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262 Part IV: Quality Assurance3. VARIABLES CHARTSIdentify characteristics and uses of x – – R andx – – s charts. (Application)Body of Knowledge IV.B.3In this section we study various control charts for variables.Shewhart X – and R Control ChartPart IV.B.3Certain rules that are widely used in preparing Shewhart X – and R control chartsare:1. Take a series of samples from the process that is under investigation.Samples consisting of four or five items taken frequently are usuallygood. This is because:a. Rational subgroups or samples of size four or five are morecost-effective.b. If samples are larger than 10, the estimate of process standarddeviation obtained using the range is not very efficient. Moreoverthe R chart is also not very effective.c. With samples of size four or five, there are fewer chances foroccurrence of any special causes during the collection of a sample.It is commonly known that if the type of variation is changing(special cause versus common cause variation) the sample sizeshould be as small as possible so that the averages of samples donot mask the changes.EXAMPLE 19.3Let 5, 6, 9, 7, 8, 6, 9, 7, 6, and 5 be a random sample from a process under investigation.Find the sample mean and the sample range.Solution:Using the above data, we have5x = + 6 + 9 + 7 + 8 + 6 + 7 + 6 + 5= 59 .10R Max x Min x 9 5= 4.= ( ) ( )=ii
Chapter 19: B. Statistical Process Control 2632. Enough samples should be collected so that the major source ofvariation has an opportunity to occur. Generally, at least 25 samplesof size four or five are considered sufficient to give a good test forprocess stability.3. During the collection of data, a complete log of any changes in theprocess, such as changes in raw materials, operators, tools or anycalibration of tools, machines, and so on, must be maintained.Keeping a log is important for finding the special causes in a process(Duncan 1986).Calculation of Sample StatisticsThe sample statistics that need to be determined initially to prepare ShewhartX – and R control chart are the sample mean (x – ) and sample range (R). Thus, forexample, let x 1 , x 2 , . . . , x n be a random sample from the process under investigation.Then, we havex + x + ... + x1 2x =nn(19.2)R Max x Min x . (19.3)= ( ) ( )iiCalculation of Control LimitsStep 1. First calculate x – i and R i for the ith sample, for i = 1, 2, 3, . . . m, wherem is the number of samples collected during the study period.Step 2. CalculateR R + R + … +=R1 2mx + x + ... + x1 2x =mmm(19.4)(19.5)Part IV.B.3Step 3. Calculate the three-sigma control limits for the X – control chart:UCL = x + 3sˆxsˆ= x + 3nR= x + 3d n2= x+A R2(19.6)
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Chapter 19: B. Statistical Process Control 263
2. Enough samples should be collected so that the major source of
variation has an opportunity to occur. Generally, at least 25 samples
of size four or five are considered sufficient to give a good test for
process stability.
3. During the collection of data, a complete log of any changes in the
process, such as changes in raw materials, operators, tools or any
calibration of tools, machines, and so on, must be maintained.
Keeping a log is important for finding the special causes in a process
(Duncan 1986).
Calculation of Sample Statistics
The sample statistics that need to be determined initially to prepare Shewhart
X – and R control chart are the sample mean (x – ) and sample range (R). Thus, for
example, let x 1 , x 2 , . . . , x n be a random sample from the process under investigation.
Then, we have
x + x + ... + x
1 2
x =
n
n
(19.2)
R Max x Min x . (19.3)
= ( ) ( )
i
i
Calculation of Control Limits
Step 1. First calculate x – i and R i for the ith sample, for i = 1, 2, 3, . . . m, where
m is the number of samples collected during the study period.
Step 2. Calculate
R R + R + … +
=
R
1 2
m
x + x + ... + x
1 2
x =
m
m
m
(19.4)
(19.5)
Part IV.B.3
Step 3. Calculate the three-sigma control limits for the X – control chart:
UCL = x + 3sˆ
x
sˆ
= x + 3
n
R
= x + 3
d n
2
= x+
A R
2
(19.6)