Transcript ™ for Quenching and Distortion Control Aparna S. Varde, Mohammed Maniruzzaman,

The QuenchMiner™ Expert System
for Quenching and Distortion Control
Aparna S. Varde, Mohammed Maniruzzaman,
Elke Rundensteiner and Richard D. Sisson Jr.
Worcester Polytechnic Institute,
Worcester, MA, USA
Introduction
• QuenchMiner™ : Started as
Web-based tool for analyzing
quenching experimental data
• Data Mining features added:
discovering interesting patterns
in data sets to guide decisions
Logo summarizing
QuenchMiner™ functions
• Enhanced into Expert System
for Decision Support in Heat
Treating
What is an Expert System
• Computer program with knowledge of specialist
• Does representation and reasoning using knowledge
• Solves problems or gives advice to users
Structure of an Expert System
QuenchMiner™ for Decision Support
• Tool to support or assist the users decisions in selecting
quenchants, parts, conditions to achieve a desired output for
quenching in the industry. Analyses user cases.
• Goal: Estimate parameters of interest computationally
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Desired Suspension of Part
Average Cooling Rate
Cooling Uniformity
Average Heat Transfer Coefficient
Residual Stress
Hardness
Distortion Tendency
Cracking Potential
• Given: Quenching experimental input
– Quenchant Information
– Part Conditions
– Manufacturing Details
System Architecture
• User interaction
through Web
• Data Mining to
get knowledge
from quenching
data, build rules
• Decision Making
using Artificial
Intelligence
techniques e.g.
Rule Interpreters
Architecture of QuenchMiner™
Expert System
QuenchMiner™ Demo
• Demo available at,
http://mpis.wpi.edu:9006/database
http://mpis.wpi.edu:9006/database
• Screendumps of demo shown here:
– Menu Screen
– Case Input Screen
– Case Output Screen
Methodology for Analysis
• Identify causes (variables) of effects (parameters of interest)
• Represent causal relationship by Fishbone (Ishikawa) diagrams
Fishbone diagram for Cooling Rate
Decision Making
Application to Distortion Control
• Distortion: One of the biggest problems in quenching. Part gets
deformed in shape and / or size while being quenched.
• QuenchMiner™
– estimates tendency for distortion in part given input conditions
(computationally, without performing experiment)
– identifies causes of distortion in given case by analysis
– Thus assists users decisions to select conditions to minimize distortion
• Example: Shown in Screen-dump
Conclusions
• QuenchMiner™ developed as an Expert System for
Decision Support in Heat Treating
• Analyzes cases in Quenching and Distortion Control
• Uses Data Mining and Artificial Intelligence techniques
• Only supports or assists users decisions
• Demo available, System Ready for use
• Ongoing research and user feedback will provide further
improvements to QuenchMiner™.