COURSE UNIT TITLE

: RESPONSE SURFACE METHODOLOGY AND APPLICATIONS IN ENGINEERING

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IND 5032 RESPONSE SURFACE METHODOLOGY AND APPLICATIONS IN ENGINEERING ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR DERYA EREN AKYOL

Offered to

INDUSTRIAL ENGINEERING (ENGLISH)
Industrial Engineering - Thesis (English) (Evening Program)
INDUSTRIAL ENGINEERING (ENGLISH)
INDUSTRIAL ENGINEERING - NON THESIS (ENGLISH)
M.Sc. Metallurgical and Material Engineering
Metallurgical and Material Engineering
INDUSTRIAL ENGINEERING - NON THESIS (EVENING PROGRAM) (ENGLISH)
Metallurgical and Material Engineering

Course Objective

This course provides insight and operational skills in designing, analyzing and optimizing quantitative models based on response surface methodology. After completing the course, the students will be able to apply the techniques independently in their own work environment.

Learning Outcomes of the Course Unit

1   Understand and be able to correctly use basic statistical terminology
2   To identify problems where response surface methodology can be applied
3   To be able to design simple experiments
4   To have a general insight into how data analysis is done in connection to designed experiments
5   To be able to build models based on response surface methodology

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Statistical Model Classifications, Approximating Response Functions, Objectives and Typical Applications of Response Surface (RS) Methodology
2 Statistical Model Classifications, Approximating Response Functions, Objectives and Typical Applications of Response Surface (RS) Methodology
3 Linear and Nonlinear Regression Models.
4 Linear and Nonlinear Regression Models.
5 Two Level Factorial Designs, Two Level Fractional Factorial Design.
6 Two Level Factorial Designs, Two Level Fractional Factorial Design.
7 Second Order RS, Second Order Approximating Function
8 Second Order RS, Second Order Approximating Function
9 Optimization by RS Methodology, Nature of Stationary Point( Canonical Analysis), Ridge Analysis of RS.
10 Optimization by RS Methodology, Nature of Stationary Point( Canonical Analysis), Ridge Analysis of RS.
11 Central Composite Designs and Box-Behnken design.
12 RS Analysis with Multiple Responses and Multi Response Optimization.
13 RS Analysis with Multiple Responses and Multi Response Optimization.
14 Response Surface Methods and Taguchi s Robust Parameter Design.

Recomended or Required Reading

Myers, R.H and Montgomery, D.C. Response Surface Methodology: Process and Product Optimization Using Designed Experiment. John Wiley and Sons Inc., 1995.

Box,G.E.P and Draper,N.R.Empirical Model Building and Response Surface. John Wiley and Sons.1986.

Planned Learning Activities and Teaching Methods

Presentations and discussions

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.25 + ASG *0.25 +FIN *0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.25 + ASG *0.25 +RST *0.50


*** 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

English

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 13 3 39
Preparation for midterm exam 1 16 16
Preparations before/after weekly lectures 13 6 78
Preparation for final exam 1 16 16
Preparing assignments 4 8 32
Final 1 2 2
Midterm 1 2 2
Presentations 2 4 8
TOTAL WORKLOAD (hours) 193

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12
LO.123
LO.223
LO.323
LO.423
LO.523