Study information

Industry 4.0 - 2023 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)

Discipline and Module Intended Learning Outcomes:

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

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

2 Understand principle mathematical tools in handling data.

3 Understand basic principles, and know-how to program a simple neural network / regression based model, for learning simple automated tasks.

4 Learn the basic principle and mechanism of robot manipulator and be able to control it to do simple tasks.

5 Understand basic principles and algorithms for machine vision.

6 Apply machine vision for an engineering problem in pattern / object recognition.

7 Develop the ability to understand complex mathematical methods in the context of real-world engineering problems.

8 Develop strong programming skills.

9 Develop presentation skills to technical and non-technical audience.

10 Work effectively as a group.

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

  • Introduction to Smart Manufacturing;
  • Introduction to Data Analysis and Artificial Intelligence;
  • Modelling to Make Sense of Data;
  • Sensors;
  • Robotic Control;
  • Machine Vision and its Applications;
  • 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 Referral/deferral 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

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:

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 Wednesday 4th October 2023
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.