenance and quality inspection • Smart factory examples and workflow optimization • Future trends: Generative AI and sustainable manufacturing