1. 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

2. 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 Enhanced into Expert System for Decision Support in Heat Treating Logo summarizing QuenchMiner ™ functions

3. 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

4. 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 –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

5. 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

6. QuenchMiner ™ Demo Demo available at, http://mpis.wpi.edu:9006/database Screendumps of demo shown here : –Menu Screen –Case Input Screen –Case Output Screen

10. Methodology for Analysis Identify causes (variables) of effects (parameters of interest) Represent causal relationship by Fishbone (Ishikawa) diagrams Fishbone diagram for Cooling Rate

11. Decision Making

12. 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

15. 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 ™.