Full Title Advanced Experimental Design

Short Title Experimental Design

Code QLTY09032
Level 09
Credit 05

Author DONOVAN, JOHN
Department Mech. and Electronic Eng.

Subject Area Quality
Attendence N/A%
Fee

Description
The student will be learn how to design conduct and analyse mixed level experiments, and interpret the data from these experiments.

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.

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.


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%

Module Dependencies
Pre Requisite Modules
None
Co Requisite Modules
None
Incompatible Modules
None

Coursework Assessment Breakdown %
Course Work / Continuous Assessment 20 %
End of Semester / Year Formal Examination 80 %

Coursework Assessment Breakdown

Description Outcome Assessed % of Total Assessment Week
Project Design and analyse own experiment 1,2,7 20 OnGoing


End Exam Assessment Breakdown

Description Outcome Assessed % of Total Assessment Week
Final Exam One final exam 2.5 hour theory paper. One computer based 2.5 hour exam involving data analysis and interpretation 1,2,3,4,5,6,7 80 End of Term


Distance Learning Mode Workload

Type Location Description Hours Frequency Avg Weekly Workload

Total Average Weekly Learner Workload 0.00 Hours

Part Time Mode Workload

Type Location Description Hours Frequency Avg Weekly Workload
Lecture Distance Learning Suite Lecture 3 Weekly 3.00
Independent Learning UNKNOWN Independent Learning 4 Weekly 4.00

Total Average Weekly Learner Workload 3.00 Hours

Full Time Mode Workload

Type Location Description Hours Frequency Avg Weekly Workload
Lecture Not Specified Lecture 3 Weekly 3.00
Independent Learning UNKNOWN Independent Learning 4 Weekly 4.00

Total Average Weekly Learner Workload 3.00 Hours

Online Learning Mode Workload

Type Location Description Hours Frequency Avg Weekly Workload

Total Average Weekly Learner Workload 0.00 Hours

Resources
Book Resources
Authors

Title

Publishers

Year

Montgomery, Douglas

Design and Analysis of Experiments, 8 th 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. 5 th Edition

John Wiley & Sons.

2012

Wu, J. & Hamada, M.,

Experiments: Planning, Analysis and Parameter Design Optimization

John Wiley & Sons.

2009
Other Resources
None
Url Resources
Additional Info