Digital Manufacturing Engineering MSc

In this course students are introduced to advanced conventional manufacturing processes including sheet/bulk metal forming and Machining operations and the relevant mechanics of the processes and materials deformation. Analytical modelling techniques are also introduced and their applications are explained in order to determine the deformation of materials under the applied loads. Fundamentals of deformation and relevant force calculations together with mechanics of machining in metallic materials will be covered as the secondary manufacturing operations. The module provides a greater range and depth of knowledge related to the deformation of materials and process analysis in primary and secondary manufacturing operations using theoretical and experimental learning methods. The students will be equipped with tools to analyse and design manufacturing operations utilising various manufacturing methods within a wider engineering context.

This module aims to provide fundamentals of what strategy is and distinguish it from activities, tactics and goals of an organisation. It explains its important role in the continual success of organisations. It also introduces how strategy can be translated into business practices, methods, procedures to achieve the goals of an organisation's strategy.

The module is designed to develop your analytical and critical skills in the strategic management issues facing engineering organisations in today's fast-changing environment. It is a unique opportunity for you to equip yourself with the essential industry-relevant skills to excel as a future leader.

The module aims to provide students with a thorough understanding of the principles of finite element modelling and its application to solve industrial engineering problems. A set of industry-relevant problems will be provided to students along with experimental results for model validation. Students will be allocated one of their preferred projects and will have to devise a modelling strategy to solve their particular problem. Knowledge will be drawn from lectures introducing the theory behind finite element modelling of dynamic problems for modal and transient analyses, non-linear problems including contact, material behaviour and large deformation as well as fracture.

The ability to use data and information from multiple sources and make informed decisions based on that data is key to many applications, e.g. manufacturing, aerospace, robotics, finance and healthcare. Through effective use of multisensory data and decision making we can reduce uncertainty, improve robustness and reliability, enhance efficiency and ultimately improve the performance of systems. In this module you will develop an in depth knowledge and understanding of multisensor and decision systems and the underlying mathematics and algorithms. You will develop your confidence in solving complex problems requiring the application of multisensory and decision techniques to a wide variety of applications.

The module gives knowledge of machine vision methods for a broad range of applications. It introduces you to image and video processing models and methods and provides you with skills on how to embed them in autonomous systems. You will be able to apply the acquired knowledge to both industrial and research areas.

An important field within artificial intelligence is machine learning, which enables systems to learn from data rather than being explicitly programmed to solve a task. Conventional machine learning algorithms tend to rely on a human to carefully engineer and extract features to present to a machine learning algorithm, which can be time-consuming and difficult. A deep learning system, by contrast, takes raw data as input and learns to extract features automatically. This approach has led to significant improvements in processing images, video, speech and audio. Deep learning has also had an impact on the design of intelligent agents, giving rise to the area of deep reinforcement learning, which is where an agent learns in a reward-based framework. An example of deep reinforcement learning is where the Google DeepMind team designed an agent that learned to play Atari computer games to better-than-human-expert level.

This module will provide you with an opportunity to demonstrate planning and management skills, to show your initiative and to display your technical skills. You will work on an industry focused research project. You will be supervised by an academic member of staff. The technical components of your project may be experimental, theoretical, analytical or design based and most projects will require proficiency in a number of these. Your project is assessed on the basis of conduct, final report and viva.

This module starts with an introduction to the different types of composite materials that either exist in nature or are man-made. Reinforcing theories are discussed as are the strengths and weaknesses of composite materials. The aim is to acquaint students with the constituents of composite materials, fibres and matrices. Running parallel to this is an examination of composite materials from a micromechanics point of view. Fibre statistics, classical laminate theory and shear lag theory (and more) are used to predict and understand the properties of composites. A series of problem classes are used to help students practise using the equations and interpreting the output.

 

This module will provide you with a comprehensive introduction to Additive Manufacturing (3D Printing), providing you with an insight into the technologies themselves, when and how they might be applied, and the broader economic, social and industrial context within which these techniques sit. Our aim is to provide you with an understanding of the underlying principles and considerations relevant to this area, so that you are able to apply this knowledge confidently and effectively during your future career.

This module introduces you to the importance of innovation in manufacturing and service organisations whose primary business activity is engineering and/or technology. Innovation management is introduced as the thoughtful combination of new product/process development and change management. Through case studies, theoretical frameworks, and tools you will come to understand innovation at multiple scales: international, national, regional, organisational and team, with particular emphasis on how organisations manage and exploit the commercial risks and opportunities inherent in innovation, and how project teams and engineers can respond to innovation challenges effectively. The module is aimed at engineering students of any discipline.

This module is designed to provide you with an understanding of both the design and manufacture of polymer composites and is presented in two sections. First, the design of composites is taught via tutorials on classical laminate theory. An extended series of worked examples provides you with the basic tools you need to design effective composite parts. Second, the manufacture of composites is taught via lectures. You will learn multiple routes for making composite parts alongside practical issues such as defects, machining/joints, failure, testing and non destructive testing, repair and SMART composites.

Robotics and autonomous systems are having an increasing impact on society and the way we live. From advanced manufacturing and surgical robots to unmanned aerial systems and driverless cars, this exciting area is presenting increasing technological challenges. This module provides you with the advanced knowledge and understanding to apply control and systems engineering concepts to the closely related disciplines of robotics and autonomous systems. The module covers theoretical and technical analysis, and design aspects of mobile and manipulator robots with reference to their applications. The module further covers advanced techniques in autonomous decision making for robots and autonomous vehicles.

This unit introduces key concepts with regards to Materials 4.0, the fourth industrial revolution. Modelling and simulation is a key enabling technology within Aerospace Technology Institute's strategy to reach zero carbon emissions by 2050. Modelling allows for the rapid insertion of new materials and manufacturing processes, in addition to the improved understanding and optimisation of current methods. The course includes key drivers in reaching zero carbon emissions, covering lithium battery manufacturing and coating technologies.

This unit aims to provide knowledge and experience of advanced manufacturing techniques that will underpin the UK's future advanced materials manufacturing base and obtain knowledge and experience of advanced manufacturing process and material modelling to solve industrial problems.