This module provides an in-depth exploration of the Industrial Internet of Things (IIoT) and Cyber Physical Systems (sensors, control boards) to enable intelligent industrial systems. Students will work with real-world industrial sensors, IIoT gateways, and edge computing devices to collect, process, and analyse manufacturing data for predictive maintenance, quality control, and process optimisation using data analytics & machine learning.

1. Evaluate the role and impact of IIoT and CPS in modern manufacturing environments.
2. Appraise best practice for real time data acquisition systems in manufacturing environments.
3. Develop and deploy edge and cloud-based IIoT architectures, including communication protocols (e.g., MQTT, OPC UA, 5G) to support connectivity, data flow, and system integration.
4. Propose solutions to integrate Cloud & Edge AI solutions to enhance decision-making at the production level.
5. Analyse and visualise industrial process data using time-series analysis and dashboards to inform real-time decision-making.
6. Select and apply Machine Learning Models on edge devices to optimise manufacturing operations.

This module provides a comprehensive introduction to machine learning (ML) with a strong emphasis on deep neural networks, tailored specifically for advanced manufacturing applications. Using PyTorch as the primary framework, students will learn to develop, train, and deploy ML models for real-world industrial challenges such as predictive maintenance, quality control, and process optimisation. The module covers: Core ML techniques (supervised/unsupervised learning) for manufacturing data.

Deep learning fundamentals, including CNNs for visual inspection and RNNs for time-series forecasting.

Cutting-edge architectures like Transformers (for sequence/vision tasks) and Generative AI (VAEs, GANs) for synthetic data generation.

Deployment strategies (ONNX, edge AI) and ethical considerations (bias, explainability).

Hands-on labs and a project will enable students to apply PyTorch-based solutions to manufacturing datasets (e.g., defect detection, tool wear prediction). By the end, students will be equipped to integrate ML into Industry 4.0 systems.

1. Analyse manufacturing datasets and select appropriate machine learning and deep learning techniques.
2. Implement PyTorch-based models (CNNs, Transformers, GANs) for industrial use cases.
3. Evaluate model performance, addressing challenges with limited or imbalanced data.
4. Deploy lightweight machine learning models for edge/real-time inference.
5. Critically assess the ethical implications of AI in manufacturing.

This module introduces students to virtual manufacturing and the creation of digital twins to simulate, optimise and enhance manufacturing processes. By leveraging advanced digital tools and techniques, students will gain hands-on experience in developing process designs, simulating process behaviour and optimising process workflow. Also, students will learn how to apply Lean principles to evaluate process performance, identify inefficiencies and propose process solutions for continuous improvement.

1. Use CAD tools to model realistic products for integration with the digital simulations.
2. Create digital twins of production lines, machines and robots to simulate their behaviours and optimise production.
3. Utilise virtual simulation tools to optimise product designs, manufacturing processes, efficiency and commissioning costs.
4. Apply process improvement techniques to analyse process performance, identify bottlenecks and reduce waste.
5. Evaluate the performance of virtual models against real-time data from physical systems.
6. Evaluate the effectiveness of digital twin integration in improving manufacturing efficiency and costs.

This module provides an in-depth understanding of quality control and digital inspection techniques in modern manufacturing. It concentrates on 3D scanning, reverse engineering, geometric dimensions and tolerances (GD&T) and machine vision to ensure product accuracy and quality compliance. Students will develop hands-on expertise in digital inspection methods using advanced scanning technologies, coordinate measurement machines (CMMs) and automated vision systems for defect detection and quality assurance. Students will adopt statistical process control (SPC) tools such as control charts and process behaviour analysis to effectively monitor and control manufacturing processes.

1. Utilise 3D scanning technology (7-axis Hexagon scan arm) for reverse engineering, precise digital inspection & quality assessment.
2. Convert scanned data into CAD models for dimensional analysis, defect detection, and redesign.
3. Apply GD&T principles to evaluate manufacturing tolerances and ensure compliance with industry standards using the portable 7-axis Hexagon co-ordinate measurement machine.
4. Implementmachine vision systems for automated quality inspection and defect detection in manufacturing processes.
5. Employ statistical process control charts to monitor and control manufacturing processes, ensuring that product quality remains consistent and within acceptable limits.
6. Integrate Machine Learning techniques to recognise when the process is out of control, identify the root cause and determine appropriate action to restore the process to a state of control.

This module equips students with advanced knowledge and hands-on experience in programming and integrating industrial robotic systems within modern manufacturing environments. It covers advanced PLC programming, electro-pneumatics, and Human Machine Interface integration, enabling the development of fully automated systems. A strong emphasis is placed on sensor integration and machine vision systems to enhance automation and system intelligence.

1. Program and optimise robotic cells for common manufacturing tasks.
2. Develop PLC programs for automated manufacturing which include subroutines, AC servo positioning and complete integration.
3. Integrate robotic and PLC systems with electro-pneumatic components for enhanced automation.
4. Develop data-driven HMI design, visualise OE performance & integrate operator alarms.
5. Implement Process Control Optimisation and Predictive Maintenance.
6. Evaluate implementation techniques for modern automation systems in regard to business planning, cost scalability and payback.

This module explores sustainability in advanced manufacturing, integrating environmental, social, and economic considerations into manufacturing processes. It explores the principles, strategies, and tools to develop more sustainable products and systems. The module will equip students with the knowledge and skills required to implement sustainability concepts within manufacturing environments, focusing on practical applications of circular economy principles to reduce waste, extend product lifecycles, and optimise resource efficiency.

1. Redesign manufacturing processes using circular economy principles, considering material efficiency, product lifecycle extension, and waste minimisation.
2. Implement sustainability strategies in advanced manufacturing, including energy-efficient processes, remanufacturing, and sustainable materials.
3. Evaluate economic and regulatory factors affecting sustainability in manufacturing, conducting ROI and cost-benefit analyses.
4. Apply sustainability assessment tools such as Life Cycle Assessment (LCA) to measure the environmental impact of products and manufacturing systems.
5. Critically assess emerging technologies and practices that can support the transition to more sustainable manufacturing systems.

This module provides the student with the opportunity to apply the theoretical knowledge and skills gained in the course to a substantial project related to the application of advanced manufacturing techniques and practices. The student will preferably practice the application of academic theories and concepts in association with a workplace environment so that the tangible benefit of how the experience has informed their knowledge can be evaluated. Ultimately, learners will critically evaluate the academic theories or concepts applied to an identified area of interest, thus embedding competency as a reflective practitioner, and quantifying the transferable skill set gained from this MEng.

1. Identify a research topic, specify appropriate research objectives, and formulate a research plan
2. Select appropriate material and critically review relevant published research in an appropriate and thorough manner
3. Engage in a sustained piece of individual, academic research on a chosen topic within the field(s) specifically relevant to the course
4. Design, implement and test an appropriate engineering solution to address the specific research objectives
5. Engage with and observe applicable ethical considerations relating to the research project content, personal conduct and the delivery of this modules components
6. Produce a dissertation that evaluates and synthesises both qualitative and quantitative research methods and displays evidence of independent research skills and reflects on the engineers responsibility to society and the environment
7. Communicate a research project to an engineering audience, defending chosen methods and provide a conclusion with recommendations for further work.