Web-based Data Mining for Quenching Data Analysis

Aparna S. Varde, Makiko Takahashi, Mohammed Maniruzzaman, Richard D. Sisson Jr.

Center for Heat Treating ExcellenceWorcester Polytechnic Institute

Worcester, MA, USA.

Introduction

Web-based Data Mining Tool “QuenchMiner” being developed at CHTE, WPI

Purpose: Analysis of experimental data generated during quenching in the heat treating of materials

Supports CHTE Quench Probe System that gathers experimental time-temperature data

Functions Existing CHTE Database QuenchPAD on the Web Advanced Features e.g. querying complex data Decision Support System (DSS)

Phase I: Query Processing

Flat Files

Web Interface

Conversion Unit

Query Processor

Integrated Store (Relational Database System)

QuenchPAD

Complex Data

Raw Data

User Input User Output

SQL Query

SQL Result

QuenchPAD on the Web for worldwide access

Integral Store for complex data, flat files, raw data

Advanced Features for queries, graphs etc.

Phase II: Decision Support System

Web Interface

Semantic Analyzer

Decision-maker

Knowledge Base

Integral Store (RDBMS)

Data Miner

User Scenario Output to User

Analytical Output Sample Decisions

DataBackground Information

ExtractionRule-building

User Case Studies and Analysis

Data Mining to acquire knowledge, build rules

Decision-making using rules and cases

Data Mining

Discovering interesting patterns/trends in large data sets for guiding future decisions

Most Important step of Knowledge Discovery in Databases (KDD)

Data Mining Techniques: Association Rules, Decision Trees etc.

Rules and action paths fed into Knowledge Base to help decision-making

Association Rules

Statement of the type “X => Y”, where X and Y are events or conditions

Examples: High carbon content => More potential for distortion Excessive agitation => Excessively high cooling rate Use of Water Quenchant => Faster heat extraction

Rules built from analysis of data using statistical measures, probability and domain knowledge

Rules serve as basis for Decision Trees

Decision Trees

Representation of paths of action taken on occurrence of certain events

Example tree for sub-case of distortion

Suggests action, based on part geometry, to minimize distortion during quenching

SuspendVertically in the Quenchant

Geometry

ThinAndLong

Has SharpCorners

Variable Cross Section

AddRounds to the Ends

AdjustCR toThickestSection

Demo of QuenchMiner

Authorized users may get this from http://mpi.wpi.edu

Query Processing screens with results

DSS screens with sample analysis and decisions

Screen-dumps of Demo shown here

Current Status

QuenchMiner Query Processing (Phase I): Alpha Version with real data in Demo

QuenchMiner DSS (Phase II): Prototype with sample data in Demo

Integral Store (Data Mart) has been built

Knowledge Base (Rules and Decisions) is being built

Conclusions

QuenchMiner does Web-based Data Mining for the CHTE Quench Probe System

It Performs Query Processing for Simple and Complex data types

It will serve as a Decision Support System for CHTE member companies

Future Issues: Introducing Artificial Intelligence to build an Expert System