insy 7200
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INSY 7200
Slip Casting Neural Net / Fuzzy Logic Control System
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Slip Casting of Sanitary Warewarm slip is piped throughout plantslip is poured into moistened moldexcess slip is drained from moldcasting takes from 50 to 70 minutesmold is opened and cast piece is air driedpiece is spray glazedpiece is kiln fired
Patriottoilet byEljer
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Slip Casting of Sanitary Ware• Casting involves many controllable and
uncontrollable variables– raw material variables– product design variables– ambient conditions– human aspects
• Casting imperfections can cause cracks or slumps which generally do not manifest until after glazing and firing
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Process Variables
• Raw Material– slip viscosity– slip thixotropy– slip
temperature– particle size
• Product Design– shape
complexity– size
• Ambient Conditions– temperature– humidity
• Human– operator skill
and experience• Other
– plaster mold condition
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General Objectives of Controlling the Slip Casting Process
• Reduce post-firing cracks which require rework or scrapping
• Analyze short term and long term trends• Optimize daily setting of controllable variables• Optimize long term setting of raw material variables• Perform “what-if” analysis without expensive test
casts• Enhance training of new engineers and technicians
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Primary Specific Objectives of Controlling the Slip Casting Process
• Set daily controllable variables
– SO4 content of slip
– Cast time for each bench• Minimize cracks and slumps using surrogate
measure of “moisture gradient”• Minimize cast time by maximizing
“cast rate”
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Possible Approaches to Control of Slip Casting
• Daily test casts and adjustments to controllable variables
• Foreman expertise and judgment• Theoretic models• Expert system• Statistical models (e.g., regression)• Artificial neural networks• Optimization algorithms
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Hybrid Computational Approach
• Data repository of relevant daily activity• Non-linear neural network models for
– Estimating cast rate– Estimating resulting moisture gradient
• Optimization algorithm to select best combination of high cast rate and low moisture gradient
• Fuzzy expert system to customize plant cast time to individual benches
• Training cases for guided “what-if” analysis
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System Architecture
User Interface - Visual Basic
Data -Access
Cast RateNeural Net
Moisture GradientNeural Net
Fuzzy ExpertSystem - TilShell, C
TrainingModule
Brainmaker, C
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Data Repository• Create data base of daily process data using
existing handwritten records (tables, control charts)
• Perform calculations (e.g., moisture gradient)• Purify records• Analyze trends graphically and numerically• Automatic generation of control charts
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Data Input Screen
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Graphing Options
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Selecting a Graphing Option
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Typical Control Chart Graph
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Dual Predictive Networks
Slip Temp
.
..
.
.. ...
MeanMoistureGradient
CastRate
Cast Time
BR10
BR100
IR
BU
Gelation
Filtrate
Cake Wt
H2O RetSO4
Plant Tempand Humidity
(8)
(8) (8)
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Neural Networks Accuracy
0.26
0.31
0.36
0.41
0.46
0.51
0.56
0.61
0.66
1 21 41 61 81 101 121 141 161 181
OBSERVATION NUMBER
CA
ST
RA
TE
PREDICTED TARGET
Network Data ErrorTrain
ErrorTest
ErrorFinal
CastRate
952 0.0162 0.0167 0.0157
MoistureGradient
367 0.0029 0.0036 0.0025
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Typical Analysis Graphs
0.32
0.325
0.33
0.335
0.34
0.345
0.35
0.355
0.36
0.365
0.37
65 70 75 80 85 90 95 100 105 110
PLANT TEMPERATURE
MAXIMUM
MEAN
MINIMUM
Cast Rate as a Function of Plant Temperature
0
0.01
0.02
0.03
0.04
0.05
0.06
86 88 90 92 94 96 98 100
SLIP TEMPERATURE
MAXIMUM
MEAN
Moisture Gradient as a Function of Slip Temperature
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Using the Predictive Models
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Process Optimization• Select best combination of variables which
can be controlled daily • Engineer inputs values of all other variables
that day• Optimization algorithm uses the neural
network predictions to find values of cast time and SO4 which yield both the smallest moisture gradient and the largest cast rate
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Using the Process Optimization Module
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Fuzzy Logic Expert System
• Plant temperature and humidity varies greatly from bench to bench
• Mold age varies greatly from bench to bench
• The plant setting of cast time from the Process Optimization Module needs to be customized to each bench
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What is an Expert System?
• Consists of qualitative rules elicited from human experts and / or induced from data
• Sample rules:
If the mold is old, the cast rate is slow.
If the temperature is low and the humidity is high, the mold is wet.
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Why is the Fuzzy Part Needed?To recommend cast time, variables must be translated
from qualitative to quantitative.
Compare Describing Temperature as Hot:
Regular Logic Fuzzy Logic
70 90Not Hot
8070 90
Hot
80
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Schematic of Expert System
Temperature
Humidity
Mold Age
RuleBase
MoldCondition
CastingRate
RuleBase
Casting Time
User Input
System Predicted
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Developing the Fuzzy Part
• Review of historic plant data to get ranges and distribution of temperature, humidity, mold age and cast rate
• Independent survey of plant ceramic engineers on rules
• Group discussion / modification of first cut rule base and membership functions
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Some Membership Functions
00.10.20.30.40.50.60.70.80.9
1
75 80 85 90 95 100 105
Plant Temperature
Mem
ber
ship Low Med High
00.10.20.30.40.50.60.70.80.9
1
40 45 50 55 60 65 70 75
Humidity
Mem
ber
ship Low Med High
00.10.20.30.40.50.60.70.80.9
1
0 1 2 3 4 5 6
Mold Age in Weeks
Mem
ber
ship New Mid Old
00.10.20.30.40.50.60.70.80.9
1
19 20 21 22 23 24 25
Cast Rate
Mem
ber
ship Low Med High
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Membership Function and Rules for Mold Condition
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7 8 9 10
Mold Condition
Me
mb
ers
hip
Very Dry
Dry Avg Wet Very Wet
AvgDry
AvgWet
Temp. Humidity / AgeLow Medium High
New Mid Old New Mid Old New Mid OldLow Dry Dry AvgWet Avg AvgWet VeryWet AvgWet Wet VeryWetMedium VeryDry Dry AvgWet Dry Avg Wet AvgDry AvgWet VeryWetHigh VeryDry VeryDry Avg VeryDry Dry AvgWet AvgDry AvgWet Wet
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System Software• Rule base and membership function developed
in TilShell by Togai Infralogic using standards - triangular membership functions, max / min composition and centroid defuzzification
• System control surface for both mold condition and casting time verified for smoothness and agreement with expert knowledge
• System compiled into C code and linked to the cast rate neural network and to the user interface
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Response Surface for Mold Condition
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Using the Expert System
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The Training Module
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Final Remarks
• A modular approach is needed for most real world complex systems
• The new computational techniques sound exotic but they can get the job done
• Combining quantitative and qualitative information can be accomplished rigorously
• Sometimes the least technically challenging parts (e.g., data repository, training module) hold great value
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