This module introduces the essentials of Industry 4.0 and Smart Manufacturing. Learn about the opportunities and challenges that Industry 4.0 presents for manufacturers. Discover the key technologies that underpin Industry 4.0. Understand what the future of Industry 4.0 looks like and the implications for companies.
Part 1 – Industry 4.0 Introduction and Overview: Introduction and background to Industry 4.0; Drivers and Opportunities by Industry 4.0; Challenges and Barriers to Industry 4.0.
Part 2 – Overview of Key Technologies in Industry 4.0: Data integration & Industrial Internet of Things; Extended Reality and Advanced Human Machine Interaction; Collaborative Robotics; Simulation and Digital Twins; Cloud, Cyber Security & Big Data Analytics; Artificial Intelligence; Additive Manufacturing.
Part 3 – Future Trends in Industry 5.0: Society 5.0 and Human-centered Manufacturing; Data and Intelligence-driven Innovation; Biological Transformation in Manufacturing; Product-service systems & 4.0 Business Models; Green, Circular & Sustainable Manufacturing; Resilient Manufacturing.

This module introduces Industrial IoT and data integration as critical elements of Smart Industries. Learn about key data analytics tools and techniques for innovative businesses. Discover the layers of Industrial IoT architecture and the considerations for systems connectivity and interoperability. Learn about the benefits of IoT gateways with edge computing capabilities.
Part 1 – Introduction to Industrial Data Analytics: Overview of Big Data; Data analytics tools and techniques (data preparation, data analysis, data mining, machine learning, data visualisation); Elements of horizontal and vertical data integration; Example of RFID for product tracking and tracing; Data integration as a basis for smart data analytics; Multi-sensor data fusion.
Part 2 – Industrial Internet of Things: Cyber-Physical Systems; Layers of Industrial IoT architecture; M2M Connectivity and Interoperability; Challenges in Industrial IoT; Retrofit devices and IoT gateways
Part 3 – Edge computing and Cyber Security: Three stages to Industrial IoT; Cyber Security Challenges and Industry Standards; Overview of Edge Computing; IoT Gateways with Edge Computing Capabilities.

This module introduces the concepts of simulation technologies and Digital Twins and their role in digital manufacturing. Discover the requirements to develop and implement Digital Twins and their industrial applications. Explore the role of Building Information Modeling and Digital Twins in Production Planning & Control.
Part 1 – Simulation in Manufacturing: Simulation across the product and production lifecycle; Levels of simulation in manufacturing and logistics (network, factory and process levels); Application of VR, AR and MR in Manufacturing
Part 2 – Digital Twins in Manufacturing: Digital Model, Digital Shadow and Digital Twin; Building a functional Digital Twin; Levels of Digital Twins (product/design level, machine level, factory/process level, supply chain level); Typical elements for implementing Digital Twins; BIM vs. Digital Twins; Industrial applications of Digital Twins.

This module provides an overview of Artificial Intelligence technologies and their application in manufacturing as a core component of Industry 4.0. Understand the different classifications of AI technologies and their role in cognitive manufacturing. Learn about fundamental machine learning and deep learning techniques. Explore real-world applications of AI in smart manufacturing.
Part 1 – Introduction to Artificial Intelligence: Examples of AI uses in different industries; AI as an enabler of cognitive manufacturing; Classification of AI technologies.
Part 2 – Overview of AI Technologies: Difference between AI, Machine Learning and Deep Learning; Overview of Machine Learning techniques (supervised, unsupervised and reinforcement learning); Introduction to Deep Learning and Convolutional Neural Networks; The role of Explainable AI
Part 3 – Examples of AI Applications in Manufacturing: Fields of application of AI in manufacturing; Training of collaborative robots for sorting parts; Predictive and Prescriptive Maintenance; Computer vision based AI for quality inspection; AI training and data mining; Increasing Enterprise Resource Planning efficiency using AI.

The module aims to provide students with a comprehensive understanding of XR technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), and their practical implications in various industrial sectors. Students will delve into the fundamental principles and underlying technologies of XR, such as computer vision, 3D modeling, and spatial mapping. Throughout the module, students will engage in practical sessions and workshops to gain hands-on experience with XR tools, software, and development platforms. They will have the opportunity to create immersive VR environments, develop AR applications for industrial use cases, and explore the potential of MR for enhancing human-machine interactions.
Part 1: Introduction to Extended Reality (XR): Fundamentals of extended reality (VR, AR, MR); XR hardware and software technologies; Computer vision and tracking in XR; Principles of 3D modeling and spatial mapping.
Part 2: Applications of XR in Industrial Processes: XR in product design and prototyping; XR-enhanced assembly and manufacturing processes; XR for maintenance and troubleshooting tasks; Training and simulation using XR technologies; Remote collaboration and communication in XR.
Part 3: Implementation and Considerations in XR: XR development platforms and tools; User experience design in XR; Cost-effectiveness and ROI of XR solutions; Data security and privacy considerations; Ethical and social implications of XR in industry; Workforce impact and XR adoption challenges.

The module aims to provide students with a comprehensive understanding of the evolving role of operators in Industry 4.0 and beyond, to explore strategies for integrating human workers and advanced technologies in a collaborative and efficient manner, to develop skills in designing and managing human-centered manufacturing systems that enhance productivity, safety, and job satisfaction. This module delves into the concept of Operator 5.0, which represents a paradigm shift in the role of human operators in the context of smart factories. It recognizes the significance of human skills, creativity, and adaptability in complementing and collaborating with automated systems. Throughout the module, students will engage in case studies, simulations, and practical exercises to apply human-centered design principles and methodologies. They will explore the potential of emerging technologies, such as wearable devices, collaborative robots, and intelligent workstations, in supporting operator effectiveness and well-being.
Part 1: Human Factors and Ergonomics in Manufacturing: Principles of human factors engineering and ergonomics; Designing ergonomic workstations and tools for operator comfort and safety; Cognitive workload assessment and optimization; Human-robot interaction and collaboration in the production environment.
Part 2: Training and Skill Development for Operator 5.0: Competency mapping and skills assessment for operator roles; Training methodologies for enhancing operator skills in a digital manufacturing setting; Augmented reality (AR) and virtual reality (VR) for operator training and on-the-job support; Continuous learning and upskilling strategies for operators in the era of Industry 4.0.
Part 3: Worker-Centric Design and Work Organization: Designing flexible and adaptive work systems for operator empowerment; Job design and enrichment techniques for enhancing job satisfaction and motivation; Participatory approaches to involving operators in process improvement and decision-making; Human-centered production planning and scheduling.

This module introduces the concept of collaborative robotics and their role in flexible and autonomous smart manufacturing. Learn about the structure, benefits and challenges of collaborative robotics. Understand the industrial applications of perception technologies and smart robots. Explore a case study of the development of a collaborative robot solution in manufacturing and the business benefits achieved.
Part 1 – Introduction to Collaborative Robots: Traditional vs. advanced and collaborative robots; Structure of collaborative robotic systems; Benefits, drivers and inhibitors of collaborative robots; Overview of collaborative robot sensor technology; Industrial application of collaborative robots.
Part 2 – Safety, Perception and Mobile Robotics: Safety requirements in collaborative robotics; Perception technologies and smart robots; Industrial applications of perception technologies; Artificial Intelligence-based prediction of operator intentions; Overview of mobile robotics.
Part 3 – Case Study of Flexible Automation using Collaborative Robotics: Overview of industrial problem; Analysis of potential solution using collaborative robotics; Development stages of the collaborative robot; Outcome and business benefits of the final collaborative robot solution.