MSc Mechatronics & Automation

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 to introduce the basic principles and techniques of engineering risk management and demonstrates the appropriate application of this knowledge within an engineering context.

The module covers: Risk definitions and basic risks in engineering; Risk management processes; 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 - 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 and Risk legislation and litigation in engineering.

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

• demonstrate awareness of and ability to make general evaluations of risk issues in the context of the particular specialisation, including health & safety, environmental and commercial risk
• demonstrate awareness of relevant regulatory requirements governing engineering activities
• demonstrate ability to work with information that may be incomplete or uncertain, quantify the effect of this on the design and, where appropriate, use theory or experimental research to mitigate deficiencies

Assessment and feedback is in the form of a group coursework to show understanding of the risk management process in practice (100% for group contribution and submission of main reports).

This module aims to provide students with skills and knowledge relating to the use of engineering practices in Project Management with particular respect to the project triple constraint: time, cost and quality.

The module covers: project management principles, concepts and processes; organisational influences, project stakeholders and project life cycle; project scoping such as project definition, project objectives, project deliverables, and work breakdown structure; Project planning and scheduling: definition of events, activities and nodes, network diagram, analysis of critical path, PERT method and use of industry standard software packages; Project controlling: cost estimate, budget setting, risk identification and assessment and contingency planning.

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

• Demonstrate a good understanding of project management practices and practical skills to manage project scope – including translating project specifications into work packages
• Define and schedule project activities using tools such as critical path and PERT methods; estimate cost and determine budget using analogous and three-point estimating methods; identify and control quality standards using cost of quality and other tools
• Develop a good understanding of the inter-dependency between various project management knowledge areas, such as managing projects under constraints; identifying and assessing risks and developing contingency plans
• Understand the importance of project stakeholders and their impact on project management, including managing stakeholder relationships

Assessment and feedback is in the form of a group report (50%) and an individual project (50%).

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.

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%).

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.

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 is a "golden thread" that runs throughout all lecture

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 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.