Design of Experiments (DOE) and Optimization 1....
Transcript of Design of Experiments (DOE) and Optimization 1....
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Hae-Jin ChoiSchool of Mechanical Engineering,
Chung-Ang University
Design of Experiments (DOE) and Optimization
1. Introduction
1DOE and Optimization
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Introduction to Lectures
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Professor : Choi, Hae-Jin, 310-531
(Email) [email protected], (Office) 02-820-5787, (Mobile) 010-6726-6096
Class : Tue (2-3pm), Thur (1-3 pm), Lecture room (301-520)
Pre-requisite:
Numerical analysis - 수치해석 (course code: 47892, Semester 1)
Text books: Montgomery, D. C., (2012), Design and Analysis of Experiments, 8th Ed., John Wiley & Sons.
Arora, J. S. (2011), Introduction to Optimum Design, 2nd Ed., Elsevier.
Lecture Note (http://isdl.cau.ac.kr/ Education section)
Software: MINITAB (http://www.minitab.com/)
MATLAB (http://www.mathworks.co.kr/products/matlab/index.html)
Pp. 18 Software Installation Guide
Assessment : Continuous assessment (10%), Design project (20%), Mid-term (30%), Final (40%)
Office hour: Tue(11-12am) or by email appointment
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What is Design of Experiments (DOE)?
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Design of Experiments (DOE) is;
Planning experiments or tests to save resources (time and money)
Analyzing results of experiments or tests to characterize your
system
Experiments are used widely in the engineering world
Process characterization & optimization
Evaluation of material properties
Product design & development
Component & system tolerance determination
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Example: Golfing
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How to improve my score in Golfing?
Practice!!!
Other than that?
Type of driver used (oversized or regular sized)
Type of ball (2 piece or 3 piece)
Walking or riding cart
Drinking water or beer
Etc…
What combination of the factors is the best for me?
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General Model of a Process or System
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System: function, model, or manufacturing process to transform input to output Golf game
Input: provided materials or energy into the process or system Physical energy to play golf
Response (output): outcome of a process or performance of a system Score
Controllable factors: factors that experimenters can control Driver type, ball type, etc.
Uncontrollable factors (noise factors): factors that experiments cannot control Course layout, grass type, weather, etc.
Response
(Output)System
(Process) y
…..
Input
x1 x2 xp
z1 z2 zq
Controllable factors
Uncontrollable factors
(Noise factors)
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What needs to be analyzed?
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Determining which variables are
most influential on the response y.
Determining where to set the
controllable factors x so that y is
almost always near the desired
nominal value.
How about in the golf experiments?
Response
(Output)System
(Process) y
…..
Input
x1 x2 xp
z1 z2 zq
Controllable factors
Uncontrollable factors
(Noise factors)
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How to find my best condition?
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One-factor-at-a time strategy
Any Problem??
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Interaction Effect between the Factors
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Interaction effect between type of driver and beverage
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Factorial Design of Experiments
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Two factors with 2 level for each factor
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Factorial Design of Experiments
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Three factors
Four factors
Any Problem??DOE and Optimization
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Fractional Factorial Design of Experiments
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16 experiments -> 8 experiments
Questions for the semester
• How to effectively reduce the number of experiments?
• How to analyze the results of experiments?
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Developing Empirical Model
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Developing relationship between
controllable (uncontrollable)
factors and response
Response Surface MethodologyResponse
(Output)System
(Process) y
…..
Input
x1 x2 xp
z1 z2 zq
Controllable factors
Uncontrollable factors
(Noise factors)
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Quality Engineering (품질공학)
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Taguchi and robust parameter design
Determining where to set the
controllable factors x so that
variability in y is small
Determining where to set the
controllable factors x so that effect
of uncontrollable factors z are
minimized.
Response
(Output)System
(Process) y
…..
Input
x1 x2 xp
z1 z2 zq
Controllable factors
Uncontrollable factors
(Noise factors)
y
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Robust Design (강건설계)
AD 1592~1598: 23 Battles
Optimized for the
fastest navigation
23 Win 0 Win
Robust navigation
under uncertainty
in operating
condition
A B
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Four Eras in the History of DOE
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The agricultural origins, 1908 – 1940s W.S. Gossett and the t-test (1908) R. A. Fisher & his co-workers Profound impact on agricultural science Factorial designs, ANOVA
The first industrial era, 1951 – late 1970s Box & Wilson, response surfaces Applications in the chemical & process industries
The second industrial era, late 1970s – 1990 Quality improvement initiatives in many companies Taguchi and robust parameter design, process robustness
The modern era, beginning circa 1990
R. A. Fisher George E. P. Box
Genichi Taguchi
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Optimization (최적화)
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How to efficiently explore the design space (factor space) to
achieve the best response ?
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Course Schedule
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Introduction to DOE
Experiments with One Factor
Analysis of Variance (ANOVA)
2k Factorial Design
Fractional Factorial Design
Response Surface Methodology
Mid-Term Exam
Taguchi Robust Design
Design Project
Introduction to Optimization
Linear Programming
Unconstrained Optimization
Constrained Optimization
Direct Search Method
Pareto Optimality
Final-Exam
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Software Installation Guide
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Minitab installation
Login at http://isdlnas2.ipdisk.co.kr
ID: isdlguest, PW: nasguest
Go to HDD1 → DOE → download “minitab18.1.0.0setup.exe”
License server IP: 165.194.3.62
Port number: 27000
Only available within CAU campus network!!
Matlab installation
Follow CAU Matlab installation guide here
Available outside of campus with the proper installation.