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Module Specifications

Archived Version 2021 - 2022

Module Title
Module Code
School

Online Module Resources

NFQ level 9 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Description

The objectives of this module are to provide skills to the participants to identify experimental design and data analysis requirements for Industry 4.0/5.0. The module will equip students with planning and analysing controlled tests to evaluate processing factors. The participants will be learn statistical design of experiments, factorial design, Box-Behnken design, artificial neural networks and related models for data analysis and process optimisation. The participants will learn Big data and Machine Learning tools for data analysis in Industry 4.0 and advanced manufacturing. The participants will get training on use of data analysis tools for real-world challenges for specific application areas.

Learning Outcomes

1. Design data analysis tools for Advanced Manufacturing.
2. Formulate experimental design and analysis methods to determine processing factors impact on response.
3. Apply and evaluate data analysis tools for discrete advanced manufacturing scenarios
4. Apply Artificial Intelligence tools for process optimisation



Workload Full-time hours per semester
Type Hours Description
Lecture24According to indicative content and learning outcomes
Online activity21Reading and using module preparatory resources
Assignment Completion40Work on assignment (Continuous Assessment 1)
Independent Study40Study of module contents prior to quiz (Continuous Assessment 2)
Total Workload: 125

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Introduction to Data Analysis for Industry 4.0
Statistical Design of Experiments; Factorial Design; Box-Behnken Response Surface Methodology; Data analysis requirements for Industry 4.0/5.0

Predictive Analysis
Introduction to predictive analysis for Industry 4.0; Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System; Machine Learning in Industry 4.0

Data Analysis Case Studies
Data analysis for laser based material processing techniques; Data analysis for defect detection

Assessment Breakdown
Continuous Assessment% Examination Weight%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Reassessment Requirement
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
Unavailable
Indicative Reading List

  • Douglas C. Montgomery: 0, Design and Analysis of Experiments, 9781119492443
  • Ernest O. Doebelin: 1995, Engineering Experimentation, ernest otto doebelin, 9780070173392
  • Alp Ustundag,Emre Cevikcan: 2017, Industry 4.0: Managing The Digital Transformation, Springer, 9783319578705
  • Charles Robert Hicks: 1993, Fundamental Concepts in the Design of Experiments, Oxford University Press, USA, 003097710X
  • Diego Galar Pascual,Pasquale Daponte,Uday Kumar: 2019, Handbook of Industry 4.0 and SMART Systems, CRC Press, 9781138316294
Other Resources

None
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