Introduce students with basic principles of planning and analyzing and implementation of experiments in quality management. Show the meaning of experiment planning in optimizing products and processes.

Student responsibilities:

Attending lectures minimum 80%.

Grading and evaluation of student work over the course of instruction and at a final exam:

Essay and practical work 50%, final exam 50%.

Upon successful completion of the course, students will be able to (learning outcomes):

1 . Identify the nature of the observed process and choose an adequate method for the analysis

2 . Design an experiment based on the principles of design and analysis of experiments (DOE)

3 . Conduct tests according to the selected plan with the rules derived from the theory of DOE

4 . Analyze the obtained data using statistical methods and up to date software algorithms

5 . Interpret the obtained results

6 . Present the obtained results in an adequate way

Lectures

1. The concept of design and analysis of experiments (DOE), basic terms and application in production systems

2. Design of experiment. Definition of experimental space, design characteristics, experimental error. Adaptive design of experiments. Definition of A, I and D optimality.

3. Factorial analysis of variance, analysis of factor significance and relation to mathematical model.

4. Full factorial designs with 2 levels. Definition of blocks, randomization, repetition, contrast, effects and interactions.

5. Fractional designs (2k-p). Use in product and process improvement systems.

6. Mathematical models: model building, stepwise regression.

7. Significance of regression coefficients. Comparison of coefficients for two or more regression equations.

8. Diagnostics of the model - analysis of residuals and model adequacy indicators.

9. Basics of response surface methodology.

10. Central composite design, rotatable and non-rotatable designs (CCD, CCF).

11. Box-Behnken design.

12. Mixture designs, simplex-lattice, pseudo simplex-lattice.

13. Mixed designs of experiments.

14. Definition of experimental optimization with one or more criteria.

15. Orthogonal arrays - Taguchi design.

Exercises

1. Arithmetic mean distribution, examples.

2. Analysis of variance, examples.

3. Analysis - random blocks models, examples.

4. Completely factorial DOE in two levels, examples 1

5. Completely factorial DOE in two levels, examples 2

6. Completely factorial DOE in several levels, examples 1

7. Completely factorial DOE in several levels, examples 2

8. Partial DOE, problems

9. Central composite designs, examples 1

10. Central composite designs, examples 2

11. Box-Behnken DOE, problems.

12. Mixture designs, examples 1

13. Mixture designs, examples 2

14. Experimental optimization based on the RSM model, examples

15. Taguchi DOE- examples.

Compulsory literature:

1. Basics of modeling and simulation, D. Landek, H. Cajner, I. Žmak, Fakultet strojarstva i brodogradnje, 2021, p. 0-0

2. Design and Analysis of Experiments - 5th edition, Douglas C. Montgomery, John Wiley & Sons, 2012, p. 0-0

Recommended literature:

3. Applied statistics and probability for engineers, Douglas C. Montgomery, John Wiley & Sons, 2006, p. 0-0