QLTY09032 2013 Advanced Experimental Design
Full Title
Advanced Experimental Design
Transcript Title
Experimental Design
Code
QLTY09032
Attendance
N/A %
Subject Area
QLTY - Quality
Department
MENG - Mech. and Electronic Eng.
Level
09 - NFQ Level 9
Credit
05 - 05 Credits
Duration
Semester
Fee
€
Start Term
2013 - Full Academic Year 2013-14
End Term
2019 - Full Academic Year 2019-20
Author(s)
JOHN DONOVAN
Programme Membership
SG_EQLTY_M09 201300 Master of Science in Quality
SG_EPROJ_M09 201300 Master of Science in Project Management
SG_EQLTY_M09 201400 Master of Science in Quality
SG_SPROJ_M09 201700 Master of Science in Project Management
SG_SPROJ_M09 201700 Master of Science in Project Management
SG_SPROJ_O09 201700 Postgraduate Diploma in Science in Project Management
SG_SPROJ_M09 201700 Master of Science in Project Management
Description
The student will be learn how to design conduct and analyse mixed level experiments, and interpret the data from these experiments.
Learning Outcomes
On completion of this module the learner will/should be able to;
1.
Conduct two and three level fractional factorial experiments and analyse the resulting data.
2.
Plan, conduct and analyse experiments using Response Surface Methodology (RSM).
3.
Design and analyse mixed level experiments.
4.
Analyse multiple response experiments and interpret the results.
5.
Analyse and interpret data from experiments involving random effects models.
6.
Formulate the expected means square rules to develop appropriate statistical models.
7.
Use Minitab to design an experiment, analyse, interpret and evaluate the resulting data.
Module Assessment Strategies
Continuous Assessment
Project work - Design and analyse own experiment 20%.
Final Examination
One written paper of 2.5 hours duration on experimental desgign theory and analysis 40%
One computer based exam of 2.5 hours duration involving data analysis and interpretation 40%
Indicative Syllabus
1. Two Level Factorial Designs : full factorial Designs. fractional factorial designs. Analysis of Residuals. Blocking in two level designs. Fold-over designs. repeat experiments vs. replicate experiments. Building the regression model and verification of the model.
2. More complex Two Level Factorial designs : Analysis of single replicate designs using probability plots and Lenth's method. Data transformations in a factorial design, variance stabilisation and Bob-Cox transformations. Analysis of multiple response experiments e.g. mean response and variability of response. Addition of centre points to a design. Robust methods of experimental design such as Taguchi methods. Solution of static and dynamic problems using Taguchi methods.
3. Three Level Factorial Designs: Full Factorial Designs, fractional factorial designs, Blocking in and designs, Analysis strategies for multiple responses.
4. Design and Analysis of Mixed Level Designs: Constructing mixed level designs using method of replacement. Analysis strategies for mixed level designs.
5. Response Surface Methods: Method of Steepest Ascent, verifying adequacy of first order model. Analysis of second order response surface. Locating the stationary point. Characterising the response surface. Ridge systems. Multiple response problem. Selecting designs for fitting response surfaces. Central composite designs. Box-Behnken designs. Face centred cube design. Blocking in Response surface designs. Introduction to mixture experiments. Evolutionary Operation.
6. Experiments with Random factors: The random effects model. Rules for Expected means squares. Repeatability and Reproducibility (R&R) studies using the random effects model. Estimation of variance components. Mixed models. Approximate F tests and Satterthwaite's method.
7. Nested and Split Plot Designs: Crossed vs. Nested designs. Two stage nested design. Diagnostic checking and estimation of variance components. Staggered nested design. The general m-stage nested design. Analysis of Split-plot designs.
Coursework & Assessment Breakdown
Coursework & Continuous Assessment
20 %
End of Semester / Year Formal Exam
80 %
Coursework Assessment
| Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
|---|---|---|---|---|---|---|
| 1 | Project Design and analyse own experiment | Coursework Assessment | UNKNOWN | 20 % | OnGoing | 1,2,7 |
End of Semester / Year Assessment
| Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
|---|---|---|---|---|---|---|
| 1 | Final Exam One final exam 2.5 hour theory paper. One computer based 2.5 hour exam involving data analysis and interpretation | Final Exam | UNKNOWN | 80 % | End of Term | 1,2,3,4,5,6,7 |
Full Time Mode Workload
| Type | Location | Description | Hours | Frequency | Avg Workload |
|---|---|---|---|---|---|
| Lecture | Not Specified | Lecture | 3 | Weekly | 3.00 |
| Independent Learning | UNKNOWN | Independent Learning | 4 | Weekly | 4.00 |
Total Full Time Average Weekly Learner Contact Time 3.00 Hours
Part Time Mode Workload
| Type | Location | Description | Hours | Frequency | Avg Workload |
|---|---|---|---|---|---|
| Lecture | Distance Learning Suite | Lecture | 3 | Weekly | 3.00 |
| Independent Learning | UNKNOWN | Independent Learning | 4 | Weekly | 4.00 |
Total Part Time Average Weekly Learner Contact Time 3.00 Hours
Module Resources
Non ISBN Literary Resources
|
Authors |
Title |
Publishers |
Year |
|
Montgomery, Douglas |
Design and Analysis of Experiments, 8th Edition |
John Wiley & Sons |
2013 |
|
Box, Hunter & Hunter |
Statistics for Experimenters, |
John Wiley & Sons. |
2005 |
|
Draper, N. & Smith, H., |
Applied Regression Analysis, |
John Wiley & Sons. |
1998 |
|
Montgomery, DC & Myers, R. |
Response Surface Methodology: Process and Product Optimization using Design Experiments |
John Wiley & Sons. |
2009 |
|
Montgomery, D.C, Peck, E.A. & Vining, G.G. |
Introduction to Linear Regression Analysis. 5th Edition |
John Wiley & Sons. |
2012 |
|
Wu, J. & Hamada, M., |
Experiments: Planning, Analysis and Parameter Design Optimization |
John Wiley & Sons. |
2009 |
Other Resources
None
Additional Information
None