Overview

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Explore the technologies driving smart factories with AI, ML and IIoT in Manufacturing. This course provides a practical introduction to the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), Machine Learning (ML), and their applications in robotic manufacturing. Learners will examine IIoT architecture, edge computing, sensor gateways, and the integration of connected systems with industrial robotics. The course also covers AI principles, Edge AI, Cloud AI, and the deployment of deep learning techniques to enhance robotic decision-making and performance. Through expert-led lessons and industry-focused examples, you will gain the knowledge needed to develop intelligent, connected, and efficient manufacturing systems. Learning Objectives By the end of this course, you will be able to: Explain key IIoT concepts, categories, and architecture layers used in smart manufacturing. Describe the role of IIoT, sensor gateways, and edge computing in robotic systems. Understand the fundamentals of AI and ML and their industrial applications. Differentiate between Edge AI and Cloud AI deployment models. Analyze how deep learning enhances robotic automation and decision-making. Apply AI, ML, and IIoT concepts to improve manufacturing efficiency and productivity. Target Learners This course is designed for: Engineering students in Mechanical, Electronics, Electrical, Mechatronics, Robotics, and Manufacturing disciplines. Professionals seeking to upskill in Industry 4.0 and smart manufacturing technologies. Automation, robotics, and IoT practitioners interested in AI-enabled industrial systems. Learners aspiring to build careers in intelligent manufacturing, digital transformation, and industrial automation.

Syllabus

IIoT in Robotic Manufacturing

This module, IIoT in Robotic Manufacturing introduces the role of the Industrial Internet of Things (IIoT) in creating intelligent, connected, and data-driven manufacturing systems. The module covers IoT categories, IIoT architecture, and the functions of sensing, communication, data processing, and application layers. It explores computing infrastructures such as on-premise servers, cloud platforms, and edge computing for real-time decision-making. Learners study IIoT gateways, their role in data aggregation and protocol translation, and communication technologies including WAN and short-range wireless protocols. The module also introduces IoT-based video analytics for quality control and presents industrial case studies in packaging, bottle manufacturing, aluminium extrusion, fastener production, excavator monitoring, metal stamping, and air compressor monitoring. Overall, it provides the foundational knowledge required to understand and implement connected robotic systems in smart manufacturing environments.

AI & ML in Robotic Manufcturing

This Module AI & ML in Robotic Manufacturing introduces the application of Artificial Intelligence (AI) and Machine Learning (ML) in creating intelligent, adaptive, and data-driven manufacturing systems. The module covers AI and ML fundamentals, supervised and unsupervised learning, model training, and evaluation, along with industrial applications such as predictive maintenance, defect detection, process optimization, and robotic control. Learners explore Edge AI, Cloud AI, and Python programming, including TensorFlow, Scikit-learn, and MicroPython for embedded AI applications. Advanced topics include deep learning, big data, self-driving car case studies, collaborative robots (cobots), and Deep Q-Learning for pick-and-place robots. Overall, the module provides the knowledge and practical skills needed to develop intelligent robotic systems for smart and autonomous manufacturing environments.