One of the major challenges of modern industry is to address the need for sustainable product development and manufacturing. International legislation and increasing costs of fiscal instruments such as the landfill tax now aim to force producers to reduce the environmental impacts of their products and processes.

Accelerating globalisation and industrialisation continue to exacerbate the complexity of sustainability. Manufacturers must attain simultaneous high quality and lower costs to maintain their competitiveness, yet labour, energy and material costs continue to rise.

This module addresses these global concerns by studying lifecycle considerations for sustainable and profitable product development and manufacture. Environmental legislation will also be studied along with product development strategies that will enable industry to meet these increasingly severe requirements. The associated industry presentations, expert guest speakers and industry-based assignments maximise the usefulness of the acquired skills in a real-life industry setting.

This module provides the background, foundations, and technical skills required to understand and successfully develop Artificial Intelligence solutions for a design, decision-making, and engineering context. Students will learn the theoretical as well as practical foundations of Data Science techniques, delving into exciting, industrially highly relevant, and cutting-edge topics like Machine Learning and Deep Learning.

Laboratory exercises augment the taught classes to deepen the learning experience. The appreciation of responsible data collection and model training, and the responsible design of sustainable solutions, is a "golden thread" that runs throughout all lectures

The "Digital Thread" is a seamless, integrated, and continuously updated data flow that connects every stage of a product's lifecycle, from design and manufacturing to operations and disposal.

This module aims to provide students with the essential knowledge, skills, and understanding of key technical concepts of Digital Manufacturing, including Generative AI. The students learn latest practices, tools and processes to enable them to develop their own innovative and creative design ideas for creating industry-level smart, sustainable, and human-centric products.

The purpose of the gamified Group Presentation in week 6 is to let the students demonstrate their new Digital Thread understanding through exploring and discussing existing smart products through a brief video production. In a Serious Games scenario, the students take the roles of members of an industrial management board of an existing company of their choice to critically discuss existing smart products in the context of the impact of the Digital Thread.

The Individual Assignment (80%) in Week 11 challenges the students to develop a novel and exciting design of their own using the latest digital enabling technologies that were introduced in class. The assignment is a 2000-word journal-style article. Creativity, presentation style, and academic depth of the explanation are key assessment criteria.

This module aims to introduce students to the concepts and basic technology of manufacturing automation and to be able to select suitable applications and specify the type of automation to be used in specific cases.

The module covers: Automation in manufacturing industry, why and where; Industrial robots, automation and typical applications; Open and closed loop control; Problems in robot design and control; Types of motion control; Control system functions; Advantages and disadvantages of electric and fluid power systems; Types of electric motors and their control techniques, simple actuators for hydraulic and pneumatic systems; Methods of programming robots; Relative economics of human labour, reprogrammable and hard automation and Safety considerations for industrial robots and other automated systems.

At the end of this module students will be able to:

• Demonstrate knowledge and understanding of why manufacturing automation is used
• Describe the conditions under which manual and/or automated production methods would be applied
• Analyse the configuration and technical specifications of an automation system suitable for a specified task
• Synthesise a manufacturing task suited to a specified automated system
• Analyse and understand the technological elements of drive and control, and machine vision, systems
• Critically appreciate the kinematic and dynamic problems associated with the control of automated systems
• Understand the implications of applying automation in human terms
• Demonstrate knowledge of safety factors that must be considered when installing automation

Assessment and feedback is in the form of one coursework submission showing technical analysis of an aspect of automation and critical thinking on the design of systems (40%), an exam (50%) and in-class participation (10%).

The module will cover the following topics: 

• mechatronic system design process: Product/system design specifications (PDS), concept generation and selection, mechatronic system design and flow chart diagrams.
• sensing and actuation: Sensing theory, sensor selection, drive design, motor control. 
• control systems: Understand and apply control theory in a mechatronic system design.
• hardware design: Processor architecture, embedded computing platforms, interface, I/Os.
• software design: Software design basics, algorithm and code design, programming tools, and software engineering principles.
• prototyping and evaluation: develop skills in selecting methods for prototyping using appropriate tools and means, including rapid prototyping and computer modelling.

This module aims to enable students to understand the concepts of virtual product modelling and techniques used to visualise products before they are fully designed and manufactured.

The module covers: an introduction to basic modelling, visualisation and evaluation techniques creating models, parts and assemblies; The representations that underpin modern CAED systems (wireframe, surface, CSG and BRep), basic computer graphics (homogeneous transformations), data exchange, information integration, product data management, economics of CAD/CAM systems (cost breakdown, potential benefits, improving cost/benefit ratio), basic systems selection and justification and organisational impact and system management.

At the end of this module students will be able to:

• demonstrate the ability to use a commercially available CAD system by creating 3-D product models and appropriate visualisations for evaluation
• demonstrate knowledge and understanding of product modelling and visualisation by demonstrating an ability to provide 2D/3D part and assembly drawings, and a variety of sectioned/dimensioned views of part/assembly models
• demonstrate knowledge and understanding of product evaluation techniques by identifying and describing suitable product evaluation techniques such as FEA and utilise for evaluation
• describe and discuss the functionality and benefits that CAED systems can bring to product development by identifying and justifying a CAED solution for an industrial problem

Assessment and feedback is in the form of coursework (100%).

This module aims for students to integrate and apply design, manufacturing and engineering management knowledge and skills to an industry based product and process development project and to develop project management skills.

The module consists of a team-based industrial project where an outline project brief is set by an industrial client. The team is expected to manage all aspects of the project through to a finished solution. This can be a product, system or process depending on the nature of the project. Teams meet with academic staff and industrial clients regularly through the project.

At the end of this module students will be able to:

• have in-depth understanding and knowledge of products and management practices in industry
• critically review and evaluate products and management practices of the particular company and the business impact of proposed solution
• demonstrate knowledge and ability in applying and using various analysis and modelling tools and techniques
• demonstrate project planning and management, presentation, consulting and team working skills
• plan, control and lead an industrial project from inception to completion
• evidence achieving deliverables which meet the client company requirements

Assessment and feedback includes a project report, a presentation to the client and any other deliverables specified in the project brief.

This module will allow students to combine the skills learned in all modules of the course and apply them within a significant project in a specific area of design, manufacture, or engineering management. This will be achieved through an individual project.

The module will teach the following:

• introduction to risk definitions and basic risks in engineering;
• risk management standards
• reliability: achieving reliability; Reliability, Availability, Maintainability and Safety (RAMS) cycle; failure rate; Mean Time Between Failure; Mean Time to Fail; Mean Life; failure stages within bathtub distribution; downtime; repair time and availability;
• risk classification and their nature in engineering; failure rate; severity and detection; As Low As Reasonably Practicable (ALARP); -
• risk identification: Failure Modes and Affects Analysis; Hazard and Operability Study; Fault and Event Tree Analysis;
• risk-based decision making – uncertainty, decision trees, Pareto optimality, Analytic Hierarchy Process;
• impact of uncertainty on engineering programmes;
• examples of application of techniques within Risk-Based Design: rule-based design; simulation of safety and impact on decision making
• programme-related risk management using ISO 16085:2021

The module will teach the following:

• Advanced Materials: Fundamentals of advanced materials, Severe plastic deformation, Materials properties, Materials characterisation.
• Advances in Machining: Machining of hard materials; High-speed machining; precision grinding technology; ultra precision diamond turning; ultra precision diamond grinding technology.
• principles and practice of Layered Manufacturing; Processes and Machines; Focuses on the manufacture of engineering functional components/systems, direct layered manufacturing and hybrid manufacturing systems, powder and filament properties, economics and design considerations.
• Advanced Surface Engineering: Physical-chemical functionalisation - thermal and plasma nitriding & Ion Implementation; Electro-deposition; CVD; PVD; Tools/Mould treatment; Nano-coating; Multi-layers coating & superlattice coating, etc.

The goal of ROVER is to design, build and develop completely autonomous, robotic vehicles to enable environmental sensing and interaction in the environment and the smart cities of the future. The project goals are flexible between years and will educate and train students in any aspect of robotic systems relevant to their interests and discipline.

This module aims to introduce students to the theories and principles of Systems Thinking. The module also introduces the methods, tools and techniques for modelling, analysing, improving and designing systems in a variety of organisations including industrial, commercial and public sector.

The module covers: Systems theory, concepts and approaches; Hard and soft systems analysis and systems dynamics; Systems and organisational performance – including leadership in a systems environment and ‘design’ in a systems environment and Practical application of Systems Thinking.

At the end of this module students will be able to:

• show clearer understanding and knowledge of hard and soft approaches and how they can be used to deal with complexity and system behaviour in a business context
• develop understanding of fundamental cybernetic principles that form the foundations of Checkland’s Soft System Methodology and Beer’s Viable System Model
• develop knowledge and skills in systems analysis and business process modelling.
• critically evaluate the most appropriate methodology to model, analyse and design engineering/business systems across a range of organisations
• demonstrate an understanding of how to model a business system and to develop a solution to solve a business system problem
• cevelop an awareness of the importance of system approaches in management interventions

Assessment and feedback is in the form of a group presentation and one coursework in the form of a reflective diary.

The module will teach the following:

• appreciation of the concept of a System, Systems Engineering, and Systems Engineering Management;
• characteristics of Systems Engineering programmes and the management of complexity;
• Model Based Systems Engineering;
• Modelling and Lines of Development;
• V-model and capability acquisition;
• ISO15288 – introduce the different processes for describing the life cycle of systems: enterprise, agreement, project, tailoring and technical
• lifecycle models for different Systems Engineering programmes
• change and configuration management and the importance of reviews and baselines
• creating a Systems Engineering culture.

The module will teach the following:

• projects and project management, key related concepts, for example, project portfolio and programmes
• time, cost and quality – as foundational project control mechanisms, expanded use of ‘best value’
• project management bodies of knowledge
• the project lifecycle
• project strategy and ambidexterity
• decisions (and decision shortcuts) in projects (including heuristics and perceptions in projects)
• leadership and emotional intelligence (including dark leadership)
• project manager personality traits (including dark personality traits)
• culture and cultural intelligence in projects
• ethics and trust
• project risk management
• wicked problems in projects
• legal issues in projects – contracts and alternative dispute resolution in projects

This module aims to introduce the basic concepts, mathematical tools and design methods of classical control theory. It also introduces students to advanced control methods and provides a basic understanding of a time-domain approach to control analysis and the design of industrial processes.

The module covers: First and second order systems, delay process, simple saturation models; Simulation tools such as GUI, SIMULINK, MATLAB; Control system performance, transient and steady-state figures of merit, time domain step response, reference tracking and disturbance rejection in time domain; Simple control principles; State space representation of linear systems; Continuous time and discrete-time system fundamentals: eigen-values & eigen-vectors, stability, controllability & observability, canonical forms for systems; State-space control methods: pole placement state feedback control with/without observer design and linear quadratic optimal control.

At the end of this module students will be able to:

• model simple systems with transfer function and state space representation, create simulations using MATLAB and Simulink
• analyse linear open loop and closed loop systems both in frequency and time domain
• understand the theoretical and practical implications of feedback control systems, design control systems using simple PID tuning methods
• assess control performance, make analytical calculations and critical evaluation of control performance-related metrics
• apply and understand the advanced control methods, principles and applications in an industrial context

Assessment and feedback is in the form of a coursework and class test in Semester 1 (15%), a project report (15%) and exam (70%) in Semester 2.

This module provides the background and skills required to develop Artificial Intelligence systems based on Neural Networks and Deep Learning.

Students will learn the theoretical as well as practical foundations of Machine Learning and Neural Networks for an engineering context. Lab exercises augment the taught classes to deepen the learning experience.