jf608: quality control - unit 3
DESCRIPTION
Unit 3 control chart for variablesTRANSCRIPT
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www.themegallery.com
UNIT 3 :UNIT 3 :CONTROL CHART FOR VARIABLES
© Mechanical Engineering Department
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LOGOOUTLINEOUTLINE
Introduction
Variation
Purpose of Control Charts
Patterns in Control Charts
X – R Charts
X – S Charts
Process Capability
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LOGOINTRODUCTIONINTRODUCTION
CONTROL CHART
“It is a statistical tool used to distinguish between variation in a process resulting from common cause
& special cause.
Its presents graphical display of process stability or instability over a time.”
OR
Control charts are particularly useful for monitoring quality and giving early warnings that a process may be going “Out of Control” and on its way to
producing defective parts.
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LOGOVARIATION
“The variation concept is a law of nature in that no two natural items
in any category are the same”
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LOGOVARIATION
The variation may be quite large and easily noticeable
The variation may be very small. It may appear that items are identical; however, precision instruments will show difference
The ability to measure variation is necessary before it can be controlled
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LOGOVARIATION
There are three categories of variation in piece part production:
1. Within-piece variation: Surface
2. Piece-to-piece variation: Among pieces produced at the same time
3. Time-to-time variation: Difference in product produced at different times of the day
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LOGOVARIATION
Materials
Tools
Operators Methods Measurement Instruments
HumanInspectionPerformance
EnvironmentMachines
Sources of Variation in production processes:
INPUTSINPUTS PROCESSPROCESS OUTPUTSOUTPUTS
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LOGOPURPOSE OF CONTROL CHARTMonitor process variation over a timeDifferentiate between special cause and common cause
variationsAssess the effectiveness of changes to improve a
processCommunicate how a process performed during specific
period Improve productivity of process Prevent defects in process Prevent unnecessary process adjustmentsCollect diagnostic informationCollect information about process capability
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LOGOTYPES OF CONTROL CHARTS
There are two main categories of Control Charts, those that display attribute data,and those that display variables data.
Attribute Data: This category of Control Chart displays data that result from counting the number of occurrences or items in a single category of similar items or occurrences. These “count” data may be expressed as pass/fail, yes/no, or presence/absence of a defect.
Variables Data: This category of Control Chart displays values resulting from the measurement of a continuous variable. Examples of variables data are elapsed time, temperature, and radiation dose.
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LOGOTYPES OF CONTROL CHARTS
Variable datax-bar and R-charts
x-bar and s-charts
Charts for individuals (x-charts)
Attribute dataFor “defectives” (p-chart, np-chart)
For “defects” (c-chart, u-chart)
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LOGOTYPES OF CONTROL CHARTS
Continuous Numerical Data
Categorical or Discrete Numerical Data
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LOGOCONTROL CHARTS ELEMENTS1. Title. The title
briefly describes the information which is displayed.
2. Legend. This is information on how and when the data were collected.
3. Data Collection Section. The counts or measurements are recorded in the data collection section.
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LOGOCONTROL CHARTS ELEMENTS4a. The Upper Graph4b. The Lower Graph5. Y-Axis. This axis
reflects the magnitude of the data collected.
6. X-Axis. This axis displays the chronological order in which the data were collected.
7. Control Limits (CL). Control limits are set at a distance of 3 sigma above and 3 sigma below the centerline.
8. Centerline. This line is drawn at the average or mean value of all the plotted data.
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LOGOCONSTRUCTING AN X-BAR AND R CHART
The X-Bar and R Control Chart is used with variables data when subgroup or sample size is between 2 and 15. The steps for constructing this type of Control Chart are:
Step 1 - Determine the data to be collected. Decide what questions about the process you plan to answer. Refer to the Data Collection module for information on how this is done.
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LOGOCONSTRUCTING AN X-BAR AND R CHART
Step 2•Collect and enter the data by subgroup.•A subgroup is made up of variables data that represent a characteristic of a product produced by a process.•The sample size relates to how large the subgroups are.
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LOGOCONSTRUCTING AN X-BAR AND R CHART
Step 3•Calculate and enter the average for each subgroup.•Use the formula below to calculate the average (mean) for each subgroup
nxxxx n.....21
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LOGOCONSTRUCTING AN X-BAR AND R CHART
Step 4•Calculate and enter the range for each subgroup. •Use the following formula to calculate the range (R) for each subgroup.
valuevalueR minmax
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LOGOCONSTRUCTING AN X-BAR AND R CHART
Step 5 - Calculate the grand mean of the subgroup’s average. The grand mean of the subgroup’s average (X-Bar) becomes the centerline for the upper plot.
Where:
subgroupsofnumberthekaveragessubgroupeachforaveragethex
averagessubgroupindividualallofmeangrandthex
kxxxxx n.......321
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LOGOCONSTRUCTING AN X-BAR AND R CHART
Step 6 - Calculate the average of the subgroup ranges. The average of all subgroups becomes the centerline for the lower plotting area.
Where:
subgroupsofnumbertheksubgroupeachforrangeindividualtheRsubgroupsallforrangestheofaveragetheR
i
kRRRR n.........21
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LOGOCONSTRUCTING AN X-BAR AND R CHARTStep 7 - Calculate the upper control limit (UCL) and lower
control limit (LCL) for the averages of the subgroups. To find the X-Bar control limits, use the following formula:
NOTE: Constants, based on the subgroup size (n), are used in determining control limits for variables charts.
RAXLCL
RAXUCL
x
x
2
2
n A2 n A2 n A2
2 1.880 7 0.419 12 0.266
3 1.023 8 0.373 13 0.249
4 0.729 9 0.337 14 0.235
5 0.577 10 0.308 15 0.223
6 0.483 11 0.285
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LOGOCONSTRUCTING AN X-BAR AND R CHARTStep 8 - Calculate the upper control limit for the ranges. When
the subgroup or sample size (n) is less than 7, there is no lower control limit. To find the upper control limit for the ranges, use the formula:
NOTE: Constants, based on the subgroup size (n), are used in determining control limits for variables charts.
n D4 n D4 n D4
2 3.267 7 1.924 12 1.717
3 2.574 8 1.864 13 1.693
4 2.282 9 1.816 14 1.672
5 2.114 10 1.777 15 1.653
6 2.004 11 1.744
RDLCL
RDUCL
R
R
3
4
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LOGOCONSTRUCTING AN X-BAR AND R CHART
Step 9•Select the scales and plot the control limits, centerline, and data points, in each plotting area. •The scales must be determined before the data points and centerline can be plotted. •The scales for both the upper and lower plotting areas should allow for future high or low out-ofcontrol data points.
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LOGOCONSTRUCTING AN X-BAR AND R CHART
Step 10 - Provide the appropriate documentation. Each Control Chart should be labeled with who, what, when, where, why, and how information to describe where the data originated, when it was collected, who collected it, any identifiable equipment or work groups, sample size, and all the other things necessary for understanding and interpreting it. It is important that the legend include all of the information that clarifies what the data describe.
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LOGOCONSTRUCTING AN X-BAR AND R CHART
Sample Size Mean Factor Upper Range Lower Range n A2 D4 D3
2 1.880 3.268 0
3 1.023 2.574 0
4 0.729 2.282 0
5 0.577 2.115 0
6 0.483 2.004 0
7 0.419 1.924 0.076
8 0.373 1.864 0.136
9 0.337 1.816 0.184
10 0.308 1.777 0.223
12 0.266 1.716 0.284
CONTROL CHART FACTORS
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LOGOCONSTRUCTING AN X-BAR AND R CHART
EXERCISE 1: A team collected the variables data recorded in the table below.
Use these data to answer the following questions and plot a Control Chart:1. What type of Control Chart would you use with these data?2. Why?3. What are the values of X-Bar for each subgroup?4. What are the values of the ranges for each subgroup?5. What is the grand mean for the X-Bar data?6. What is the average of the range values?7. Compute the values for the upper and lower control limits for both the upper and lower plotting areas.8. Plot the Control Chart.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
X1 6 2 5 3 2 5 4 7 2 5 3 6 4 5 3 6
X2 5 7 6 6 8 4 6 4 3 5 1 4 3 4 4 2
X3 2 9 4 6 3 8 3 4 7 2 6 2 6 6 7 4
X4 7 3 2 7 5 4 6 5 1 6 5 2 6 2 3 4
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LOGOCONSTRUCTING AN X-BAR AND R CHART
EXERCISE 1 ANSWER KEY:1. X-Bar and R.2. There is more than one measurement within each subgroup.5. Grand Mean of X = 4.526. Average of R = 4.387. UCLx = 7.71 LCLx = 1.33 UCLR = 10.0 LCLR = 0
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LOGOCONSTRUCTING AN X-BAR AND R CHART
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LOGOX-BAR AND R CHART FORMULA
1 1
g g
i i
i i
i
i
X RX and R
g g
where
X average of subgroup averages
X average of the ith subgroup
g number of subgroups
R average of subgroup ranges
R range of the ith subgroup
Central Lines are obtained using:
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LOGOX-BAR AND R CHART FORMULA
3 3
3 3
R RX X
R RX X
X
R
UCL X UCL R
LCL X LCL R
where
UCL=upper control limit
LCL=lower control limit
population standard deviation of the subgroup averages
population standard deviation of the range
If σ are known, control limits are obtained using :
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LOGOX-BAR AND R CHART FORMULA
If σ are unknown, control limits are obtained using :
2 4
2 3
RX
RX
UCL X A R UCL D R
LCL X A R LCL D R
NOTE :A2,D3 and D4 are factors that vary with the subgroup size and are found in Table.
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LOGOX-BAR AND R CHART FORMULA
Revised Central LinesRevised Central Lines
d dnew new
d d
d
d
d
X X R RX and R
g g g g
where
X discarded subgroup averages
g number of discarded subgroups
R discarded subgroup ranges
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LOGOX-BAR AND R CHART FORMULA
Standard ValuesStandard Values
00 0 0
2
new newR
X X R R andd
0 0 2 0
0 0 1 0
RX
RX
UCL X A UCL D
LCL X A LCL D
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LOGOCONSTRUCTING AN X-BAR AND R CHART
Sample Std. Deviation Chart (x - s control chart ) Both R and s measure dispersion of
data R chart - simple, only use XH
(highest) and XL(lowest) s chart - more calculation - use ALL
xi’s more accurate, need calculate sub-group sample standard deviation
When n < 10 R chart s chart n 10 - s chart better , R not
accurate any more
sAXUCL 3x
sAXLCL 3x
sBUCL 4s sBLCL 3s
44
oo C
sCs
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LOGOX-BAR AND S CHART FORMULA
For subgroup sizes >=10, an s chart is more accurate than an R Chart. Control limits are given by:
1 1
3 4
3 3
g gi ii i
sX
sX
s Xs X
g g
UCL X A s UCL B s
LCL X A s LCL B s
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LOGOX-BAR AND S CHART FORMULA
0
00 0
4
0 0 6 0
0 0 5 0
4 5 6, , ,
dnew
d
dnew
d
sX
sX
d
X XX X
g g
s s ss s
g g c
UCL X A UCL B
LCL X A LCL B
where
s discarded subgroup averages
c A B B factors found in Table B
Revised Limits for s chartRevised Limits for s chart
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LOGOPATTERNS IN CONTROL CHARTS
Upper control limit
Target
Lower control limit
Normal behavior. Process is “in control.”
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LOGOPATTERNS IN CONTROL CHARTS
One plot out above (or below). Investigate for cause. Process is “out of control.”
Upper control limit
Target
Lower control limit
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LOGOPATTERNS IN CONTROL CHARTS
Upper control limit
Target
Lower control limit
Trends in either direction, 5 plots. Investigate for cause of progressive change.
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LOGOPATTERNS IN CONTROL CHARTS
Upper control limit
Target
Lower control limit
Two plots very near lower (or upper) control. Investigate for cause.
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LOGOPATTERNS IN CONTROL CHARTS
Upper control limit
Target
Lower control limit
Run of 5 above (or below) central line. Investigate for cause.
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LOGOPATTERNS IN CONTROL CHARTS
Upper control limit
Target
Lower control limit
Erratic behavior. Investigate.
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LOGOPATTERNS IN CONTROL CHARTS
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LOGOPATTERNS IN CONTROL CHARTS
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LOGOPATTERNS IN CONTROL CHARTS
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LOGOProcess is OUT of control if :
One or multiple points outside the control limits
Eight points in a row above the average value
Multiple points in a row near the control limits
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LOGOProcess is IN of control if :
The sample points fall between the control limits
There are no major trends forming, i.e.. The points vary, both above and below the average value.
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LOGOSTATE OF CONTROL
When the process is in control:
1. Individual units of the product or service will be more uniform
2. Since the product is more uniform, fewer samples are needed to judge the quality
3. The process capability or spread of the process is easily attained from 6ơ
4. Trouble can be anticipated before it occurs
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LOGOSTATE OF CONTROL
When the process is in control:
5. The % of product that falls within any pair of values is more predictable
6. It allows the consumer to use the producer’s data
7. It is an indication that the operator is performing satisfactorily
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LOGOPROCESS CAPABILITY (CP)
The range over which the natural variation of a process occurs as determined by the system of common causes
Measured by the proportion of output that can be produced within design specifications
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LOGOPROCESS CAPABILITY (CP)
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LOGOPROCESS CAPABILITY (CP)
Cp = Upper Specification - Lower Specification
6
A capable process must have a Cp of at least 1.0
Does not look at how well the process is centered in the specification range
Often a target value of Cp = 1.33 is used to allow for off-center processes
Six Sigma quality requires a Cp = 2.0
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LOGOPROCESS CAPABILITY (CP)
Example
Cp = Upper Specification - Lower Specification6
Insurance claims process
Process mean x = 210.0 minutesProcess standard deviation = .516 minutesDesign specification = 210 ± 3 minutes
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LOGOPROCESS CAPABILITY (CP)Calculation
Cp = Upper Specification - Lower Specification6
Insurance claims process
Process mean x = 210.0 minutesProcess standard deviation = .516 minutesDesign specification = 210 ± 3 minutes
= = 1.938213 - 2076(.516)
Process is capable
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LOGOPROCESS CAPABILITY INDEX (Cpk)
A capable process must have a Cpk of at least 1.0
A capable process is not necessarily in the center of the specification, but it falls within the specification limit at both extremes
Cpk = minimum of ,
UpperSpecification - xLimit
Lowerx - Specification
Limit
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LOGO
Example
New Cutting MachineNew process mean x = .250 inchesProcess standard deviation = .0005 inchesUpper Specification Limit = .251 inchesLower Specification Limit = .249 inches
PROCESS CAPABILITY INDEX (Cpk)
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LOGO
Calculation
New Cutting MachineNew process mean x = .250 inchesProcess standard deviation = .0005 inchesUpper Specification Limit = .251 inchesLower Specification Limit = .249 inches
Cpk = = 0.67.001
.0015
New machine is NOT capable
Cpk = minimum of ,(.251) - .250
(3).0005.250 - (.249)
(3).0005
Both calculations result in
PROCESS CAPABILITY INDEX (Cpk)
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LOGO
Interpreting Cpk
Cpk = negative number
Cpk = zero
Cpk = between 0 and 1
Cpk = 1
Cpk > 1
PROCESS CAPABILITY INDEX (Cpk)
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LOGOEXERCISE
Dalam suatu proses pengeluaran sebuah mentol lampu, didapati purata jangka hayat µ=900jam dan σ=77jam. Nilai norminal bagi julat kelegaan adalah 1000 jam dengan spesifikasi atas = 1200jam dan spesifikasi bawah = 800jam. Jika anda sebagai seorang pengurus operasi, tentukan adakah proses berupaya untuk menghasilkan spesifikasi mentol lampu. Huraikan dapatan anda.
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