2020/21 Full-time Postgraduate course
Master of Science
Faculty of Computing, Engineering and the Built Environment
School of Computing, Engineering and Intelligent Systems
Providing professionals with expert, multi-discipline knowledge in the principles and application of Smart Manufacturing.
In this section
Five core technologies that are essential to raise the development of manufacturing and productivity to the levels required in the next 50 years are; Artificial intelligence (AI), robotics, automation, internet of things (IoT) and industrial digital technologies (IDTs). The vast majority of industrial and commercial organisations in N. Ireland are substantially under-prepared for the challenges presented by these advances in technology and will need significant support to develop the capability and capacity to survive, compete internationally with innovative products while extracting greatly increased efficiencies from their processes. Smart Manufacturing Systemsdraws on each of these 5 key enablers to provide a platform for development where intelligent factories become the norm with connectivity data and IDTs at the heart of the workplace.
Within Northern Ireland, there is a requirement to focus on research and teaching development of the integration of the five key technologies which are crucial for future industrialisation. This MSc has been designed to provide tutelage in each of the key areas for industrial manufacture including, but not limited to robotics, automation and internet of things technologies, CAD/CAM, process management and the application of AI/machine learning in industry and business. The course provides an opportunity for the support of transfer of skills and expertise to company staff. It will be a location for them to re-train their staff in IDTs and become enabled, competent and confident in IDTs.
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In this section
The MSc in Smart Manufacturing Systems is a collaboration between the school of Computing, Engineering and Intelligent Systems in the Faculty of Computing, Engineering and the Built Environment, and the Department of Global Business and Enterprise in the Ulster Business School; both are based at the Magee campus. The overall aim of the course is to provide postgraduate education and training in the concepts and methods within the area of manufacturing and mechanical engineering with the application of industrial robotics, data analytics and artificial intelligence (AI)/ machine learning to meet the needs of manufacturing in Industry 4.0. The programme aims to equip students with expert knowledge across multi-disciplines in both the underlying science and application of manufacturing and mechanical engineering with the computing topics of intelligent data analytics and robotics. In addition, the programme develops knowledge and skills to understand the nature of the global business environment and global business operations. Therefore, the MSc Smart Manufacturing Systems seeks to provide develop the human resources to provide pathways to develop innovative products and manufacturing processes allowing existing companies to increase productivity and establish new business by remaining competitive through the use of the emerging digital and smart manufacturing technologies in their operations.
Beginning in September, the degree will normally be completed in three semesters across a single academic year. Each lecture will be hosted at the Magee campus. The project is completed in the third semester.
Students are expected to attend all classes associated with the programme and be punctual and regular in attendance.
A student who has not been in attendance for more than three days through illness or other cause must notify immediately the Course Director. The student shall state the reasons for the absence and whether it is likely to be prolonged. Where the absence is for a period of more than five working days, and is caused by illness which may affect their studies, the student shall provide appropriate medical certification in accordance with the General Regulations for Students.
Students who are absent without good cause for a substantial proportion of classes may be required to discontinue studies, in accordance with the General Regulations for Students.
The aims and learning outcomes of the programme are achieved through the application of a variety of teaching and learning methods across the modules. The modules allow a varied and interesting mix of methods to be used to enhance knowledge and understanding as well as allowing students to practise and develop their professional skills.
Teaching methods are diverse and planned in a manner thought appropriate for an advanced education in engineering. As well as aiming to cover an appropriate curriculum, the course team endeavour to take account of the following factors:
Lectures: Lectures are considered an effective way of engaging students and communicating knowledge coherently to groups and are therefore used extensively for the presentation of material. However, the ‘flipped classroom’ model is also used when opportunities arise, and it is deemed appropriate to employ. In addition, digital enhanced learning is facilitated by the utilisation of such tools as Blackboard Collaborate. For example, the module “Global Business in Context” is delivered fully online.
Practical Work: Practical, laboratory-based work is a central activity in all modules, and the School has a number of state-of-the-art laboratories designed for this purpose including mechanical, electronic and computing based. Due to the practical nature of the course the students are timetabled for extensive laboratory work, where they have the support of a number of tutors to provide them with “hands on” guidance. This is an essential learning component for the students as “learning by doing” is for example recognised as the best way to develop design thinking skills; e.g. prototype, test etc.
Tutorials: Many modules use tutorial work to further advance study and practical skills. In addition to laboratory sessions, tutorials are considered crucial not only for the acquisition of knowledge but also for enhancing contact and communication between staff and students.
Pre-induction boot camp:To support the computer programming required in modules COM737 and EEE837, several weeks in advance of the course starting students will be offered early training on the programming language used and an induction on programming for beginners (or refresher). Online access to worked tutorial material will be provided to develop or fresh programming basics.
All modules, as a minimum, have an online presence on the University’s VLE, Blackboard Learn (BBL). Lecture notes, practical exercises and any other supplementary materials are available to students in advance of classes. Module coordinators use the BBL provision to administer the module, for example posting notices and instructions. Each programme of study has a dedicated BBL Course Support Area (CSA) where all information from enrolment to graduation can be easily accessed. Students, on logging into BBL, have access to the CSA and to the modules on which they have enrolled. The CSA provides links to all information – general and specific – that students might require throughout their studies.
Assessment and Feedback
In 2011 the University adopted 7 Principles of Assessment and Feedback for Learning, based on the Reengineering Assessment Practices (REAP) principles (www.reap.ac.uk).
Help to clarify, from the early stages of a programme, what good performance means (goals, criteria, standards); Encourage ‘time and effort’ on challenging learning tasks which recognise the importance of learning from the tasks, not just demonstrating learning through the tasks; Deliver timely learner-related feedback information that helps students to self-correct and communicate clear, high expectations and professionalism; Provide opportunities for students to act on feedback and close any gap between current and desired performance through complementary and integrated curriculum design and pedagogic practice; Ensure that all assessment has a beneficial, constructive impact on student learning, encouraging positive motivational beliefs, confidence and self-esteem; Facilitate the development of self- and peer-assessment skills and reflection on learning, to enable students to progressively take more responsibility for their own learning, and to inspire a lifelong capacity to learn; Encourage interaction and dialogue around learning and professional practice (student-student, lecturer-student and lecturer-lecturer) including supporting the development of student learning groups and peer learning communities.
The implementation of these principles inevitably influences curriculum design, delivery and educational practice.
Assessment strategies are closely related to the aims and learning outcomes of individual modules, but similar types of strategies are assessed and given feedback by standard methods to promote consistency across modules. Central to any assessment strategy is the need to assess whether learning outcomes have been met by candidates in relation to not only the course aims and objectives but also as a form of feedback to students in terms of their learning progression. Students are provided with comprehensive information at the start of each taught module detailing assessment schedules throughout. Individual Assessment Specifications clearly articulate requirements (including submission and return deadlines) and a marking scheme. In the event of any elements of group work that are assessed in a summative method, at least 25% of each student’s assessment in the group work shall be based on his or her individual contribution.
The Project module is different as it requires a more sustained research effort over a longer period of time with a more substantial output. The approach taken for the Project module is to agree a research proposal specification with the student prior to embarking on the research. During the project the student will meet with the designated supervisor on a weekly basis to get feedback on the work carried out and receive guidance on addressing the challenges ahead. The assessment associated with the Project module will include an evaluation of a Research Paper produced as a result of the student's work and a Presentation/Oral examination. During this assessment, which will take place shortly after the twelve weeks of research activity have elapsed, consideration will be given to how well the final output meets the agreed specification, with the marking categories documented in the module employed to arrive at the final result.
Innovation in assessment and feedback is strongly encouraged, and best practice is disseminated at Course Committees and School Boards. All coursework material is both internally and externally moderated prior to it being made accessible to students, and also following its marking to ensure adequate validity, reliability and fairness.
In line with the University’s Assessment and Feedback Principles, staff members strive to provide prompt and detailed feedback to all students. Since the Faculty is moving to the “paperless office” mode of operation it is expected that students are facilitated to upload all assignment work to Blackboard and receive feedback using the same mode of communication. The Schools within the Faculty comply with the University policy of providing feedback within fifteen working days.
The Course Team currently monitors the assessment burden on students in each year and takes action where necessary. In advance of each academic semester all staff are required to submit the assessment schedule for their module/s. This is then carefully considered by the Course Director and suggestions made to avoid significant submission clashes. This often results in consultation to consider alternative submission dates to avoid overloading of assessment deadlines.
In accordance with SENDO (NI) (amended 2008) and the University’s ethos of inclusion, the facilitation of special arrangements for students with disabilities is applied where appropriate. Students with disabilities are accommodated as their needs dictate and as specified by the Student Disability Officer within Student Support services. Students who declare a disability not already formally disclosed are directed to Student Support to ensure that their specific needs are assessed. In relation to assessments, every effort is made to ensure that students with disabilities have the same opportunities as their peers to demonstrate the achievement of learning outcomes.
The content for each course is summarised on the relevant course page, along with an overview of the modules that make up the course.
Each course is approved by the University and meets the expectations of:
As part of your course induction, you will be provided with details of the organisation and management of the course, including attendance and assessment requirements - usually in the form of a timetable. For full-time courses, the precise timetable for each semester is not confirmed until close to the start date and may be subject to some change in the early weeks as all courses settle into their planned patterns. For part-time courses which require attendance on particular days and times, an expectation of the days and periods of attendance will be included in the letter of offer. A course handbook is also made available.
Courses comprise modules for which the notional effort involved is indicated by its credit rating. Each credit point represents 10 hours of student effort. Undergraduate courses typically contain 10- or 20-credit modules (more usually 20) and postgraduate course typically 15- or 30-credit modules.
The normal study load expectation for an undergraduate full-time course of study in the standard academic year is 120 credit points. This amounts to around 36-42 hours of expected teaching and learning per week, inclusive of attendance requirements for lectures, seminars, tutorials, practical work, fieldwork or other scheduled classes, private study, and assessment. Part-time study load is the same as full-time pro-rata, with each credit point representing 10 hours of student effort.
Postgraduate Master’s courses typically comprise 180 credits, taken in three semesters when studied full-time. A Postgraduate Certificate (PGCert) comprises 60 credits and can usually be completed on a part-time basis in one year. A 120-credit Postgraduate Diploma (PGDip) can usually be completed on a part-time basis in two years.
Class contact times vary by course and type of module. Typically, for a module predominantly delivered through lectures you can expect at least 3 contact hours per week (lectures/seminars/tutorials). Laboratory classes often require a greater intensity of attendance in blocks. Some modules may combine lecture and laboratory. The precise model will depend on the course you apply for and may be subject to change from year to year for quality or enhancement reasons. Prospective students will be consulted about any significant changes.
Assessment methods vary and are defined explicitly in each module. Assessment can be a combination of examination and coursework but may also be only one of these methods. Assessment is designed to assess your achievement of the module’s stated learning outcomes. You can expect to receive timely feedback on all coursework assessment. The precise assessment will depend on the module and may be subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Coursework can take many forms, for example: essay, report, seminar paper, test, presentation, dissertation, design, artefacts, portfolio, journal, group work. The precise form and combination of assessment will depend on the course you apply for and the module. Details will be made available in advance through induction, the course handbook, the module specification and the assessment timetable. The details are subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Normally, a module will have 4 learning outcomes, and no more than 2 items of assessment. An item of assessment can comprise more than one task. The notional workload and the equivalence across types of assessment is standardised.
Calculation of the Final Award
The class of Honours awarded in Bachelor’s degrees is usually determined by calculation of an aggregate mark based on performance across the modules at Levels 5 and 6, (which correspond to the second and third year of full-time attendance).
Level 6 modules contribute 70% of the aggregate mark and Level 5 contributes 30% to the calculation of the class of the award. Classification of integrated Master’s degrees with Honours include a Level 7 component. The calculation in this case is: 50% Level 7, 30% Level 6, 20% Level 5. At least half the Level 5 modules must be studied at the University for Level 5 to be included in the calculation of the class.
All other qualifications have an overall grade determined by results in modules from the final level of study. In Master’s degrees of more than 200 credit points the final 120 points usually determine the overall grading.
The University employs over 1,000 suitably qualified and experienced academic staff - 59% have PhDs in their subject field and many have professional body recognition.
Courses are taught by staff who are Professors (25%), Readers, Senior Lecturers (18%) or Lecturers (57%).
We require most academic staff to be qualified to teach in higher education: 82% hold either Postgraduate Certificates in Higher Education Practice or higher. Most academic staff (81%) are accredited fellows of the Higher Education Academy (HEA) - the university sector professional body for teaching and learning. Many academic and technical staff hold other professional body designations related to their subject or scholarly practice.
The profiles of many academic staff can be found on the University’s departmental websites and give a detailed insight into the range of staffing and expertise. The precise staffing for a course will depend on the department(s) involved and the availability and management of staff. This is subject to change annually and is confirmed in the timetable issued at the start of the course.
Occasionally, teaching may be supplemented by suitably qualified part-time staff (usually qualified researchers) and specialist guest lecturers. In these cases, all staff are inducted, mostly through our staff development programme ‘First Steps to Teaching’. In some cases, usually for provision in one of our out-centres, Recognised University Teachers are involved, supported by the University in suitable professional development for teaching.
Figures correct for academic year 2019-2020.
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Here is a guide to the subjects studied on this course.
Courses are continually reviewed to take advantage of new teaching approaches and developments in research, industry and the professions. Please be aware that modules may change for your year of entry. The exact modules available and their order may vary depending on course updates, staff availability, timetabling and student demand. Please contact the course team for the most up to date module list.
In this section
Businesses now operate in a globalised, highly connected multi-layered business environment that presents many challenges, and is typified by volatility, uncertainty, complexity and ambiguity to the firm. As the leaders/managers and decision makers of the future, this module will help students understand key concepts and current trends in Global Business, prepare for work in multi-layered environments, and to understand the need for flexibility and adaptability, both in the domestic and regional and global contexts.
This module covers Machine Learning both conceptually and practically. Students will be introduced to a variety of unsupervised and supervised Machine Learning techniques. Once the core concepts have been introduced they will be given practical experience of their use, application and evaluation through laboratory exercises and a project. The students will develop an in-depth understanding of the potential and scope of applying and evaluating the different forms of Machine Learning. This will allow them to develop a range of applications from simple practical implementations to large scale implementations.
The aim of the project is to allow the student to demonstrate their ability in undertaking an independent research project for developing theoretical perspectives, addressing research questions using data, or analysing and developing real world solutions. They will be expected to utilise appropriate methodologies and demonstrate the skills gained earlier in the course when implementing the project.
The module introduces postgraduate students to the research domain of Smart Embedded Systems and provides both theoretical and practical description of how such systems are designed and implemented. In particular, focus is placed on the design issues and considerations for implementing machine learning techniques on resource-constrained systems. The elements of the module are presented through lectures, tutorials and practical sessions, and assessed via continuous assessment.
This module provides the student with the ability to devise and direct the specification and installation of advanced manufacturing processes compliant with Industry 4.0 principles within an engineering company.
This module provides an overview of modern manufacturing management and manufacturing improvement techniques. State of the art industrial automation machines, processes and the use of Internet of Things will be analysed to identify where these resources could be applied to current industrial process to improve automation. Theoretical and practical exercises will provide the student with the required knowledge to apply their skills to real world industrial based applications.
The module develops the area of Manufacturing and the impacts that Computer Aided systems have on productivity and realisation of advanced design and manufacture techniques on the current industrial scene. The development of skills in this area will promote the Industry 4.0 principles and will provide the opportunity for integration of such in the current manufacturing industry.
We recognise a range of qualifications for admission to our courses. In addition to the specific entry conditions for this course you must also meet the University’s General Entrance Requirements.
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(a) have gained:
(i) a second class honours degree or better in the subject area of Mechanical Engineering, Electrical\Electronic Engineering, Computer Engineering, or a related discipline, from a university of the United Kingdom or the Republic of Ireland, or from a recognised national awarding body, or from an institution of another country which has been recognised as being of an equivalent standard; or
(ii) an equivalent standard (normally 50%) in a Graduate Diploma, Graduate Certificate, Postgraduate Certificate or Postgraduate Diploma or an approved alternative qualification and the qualification must be in the subject area of Mechanical Engineering, Electrical\Electronic Engineering, Computer Engineering, or a related discipline
(b) provide evidence of competence in written and spoken English (GCSE grade C or equivalent).
In exceptional circumstances, as an alternative to (a) (i) or (a) (ii) and/or (b), where an individual has substantial and significant experiential learning, a portfolio of written evidence demonstrating the meeting of graduate qualities (including subject-specific outcomes, as determined by the Course Committee) may be considered as an alternative entrance route. Evidence used to demonstrate graduate qualities may not be used for exemption against modules within the programme.
English language requirements for international applicants
The minimum requirement for this course is Academic IELTS 6.0 with no band score less than 5.5. Trinity ISE: Pass at level III also meets this requirement for Tier 4 visa purposes.
Ulster recognises a number of other English language tests and comparable IELTS equivalent scores.
Studies pursued and examinations passed in respect of other qualifications awarded by the University or by another university or other educational institution, or evidence from the accreditation of prior experiential learning, may be accepted as exempting candidates from part of the programme provided that they shall register as students of the University for modules amounting to at least the final third of the credit value of the award, at the highest level;
No exemption shall be permitted from the Master’s Research Project for the Master of Science Award.
Typically we require applicant for taught programmes to hold the equivalent of a UK first degree (usually in a relevant subject area). Please refer to the specific entry requirements for your chosen course of study as outlined in the online prospectus. We consider students who have good grades in the following:
|Level 12 English Lang in HSD|
In this section
Smart Manufacturing is a rapidly developing field of study within both academia and industry. The MSc Smart Manufacturing Systems aims to prepare students for a successful career as a modern manufacturing engineer where Industry 4.0 machine-to-machine (M2M) communication (products talk to machines) is producing large amounts of collected data, where analytics now plays a key role in understanding and modelling process improvements, alongside competitive demands within a global business context. Hence, there is a need for skills in advanced manufacturing processes, industrial robotics, intelligent automation, factory modelling and simulation, zero defect manufacturing with data analytics, embedded sensing and M2M, and commercial awareness. These types of skills are typically in high demand in manufacturing industries from plastics, semiconductor to construction, as the sectors moves to Industry 4.0.
This programme of study is firmly based under the STEM umbrella and additionally due to the nature of the skills and knowledge developed during the programme, resulting graduates will have the potential to be employed across a spectrum of industries which will address the skills shortage in smart manufacturing capabilities.
Students from this course would be eligible to directly enter graduate employment or proceed to further study at PhD level. There is currently an extensive demand for engineering graduates, particularly those with the high-level skills to develop and operate smart factories, which will be provided by this course.
There is no work-based learning or supervised work experience/placement within the MSc programme.
Dr Emmett Kerr
School of Computing, Engineering and Intelligent Systems