كتاب Applied Design of Experiments and Taguchi Methods
منتدى هندسة الإنتاج والتصميم الميكانيكى
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منتدى هندسة الإنتاج والتصميم الميكانيكى
بسم الله الرحمن الرحيم

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 كتاب Applied Design of Experiments and Taguchi Methods

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تاريخ التسجيل : 01/07/2009
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العمل : مدير منتدى هندسة الإنتاج والتصميم الميكانيكى

كتاب Applied Design of Experiments and Taguchi Methods Empty
مُساهمةموضوع: كتاب Applied Design of Experiments and Taguchi Methods   كتاب Applied Design of Experiments and Taguchi Methods Emptyالخميس 05 مارس 2015, 12:44 am

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أحضرت لكم كتاب
Applied Design of Experiments and Taguchi Methods
K. Krishnaiah
P. Shahabudeen

كتاب Applied Design of Experiments and Taguchi Methods A_d_e_10
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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