Industry 4.0 - 2021 entry
| MODULE TITLE | Industry 4.0 | CREDIT VALUE | 15 |
|---|---|---|---|
| MODULE CODE | ENG2006 | MODULE CONVENER | Dr Halim Alwi (Coordinator) |
| DURATION: TERM | 1 | 2 | 3 |
|---|---|---|---|
| DURATION: WEEKS | 11 |
| Number of Students Taking Module (anticipated) |
|---|
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 a 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 |
|---|
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
Ref/Def assessment is via resubmission of failed coursework in August.
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
information that you are expected to consult. Further guidance will be provided by the Module Convener
Reading list 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 8th January 2021 | LAST REVISION DATE | Monday 28th March 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.
