ENGM021 | 2024/5 | University of Exeter

MODULE TITLE	Smart Production Systems	CREDIT VALUE	15
MODULE CODE	ENGM021	MODULE CONVENER	Dr Baris Yuce (Coordinator)

DURATION: TERM	1	2	3
DURATION: WEEKS		11

Number of Students Taking Module (anticipated)	150

DESCRIPTION - summary of the module content

The concept of a smart production systems emerged from the future factories, future cities and autonomous and adaptive robotic systems production philosophies that then has been supported with recent technological advances in IoT systems, communication technologies, high -performance computing, intelligent systems and algorithms and augmented reality. The concept initially developed and applied on the Industry 4.0 framework which is the 4th industrial revolution to digitise the manufacturing environment to provide optimal production conditions for the manufacturing environment and interoperability between cyber and physical systems. Later, this philosophy has been enhanced for different systems which has paved new enhancements and philosophies like digital twins, smart cities and 4.0 frameworks for manufacturing systems. The module is an interdisciplinary module that covers the manufacturing and production systems, applications of IoT systems, wireless communications systems and microcontroller applications, cyber-physical systems and overall smart production architectures in production systems and smart cities.

By the end of the module, you will have a good understanding of digitalised manufacturing and service systems, cyber and physical systems, cloud computing, digital twin concept and artificial intelligence applications in manufacturing systems and smart cities which will be underpinned with several use cases, practical applications and projects.

Pre-requisite or Co-requisite modules: None.

AIMS - intentions of the module

This module aims to provide essential knowledge in the fields of the smart systems and their application in smart production and city concepts, and detailed understanding for the Industry 4.0 framework and its applications in several enabling areas like smart logistics management, smart building and smart energy managements systems and real world examples. Further, students will have an opportunity to develop smart solutions using artificial intelligence techniques, high-performance computing technologies and sensory based systems for different industries including manufacturing, logistics, water, energy and built environments.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

This is a constituent module of one or more-degree programmes which are accredited by a professional engineering institution under licence from the Engineering Council. The learning outcomes for this module have been mapped to the output standards required for an accredited programme, as listed in the current version of the Engineering Council’s ‘Accreditation of Higher Education Programmes’ document (AHEP-V3). On successful completion of this module, the following learning outcomes will be achieved: SM1m, SM3m, SM5m, SM6m, SM1fl, SM3fl, EA1mEA5m, EA1fl, EA2fl, D3m, D4m, D7m, D8m, D1fl-D3fl, EP2m, EP4m, EP9m, EP1fl, EP2fl, G1m-G4m, G1fl-G4fl.

On successful completion of this module, you should be able to: On successful completion of this module you should be able to:

Module Specific Skills and Knowledge (SM1m, SM1fl, EA1m-EA5m, EA1fl, EA2fl, EA3fl, D3m, D1dl.)

1. Understanding manufacturing and production systems and various business types.
2. Grasp the Industry 4.0 framework, 4.th industrial revolution, Smart Factories and Smart Cities concepts.

3. Understand the new business models in Industry 4.0 frameworks and Smart Cities concepts.

4. Understand the key elements of the Industry 4.0 framework including sensing infrastructure, IoT connectivity, and networking, cyber and physical systems.

5. Grasp the digital twin concept and overall architecture of the physical systems.

Discipline Specific Skills and Knowledge (SM3m, SM5m, SM6m, SM3fl, D4m, EP2m, EP4m, EP9m, EP1fl, EP2fl.)

6. Understand the concepts of Artificial Intelligence technologies and apply in Industry 4.0 frameworks and Smart Cities.
7. Demonstrate the Big Data applications in future manufacturing and engineering environment including, the industries supported with Industry 4.0 frameworks or Smart Cities technologies.

8. Understand engineering and industrial requirements of the Cybersecurity in production environment supported with Industry 4.0 frameworks and Smart Cities concepts.

9. Analyse the requirements of the new business models for the industries supported with Industry 4.0 frameworks.

Personal and Key Transferable / Employment Skills and Knowledge

10. Apply enhanced problem-solving ability and illustrate in Industry 4.0 laboratory applications
11. Develop communication skills.

12. Demonstrate report writing skills, project management and organizational skills.

SYLLABUS PLAN - summary of the structure and academic content of the module

Introduction to production systems.
4th Industrial revolution, Production Industry 4.0 framework, Smart Factories and Smart Cities concepts.
Manufacturing process, Quality Control and Supply Chain Management in Industry 4.0 framework.
Introduction to sensing and actuation, and IoT technologies (connectivity and networking).
Digital twins and Cyber-Physical systems.
Artificial Intelligence technologies, (Artificial Neural Network, Fuzzy Logic, Genetic Algorithm and others).
Big Data Analytics in Industry 4.0 framework and Smart cities.
Cybersecurity in its applications Industry 4.0 and Smart cities.
Industry 4.0 framework laboratory applications

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	33	Guided Independent Study	117	Placement / Study Abroad	0

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category	Hours of Study Time	Description
Scheduled learning and teaching activities	22	Lectures
Scheduled learning and teaching activities	7	Tutorials
Scheduled learning and teaching activities	4	Laboratories
Guided independent study	117	Lecture and assessment preparation; wider reading

ASSESSMENT

FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

SUMMATIVE ASSESSMENT (% of credit)

Coursework	0	Written Exams	100	Practical Exams

DETAILS OF SUMMATIVE ASSESSMENT

Form of assessment	% of credit	Size of the assessment e.g. duration/length	ILOs assessed	Feedback method
Written exam – closed book	100	3 hours	All	Oral, by request

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)

Original form of assessment	Form of re-assessment	ILOs-reassesed	Time-scale for assessment
Exam	Written exam 100% 3 hours	All	Referral/deferral period

RE-ASSESSMENT NOTES

Deferrals: Reassessment will be by coursework and/or exam in the deferred element only. For deferred candidates, the module mark will be uncapped.

Referrals: Reassessment will be by a single written exam worth 100% of the module. As it is a referral, the mark will be capped at 50%.

RESOURCES

INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Armendia, M, Ghassempouri, M, Ozturk, E and Peysson F	Digital Twin Approach to Improve Machine Tools Lifecycle		Springer Nature	2019
Bessis, N and Dobre, C	Big Data and Internet of Things: A Road map for Smart Environments		Springer International Publishing	2014
Dehghantanha, A and Raymond Choo, K K	Handbook of Big Data and IoT Security		Springer International Publishing	2019
Ejaz, W and Anpalagan, A	Internet of Things for Smart Cities: Technologies, Big Data and Security		Springer International Publishing	2019
Guo, S and Zeng, D	Cyber-Physical Systems: Architecture, Security and Application		Springer International Publishing	2019
Mathur, P	Machine Learning Applications Using Python: Case Studies from Healthcare, Retaila and Finance		Apress	2019
Mohammed, M, Khan, M B, Bashier, E and Bashier, M	Machine Learning Algorithms and Applications		CRC Press	2017
Sendler, U	The internet of Things: Industrie 4.0 Unleashed		Springer-Verlag	2016
Slack, N, Brandon-Jones, A and Johnston, R	Operations Management	7th	Pearson Education	2013
Sun, H, Wang, C and Ahmad, B	From Internet of Things to Smart Cities: Enabling Technologies		CRC Press; Chapman and Hall	2017

Reading list for this module:

Type	Author	Title	Edition	Publisher	Year
Set	Armendia, M, Ghassempouri, M, Ozturk, E and Peysson F	Digital Twin Approach to Improve Machine Tools Lifecycle		Springer Nature	2019
Set	Bessis, N and Dobre, C	Big Data and Internet of Things: A Road map for Smart Environments		Springer International Publishing	2014
Set	Dehghantanha, A and Raymond Choo, K K	Handbook of Big Data and IoT Security		Springer International Publishing	2019
Set	Ejaz, W and Anpalagan, A	Internet of Things for Smart Cities: Technologies, Big Data and Security		Springer International Publishing	2019
Set	Guo, S and Zeng, D	Cyber-Physical Systems: Architecture, Security and Application		Springer International Publishing	2019
Set	Mathur, P	Machine Learning Applications Using Python: Case Studies from Healthcare, Retaila and Finance		Apress	2019
Set	Mohammed, M, Khan, M B, Bashier, E and Bashier, M	Machine Learning Algorithms and Applications		CRC Press	2017
Set	Sendler, U	The internet of Things: Industrie 4.0 Unleashed		Springer-Verlag	2016
Set	Slack, N, Brandon-Jones, A and Johnston, R	Operations Management	7th	Pearson Education	2013
Set	Sun, H, Wang, C and Ahmad, B	From Internet of Things to Smart Cities: Enabling Technologies		CRC Press; Chapman and Hall	2017

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	None
CO-REQUISITE MODULES	None

NQF LEVEL (FHEQ)	7	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Friday 22nd March 2024	LAST REVISION DATE	Thursday 5th December 2024

KEY WORDS SEARCH	Industry 4.0, smart cities, cyber physical systems, production systems

Smart Production Systems - 2024 entry