There is a perceived lack of critical understanding and training in modern industrial design methods using state-of-the-art CAD/CAE/CAM technology and design optimization. This module aims to address this imbalance by providing exposure to the advanced aspects of many software tools such as finite element analysis (FEA), cutting path analysis and product data management (PDM): all key tools in many decision-making and design optimisation processes.

Students are introduced to the use of computer-based tools for strategic decision making in industry. It is not a training workshop on how to use specific software; it is instead a study on how to use the software well, in particular to facilitate making engineering decisions. During the module, the students will learn what engineering models are required in industry and how that data is managed using PDM and PLM. They shall also learn how to prove that their numerical analyses is of a good enough standard to be used in decision making; and how cutting path analysis can be used as a strategic tool for cost reduction.

Increasingly, employers are expecting graduates to leave university already versed in tools used in industry, so the importance of this module in terms of employment opportunities cannot be overstated.

At the end of this module, the students will be able to use their understanding of solid mechanics to devise appropriate Finite Element Analysis methodologies and assess the validity of their analysis, and shall be able to create designs that can be reliably realised using Computer Aided Manufacturing methodologies. They will also gain a comprehensive understanding of the use of Product Data Management and be able to judge when it is to be used as compared to alternative methods.

Students are given a comprehensive introduction to Systems Thinking and its application to engineering (also known as Systems Engineering) during this module. To that end, the module introduces the tools to help in gathering and analysing requirements and continuing through system architecting to solution generation, evaluation and selection.

The modern integration of mechanical, electronic, chemical and software engineering technologies demands an approach to design that enables the designers to handle the inherent complexity. As such, students will be taught how to design complex systems to meet the desires and expectations of customers; this will increase their employability in the engineering industry.

Students will be educated in the importance of a structured approach to system and product design, including the skills for eliciting, capturing and analysing customer requirements. They shall also learn how to introduce functional modelling methods for the analysis and synthesis of a set of requirements and introduce a systems framework for design, in terms of people, processes and tools. A key aim is to enable the students to develop skills in creative thinking, allowing them to, among other things, use tools to generate and select conceptual system design solutions.

By the end of this module, students will have gained a theoretical understanding of the systems approach to system design, including how it relates to systems engineering and its principles through divergent and convergent thinking processes.

This module involves the development and delivery of an individual engineering project. Project topics are usually aligned with an academic supervisor’s research interests or contribute to industrially collaborative areas of technical enquiry of significance to the department.

Projects can vary from research-orientated investigations of new methods or techniques through to the design and verification of components for manufacture. Part-time students may undertake a project linked to their company subject to approval, and in such cases, students are assigned an academic supervisor from the university and a technical contact within the company.

The emphasis is on applying skills learnt during the course. This will require students to self-manage their project; to select and apply engineering modelling and analysis, and to use this to deliver a technically justified solution to an engineering problem. Students will undertake a feasibility study, write up their project findings in an individual technical report, and produce a poster suited to the communication of technical information and project outcomes.

Where possible, projects are connected to an industrial partner and are structured to enable the student to develop and demonstrate several of the professional engineering competences defined by the Engineering Council.

Projects are obtained from local companies who have a genuine engineering problem, design or development requirement. The three-week project commences with a team and project assignment and briefing lecture. Each team then meets their company and is assigned an industrial contact and academic supervisor for the project. Communication with the company and academic supervisor for most of the project is at the discretion of the team. The modules ends with a presentation session to which the company and all academic project supervisors are invited.

This module gives the opportunity to apply the technical, problem analysis and project management skills learned in earlier modules to a real industrial environment.

Gaining professional experience solving problems in the industry can greatly increase the employability of postgraduates. Students can also forge useful connections within the industry during their communication with the company.

During the project, students will learn how to structure a technical problem; assess the technologies required to meet the requirements using available literature and resources; work creatively to develop possible solutions; and apply multidisciplinary scientific and engineering skills to assess the technical validity of those solutions.

This module is only available to full time MSc students.

The design and application of intelligent control systems, with a focus on modern algorithmic computer-aided design methods, is what students will be introduced to during this module. Starting from the well-known proportional-integral algorithm, essential concepts such as digital and optimal control will be familiarised using straightforward algebra and block diagrams.

The module addresses the needs of students across the engineering discipline who would like to advance their knowledge of automatic control and optimisation, with the lectures being supported by practical worked-examples based on recent research into robotics, mechatronic and environmental systems, among other areas.

Students shall also be taught statistical modelling concepts that have a wide ranging application for control, signal processing and forecasting, with applications beyond engineering into health and medicine, economics, etc.

The concept of state variable feedback is utilised as a unifying framework for generalised digital control system design. This approach provides a relatively gentle learning curve, from which potentially difficult topics, such as optimal, stochastic and multivariable control, can be introduced and assimilated in an interesting and straightforward manner. The module also aims to develop an appreciation of the constraints under which industrial applications of control operate, and to introduce the computational tools needed for designing these control systems.

Major global companies across the engineering discipline, including automotive and communications companies, have positions for graduates with a control engineering background. This module is also very useful for those who wish to work with robotics and autonomous systems.

Ultimately, students will come to understand various hierarchical architectures of intelligent control. They will also be able to design optimal model-based control systems and design and evaluate system performance for practical applications.

This module introduces students to the recent advances in artificial intelligence, machine learning, and cutting-edge deep learning methods. Students will learn how to examine the technologies that apply to various aspects of engineering, such as searching and planning algorithms, supervised learning, unsupervised learning, reinforcement learning, deep neural networks, convolution neural networks, recurrent neural networks, and generative adversarial network.

The module aims to equip students with key knowledge and understanding of their application in industrial robots, smart manufacturing, predictive maintenance, design optimisation and digital twin. Students will also learn how to implement the machine learning algorithms by practicing this in our labs, keeping the legal, social and ethical considerations in mind when applying machine learning technologies.

On successful completion of this module, students will be able to demonstrate the impact of emerging machine learning technologies by understanding the underlying principles of machine learning, typical algorithms, and deep learning methods. Students will be able to analyse real-world problems, such as design optimisation, manufacturing process optimisation, fault diagnosis and prognosis, and be able to design machine learning models to solve them.

This module aims to help students identify, understand and then set out the mechanism and mechanical design requirements for products and, in particular, actuators. It also covers actuation system mechanics and kinematics with the analytical techniques for analysing actuators and their dynamics.

Students shall be taught the operating principles of different types of actuators and how they are selected for a dedicated application. They will learn the principles of precision location and of the guidance of moving parts, and use kinematic design to integrate actuators into their systems. Other tasks will include studying the dynamic analysis of the actuation systems within different industrially relevant drive mechanisms, including drive circuitry effects. These applications of knowledge will allow students to appreciate the mechanics of robotic manipulators, their use in manufacturing and their programming. It will also provide an understanding of actuator operating principles.

Students will become skilled in analysing the dynamics of real systems via applying appropriate approaches including the formulation of actuator system models, time-series analysis and frequency response analysis. They shall also come to understand the meaning and significance of factors which determine the performance and stability of machine systems, and be able to set out the scheme design of a machine system which incorporates principles derived from this understanding.

During this module, students will learn the basics of how the behaviour of device structures change as dimensions shrink, along with how to design these structures and predict their static and dynamic behaviour across a range of energy domains (electro-magnetic, electro-static, thermal, mechanical etc.).

In addition to this, the module will also teach the principles of sensing and actuation in the main application areas that range from mobile communications to bio-chemical analysis. Students will gain knowledge regarding the manufacturing technologies for micro & nanoscale technologies; the product engineering process, including how to achieve high reliability; and the emerging technologies that utilise nanoscale structures.

Semiconductor industry companies like Intel and Texas Instruments have positions across the entire design and manufacturing flow for graduates with a microengineering background. Most of the smaller design companies have activity in MEMS and microengineering.

As a result of undertaking this module, students will come to understand the underpinning engineering science associated with micro-mechanics and microfluidics. Other topics they will discuss include the fundamental principles of solid state physics and materials used within devices involving sub 100nm dimensions; micropackaging concepts; and the mechanics of scaling across multiple energy domains down to sub 100nm dimensions.

In the end, students will be able to demonstrate a wide knowledge and comprehensive understanding of design processes and methodologies for microsystems, this will include an awareness of developing technologies in the areas of microsystems and heterogeneous systems.

The aim is to develop students' understanding of the key aspects underlying engineering science, relating to the production of nuclear fuels and the conversion of nuclear energy. The unique hazards associated with handling the materials in the manufacturing train, such as criticality, radioactive exposure, chemical toxicity and flammability, will be highlighted together with methods for their safe management. Students will be able to study advanced material balancing methods suited to the special requirements of nuclear materials including methods of reconciliation and active material accountancy.

Additionally, students will extend their knowledge of heat transfer with particular reference to the design of nuclear reactors and the complex boiling processes occurring in theory geometries.

Ultimately, this module will provide understanding of a range of nuclear fuels, their associated manufacturing processes, and their relationship with the civil/military controversy.

Manufacturing is a key component of engineering. The ability to design and manufacture, high quality, high value products, with short lead times, is essential for industries to be competitive in the modern "digital" age. This module will introduce the context of new product introduction and examine the technologies available to both shorten total lead times and increase confidence in the product. You will study, in detail, a range of rapid product development tools and technologies including specific process principles and engineering applications. Topics covered include Concurrent Engineering, Rapid Prototyping, Rapid Tooling, Additive Manufacturing, Reverse Engineering, Virtual Prototyping and Responsive Manufacturing.

The Renewable Energy module provides students with specialist training in this field, with strong emphasis on engineering design, but also includes discussions of costs, grid integration, optimal resource exploitation and environmental aspects. The aim of this module is to introduce students to the fundamentals of a range of sources of renewable energy and the means of its conversion into mechanical and/or electrical power. In addition, the technical, economical, environmental and ethical issues associated with the exploitation of renewable energy sources are highlighted and discussed.

Students will be provided with a good overview of well established and rapidly growing forms of renewable energy, learning fundamental design concepts of horizontal and vertical axis wind and tidal current turbines, and hydraulic turbomachinery, and analysing key power and load control strategies. An introduction to solar energy for electrical and heat power generation is also included. Student will be taught how to assess renewable energy resources, and how to reliably determine the maximum share of the available source that can be converted into electricity or heat.

Using engineering, physical and mathematical models, students will learn about the formulation and solution of multidisciplinary problems of renewable energy engineering. The discussion of realistic engineering problems and machine design/usage challenges will expose students to technologies presently used in the research and development departments of modern renewable energy organisations.