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 |  موضوع: كتاب Applied Design of Experiments and Taguchi Methods الخميس 05 مارس 2015, 12:44 am | |
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Applied Design of Experiments and Taguchi Methods
K. Krishnaiah
P. Shahabudeen
ويتناول الموضوعات الأتية :
REVIEW OF STATISTICS
Introduction
Normal Distribution
Standard Normal Distribution
Distribution of Sample Means
The t-Distribution
The F-Distribution
Confidence Intervals
Confidence Interval on a Single Mean ()
Confidence Interval for Difference in Two Mean
Hypotheses Testing
Tests on a Single Mean
Tests on Two Means
Dependent or Correlated Samples
Problems
FUNDAMENTALS OF EXPERIMENTAL DESIGN
Introduction
Experimentation
Conventional Test Strategies
Better Test Strategies
Efficient Test Strategies
Need for Statistically Designed Experiments
Analysis of Variance
Basic Principles of Design
Replication
Randomization
Blocking
Terminology Used in Design of Experiments
Steps in Experimentation
Problem Statement
Selection of Factors, Levels and Ranges
Selection of Response Variable
Choice of Experimental Design
Conducting the Experiment
Analysis of Data
Conclusions and Recommendations
Choice of Sample Size
Variable Data
Attribute Data
Normal Probability Plot
Normal Probability Plotting
Normal Probability Plot on an Ordinary Graph Paper
Half-normal Probability Plotting
Brainstorming
Cause and Effect Analysis
Linear Regression
Simple Linear Regression Model
Multiple Linear Regression Model
P r o b l e m s
SINGLE-FACTOR EXPERIMENTS –
Introduction
Completely Randomized Design
The Statistical Model
Typical Data for Single-factor Experiment
Analysis of Variance
Computation of Sum of Squares
Effect of Coding the Observations
Estimation of Model Parameters
Model Validation
Analysis of Treatment Means
Multiple Comparisons of Means Using Contrasts
Randomized Complete Block Design
Statistical Analysis of the Model
Estimating Missing Values in Randomized Block Design
Balanced Incomplete Block Design (BIBD)
Statistical Analysis of the Model
Latin Square Design
The Statistical Model
Graeco-Latin Square Design
The Statistical Model
Problems
MULTI-FACTOR FACTORIAL EXPERIMENTS –
Introduction
Two-factor Experiments
The Statistical Model for a Two-factor Experiment
Estimation of Model Parameters
The Three-factor Factorial Experiment
The Statistical Model for a Three-factor Experiment
Randomized Block Factorial Experiments
The Statistical Model
Experiments with Random Factors
Random Effects Model
Determining Expected Mean Squares
The Approximate F-test
Rules for Deriving Degrees of Freedom and Sum of Squares
Rule for Degrees of Freedom
Rule for Computing Sum of Squares
Problems
THE k FACTORIAL EXPERIMENTS –
Introduction
The Factorial Design
Determining the Factor Effects
Development of Contrast Coefficients
The Regression Model
The Factorial Design
Development of Contrast Coefficient Table
Yates Algorithm for the k Design
The Regression Model
Statistical Analysis of the Model
The General k Design
The Single Replicate of the k Design
Addition of Center Points to the k Design
Problems
BLOCKING AND CONFOUNDING IN k FACTORIAL DESIGNS –
Introduction
Blocking in Replicated Designs
Confounding
The k Factorial Design in Two Blocks
Assignment of Treatments to Blocks Using Plus–Minus Signs
Assignment of Treatments Using Defining Contrast
Data Analysis from Confounding Designs
Complete Confounding
Partial Confounding
Confounding k Design in Four Blocks
Confounding k Factorial Design in m Blocks
Problems
TWO-LEVEL FRACTIONAL FACTORIAL DESIGNS –
Introduction
The One-half Fraction of the k Design
Design Resolution
Construction of One-half Fraction with Highest Resolution
The One-quarter Fraction of k Design
The k–m Fractional Factorial Design
Fractional Designs with Specified Number of Runs
Fold-over Designs
Problems
RESPONSE SURFACE METHODS –
Introduction
Response Surface Designs
Designs for Fitting First-order Model
Central Composite Design (CCD)
Box–Behnken Designs
Analysis of Data From RSM Designs
Analysis of First-order Design
Analysis of Second-order Design
Problems
QUALITY LOSS FUNCTION –
Introduction
Taguchi Definition of Quality
Taguchi Quality Loss Function
Quality Loss Function (Nominal—the best case)
Quality Loss Function (Smaller—the better case)
Quality Loss Function (Larger—the better case)
Estimation of Quality Loss
Traditional Method
Quality Loss Function Method
Problems
TAGUCHI METHODS –
Introduction
Taguchi Methods
Development of Orthogonal Designs
Robust Design
System Design
Parameter Design
Tolerance Design
Basis of Taguchi Methods
Steps in Experimentation
P r o b l e m s
DESIGN OF EXPERIMENTS USING ORTHOGONAL ARRAYS –
Introduction
Assignment of Factors and Interactions
Linear Graph
Selection and Application of Orthogonal Arrays
Problems
DATA ANALYSIS FROM TAGUCHI EXPERIMENTS –
Introduction
Variable Data with Main Factors Only
Variable Data with Interactions
Variable Data with a Single Replicate and Vacant Column
Attribute Data Analysis
Treating Defectives as Variable Data
Considering the Two-Class Data as and
Transformation of Percentage Data
Confirmation Experiment
Confidence Intervals
Confidence Interval for a Treatment Mean
Confidence Interval for Predicted Mean
Confidence Interval for the Confirmation Experiment
Problems
ROBUST DESIGN –
Introduction
Factors Affecting Response
Objective Functions in Robust Design
Advantages of Robust Design
Simple Parameter Design
Inner/Outer OA Parameter Design
Relation between S/N Ratio and Quality Loss
Problems
MULTI-LEVEL FACTOR DESIGNS –
Introduction
Methods for Multi-level Factor Designs
Merging Columns
Dummy Treatment
Combination Method
Idle Column Method
Problems
MULTI-RESPONSE OPTIMIZATION PROBLEMS –
Introduction
Engineering Judgment
Assignment of Weights
Data Envelopment Analysis based Ranking Method
Grey Relational Analysis
Factor Analysis
Genetic Algorithm
P r o b l e m s
CASE STUDIES –
Maximization of Food Color Extract from a Super Critical Fluid
Extraction Process
Automotive Disc Pad Manufacturing
A Study on the Eye Strain of VDT Users
Optimization of Flash Butt Welding Process
Wave Soldering Process Optimization
Application of L OA
A p p e n d i c e s –
R e f e r e n c e s –
Index
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