manufacturing process a sequence of activities that is intended to achieve a result (juran)
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Manufacturing Process A sequence of activities that is intended to achieve a result (Juran). Quality of Manufacturing Process depends on Entry Criteria Task Definitions Validation Definitions Exit Criteria. Task Definitions. Entry Criteria. Exit Criteria. Validation Definitions. - PowerPoint PPT PresentationTRANSCRIPT
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Manufacturing Process
A sequence of activities that is intended to achieve a result (Juran).
Quality of Manufacturing Process depends on
• Entry Criteria• Task Definitions• Validation Definitions• Exit Criteria E
ntr
y C
rite
ria
Exi
t Cri
teri
a
Validation Definitions
Task Definitions
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Variation of Process Quality
• Outcomes of the process vary along the process life.
• The variation should follow a Normal Distribution with a
level of acceptable dispersion.
Causes of Variation
• Common Causes (Natural variation)Small, random forces that act continuously on the process.
• Special Causes (Assignable variation)
Extraneous to the process and interfere with the routine operation and normal dynamics of the process.
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Average ?
Standard Deviation ?
Variance?
Allowed tolerance
Allowed tolerance
Ideal specification
*Allowed tolerance* is not equal to
Design tolerance
Design tolerance
Spec Width?
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Average ?
Standard Deviation ?
Variance?
Ideal specification
Allowed tolerance
Allowed tolerance
*Allowed tolerance* is not equal to
Design tolerance
Design tolerance
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Objective
To determine whether a process is staying in control or is
potentially moving out of control at a given point of time --
Process Monitoring
SPC Procedure
• Periodically select a sample of items, inspect and note the
result
• Determine a type of variation cause related to the result
• Take remedy actions, relevant to sources of variation
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• Measures of Central Tendency
• Measures of Dispersion
• Population Distribution
• Sampling Distribution
• Central Limit Theorem
• Normal Distribution (Average, Standard Deviation)
• Standardized Normal Distribution; Z (0,1)
• Level of Confidence Interval
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Data Collection and Plotting points
Sampling Distribution
Central Limit Theorem
Control LimitsRandomness
Positions of Upper and Lower Control Limits
Concept
Calculations
Control Limits Adjustment
Significant change of the process Signals of going “out of control”
Risks of Error: Type I error & Type II error
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Variables Charts
• X-bar Charts
• R Charts
What are Variables Measurement
in a process ?
Attributes Charts
• P Chart
• C Chart
What are AttributesMeasurement
in a process ?
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X-bar Charts
To monitor process central tendency based on estimated process mean
R Charts
To monitor process variability based on estimated process range
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P Chart
To monitor proportion or fraction of process in a category
C Chart
To monitor count, or number of occurrences
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• original process• a change in process mean• a change in process variation• a change in both mean & variation
UCL
LCL
CL
Back to 25
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Center line, CL = Average of Sample Averages,
For 3-Sigma* limits,
Upper Control Limit, UCL
Lower Control Limit, LCL
Center Line, CL
X bar, sampling average
Sigma* = sigma of sampling distribution
X
RXUCL = + A2
LCL = - A2
RXTable 14.4 Control Chart Constants
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Upper Control Limit, UCL
Lower Control Limit, LCL
Center Line, CL
R, sampling range
Sigma* = sigma of sampling distribution
Center line, CL = Average of Sample Ranges,
For 3-Sigma* limits, RUCL = D4
LCL = D3R
R
Table 14.4 Control Chart Constants
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Sample Size(n) A2 D4D3
10
3
4
5
2.57501.023
2.28200.729
1.7770.2230.308
2.11400.577
…
…
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Center Line, p bar
Upper Control Li
mit, UCLp
Lower Control
Limit, LCLp
Fraction defective, p
Center line, CL = Average of Sample proportion,
For 3-Sigma* limits,
p
UCL = + 3 p p (1- )pn
p p (1- )pn
LCL = - 3
Sigma* = sigma of sampling distribution
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Upper Control
Limit, UCLc
Lower Control Li
mit, LCLc
Center Line, c bar
Number of defective, c
Center line, CL = Average number of characteristics,
For 3-Sigma* limits,
c
UCL = + 3 cc LCL = - 3 cc
Sigma* = sigma of sampling distribution
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To get Estimated process parameters :
1) How do we know the estimators are good enough?
2) How many samples should we need, and
How many groups of them?
3) What factors do we consider?
X p cR
Let’s discuss this !!Let’s discuss this !!Let’s discuss this !!Let’s discuss this !!
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Sampling Basis: Concept of Rational sampling
• Homogenous items(Within-Groups and Among-Groups variations)
• Time-order Consecutive items
• Time-order Distributed items
Sample Size:
-- The most common “n” is 5
-- Large enough “n” to detect a defect count
X R
p c
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Sampling Frequency
Depends on the nature of process and Opportunity of a
ssignable variation exposure
Initial Number of Samples, m
To make sure that we are observing a stable process, pr
actically 20, or 30, 40 of “m” should be located within C
ontrol Limits.
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Easiness• Efforts
• Costs
Usefulness• Value of obtained information
• Company image
Then, which one we select, and Why So?
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• A point lies outside the control limits• Any 2 of 3 consecutive points fall in the same A zone• 4 out of 5 consecutive points fall in the same B zone• 8 or more consecutive points lie on the same side of CL• 8 or more consecutive points move continuously in the same
direction either upward or downward
UCL (3 Sigma*)
LCL (3 Sigma*)
CL
Parameter
Sigma* = sigma of sampling distribution
Zone B
Zone B
Zone A
Zone A2 Sigma*
2 Sigma*
1 Sigma*
1 Sigma*
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• Definition of a Stable Process
• Uses of Control Charts
Variable Charts
Attributes Charts
• Control Chart Restructuring: Why & When?
• Pre-Control
• Process Capability Study
• Process Improvement
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GREEN ZONE
USL
LSL
Target, CL
X
Red Zone
Red Zone
Yellow Zone
Yellow Zone
Initial Set-up: All 5 consecutive items must fall in Green zone
Periodically check: 2 items at a time
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To See No. 10
Quiz !?!
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• Run Diagram VS Control Charts
• Specifications VS Control Limits
• Customer spec.
• Design spec.
• Detection VS Prevention Approach
• Points of control
• Rapid feedback system
• “Quality cannot be inspected into products”
Value of information?
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• Inspection and Measurement errors
• Human Error
• Instrument Error: Standard & Calibration
• Management & Shop-floor Responsibility
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In Control
Capable
Out of Control
Not Capable
IDEAL
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• Conformity of outputs
• Process Capability Index
The range over which the natural variation of a process occurs as
determined by the system of common causes, i.e., “what the
process can achieve under stable conditions”
• Quality Assurance and Acceptance Sampling
A method of measuring random samples of lots or batches of
products against predetermined standards
• Risks of Error: Producer’s Risk VS Consumer’s Risk
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Techniques• Failure Mode and Effects Analysis (FMEA)
Identification of all the ways in which a failure can occur, its e
ffect and seriousness estimation as well as corrective actions
recommendation
• Experimental Design or Design of Experiments (DOE)
Further study on Multi-Analysis of Variance (MANOVA)
• Taguchi Loss Function
Tolerance Design: The larger deviation from target the increa
singly larger losses incurred from variation allowed
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Management Frameworks
• Feigenbaum: SPC & Total Quality Control
• Deming and Total Quality Management (TQM)
• ISO 9000
• Six Sigma: Commitment of 3.4 ppm defect
SPC TQC CWQC TQM