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E-raamat: Introduction to Statistical Process Control [Wiley Online]

  • Formaat: 304 pages
  • Ilmumisaeg: 06-Oct-2020
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119528429
  • ISBN-13: 9781119528425
Teised raamatud teemal:
  • Wiley Online
  • Hind: 131,05 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Formaat: 304 pages
  • Ilmumisaeg: 06-Oct-2020
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119528429
  • ISBN-13: 9781119528425
Teised raamatud teemal:

An Introduction to the Fundamentals and History of Control Charts, Applications, and Guidelines for Implementation 

Introduction to Statistical Process Control examines various types of control charts that are typically used by engineering students and practitioners. This book helps readers develop a better understanding of the history, implementation, and use-cases. Students are presented with varying control chart techniques, information, and roadmaps to ensure their control charts are operating efficiently and producing specification-confirming products. This is the essential text on the theories and applications behind statistical methods and control procedures.

This eight-chapter reference breaks information down into digestible sections and covers topics including:

&;      An introduction to the basics as well as a background of control charts

&;      Widely used and newly researched attributes of control charts, including guidelines for implementation

&;      The process capability index for both normal and non-normal distribution via the sampling of multiple dependent states

&;      An overview of attribute control charts based on memory statistics

&;      The development of control charts using EQMA statistics 

For a solid understanding of control methodologies and the basics of quality assurance, Introduction to Statistical Process Control is a definitive reference designed to be read by practitioners and students alike. It is an essential textbook for those who want to explore quality control and systems design.

About the Authors xi
Preface xiii
Acknowledgments xvii
1 Introduction and Genesis
1(22)
1.1 Introduction
1(2)
1.2 History and Background of Control Charts
3(2)
1.3 What Is Quality and Quality Improvement?
5(4)
Types of Quality-Related Costs
7(2)
1.4 Basic Concepts
9(10)
1.4.1 Descriptive Statistics
9(5)
1.4.2 Probability Distributions
14(1)
Continuous Probability Distributions
14(4)
Discrete Probability Distributions
18(1)
1.5 Types of Control Charts
19(2)
1.5.1 Attribute Control Charts
19(1)
1.5.2 Variable Control Charts
20(1)
1.6 Meaning of Process Control
21(2)
References
22(1)
2 Shewhart Type Control Charts for Attributes
23(34)
2.1 Proportion and Number of Nonconforming Charts
24(8)
2.1.1 Proportion of Nonconforming Chart (p-Chart)
25(3)
Variable Sample Size
28(1)
Improved p-Chart
29(1)
2.1.2 Number of Nonconforming Chart (np-Chart)
30(1)
2.1.3 Performance Evaluation Measures
30(2)
2.2 Number of Nonconformities and Average Nonconformity Charts
32(8)
2.2.1 Number of Nonconformities (c-) Chart
33(1)
2.2.2 Average Nonconformities (u-) Chart
34(4)
2.2.3 The Performance Evaluation Measure
38(1)
Dealing with Low Defect Levels
39(1)
2.3 Control Charts for Over-Dispersed Data
40(4)
2.3.1 Dispersion of Counts Data
40(1)
2.3.2 g-Chart and h-Chart
40(4)
2.4 Generalized and Flexible Control Charts for Dispersed Data
44(8)
2.4.1 The gc- and the gu-Charts
45(1)
2.4.2 Control Chart Based on Generalized Poisson Distribution
46(1)
Process Monitoring
47(1)
A Geometric Chart to Monitor Parameter θ
48(1)
2.4.3 The Q- and the T-Charts
49(3)
The OC Curve
52(1)
2.5 Other Recent Developments
52(5)
References
54(3)
3 Variable Control Charts
57(34)
3.1 Introduction
57(1)
3.2 X Control Charts
58(14)
3.2.1 Construction of X and R Charts
59(3)
3.2.2 Phase II Control Limits
62(1)
3.2.3 Construction of X Chart for Burr Distribution Under the Repetitive Sampling Scheme
63(9)
3.3 Range Charts
72(1)
3.4 Construction of S-Chart
72(3)
3.4.1 Construction of X Chart
74(1)
3.4.2 Normal and Non-normal Distributions for X and S-Charts
75(1)
3.5 Variance S2-Charts
75(16)
3.5.1 Construction of S2-Chart
76(1)
3.5.2 The Construction of Variance Chart for Neutrosophic Statistics
77(4)
3.5.3 The Construction of Variance Chart for Repetitive Sampling
81(6)
References
87(4)
4 Control Chart for Multiple Dependent State Sampling
91(30)
4.1 Introduction
91(1)
4.2 Attribute Charts Using MDS Sampling
91(7)
4.2.1 The np-Control Chart
92(6)
4.3 Conway--Maxwell--Poisson (COM-Poisson) Distribution
98(8)
4.4 Variable Charts
106(1)
4.5 Control Charts for Non-normal Distributions
107(2)
4.6 Control Charts for Exponential Distribution
109(2)
4.7 Control Charts for Gamma Distribution
111(10)
References
118(3)
5 EWMA Control Charts Using Repetitive Group Sampling Scheme
121(40)
5.1 Concept of Exponentially Weighted Moving Average (EWMA) Methodology
121(5)
5.2 Attraction of EWMA Methodology in Manufacturing Scenario
126(1)
5.3 Development of EWMA Control Chart for Monitoring Averages
127(1)
5.4 Development of EWMA Control Chart for Repetitive Sampling Scheme
127(1)
5.5 EWMA Control Chart for Repetitive Sampling Using Mean Deviation
128(11)
5.6 EWMA Control Chart for Sign Statistic Using the Repetitive Sampling Scheme
139(8)
5.7 Designing of a Hybrid EWMA (HEWMA) Control Chart Using Repetitive Sampling
147(14)
References
154(7)
6 Sampling Schemes for Developing Control Charts
161(30)
6.1 Single Sampling Scheme
161(1)
6.2 Double Sampling Scheme
162(3)
6.3 Repetitive Sampling Scheme
165(11)
6.3.1 When a Shift of μ1 = μ + kσ Occurs in the Process
169(7)
6.4 Mixed Sampling Scheme
176(4)
6.4.1 Mixed Control Chart Using Exponentially Weighted Moving Average (EWMA) Statistics
179(1)
6.5 Mixed Control Chart Using Process Capability Index
180(11)
6.5.1 Analysis Through Simulation Approach
187(1)
References
187(4)
7 Memory-Type Control Charts for Attributes
191(40)
7.1 Exponentially Weighted Moving Average (EWMA) Control Charts for Attributes
191(18)
7.1.1 Binomial EWMA Charts
192(2)
7.1.2 Poisson EWMA (PEWMA) Chart
194(2)
Performance Evaluation Measure
196(1)
Calculation of ARLs Using the Markov Chain Approach
196(6)
7.1.3 Other EWMA Charts
202(1)
Geometric EWMA Chart
202(2)
Conway--Maxwell--Poisson (COM--Poisson) EWMA Chart
204(5)
7.2 CUSUM Control Charts for Attributes
209(11)
7.2.1 Binomial CUSUM Chart
210(5)
7.2.2 Poisson CUSUM Chart
215(2)
7.2.3 Geometric CUSUM Chart
217(2)
7.2.4 COM--Poisson CUSUM Chart
219(1)
Performance Measure
219(1)
7.3 Moving Average (MA) Control Charts for Attributes
220(11)
7.3.1 Binomial MA Chart
221(2)
7.3.2 Poisson MA Chart
223(2)
7.3.3 Other MA Charts
225(1)
References
226(5)
8 Multivariate Control Charts for Attributes
231(20)
8.1 Multivariate Shewhart-Type Charts
231(12)
8.1.1 Multivariate Binomial Chart
231(2)
Choice of Sample Size
233(1)
8.1.2 Multivariate Poisson (MP) Chart
234(5)
8.1.3 Multivariate Conway--Maxwell--Poisson (COM--Poisson) Chart
239(4)
8.2 Multivariate Memory-Type Control Charts
243(3)
8.2.1 Multivariate EWMA Charts for Binomial Process
243(1)
Design of MEWMA Chart
244(1)
8.2.2 Multivariate EWMA Charts for Poisson Process
245(1)
8.3 Multivariate Cumulative Sum (CUSUM) Schemes
246(5)
8.3.1 Multivariate CUSUM Chart for Poisson Data
247(1)
References
248(3)
Appendix A Areas of the Cumulative Standard Normal Distribution 251(2)
Appendix B Factors for Constructing Variable Control Charts 253(2)
Index 255
MUHAMMAD ASLAM, Ph.D., is a Professor in the Department of Statistics at King Abdulaziz University at Jeddah, Saudi Arabia. He was awarded the "Research Productivity Award for the year" in 2012 by Pakistan Council for Science and Technology. He is the founder of neutrosophic statistical quality control and neutrosophic inferential statistics.

AAMIR SAGHIR, Ph.D., is a Professor in the Department of Mathematics at Mirpur University of Science and Technology. He received his Ph.D. in Statistics from Zhejiang University in China.

LIAQUAT AHMAD, Ph.D., is an Associate Professor in the Department of Statistics and Computer Science at the University of Veterinary and Animal Sciences, Lahore, Pakistan. He's taught Statistics for over 24 years at the Ph.D. and M. Phil levels.