Study information

Industry 4.0 - 2022 entry

MODULE TITLEIndustry 4.0 CREDIT VALUE15
MODULE CODEENG2006 MODULE CONVENERDr Halim Alwi (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 11 0
Number of Students Taking Module (anticipated) 200
DESCRIPTION - summary of the module content
Industry 4.0 or the “fourth industrial revolution” is the trend towards automation and data exchange in new manufacturing technologies - to deliver so called smart manufacturing. In this module you will be given an introduction to smart manufacturing, the mathematical tools behind it and how it can be applied to real world manufacturing problems.
 
The teaching style in this module emphasises hands on learning. For example, you will learn how to manipulate a desktop robot to perform automated tasks. The module will build on mathematical and programming skills developed in the first year and modelling of engineering systems in the second year. Assessment in this module is 100% coursework and is based around 2 practical build activities built around real world engineering problems.
 
AIMS - intentions of the module

The aim of this module is to introduce the fundamental principles behind industry 4.0, with a hands on practical approach.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)
ILO # Intended Learning Outcome AHEP* ILO - MEng AHEP ILO - BEng
ILO #1 Understand the key economic drivers behind industry 4.0/smart
manufacturing, including knowledge of good industrial case studies.		 D1m, ET2m D1p, ET2p
ILO #2 Understand principle mathematical tools in handling data SM2m, EA3, EA5m SM2p, EA3p
ILO #3 Understand basic principles, and know-how to program a simple neural network / regression based model, for learning simple automated tasks. SM2m, EA2m, Ea3m, D3m, D4m, EP1m SM2p, EA3p, SM2p, D3p, D4p, EP1p, EP8p
ILO #4 Learn the basic principle and mechanism of robot manipulator and be able to  control it to do simple tasks. SM2m, SM4m, SM5m, EA2m, EA3m, EA4m SM2p, EA1p, EA2p, EA4p, D4p, EP1p, EP2p, EP3p, G1p
ILO #5 Understand basic principles and algorithms for machine vision   SM2p, EA2p, D4p
ILO #6 Apply machine vision for an engineering problem in pattern / object recognition    
ILO #7 Develop the ability to understand complex mathematical methods in the context of real-world engineering problems   SM2p, D4p, G1p, G2p
ILO #8 Develop strong programming skills SM5m, EA3m SM2p, EP3p
ILO #9 Develop presentation skills to technical and non-technical audience   D6p
ILO #10 Work effectively as a group EP11m EP9p, G4p
*Engineering Council Accreditation of Higher Education Programmes (AHEP) ILOs for MEng and BEng Degrees

 

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

1: Introduction to Smart Manufacturing;

2: Introduction to Data Analysis and Artificial Intelligence;

3: Modelling to Make Sense of Data;

4: Sensors;

5: Robotic Control;

6: Machine Vision and its Applications;

7: Neural Networks, Model Fitting and Sensitivity Analysis.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 24 Guided Independent Study 126 Placement / Study Abroad
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching Activities 11 Weekly lectures
Scheduled Learning and Teaching Activities 11 Weekly programming tutorials
Scheduled Learning and Teaching Activities 2 Practical Lab Sessions
Guided Independent Study 126  

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Weekly Problem Sheets 2 hours 2, 3  

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework - Robotics 45 4000-word report (less than 10 A4 pages) 1-4, 7-8 eBart
Coursework - Machine Vision 45 Approximately 1000 lines of Python code (maximum) 2, 3, 5-10 eBart
Coursework – Smart manufacturing 10 700 word report 1, 2 eBart

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-reassessment
Coursework Re-submission of failed coursework (100%) All August Ref/Def Period

 

RE-ASSESSMENT NOTES

Reassessment will be by a single coursework assignment only worth 100% of the module. For deferred candidates, the mark will be uncapped. For referred candidates, the mark will be capped at 40%.

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

Basic reading:

 

ELE:

 

Web based and Electronic Resources:

 

Other Resources:

  •  A. Kusiak, "Smart manufacturing," International Journal of Production Research, vol. 56, no. 1-2, pp. 508-517, 2018/01/17 2018, doi: 10.1080/00207543.2017.1351644

 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Corke, P. Robotics: Vision and control – fundamental algorithm in Matlab 2nd Springer 2016
Set Craig, J. J. Introduction to robotics, mechanics and control 2014 978-0133489798
Set Hermann, M. “Design Principles for Industrie 4.0 Scenarios: A Literature Review” 10.13140/RG.2.2.29269.2224 Technische universität Dortmund 2015
Set Lynch, M; Park, F. C. Modern Robotics: Mechanics, Planning, and Control Cambridge University Press 2017 978-1107156302
Set Niku, S. B. Introduction to robotics: analysis, control, applications 3rd Edition Wiley 2006 978-1119527626
Set Mehrotra, K Element of Artificial neural networks MIT Press 1997 9780262133289
Set Pascual, D. G., P. Daponte, U. Kumar Handbook of Industry 4.0 and SMART Systems CRC Press 2019
Set Patterson, D.W. Artificial Neural Networks: Theory and Application Prentice Hall 1996 9780132953535
Set Soroush, M., M. Baldea, and T. F. Edgar Smart Manufacturing: Concepts and Methods Elsevier Science Publishing 2020
Set Spong, M. W. Robot Modeling and Control 2nd Edition Wiley 2006 978-1119523994
Set Tao, F., Q. Qi, L. Wang, and A. Y. C. Nee "Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison," Engineering, vol. 5, no. 4, pp. 653-661, 2019/08/01/ 2019 doi: https://doi.org/10.1016/j.e
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 5 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Thursday 16th December 2021 LAST REVISION DATE Friday 22nd July 2022
KEY WORDS SEARCH None Defined

Please note that all modules are subject to change, please get in touch if you have any questions about this module.