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ME5108 Data Analysis for Process and Product Development
This module introduces a systems engineering approach to design of experiments, data analysis and modelling, towards product and process improvement and optimisation. Students will learn about the Taguchi method for robust engineering design which is a system engineering approach for Quality by Design for process and product development.
ME5108: Data Analysis for Process and Product Development
15 months long | Credits: 5
This module introduces a systems engineering approach to design of experiments, data analysis and modelling, towards product and process improvement and optimisation. Students will learn about the Taguchi method for robust engineering design which is a system engineering approach for Quality by Design for process and product development. They will explore a range of statistical models and tools and learn how to apply them in a range of problem based learning scenarios using surveys, product and process developmental data. Topics and approaches to be covered include :
• ANoVA, DoE with orthogonal arrays, optimisation and minimisation of variability with Signal-to-Noise ratio
• Model-based analysis and response surface methodology
• Unconfounded experimental designs
• Multi-response approaches (PCA and Factor Analysis)
Students will also consider affective product design and kansei engineering approaches in the context of real life practice problems.
(Language of instruction: English)
Learning Outcomes
- Plan experiments and data collection strategies to determine which factors are more influential on a particular system of interest
- Apply a Taguchi approach to identify optimum combination of options of the most influential design factors to minimise non-conformity costs
- Fit mathematical models to data using least square regression, determining confidence intervals and error region of model predictions
- Use experimental designs without confounding and polynomial models to estimate points of optimum
- Apply a factor analysis to extract high-level responses (Principal Components) from a large number of individual responses
- Draw quality charts, design tables and Pareto charts.
Assessments
This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.
- Continuous Assessment (100%)
Module Director
- SUZANNE GOLDEN: Research Profile | Email
Lecturers / Tutors
- KATHRYN CORMICAN: Research Profile
- AISLING MONAHAN: Research Profile
- NIAMH NOLAN: Research Profile
Reading List
- "Design of Experiments Using The Taguchi Approach" by Ranjit K. Roy
ISBN: 0-471-36101-1.
Publisher: John Wiley & Sons - "Design and Analysis of Experiments" by Douglas Montgomery
ISBN: 978-0-470-398.
Publisher: John Wiley & Sons
Note: Module offerings and details may be subject to change.
They will explore a range of statistical models and tools and learn how to apply them in a range of problem based learning scenarios using surveys, product and process developmental data. Topics and approaches to be covered include : • ANoVA, DoE with orthogonal arrays, optimisation and minimisation of variability with Signal-to-Noise ratio • Model-based analysis and response surface methodology • Unconfounded experimental designs • Multi-response approaches (PCA and Factor Analysis) Students will also consider affective product design and kansei engineering approaches in the context of real life practice problems.
On successful completion of this module the learner will be able to:
Plan experiments and data collection strategies to determine which factors are more influential on a particular system of interest
Apply a Taguchi approach to identify optimum combination of options of the most influential design factors to minimise non-conformity costs
Fit mathematical models to data using least square regression, determining confidence intervals and error region of model predictions
Use experimental designs without confounding and polynomial models to estimate points of optimum
Apply a factor analysis to extract high-level responses (Principal Components) from a large number of individual responses
Draw quality charts, design tables and Pareto charts.
Funding for a limited number of places is available under the Modular Skills Provision. Details are available at www.nuigalway.ie/cpd
Contact Information
https://www.nuigalway.ie/adult-learning/courses/business-&-management/