Study information

Industry 4.0 - 2024 entry

MODULE TITLEIndustry 4.0 CREDIT VALUE15
MODULE CODEENG2006 MODULE CONVENERUnknown
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)

Programmes that are accredited by the Engineering Council are required to meet Accreditation of Higher Education  

Programmes (AHEP4) Learning Outcomes. 

The following Engineering Council AHEP4 Learning Outcomes are covered on this module: 

Module Specific Skills and Knowledge: 

1 Understand the key economic drivers behind industry 4.0/smart manufacturing, including knowledge of good industrial case  

studies. (B&C&M 4) 

2 Understand principle mathematical tools in handling data. (B&C&M 1, 2, 3) 

3 Understand basic principles, and know-how to program a simple neural network / regression based model, for learning simple automated tasks. (B&C&M 1, 2, 3) 

4 Learn the basic principle and mechanism of robot manipulator and be able to control it to do simple tasks. (B&C&M 1, 2, 3, 5, 12) 

5 Understand basic principles and algorithms for machine vision. (B&C&M 1, 2, 3, 5) 

6 Apply machine vision for an engineering problem in pattern / object recognition. (B&C&M 3, 5) 

 

Discipline Specific Skills and Knowledge: 

7 Develop the ability to understand complex mathematical methods in the context of real-world engineering problems. (B&C&M 1,  

2, 3) 

 

Personal and Key Transferable/ Employment Entrepreneurship Skills and Knowledge: 

8 Develop strong programming skills. (B&C&M 3) 

9 Develop presentation skills to technical and non-technical audience. (B&C&M 17) 

10 Work effectively as a group. (B&C&M 16) 

 

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 1 23 4000-word report (less than 10 A4 pages) 1-4, 7-8 (B&C&M 1, 2, 3, 5, 12,16, 17) Written

Coursework – Robotics 2 (Matlab + Simulink code)

 22 Matlab code + Simulink model (less than 1000 lines of code) 2,-6, 9, 10 (B&C&M 1, 2, 3, 5, 12,16, 17) Written
Coursework - Machine Vision 45 Approximately 1000 lines of Python code (maximum) 2, 3, 5-10 (B&C&M 1, 2, 3, 5) Written
Coursework – Smart manufacturing 10 700 word report 1, 2 (B&C&M 4, 16, 17) Written

 

 
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 Referral/deferral period

 

RE-ASSESSMENT NOTES

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

Referrals: Reassessment will be by a single 100%v coursework assessment. As it is a referral, 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

Reading list for this module: 

  1. D. G. Pascual, P. Daponte, U. Kumar, Handbook of Industry 4.0 and SMART Systems, CRC Press, 2019 

  1. I-Scoop: Industry 4.0: Industry 4.0 and the fourth industrial revolution explained, https://www.i-scoop.eu/industry-4-0/ (01/01/2022) 

  1. 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 

  1. M. Hermann “Design Principles for Industrie 4.0 Scenarios: A Literature Review”. Technische universität Dortmund. (2015). 10.13140/RG.2.2.29269.2224 . 

  1. F. Tao, 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.eng.2019.01.014 . 

  1. P. Corke, Robotics: vision and control – fundamental algorithm in Matlab, Springer, 2nd Edition, 2016. 

  1. S. B. Niku, Introduction to robotics: analysis, control, applications, Wiley, 3rd Edition 2019 

  1. M. W. Spong, S. Hutchinson and M. Vidyasagar, Robot Modeling and Control, Wiley  2006. 

  1. J. J. Craig, Introduction to robotics, mechanics and control, Pearson Higher Education, 2014 

  1. F. C. Park, K. M. Lynch, Modern Robotics: Mechanics, Planning, and Control, Cambridge University Press, 2017 

  1. K. Mehrotra, C. K. Mohan, S. Ranka , Element of Artificial neural networks , MIT Press, 1997 

  1. D.W. Patterson, Artificial Neural Networks: Theory and Application, Prentice Hall, 1996   

  1. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016 

 

Reading list for this module:

There are currently no reading list entries found for this module.

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 Friday 27th January 2023 LAST REVISION DATE Tuesday 10th September 2024
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.