### Engineering statistics

1. EDA Introduction

What is EDA?

EDA vs Classical & Bayesian

EDA vs Summary

EDA Goals

The Role of Graphics

An EDA/Graphics Example

General Problem Categories

2. EDA Assumptions

Underlying Assumptions .

Importance Techniques for Testing Assumptions

Interpretation of 4-Plot .

Consequences

3. EDA Techniques

Introduction

Analysis Questions

Graphical Techniques: Alphabetical

Graphical Techniques: By Problem

Category

Quantitative Techniques

Probability Distributions

4. EDA Case Studies

Introduction

By Problem Category

Detailed Chapter Table of Contents

References

Dataplot Commands for EDA Techniques

II.Measurement Process Characterization

1. Characterization

Issues

Check standards

2. Control Issues

Bias and long-term variability

Short-term variability

3. Calibration

Issues

Artifacts

Designs 3.

Catalog of designs

Artifact control

Instruments

Instrument control

4. Gauge R & R studies

Issues

Design

Data collection

Variability

Bias

Uncertainty

5. Uncertainty analysis

Issues

Approach .

Type A evaluations

Type B evaluations

Propagation of error

Error budget

Expanded uncertainties

Uncorrected bias

6. Case Studies

Gauge study

Check standard

Type A uncertainty

Type B uncertainty

III.Production Process Characterization

The goal of this chapter is to learn how to plan and conduct a Production Process

Characterization Study (PPC) on manufacturing processes. We will learn how to model

manufacturing processes and use these models to design a data collection scheme and to

guide data analysis activities. We will look in detail at how to analyze the data collected

in characterization studies and how to interpret and report the results. The accompanying

Case Studies provide detailed examples of several process characterization studies.

1. Introduction

Definition

Uses Terminology/Concepts

PPC Steps

2. Assumptions

General Assumptions

Specific PPC Models

3. Data Collection

Set Goals

Model the Process

Define Sampling Plan

4. Analysis

First Steps

Exploring Relationships

Model Building

Variance Components

Process Stability

Process Capability

Checking Assumptions

5. Case Studies

Furnace Case Study

Machine Case Study

IV.Process Modeling

The goal for this chapter is to present the background and specific analysis techniques

needed to construct a statistical model that describes a particular scientific or

engineering process. The types of models discussed in this chapter are limited to those

based on an explicit mathematical function. These types of models can be used for

prediction of process outputs, for calibration, or for process optimization.

1. Introduction

Definition

Terminology

Uses

Methods

2. Assumptions

Assumptions

3. Design

Definition

Importance

Design Principles

Optimal Designs

Assessment

4. Analysis

Modeling Steps

Model Selection

Model Fitting

Model Validation

Model Improvement

5. Interpretation & Use

Prediction

Calibration

Optimization

6. Case Studies

Load Cell Output

Alaska Pipeline

Ultrasonic Reference Block

Thermal Expansion of Copper

V.Process Improvement

1. Introduction

Definition of experimental design

Uses

Steps

2. Assumptions

Measurement system capable

Process stable

Simple model

Residuals well-behaved

3. Choosing an Experimental Design

Set objectives

Select process variables and levels

Select experimental design

Completely randomized designs

Randomized block designs

Full factorial designs

Fractional factorial designs

Plackett-Burman designs

Response surface designs

Adding center point runs

Improving fractional design resolution

Three-level full factorial designs

Three-level, mixed-level and fractional factorial designs

4. Analysis of DOE Data

DOE analysis steps

Plotting DOE data

Modeling DOE data

Testing and revising DOE models

Interpreting DOE results

Confirming DOE results

DOE examples

Full factorial example

Fractional factorial example

Response surface example

5. Advanced Topics

When classical designs don’t work

Computer-aided designs

D-Optimal designs

Repairing a design

Optimizing a process

Single response case

Multiple response case

Mixture designs

Mixture screening designs

Simplex-lattice designs

Simplex-centroid designs

Constrained mixture designs

Treating mixture and process

variables together

Nested variation

Taguchi designs

John’s 3/4 fractional factorial designs

Small composite designs

An EDA approach to experiment design

Case Studies

Eddy current probe sensitivity study

Sonoluminescent light intensity

study

References

VI.Process or Product Monitoring and Control

This chapter presents techniques for monitoring and controlling processes and signaling

when corrective actions are necessary.

1. Introduction

History

Process Control Techniques

Process Control

“Out of Control”

“In Control” but Unacceptable

Process Capability

2. Test Product for Acceptability

Acceptance Sampling

Kinds of Sampling Plans

Choosing a Single Sampling Plan

Double Sampling Plans

Multiple Sampling Plans

Sequential Sampling Plans

Skip Lot Sampling Plans

3. Univariate and Multivariate Control

Charts

Control Charts

Variables Control Charts

Attributes Control Charts

Multivariate Control charts

4. Time Series Models

Definitions, Applications and Techniques

Moving Average or Smoothing

Techniques

Exponential Smoothing

Univariate Time Series Models

Multivariate Time Series Models

5. Tutorials

What do we mean by “Normal” data?

What to do when data are non-normal

Elements of Matrix Algebra

Elements of Multivariate Analysis

Principal Components

6. Case Study

Lithography Process Data

Box-Jenkins Modeling Example

VII.Product and Process Comparisons

This chapter presents the background and specific analysis techniques needed to

compare the performance of one or more processes against known standards or one

another.

1. Introduction

Scope

Assumptions

Statistical Tests

Confidence Intervals

Equivalence of Tests and Intervals

Outliers

Trends

2. Comparisons: One Process

Comparing to a Distribution

Comparing to a Nominal Mean

Comparing to Nominal Variability

Fraction Defective

Defect Density

Location of Population

Values

3. Comparisons: Two Processes

Means: Normal Data

Variability: Normal Data

Fraction Defective

Failure Rates

Means: General Case

4. Comparisons: Three +

Processes

Comparing Populations

Comparing Variances

Comparing Means

Variance Components

Comparing Categorical

Datasets

Comparing Fraction

Defectives

Multiple Comparisons

VIII. Assessing Product Reliability

This chapter describes the terms, models and techniques used to evaluate and predict

product reliability.

1. Introduction

Why important?

Basic terms and models

Common difficulties

Modeling “physical acceleration”

Common acceleration models

Basic non-repairable lifetime distributions

Basic models for repairable systems

Evaluate reliability “bottom-up”

Modeling reliability growth

Bayesian methodology

2. Assumptions/Prerequisites

Choosing appropriate life distribution

Plotting reliability data

Testing assumptions .

Choosing a physical acceleration model

Models and assumptions for Bayesian methods

Reliability Data Collection

Planning reliability assessment tests

4. Reliability Data Analysis

Estimating parameters from censored data

Fitting an acceleration model

Projecting reliability at use conditions

Comparing reliability between two or more populations

Fitting system repair rate models

Estimating reliability using a Bayesian gamma prior

Assessing Product Reliability**Download**

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