7.Quality Control
Transcript of 7.Quality Control
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Engineering Mangement & Industial Economics (MEng
610)
Quality control
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What is Quality ?
Quality is a measure of the customer’s experience with the product or service with respect to specifications, requirements and expectations.
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QualityMeeting Customer’s Expectations Through Design and Mfg
ReliabilityEnsuring the Ability to Retain Quality Over a Period of Time
Product SupportProviding Necessary Support to Ensure Reliability
Competitive Edge
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Customer Supplier Sample Insp.
Sample Insp.
Customer Supplier Sample Insp.
Process control
Customer Supplier 100% Insp.
100%Insp.
Customer Supplier
Trends in quality control process
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Process of quality control - Sony produced TV’s in the U.S. and Japan. Sony had significantly
more complaints from the U.S. TV’s even though both factories produced zero defects. The Japanese factory manufactured “on target” using Taguchi techniques.
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A procedure for sentencing incoming batches or lots of items without doing
100% inspection
What is acceptance sampling?
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Why not 100% inspection?
-Very expensive
-Can’t use when product must be destroyed to test
-Handling by inspectors can itself induce defects
-Inspection becomes tedious in order to prevent defective items from slipping through inspection
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Purpose of acceptance sampling?
Assessing the average quality level of an incoming shipment or at the end of production and judge whether quality level is within the level that has been predetermined.
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The general approach
N(Lot) n
Count Number
ConformingAccept orReject Lot
Specify the sampling plan For a lot size N, determine
the sample size (s) n, and Select acceptance criteria for good lots Select rejection criteria bad lots
Accept the lot if acceptance criteria are satisfied Specify course of action if lot is rejected
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What's a good and bad lot ?
• Acceptance quality level (AQL)
The smallest percentage of defectives that will make the lot definitely acceptable. A quality level that is the base line requirement of the customer
• RQL or Lot tolerance percent defective (LTPD)
Impacts discriminating power of plan-
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d Ac ?
Reject lot
YesAccept
lot
Do 100% inspectio
n
Return lot to
supplier
Inspect all items in the sample
Defectives found = d
No
Take a randomized
sample of size n from the lot
N
Example : Single Sampling procedure
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Sampling plans are based on sample statistics and the theory says that since we inspect only a sample and not the whole lot, there is a chance of making an error.
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We need to consider two types of errors that result in wrong decisions
Type 1 Error No Error
No Error Type 2 Error
Reject Accept
Good lot
Bad lot
TRUTH
DECISION
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TYPE I ERROR = P (reject good lot)
or Producer’s risk
5% is common
TYPE II ERROR = P (accept bad lot)
or Consumer’s risk
10% is typical value
Errors and Risks
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-- Consumer’s risk
Receive shipment, assume good quality, actually bad quality
Beta () risk
= Prob (committing Type II error)
= Prob (accepting a lot at RQL quality level)
Producer’s & Consumer’s Risks
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CONTROL CHARTS
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In Control Process
Lets represent it with
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Process Shift
Shift in Avg Shift in Std Dev
Shift in Avg and Std Dev
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Detect using a control chart and Restore
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If we can understand variation that occurs in any process, then we begin to understand how to control that process by eliminating special causes of variation
For a manufacturing process, this means reducing the variability of the process by using a mechanism for minimizing the number of non-conformances
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Process Performance Measures
• Mean• Standard deviation
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Process Performance Measures
= ( y) / n Mean
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Process Performance Measures
Standard deviation
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Data Analysis
10 mm
9.57 9.91 9.45 9.85 9.899.75 9.62 9.71 10.59 10.239.54 10.07 9.77 10.26 9.979.89 10.38 9.36 10.71 9.849.99 10.36 9.83 9.80 10.4010.01 10.52 9.88 10.50 9.979.90 9.34 10.04 9.52 9.7910.66 9.93 9.89 10.16 9.499.48 10.33 9.90 10.27 9.949.78 9.67 9.89 10.58 10.20
Measured sample Averages
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Histogram
5 10 150
10
20
Data Points
Histogram
Freq
uenc
y C
ount
9.57 9.91 9.45 9.85 9.899.75 9.62 9.71 10.59 10.239.54 10.07 9.77 10.26 9.979.89 10.38 9.36 10.71 9.849.99 10.36 9.83 9.80 10.4010.01 10.52 9.88 10.50 9.979.90 9.34 10.04 9.52 9.7910.66 9.93 9.89 10.16 9.499.48 10.33 9.90 10.27 9.949.78 9.67 9.89 10.58 10.20
Measured sample Averages
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Common Causes Variation inherent in a process Can be eliminated only through improvements in
the system
Special Causes Variation due to identifiable factors Can be modified through operator or management
action
Causes of variation
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x xi
i1
n
n
xi x ) 2
n 1
Common Causes
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Assignable Causes
Average
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Process Tracking
0 10 20 30 40 50 60 70 80 90 1000
2
4
6
8
10
12
14
16
18
.
LCL=0.8236
CL=9.85
UCL=18.88
Data1_A
Individuals (X) Chart
Indi
vidu
als
Subgroup No.
• 1 Sigma = 68.25 % Yield • 2 Sigma = 95.5 % Yield • 3 Sigma = 99.73 % Yield • 6 Sigma = 99.99998 % Yield
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Control Charts
UCL
Nominal
LCL
1 2 3Samples
Assignable causes
likely
Sampling distribution
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Control Limits and Errors
UCL
LCL
Processaverage
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Control Limits and Errors
Type I error:Probability of searching for a cause when none exists
UCL
LCL
Processaverage
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Control Limits and Errors
Type I error:Probability of searching for a cause when none exists
Shift in process average
UCL
LCL
Processaverage
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Control Limits and Errors
Type I error:Probability of searching for a cause when none exists
Type II error:Probability of concludingthat nothing has changed
Shift in process average
UCL
LCL
Processaverage
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Type 1 Error No Error
No Error Type 2 Error
Alarm No Alarm
In-Control
Out-of-Control
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Calculating Type 1 error
1. Z = (UCL-CL) / ( / n)2. For this z find P( x UCL)3. Find 1 - P( x UCL)4. Multiply by 2 to get two tailed value
Relation between population and sampling distribution
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Calculating Type 2 error
1. Z1 = (UCL- shifted CL) / ( / n)2. Z2 = (LCL- shifted CL) / ( / n)3. Find P( x UCL) and P( x LCL) 4. P (type 2 error) = P( x UCL) - P( x LCL)
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Example 1 : If CL= 3, = 2, n = 5, find probability of type 1 error corresponding to a UCL of 5.
Example 2 : If CL shifts to 4 for the above process, find the probability of type 2 error.
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a CDF a CDF-4.000 0.00003 0.000 0.50000-3.900 0.00005 0.100 0.53983-3.800 0.00007 0.200 0.57926-3.719 0.00010 0.300 0.61791-3.700 0.00011 0.400 0.65542-3.600 0.00016 0.431 0.66667-3.500 0.00023 0.500 0.69146-3.400 0.00034 0.600 0.72575-3.300 0.00048 0.674 0.75000-3.291 0.00050 0.700 0.75804-3.200 0.00069 0.800 0.78814-3.100 0.00097 0.900 0.81594
a CDF a CDF-3.090 0.00100 1.000 0.84134-3.000 0.00135 1.100 0.86433-2.900 0.00187 1.200 0.88493-2.800 0.00256 1.282 0.90000-2.700 0.00347 1.300 0.90320-2.600 0.00466 1.400 0.91924-2.576 0.00500 1.500 0.93319-2.500 0.00621 1.600 0.94520-2.400 0.00820 1.645 0.95000-2.326 0.01000 1.700 0.95543-2.300 0.01072 1.800 0.96407-2.200 0.01390 1.900 0.97128-2.100 0.01786 1.960 0.97500-2.000 0.02275 2.000 0.97725
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a CDF a CDF
-1.960 0.02500 2.100 0.98214-1.900 0.02872 2.200 0.98610-1.800 0.03593 2.300 0.98928-1.700 0.04457 2.326 0.99000-1.645 0.05000 2.400 0.99180-1.600 0.05480 2.500 0.99379-1.500 0.06681 2.576 0.99500-1.400 0.08076 2.600 0.99534-1.300 0.09680 2.700 0.99653-1.282 0.10000 2.800 0.99744-1.200 0.11507 2.900 0.99813-1.100 0.13567 3.000 0.99865-1.000 0.15866 3.090 0.99900-0.900 0.18406 3.100 0.99903
a CDF a CDF
-0.800 0.21186 3.200 0.99931-0.700 0.24196 3.291 0.99950-0.674 0.25000 3.300 0.99952-0.600 0.27425 3.400 0.99966-0.500 0.30854 3.500 0.99977-0.431 0.33333 3.600 0.99984-0.400 0.34458 3.700 0.99989-0.300 0.38209 3.719 0.99990-0.200 0.42074 3.800 0.99993-0.100 0.46017 3.900 0.99995
4.000 0.99997