IE 361 Module 12
EFC and SPM: One Thing Control Charting is Not
Reading: Section 3.6 of Statistical Quality Assurance Methods for
Engineers
Prof. Steve Vardeman and Prof. Max Morris
Iowa State University
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IE 361 Module 12
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EFC and SPC
In this short module, we try to make explicit the di¤erence between
"engineering control" (as understood, for example, by EE’s and ME’s) and
"SPC." Recall from Module 10 the basic insight that Shewhart "control
charting" is about process watching for purposes of change detection as
illustrated in the …gure below repeated from that module.
Figure: Statistical Process Monitoring
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EFC and SPM
Control Charts are NOT Process-Tweaking/Process-Guidance Tools
Engineering Feedback Control is process guidance/on-line adjustment as
represented in the …gure below, where repeatedly an output Y is compared
to a target value T and accordingly a change is ordered in some process
variable X .
Figure: Schematic of an Engineering Control Scheme
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EFC and SPM
Control Charts are NOT Process-Tweaking/Process-Guidance Tools
The …gures on panels 2 and 3 and the methodologies they represent are
NOT the same. Automatic online process adjustment is not the same
thing as process watching for purposes of change detection.
SPM and EFC are NOT Competing Technologies
both have their places
both can be badly done
both can contribute to variation reduction in an industrial process
neither is a "weak version" of the other
In many industrial applications, EFC creates the physical stability that
SPM monitors.
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IE 361 Module 12
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EFC and SPM
Control Charts are NOT Process-Tweaking/Process-Guidance Tools
Here are some contrasts between EFC and SPM
EFC
is "automatic" (digital or mechanical)
is compensation-oriented
expects process "drift"
is on-going "tweaking"
is typically computer-controlled
maintains optimal adjustment
is tactical
can exploit dynamic models
SPM
is often manual
is detection-oriented
expects "stability"
triggers "rare" interventions
typically alerts a human agent
warns of "special cause" changes
is strategic
warns of departure from stability
The technologies of EFC and SPM are quite di¤erent. SPM deals in
control charts. EFC deals in feedback algorithms that prescribe exactly
how adjustment variables are to be manipulated on the basis of process
data. "PID" control algorithms are a common variety of these (see
Section 3.6 of SQAME for a few details on PID control).
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An EFC Example (Paper Making)
From Section 3.6 of SQAME
Prof. Jobe worked with the Miami U. Paper Science Department on the
control of a paper-making machine. A schematic of that machine is
below. Fundamentally, a pump feeds pulp onto a moving conveyor where
the pulp dries to make paper.
Figure: Schematic of a Paper-Making Process
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An EFC Example (Paper Making)
From Section 3.6 of SQAME
Issues in developing an appropriate control algorithm included:
pulp mix thickness WILL vary, but pump speed can be used to
compensate,
this is NOT an SPC problem! (it is an automated compensation
problem),
target paper density is 70 g/ m2 ,
1 "tick" on pump dial changes density about .3 g/ m2 , and
time delay and potential for over-compensation/oscillation are serious
issues.
To remove the time delay issue, a 5-minute sampling/adjustment interval
was adopted. Through a series of experiments, an appropriate PID
control algorithm was developed (see SQAME exposition and problems
and the appendix to this module for details).
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IE 361 Module 12
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An EFC Example (Paper Making)
From Section 3.6 of SQAME
The following shows "uncontrolled"/baseline density data for 12 periods
and data from 6 periods after the …nal algorithm was implemented and
had been allowed to take e¤ect.
Figure: "Before" and "After" Paper Density
To this point, we have an EFC success story. SPM now could have a role
in monitoring for unexpected changes from this behavior.
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IE 361 Module 12
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Appendix: Some Details on the PID Controller for the
Paper Machine
A PID (proportional/integral/derivative) control algorithm for the paper
making example is
∆X (t ) = κ 1 ∆E (t ) + κ 2 E (t ) + κ 3 ∆2 E (t )
for
Y (t ) = measured paper density at time t
∆X (t ) = the knob position change (in "ticks") after observing Y (t )
E (t ) = the "error" at time t
= T (t )
∆E (t ) = E (t )
Y (t )
E (t
1)
and
∆2 E (t ) = ∆ (∆E (t )) = ∆E (t )
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IE 361 Module 12
∆E (t
1)
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Appendix: Some PID Control Details
The
"Integral" part of the algorithm, κ 2 E (t ), reacts to deviation from
target/o¤set,
"Proportional" part of the algorithm, κ 1 ∆E (t ), reacts to changes in
error (/level), and
"Derivative" part of the algorithm, κ 3 ∆2 E (t ), reacts to curvature on
plots of error.
Jobe’s …nal control algorithm was
∆X (t ) = .83∆E (t ) + 1.66E (t ) + .83∆2 E (t )
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Appendix: Some PID Control Details
The following table shows some hypothetical values for Y (t ) and
computation of corresponding values of ∆X (t ) for Jobe’s control
algorithm.
t
1
2
3
4
5
6
T (t )
70.0
70.0
70.0
70.0
70.0
70.0
Y (t )
65.0
67.0
68.6
68.0
67.8
69.2
E (t )
5.0
3.0
1.4
2.0
2.2
.8
Vardeman and Morris (Iowa State University)
∆E (t )
∆2 E (t )
∆X (t )
2.0
1.6
.6
.2
1.4
.4
2.2
.4
1.6
1.328
5.644
3.486
1.162
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