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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EMT 3069 DESIGN OF EXPERIMENTS AND INDUSTRIAL APPLICATIONS ELECTIVE 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

Offered to

Econometrics Econometrics (Evening)

Course Objective

The DOE can economically satisfy the needs of problem solving and product/process design optimization projects. By learning and applying this technique, engineers, scientists, and researchers can significantly reduce the time required for experimental investigations. This course is intended as a guide for candidates of practitioners and/or managers involved in product or process experimentation and development.

Learning Outcomes of the Course Unit

1   1. Understand the importance of statistical design of experiments 2   2. Learn the experimental designs most widely used in practice 3   3. Choose an appropriate experimental design based on the study objectives 4   4. Construct and implement the design selected 5   5. Interpret the results of the experiment and report the conclusions 6   6. Analyze the data collected based on the design used and its underlying assumptions

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction 2 Quality Characteristics, Loss function concept 3 Factors and Levels 4 Interaction Between Factors Noise Factors and Outer Arrays 5 Scope and Size of Experiments 6 Order of Running Experiments Repetitions and Replications 7 Midterm 8 Available Orthogonal Arrays 9 Triangular Table and Linear Graphs Upgrading Columns 10 Dummy Treatments 11 S/N Ratios for Static and Dynamic Systems 12 Why Taguchi Approach and Taguchi vs. Classical DOE 13 Applications 14 Final Exam

Recomended or Required Reading

Taguchi Techniques for Quality Engineering: Loss Function, Orthogonal Experiments, Parameter and Tolerance Design 2nd Edition by Phillip J.Ross (1996) McGraw-Hill

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 MTEG MIDTERM GRADE MTEG * 1 3 FIN FINAL EXAM 4 FCGR FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTEG * 0.40 + RST * 0.60 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 12 3 36 Preparations before/after weekly lectures 12 3 36 Preparation for midterm exam 1 20 20 Preparation for final exam 1 20 20 Preparing assignments 4 1 4 Midterm 1 1 1 Final 1 1 1 TOTAL WORKLOAD (hours) 118

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 LO.1 1 1 LO.2 1 1 LO.3 1 1 LO.4 1 1 LO.5 1 1 LO.6 1 1