Assist. Prof. Figen (KOSEBALABAN) TOKATLI
Izmir Institute of Technology figentokatli@iyte.edu.tr
EM 535
STATISTICAL QUALITY CONTROL
Three hours lecture, three credit hours
(3-0) 3
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Prerequisites : EM 505 Statistics for Engineers.
Text Book :
Montgomery, Douglas C., Introduction to Statistical Quality Control , John Wiley and Sons, 5th ed., 2004.
Reference Book(s):
Oakland, John S., Statistical Process Control , Butterworth-Heinemann, 3rd ed., 1996
Course Description:
Statistical process monitoring (SPM) is a method of monitoring and controlling the process variation through statistical analysis. The purpose is to detect abnormality in the process and identify the causes behind this variation.
This course is about statistical techniques to monitor and control process and quality variables. The objective is to give the students the idea about SPM and how it can improve quality and productivity of a process. Therefore, statistical tools introduced in this course can be used with various kinds of data from different disciplines.
Extensive statistical knowledge is not required. Basic statistical concepts such as probability distributions hypothesis testing and confidence intervals will be covered. After introducing univariate control charts for attribute variables and process variables (Shewhart charts, p and u charts,etc.), the autocorrelation and crosscorrelation in process data and how they affect the construction of control charts will be given. Then, multivariate SPM charts will be introduced.
Also, the basics of design of experiments will be covered.
Course Outline
I. Introduction
I1. What is Statistical Process Monitoring (SPM)
I2. Continuous and discrete probability distributions, hypothesis tests and confidence intervals.
I3. Tools of Statistical Process Control: Histogram, pareto chart, cause and effect diagram, scatter diagram, control chart
II. Univariate Control Charts
II.1. Control Charts for Attributes: Charts for defects and nonconformities
II.2. Control Charts for Variables: X, Range, and S charts
II.3. Cumulative sum (CUSUM) and EWMA Charts
II.5. Process Capability Analysis
III. Autocorrelation in Data
III.1. Definitions
III.2. Diagnosis and effects of Autocorrelation
III.4. Control Charts for Autocorrelated data
IV. SPM of Multivariate Process
IV.1. Why do we need Multivariate SPM Techniques?
IV2. Multivariate SPM Charts
V. Design of Experiments
V.1. What is Experimental Design?
V.2. Experiments with one Factor
V.3. Factorial Experiments (more than one factor)