INSY 7200 Slip Casting Neural Net / Fuzzy Logic Control System

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