1. INSY 7200 Slip Casting Neural Net / Fuzzy Logic Control System

2. 2 Slip Casting of Sanitary Ware Êwarm slip is piped throughout plant Ëslip is poured into moistened mold Ìexcess slip is drained from mold Ícasting takes from 50 to 70 minutes Îmold is opened and cast piece is air dried Ïpiece is spray glazed Ðpiece is kiln fired Patriot toilet by Eljer

3. 3 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

4. 4 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

5. 5 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

6. 6 Primary Specific Objectives of Controlling the Slip Casting Process Set daily controllable variables –SO 4 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”

7. 7 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

8. 8 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

9. 9 System Architecture User Interface - Visual Basic Data - Access Cast Rate Neural Net Moisture Gradient Neural Net Fuzzy Expert System - TilShell, C Training Module Brainmaker, C

10. 10 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

11. 11 Data Input Screen

12. 12 Graphing Options

13. 13 Selecting a Graphing Option

14. 14 Typical Control Chart Graph

15. 15 Dual Predictive Networks Slip Temp......... Mean Moisture Gradient Cast Rate Cast Time BR10 BR100 IR BU Gelation Filtrate Cake Wt H 2 O Ret SO 4 Plant Temp and Humidity (8)

16. 16 Neural Networks Accuracy

17. 17 Typical Analysis Graphs Cast Rate as a Function of Plant Temperature Moisture Gradient as a Function of Slip Temperature

18. 18 Using the Predictive Models

19. 19 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 SO 4 which yield both the smallest moisture gradient and the largest cast rate

20. 20 Using the Process Optimization Module

21. 21 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

22. 22 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.

23. 23 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 7090 Not Hot 807090 Ho t 80

24. 24 Schematic of Expert System Temperature Humidity Mold Age Rule Base Mold Condition Casting Rate Rule Base Casting Time User Input System Predicted

25. 25 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

26. 26 Some Membership Functions

27. 27 Membership Function and Rules for Mold Condition

28. 28 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

29. 29 Response Surface for Mold Condition

30. 30 Using the Expert System

31. 31 The Training Module

32. 32 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