Manufacturing for Industry 4.0
Industry 4.0 brings automation, operational intelligence and connectedness to manufacturing to create smart factories, with processes and workflows monitored in real time. Digital twins – accurate computer models of the layout and operation of the factory, provide predictive simulation tools for manufacturing managers. Alternative layouts and workflows can be modelled, analysed, and optimised prior to investing in their implementation.
Students will work with case studies and details of manufacturing cells and workflows, developing accurate digital twin models and applying the principles of predictive simulation to quantify and optimise the benefits, risks and costs of modifications.
Applied Materials for Modernisation
Materials Science is one of the key disciplines of the 21st Century. Innovations in the design and modification of materials will make significant contributions in the years and decades to come, providing intelligent products for areas such as health, energy, mobility and communication, as well as environment and climate management.
You will learn about the impact of the latest developments in materials science, and how material properties and characteristics can be enhanced using appropriate additive methods, to improve production methods and engineering design in general.
A resilient and sustainable economic future depends on engineering products and processes that use resources and energy at a rate that does not compromise the natural environment, or the ability of future generations to meet their own needs.
Students will learn about the latest developments in harvesting energy from natural resources, such as wind, solar, bioenergy, hydrogen fuel cells, gaseous and liquid biofuels, etc. and about what informs business decision-making when evaluating sustainable plant performance and materials selection.
Systems Engineering for Industry
A range of interdisciplinary skills in systems thinking and systems analysis will be required to support the delivery of new engineering products, enterprise and services for Industry 4.0.
Students will learn how analysis and decision making during the operational and developmental phases of the system life cycle for complex systems is supported by hierarchical models of the system’s structure and its functional and physical building blocks.
Management of Projects Professional Practice
The aim of this Unit is to develop student knowledge in the management of projects in a professional practice setting via problem based learning. Students will be expected to analyse, critique and recommend solutions for an existing project.
Using a problem based learning approach, with a case study provided by an industrial partner, students will explore the management strategies, processes and stakeholders involved in the project, and develop communication and coordination plans to monitor and control the project information.
Group Engineering Project
Students will work in teams to deliver substantial design solutions to a complex problem in engineering.
Computer Aided Engineering
In Industry 4.0, computer models are used to create “digital twins” of physical components and assemblies, so that potential modifications can be tested and improved, prior to actual implementation. Computer Aided Engineering (CAE) provides the computational simulation tools for engineers to model and validate their designs, using techniques such as Finite Element Analysis (FEA).
Students will learn how to apply the principles of computational mechanics to create and analyse system models, with an emphasis on the importance of verification and validation of models to assess the reliability and accuracy of any recommendations based upon them. Case studies of applications will be considered in areas such as bioengineering, renewables, and manufacturing methods.