This module provides the background and skills required to develop autonomous systems based on Machine Learning and Artificial Intelligence. Students will learn the theoretical as well as practical foundations of Data Science (Machine Learning, Deep Learning) for a design and engineering context. 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 are themes that run throughout all lectures.

This module provides the background and skills required to develop artificial intelligent systems based on Neural Networks and Deep Learning. Students will learn how to apply the main methods used for machine learning and neural networks and to solve engineering challenges. They will also gain an understanding and appreciation of the breadth of applications, issues and limitations of these tools, and their role in supporting automated decision-making.

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 provide an introduction and overview to the various core aspects of robotics which include design, control, sensing and localisation. It provides a solid base of understanding through theory and examples. Intuition is encouraged through numerous hands-on examples.

The module covers: Robotic systems including background, classification of robots based on design construction, control systems; Performance characteristics of typical robots; forward kinematics of robots including Denavit-Hartenberg (D_H) algorithm and inverse kinematics; Robotic control including principles of system modelling, Matlab implementation, time and frequency domain analysis and control system analysis; Bayesian robot localisation including linearization and Kalman Filtering; Robotic computer vision in particular when applied to mapping and localisation.

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

• describe types of robotic systems, their dynamic and mechanical architecture and associated sensor technology
• describe appropriate path-planning techniques taking into account ways to perform collision avoidance and speed up optimal path evaluation
• understand standard camera models and common approaches to image registration
• use computer-based tools to evaluate designs, measure, record and report experimental and numerical data relevant to robotic and other computer control systems
• formulate models from given relevant information and design control systems to drive these models to specified positions and within required accuracy, speed and other performance-related parameters

Assessment and feedback is in the form of a final exam (60%) and coursework which will be a mixture of multiple choice quizzes and laboratory work (40%).

This module will provide you with a broad appreciation of modern sensor technologies and their implementation in industrially relevant applications.

The aim of this class is to provide you with support for your general academic and professional development.

You'll undertake an advanced investigation of an electronic or electrical engineering topic of your choice, to enhance your learning, and develop presentation and communication skills.

This module aims to provide a series of strategic frameworks for managing high-technology businesses. The main focus is on the acquisition of a set of powerful analytical tools which are critical for the development of a technology strategy as an integral part of business strategy. These tools can provide a guiding framework for deciding which technologies to invest in, how to structure those investments and how to anticipate and respond to the behaviour of competitors, suppliers, and customers. The course should be of particular interest to those interested in managing a business for which technology is likely to play a major role, and to those interested in consulting or venture capital.

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

• Demonstrate a comprehensive understanding of the role and importance of technology in business strategy formulation process
• Develop the ability to critically assess concepts, tools and techniques of managing technology for both stable and turbulent business environments
• Evaluate complexity and develop appropriate technology strategy models for specific cases

Grades will be determined by class participation assessed through four two-page papers on case studies, which may be written in groups of 4 people (40%), and an individual final technology strategy report based on an in-depth exploration of technology strategy in an assigned industry (60%). There is no final exam.

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 to enable students to select and apply appropriate design methods as a part of the design process.

The selection and use of design methods within the context of modern design practices and the new product development process will be explored. Emphasis will be placed on recently developed product independent design methods and their application within industrial environments. Specific topics include the design process management frameworks, user understanding methods, product specification methods, creative methods, design for production and cost methods, design for safety and reliability methods and design for the environment.

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

• select and apply appropriate design methods for a design project to solve product design oriented problems by understanding specific design methods and recognising their strengths and weaknesses
• integrate appropriate design methods into a design process to ensure fitness of purpose of all aspects of the problem/context by demonstrating how manufacture, costing, environmental, disposal and customer needs may be addressed in the design process through design methods
• analyse literature sources to identify design methods suitable for a particular situation by undertaking a critical literature review to identify current developments in design methods in research and practice and synthesise the results of the literature review into a report

Assessment and feedback will be in the form of an exam (60%), a report (35%) and a presentation (5%).

This module aims to provide students with knowledge and understanding of the key concepts for Digital Manufacturing, current practices, tools and processes, and possible future development routes.

The module covers the current state of digital manufacturing, including tools and processes and identification of challenges and areas requiring further development in terms of research and technology innovation, product and service development, supplier management, production, routes to market, delivery, in service, maintenance, repair, remanufacture and reuse, and business plan development and management aspects. Digital manufacturing developments are also considered including the exploration of life-phases, challenges and technologies, Industry 4.0, Smart Products, Internet of Things, Cyber Physical Systems, value chains and value creation through life.

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

• Demonstrate an understanding of the key concepts for digital manufacturing and stages of development of the manufacture of a chosen product
• Discuss different digital manufacturing approaches
• Provide an overview of the tools, processes and practices currently employed in digital manufacturing
• Identify challenges and opportunities for improvement
• Understanding of current worldwide initiatives for the future development of digital manufacturing, and exploration of how proposals for future development given would affect the current processes

Assessment and feedback is in the form of coursework (100%) including a group presentation and a report.

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
• develop 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.

This class provides hands-on experience in translating Digital Signal Processing concepts into real-time embedded systems applications.

Through a combination of lectures, up-to-date technical discussions and hardware programming, you'll learn to design and implement real-time embedded systems through familiarisation with Digital Signal Processors and FPGAs.

The module will teach the following:

• background and design for competitiveness and sustainability
• integrated Product Development, and different approaches and aspects to design development including concurrent engineering, team engineering, product management, design management, distributed design, and decision support
• the design activity, methods and process models including the role of the market, specification, conceptual and detailed design
• basic team and management structures (organisation)
• key issues related to design complexities (e.g. relating to the people, processes, resources, product, key considerations, knowledge and information, decision making) and the key aspects of design co-ordination 
• design performance and innovation

This module aims to provide students with knowledge and understanding of the key concepts for the Design for Industry 4 and Smart Products, current practices, tools and processes, and possible future development routes.

The module covers the current and latest state-of-the-art in Design for Industry 4 and Smart Products, including the identification of challenges and areas requiring further development in terms of research and technology innovation, product and service development, supplier management, production, routes to market, delivery, in service, maintenance, repair, remanufacture and reuse, and business plan development and management aspects. It also explores the latest initiatives worldwide that tie with Design for Industry 4 and Smart products (Industrial Internet of Things (IIoT), Cyber Physical Systems, Cloud Manufacturing, Big Data analytics and Edge Analytics, Additive Manufacturing for Smart Products, IIoT Security aspects) and Through-Life Engineering and Through-Life Engineering Services (TES) concepts.

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

• Formulate an overview of the tools, processes and best practice currently employed in Design for Industry 4 and Smart Products
• Understand initiatives currently undertaken worldwide for the future development of Design for Industry 4 and Smart Products, and assess how proposals for future development given would affect the current processes.

Assessment and feedback is in the form of classwork (100%) including a group presentation and a report.

This module aims for the student to acquire: (1) knowledge of the fundamentals of micro- and nano-products and of the manufacturing of such products (MEMS, micro-fluidic devices, micro-medical devices, micro-motors, microrobots, MOEMS, etc.), size-effects, material/interface behaviour at the micro-/nano-scale, challenges to manufacturing at low length-scales, etc.; (2) knowledge of micro-/nano-materials processing methods, techniques, industrially-viable processes, etc. and (3) experience and skills in the design/selection of micro- /nano-manufacturing processes, tools and equipment for real-world products.

It covers material behaviour, challenges, processes (subtractive, additive, deformation, replication, joining, hybrid processes including mechanical, thermal, chemical, electrochemical, electrical methods) and tools, machines and manufacturing systems.

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

• Explain key techniques used in the processes for the manufacture of micro-products
• Correctly select technologies for specified products and materials
• Demonstrate calculations of forming/cutting forces involved and analysis of stresses/temperatures involved in tools/machine-frames/workpiece as appropriate
• Deliver a machine design (either for micro-machining or micro-forming) with detailed analysis and module designs, including a cost analysis on the machine designed.

Assessment and feedback is in the form of coursework (40%) and a project (60%), including a group project presentation and project report and individual assignment.

This module aims to provide students with knowledge and understanding of the underlying principles of the metal forming theory and practice as applied to modern metal forming machines, tools and processes.

The module covers concepts and definitions including stress, yield condition, strain, flow laws, plastic work, evolution equations, meso and micro-scale approaches; limiting phenomena (shape accuracy, plastic flow localisation, fracture, tool strength, friction, microstructure); metal forming machines and tooling; bulk metal forming; sheet metal forming and incremental forming.

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

• Describe stress/strain relationship for metals undergoing plastic deformation
• Explain the mechanism of plastic deformation at the meso and micro scale
• Explain the effect of different factors on the net-shape forming capability
• Discuss metal forming problems resulting from material and tool interaction
• Explain limitations of the metal forming technology due to a tool/machine system
• Discuss major elements and challenges for a forging system
• Explain the idea and give examples of incremental metal forming operations

Assessment and feedback is in the form of an exam (80%) and coursework (20%)

This module aims to provide students with an introduction to the fundamentals of advanced materials, characterisation and advanced surface engineering. The module also covers advanced machining processes and technologies and the principles and practices of rapid prototyping and manufacturing.

The module covers: Severe plastic deformation, materials properties and characterisation; Advances in Machining including the machining of hard materials, high-speed machining, precision grinding technology; ultra precision diamond turning and grinding technology; Principles and practice of Layered Manufacturing; Advanced Surface Engineering including physical-chemical functionalisation, electro-deposition, CVD, PVD, tools/mould treatment, nano- and multi-layered coating.

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

• Describe processes of materials selection, characterisation, ultra-precision machining, rapid prototyping and advanced surface engineering
• Demonstrate know-how on key processing parameters and show numerical and analytical skills relating to the materials and process selections and parameter setting
• Identify key process parameters/variables in relation to process control and product quality
• Specify machines or manufacturing systems for the manufacture/creation of specified products/models or to propose design solutions for a manufacturing machine/system to address the manufacturing requirements identified

Assessment and feedback is in the form of four pieces of coursework (25% each).

Provide advanced competence in the use of industry standard microcontrollers programmed in low and high level languages in real time applications.

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.

Develop the necessary skills that will allow you to analyse, design, implement and simulate advanced DSP techniques and algorithms for a variety of communications and general engineering problems.

This module aims to give students an understanding of the types of knowledge, techniques and systems used in building knowledge-based systems and discussion on the application of these techniques; and, an understanding of the types of different approaches, techniques and systems used in building information-based systems.

The module covers an introduction to knowledge based systems; knowledge representations; reasoning, chaining and searching and an introduction to information systems; information input and retrieval; information modelling process and techniques; information normalisation; visual modelling; information structure and organisation; and integration of information systems.

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

• Demonstrate an understanding of Knowledge and Information Management
• Demonstrate an understanding of Knowledge Models and Methods
• Demonstrate an understanding of Knowledge Engineering and Development Processes
• Design, develop, implement and report on an appropriate information system to meet the identified information requirements

Assessment and feedback is in the form of group coursework (50%) and individual coursework (50%), there is no exam

The aim of the research project is to provide you with an opportunity to bring your knowledge and skills together and deploy them in a significant practical investigation, using relevant engineering literature, and where relevant, initial experiments or simulations.