Design of Expert At Enginnering
Industrial Engineering
Institute of Natural and Applied Sciences
Third Cycle (Doctorate Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Design of Expert At Enginnering | MEN605 | Elective | Doctorate degree | 1 | Fall | 8 |
Name of Lecturer(s)
Prof. Dr. Kasım BAYNAL
Learning Outcomes of the Course Unit
1) Clarify the basic concepts and strategies of experimental design and application areas
2) Define the relationship between experimental design and statistics
3) Carry out the statistical analysis of single and multi-factorial experiments
4) Understand and analyze the interactions of factors
5) Establish the hypotheses related to the event and making the ANOVA of factors
6) Set and implement of the full factorial and fractional factorial experimental designs
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||
| 1 | 2 | 3 | 4 | 5 | ||
| Learning Outcomes | ||||||
| 1 | No relation | No relation | No relation | No relation | No relation | |
| 2 | No relation | No relation | No relation | No relation | No relation | |
| 3 | No relation | No relation | No relation | No relation | No relation | |
| 4 | No relation | No relation | No relation | No relation | No relation | |
| 5 | No relation | No relation | No relation | No relation | No relation | |
| 6 | Low | No relation | Low | No relation | Low | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not Required
Course Contents
In this lecture, candidates are provided with in-depth knowledge on DOE, basic consepts, startegies of experiment, basic application fields, main procedures in DOE, background of statical design, random sampling and identify data, sample, mean and variance sample, tests of hypothesis for sampling, simple linear regression, multiple linear regression, experiments of one factor, ANOVA and carrying out and consequence assessment of experiments. During this lecture, the block design in one factor experiments, full random one factor experiments, full and fractional factoriel, Latin square, Taguchi and response sureface methods, 2k factoriel DOE, sum of squares, multi-faktor experiments, interactions analysis of factors, ANOVA of two-factors, ANOVA of Multi-factors and designs of Taguchi and applications will be analyzed.
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
50% |
||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||||||
Contribution of Final Examination to Course Grade |
50% |
||||||||||||||
Total | 100% | ||||||||||||||
Language of Instruction
Other
Work Placement(s)
Not Required