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E-raamat: Introduction to Acceptance Sampling and SPC with R [Taylor & Francis e-raamat]

(Brigham Young University, USA)
  • Formaat: 298 pages, 42 Tables, black and white; 102 Illustrations, black and white
  • Ilmumisaeg: 25-Feb-2021
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9781003100270
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Introduction to Acceptance Sampling and SPC with RSuurem pilt
  • Formaat: 298 pages, 42 Tables, black and white; 102 Illustrations, black and white
  • Ilmumisaeg: 25-Feb-2021
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9781003100270
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003100270

An Introduction to Acceptance Sampling and SPC with R is an introduction to statistical methods used in monitoring, controlling and improving quality. Topics covered include acceptance sampling; Shewhart control charts for Phase I studies; graphical and statistical tools for discovering and eliminating the cause of out-of-control-conditions; Cusum and EWMA control charts for Phase II process monitoring; and the design and analysis of experiments for process troubleshooting and discovering ways to improve process output. Origins of statistical quality control and the technical topics presented in the remainder of the book are those recommended in the ANSI/ASQ/ISO guidelines and standards for industry. The final chapter ties everything together by discussing modern management philosophies that encourage the use of the technical methods presented earlier.

In the modern world sampling plans and the statistical calculations used in statistical quality control are done with the help of computers. As an open source high-level programming language with flexible graphical output options, R runs on Windows, Mac and Linux operating systems, and has add-on packages that equal or exceed the capability of commercial software for statistical methods used in quality control. In this book, we will focus on several R packages. In addition to demonstrating how to use R for acceptance sampling and control charts, this book will concentrate on how the use of these specific tools can lead to quality improvements both within a company and within their supplier companies.

This would be a suitable book for a one-semester undergraduate course emphasizing statistical quality control for engineering majors (such as manufacturing engineering or industrial engineering), or a supplemental text for a graduate engineering course that included quality control topics.

Preface ix
List of Figures
xiii
List of Tables
xvii
1 Introduction and Historical Background
1(10)
1.1 Origins of Statistical Quality Control
1(2)
1.2 Expansion and Development of Statistical Quality Control during WW II
3(2)
1.3 Use and Further Development of Statistical Quality Control in Post-War Japan
5(2)
1.4 Re-emergence of Statistical Quality Control in U.S. and the World
7(4)
2 Attribute Sampling Plans
11(32)
2.1 Introduction
11(1)
2.2 Attribute Data
11(1)
2.3 Attribute Sampling Plans
12(2)
2.4 Single Sample Plans
14(3)
2.5 Double and Multiple Sampling Plans
17(6)
2.6 Rectification Sampling
23(2)
2.7 Dodge-Romig Rectification Plans
25(1)
2.8 Sampling Schemes
26(1)
2.9 Quick Switching Scheme
27(3)
2.10 MIL-STD-105E and Derivatives
30(10)
2.11 MIL-STD-1916
40(1)
2.12 Summary
41(1)
2.13 Exercises
42(1)
3 Variables Sampling Plans
43(30)
3.1 The k-Method
44(9)
3.1.1 Lower Specification Limit
44(1)
3.1.1.1 Standard Deviation Known
44(5)
3.1.1.2 Standard Deviation Unknown
49(3)
3.1.2 Upper Specification Limit
52(1)
3.1.2.1 Standard Deviation Known
52(1)
3.1.2.2 Standard Deviation Unknown
52(1)
3.1.3 Upper and Lower Specification Limits
53(1)
3.1.3.1 Standard Deviation Known
53(1)
3.1.3.2 Standard Deviation Unknown
53(1)
3.2 The M-Method
53(6)
3.2.1 Lower Specification Limit
53(1)
3.2.1.1 Standard Deviation Known
53(2)
3.2.1.2 Standard Deviation Unknown
55(1)
3.2.2 Upper Specification Limit
56(1)
3.2.2.1 Standard Deviation Known
56(1)
3.2.2.2 Standard Deviation Unknown
57(1)
3.2.3 Upper and Lower Specification Limit
57(1)
3.2.3.1 Standard Deviation Known
57(1)
3.2.3.2 Standard Deviation Unknown
57(2)
3.3 Sampling Schemes
59(4)
3.3.1 MIL-STD-414 and Derivatives
59(4)
3.4 Gauge R&R Studies
63(4)
3.5 Additional Reading
67(1)
3.6 Summary
67(4)
3.7 Exercises
71(2)
4 Shewhart Control Charts in Phase I
73(46)
4.1 Introduction
73(3)
4.2 Variables Control Charts in Phase I
76(8)
4.2.1 Use of Jt-R charts in Phase I
77(1)
4.2.2 X and R Charts
78(4)
4.2.3 Interpreting Charts for Assignable Cause Signals
82(1)
4.2.4 X and s Charts
83(1)
4.2.5 Variable Control Charts for Individual Values
83(1)
4.3 Attribute Control Charts in Phase I
84(7)
4.3.1 Use of a p Chart in Phase I
85(4)
4.3.2 Constructing other types of Attribute Charts with qcc
89(2)
4.4 Finding Root Causes and Preventive Measures
91(15)
4.4.1 Introduction
91(2)
4.4.2 Flowcharts
93(1)
4.4.3 PDCA or Shewhart Cycle
94(2)
4.4.4 Cause-and-Effect Diagrams
96(3)
4.4.5 Check Sheets or Quality Information System
99(2)
4.4.6 Line Graphs or Run Charts
101(1)
4.4.7 Pareto Diagrams
102(2)
4.4.8 Scatter Plots
104(2)
4.5 Process Capability Analysis
106(3)
4.6 OC and ARL Characteristics of Shewhart Control Charts
109(4)
4.6.1 OC and ARL for Variables Charts
110(3)
4.6.2 OC and ARL for Attribute Charts
113(1)
4.7 Summary
113(2)
4.8 Exercises
115(4)
5 DoE for Troubleshooting and Improvement
119(54)
5.1 Introduction
119(4)
5.2 Definitions
123(2)
5.3 2fc Designs
125(14)
5.3.1 Examples
128(1)
5.3.2 Example 1: A 23 Factorial in Battery Assembly
129(4)
5.3.3 Example 2: Unreplicated 24 Factorial in Injection Molding
133(6)
5.4 2k~p Fractional Factorial Designs
139(15)
5.4.1 One-Half Fraction Designs
139(4)
5.4.2 Example of a One-half Fraction of a 25 Designs
143(5)
5.4.3 One-Quarter and Higher Order Fractions of 2k Designs
148(1)
5.4.4 An Example of a |th Fraction of a 27 Design
149(5)
5.5 Alternative Screening Designs
154(4)
5.5.1 Example
155(3)
5.6 Response Surface and Definitive Screening Experiments
158(9)
5.7 Additional Reading
167(1)
5.8 Summary
168(1)
5.9 Exercises
169(4)
6 Time Weighted Control Charts in Phase II
173(36)
6.1 Time Weighted Control Charts When the In-control μ and σ are known
173(13)
6.1.1 Cusum Charts
176(2)
6.1.1.1 Headstart Feature
178(1)
6.1.1.2 ARL of Shewhart and Cusum Control Charts for Phase II Monitoring
179(2)
6.1.2 EWMA Charts
181(3)
6.1.2.1 ARL of EWMA, Shewhart and Cusum Control Charts for Phase II Monitoring
184(1)
6.1.2.2 EWMA with FIR Feature
185(1)
6.2 Time Weighted Control Charts of Individuals to Detect Changes in σ
186(3)
6.3 Examples
189(3)
6.4 Time Weighted Control Charts Using Phase I estimates of μ and σ
192(5)
6.5 Time Weighted Charts for Phase II Monitoring of Attribute Data
197(9)
6.5.1 Cusum for Attribute Data
198(7)
6.5.2 EWMA for Attribute Data
205(1)
6.6 Exercises
206(3)
7 Multivariate Control Charts
209(38)
7.1 Introduction
209(2)
7.2 T2-Control Charts and Upper Control Limit for T2 Charts
211(3)
7.3 Multivariate Control Charts with Sub-grouped Data
214(17)
7.3.1 Phase I T2 Control Chart with Sub-grouped Data
214(6)
7.3.2 Multivariate Control Charts for Monitoring Variability with Sub-grouped Data
220(8)
7.3.3 Phase II T2 Control Chart with Sub-grouped Data
228(3)
7.4 Multivariate Control Charts with Individual Data
231(13)
7.4.1 Phase I T2 with Individual Data
231(3)
7.4.2 Phase II T2 Control Chart with Individual Data
234(2)
7.4.3 Interpreting Out-of-control Signals
236(1)
7.4.4 Multivariate EWMA Charts with Individual Data
237(7)
7.5 Summary
244(1)
7.6 Exercises
245(2)
8 Quality Management Systems
247(20)
8.1 Introduction
247(1)
8.2 Quality Systems Standards and Guidelines
248(9)
8.2.1 ISO 9000
249(4)
8.2.2 Industry Specific Standards
253(1)
8.2.3 Malcolm Baldridge National Quality Award Criteria
253(4)
8.3 Six-Sigma Initiatives
257(7)
8.3.1 Brief Introduction to Six Sigma
257(2)
8.3.2 Organizational Structure of a Six Sigma Organization
259(1)
8.3.3 The DMAIC Process and Six Sigma Tools
260(1)
8.3.4 Tools Used in the DMAIC Process
261(1)
8.3.5 DMAIC Process Steps
261(1)
8.3.6 History and Results Achieved by the Six Sigma Initiative
262(1)
8.3.7 Six Sigma Black Belt Certification
263(1)
8.4 Additional Reading
264(1)
8.5 Summary
264(3)
Bibliography 267(8)
Index 275
John Lawson is a Professor Emeritus from the Statistics Department at Brigham Young University where he taught from 1986-2019. He is an ASQ-CQE and he has a Masters Degree in Statistics from Rutgers University and a PhD in Applied Statistics from the Polytechnic Institute of N.Y. He worked as a statistician for Johnson & Johnson Corporation from 1971 to 1976, and he worked at FMC Corporation Chemical Division from 1976 to 1986 where he was the Manager of Statistical Services. He is the author of Design and Analysis of Experiments with R, CRC Press, and the co-author (with John Erjavec) of Basic Experimental Strategies and Data Analysis for Science and Engineering, CRC Press.
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