PDF Download Statistical Process Adjustment for Quality Control By Enrique del Castillo

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Statistical Process Adjustment for Quality Control By Enrique del Castillo

PDF Download Statistical Process Adjustment for Quality Control By Enrique del Castillo

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Sales Rank: #3449083 in Books
Published on: 2002-04-04
Original language: English
Number of items: 1
Dimensions: 9.61" h x .94" w x 6.34" l, 1.45 pounds
Binding: Hardcover
357 pages

Review
"It is definitely a book that I would gladly buy, and one that I would have no hesitation in recommending to fellow professionals." (IIE Transactions-Quality and Reliability Engineering, November 2005)

"…I think that this is a great book that is well worth its price…from those working in the manufacturing area…this book is a valuable resource." (Journal of the American Statistical Association, June 2004)

"...presents process adjustment techniques based on EPC methods and discusses them from the perspective of product quality control..." (SciTech Book News, Vol. 26, No. 2, June 2002)

"...the author attempts consolidation of [controlling process variables and monitoring product attributes] along technical tools...this book successfully achieves the intended purpose…useful…easy-to-read..." (Mathematical Reviews, 2003a)

"I like this book.... If you are a statistician interested in EPC or a process control engineer wondering how SPC relates to EPC, then this book is for you." (Technometrics, Vol. 45, No. 1, February 2003)

"...I like this book very much and it should make a useful addition to both academic and industrial provider libraries..." (Measurement & Control, February 2003)

"...fills the need for a comprehensive presentation of control theory at an elementary level..." (Zentralblatt Math, Vol.1002, No.02, 2003)

From the Back Cover
A comprehensive presentation of control theory for the SPC community

Quality control has become a major concern in today's competitive industrial environment, and industrial engineers are constantly seeking to make process adjustments that will optimize production efficiency and improve product quality. Statistical Process Adjustment for Quality Control fills the need for a comprehensive presentation of control theory at the elementary level, focusing on statistical methods used in process adjustment (Engineering Process Control Methods or EPC) and their relation to the classical methods of process monitoring, particularly those using SPC control charts. The author presents the severe effects of autocorrelated data on control chart performance and advocates the use of active adjustment methods for improving the quality of products and processes. He uses a detailed explanation of Deming's funnel experiment to illustrate the need for process adjustment when there are process dynamics, and presents an in-depth desciption of ARIMA models and Transfer Function models from a statistical point of view. He offers several adjustment strategies, including Minimum Variance Controllers, PID controllers, EWMA controllers, deadband policies, and constrained variance controllers. The book also offers integration strategies for SPC and EPC methods, discusses multivariate ARMAX models used for multivariate adjustment, and provides readers with a brief introduction to frequency domain and state-space methods.

Statistical Process Adjustment for Quality Control is a timely resource for students, industrial engineers, and applied statisticians in both academic and industrial settings. Unique features include:
* A strong focus on quality control of products and processes
* Broad coverage of SPC, adjustments, and time series under one cover
* Abundant examples using both real process data and simulations
* Detailed explanations on how to use SAS and MATLAB on an accompanying ftp site
* Coverage of spreadsheet simulation and optimization models in Microsoft(r) Excel
* Numerous chapter problems and detailed bibliography of relevant literature for further reading

About the Author
ENRIQUE del CASTILLO is an Associate Professor in the Department of Industrial Manufacturing and Engineering at the Pennsylvania State University.

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