Statistical Process Control (SPC)
Quality Control (QC) Control – the activity of ensuring conformance to requirements and taking corrective action w...
Quality Control (QC) Control – the activity of ensuring conformance to requirements and taking corrective action when necessary to correct problems Importance
Daily management of processes Prerequisite to longer-term improvements
Designing the QC System
Quality Policy and Quality Manual
Contract management, design control and purchasing Process control, inspection and testing Corrective action and continual improvement Controlling inspection, measuring and test equipment (metrology, measurement system analysis and calibration) Records, documentation and audits
Example of QC: HACCP System 1. 2. 3. 4. 5. 6. 7.
Hazard analysis Critical control points Preventive measures with critical limits for each control point Procedures to monitor the critical control points Corrective actions when critical limits are not met Verification procedures Effective record keeping and documentation
Inspection/Testing Points Receiving
inspection In-process inspection Final inspection
Acceptance Sampling Lot received for inspection Sample selected and analyzed Results compared with acceptance criteria
Accept the lot Send to production or to customer
Reject the lot Decide on disposition 7
Pros and Cons of Acceptance Sampling
Arguments for: Provides an assessment of risk Inexpensive and suited for destructive testing Requires less time than other approaches Requires less handling Reduces inspector fatigue
Arguments against: Does not make sense for stable processes Only detects poor quality; does not help to prevent it Is non-value-added Does not help suppliers improve
In-Process Inspection
What to inspect?
Where to inspect?
Key quality characteristics that are related to cost or quality (customer requirements) Key processes, especially high-cost and value-added
How much to inspect?
All, nothing, or a sample 9
Economic Model C1 = cost of inspection and removal of nonconforming item C2 = cost of repair p = true fraction nonconforming Breakeven Analysis: p*C2 = C1 If p > C1 / C2 , use 100% inspection If p < C1 / C2 , do nothing 10
Human Factors in Inspection complexity defect rate repeated inspections inspection rate
Inspection should never be a means of assuring quality. The purpose of inspection should be to gather information to understand and improve the processes that produce products and services.
Metrology - Science of Measurement Accuracy - closeness of agreement between an observed value and a standard Precision - closeness of agreement between randomly selected individual measurements
Repeatability and Reproducibility Repeatability (equipment variation) – variation in multiple measurements by an individual using the same instrument. Reproducibility (operator variation) variation in the same measuring instrument used by different individuals
Repeatability and Reproducibility Studies
Quantify and evaluate the capability of a measurement system Select m operators and n parts Calibrate the measuring instrument Randomly measure each part by each operator for r trials Compute key statistics to quantify repeatability and reproducibility
Reliability and Reproducibility Studies(2) Measurement (M) made by Operators (i from 1 to m) on Parts (j from 1 to n) in Trials (k from 1 to r) ⎞ ⎛ ⎜ ∑∑ M ijk ⎟ ⎟ ⎜ j k ⎠ average for each operator ⎝ xi = n⋅r xD = max( xi ) − min ( xi ) difference (range) of operator averages i
i
R ij = max( M ijk ) − min ( M ijk ) range for each part for each operator k
k
⎞ ⎛ ⎜ ∑ Rij ⎟ ⎟ ⎜ j ⎠ average range for each operator ⎝ Ri = n ⎞ ⎛ ⎜ ∑ Ri ⎟ ⎠ average range of all R =⎝ i m
Reliability and Reproducibility Studies(3) Control limit of ranges Rij = D4 ⋅ R Use number trials (r) for n in table. Check for randomness of errors. Repeatability or Equipment Variation EV = K1 ⋅ R
K1 is a constant tied to # of trials
Reproducibility or operator (appraisal) variation ⎛ EV 2 ⎞ ⎟⎟ K 2 is a constant tied to # of operators AV = (K 2 ⋅ xD ) − ⎜⎜ n r ⋅ ⎠ ⎝ Repeatability and Reproducibility 2
R&R =
(EV )2 + ( AV )2
Results are in actual units measured. Customary to express as percentages. Under 10% - Acceptable 10 - 30% - ? based on importance and repair cost Over 30% - Unacceptable
R&R Constants Number of Trials K1 Number of Operators K2
2
3
4
5
4.56 3.05 2.50 2.21 2 3 4 5 3.65 2.70 2.30 2.08
R&R Evaluation Under
10% error - OK 10-30% error - may be OK over 30% error - unacceptable
R&R Example
R&R Study is to be conducted on a gauge being used to measure the thickness of a gasket having specification of 0.50 to 1.00 mm. We have three operators, each taking measurement on 10 parts in 2 separate trials.
x 1 = 0 . 830 x
2
= 0 . 774
x
3
= 0 . 829
R
1
= 0 . 037
R
2
= 0 . 034
R
3
= 0 . 017
Calibration Calibration - comparing a measurement device or system to one having a known relationship to national standards Traceability to national standards maintained by NIST, National Institute of Standards and Technology
Statistical Process Control (SPC) A methodology for monitoring a process to identify special causes of variation and signal the need to take corrective action when appropriate SPC relies on control charts
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Common Causes
Special Causes
Histograms do not take into account changes over time.
Control charts can tell us when a process changes
Control Chart Applications Establish
state of statistical
control Monitor a process and signal when it goes out of control Determine process capability
27
Commonly Used Control Charts Variables
data
x-bar
and R-charts x-bar and s-charts Charts for individuals (x-charts) Attribute
data
For
“defectives” (p-chart, np-chart) For “defects” (c-chart, u-chart) 28
Developing Control Charts 1.
Prepare
2.
Choose measurement Determine how to collect data, sample size, and frequency of sampling Set up an initial control chart
Collect Data
Record data Calculate appropriate statistics Plot statistics on chart
Next Steps 3.
Determine trial control limits
4.
Center line (process average) Compute UCL, LCL
Analyze and interpret results
Determine if in control Eliminate out-of-control points Recompute control limits as necessary
Typical Out-of-Control Patterns
Point outside control limits Sudden shift in process average Cycles Trends Hugging the center line Hugging the control limits Instability
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Shift in Process Average
Identifying Potential Shifts
Cycles
Trend
Final Steps 5.
Use as a problem-solving tool
6.
Continue to collect and plot data Take corrective action when necessary
Compute process capability
Process Capability
Capability Indices
UTL − LTL 6σ if C p ≥ 1 is defined as capable (1.5 more often the minimum)
Cp =
Example : Part specification is 10.75mm ± .25mm σ = 0.0868mm
11.00−10.50 Cp = = 0.96 6• 0.0868
Process Capability (2) UTL − µ 3σ µ − LTL C pl = 3σ C pk = min (C pl , C pu ) C pu =
C pu =
Cpl =
C pk = C p (1 − K ) where K =
2⋅ µ −T
11.0 − 10.7171 = 1.086 3 • 0.0868
10.7171−10.5 = 0.834 3• 0.0868
Tolerance Example : same as above, but assume process is centered at 10.7171mm
Cp
C pm = 1+
(µ − T )
2
T is the Target
σ2 0.960
C pm =
(10.7171 − 10.75)
2
1+
0.8682
= 0.8977
Capability Versus Control Control Capability Capable