Code
Location
Carlow
Campus
Kilkenny Road
- Level Level 9
- Duration 5 weeks
- Credits 5
- Delivery Part-time
- Application Status Open
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 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.
Entry to the Certificate in Innovative Materials for Industry 4.0 programme and the associated minor awards is via direct entry with the following requirements:
Graduates with a Level 8 Honours degree engineering or science or equivalent
or
Graduates with a Level 7 Distinction award in Engineering or Science or equivalent with a minimum 2 years relevant post qualification experience.
or
EU and International graduates whose qualifications which are in line with the Bologna agreement or the international NARIC system for Level 8 comparison. International learners must have an English Language score of 6.0 ILETS or a Duolingo score of 100.
RPL (Recognition of Prior Learning)
All programmes are mapped to the SETU Carlow Recognition of Prior Learning (RPL) policy https://www.itcarlow.ie/resources/quality/quality-policies-procedures.htm and applicants who wish to apply using this route can request the specific RPL for this programme.
SETU Carlow’s RPL Policy offers clear pathways to the learner for recognition of previous learning undertaken and offer every credit to the learner in completing such learning.
The Programme Board will be responsible for the assessment of entry standards and applications as part of any RPL Process.
Introduction to Data Analysis for Industry 4.0
Statistical Design of Experiments; Factorial Design; Box-Behnken Response Surface Methodology; Data analysis requirements for Industry4.0/5.0
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