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Web-based Data Mining for Quenching Data Analysis

Web-based Data Mining for Quenching Data Analysis. Aparna S. Varde, Makiko Takahashi, Mohammed Maniruzzaman, Richard D. Sisson Jr. Center for Heat Treating Excellence Worcester Polytechnic Institute Worcester, MA, USA. Introduction.

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Web-based Data Mining for Quenching Data Analysis

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  1. Web-based Data Mining for Quenching Data Analysis Aparna S. Varde, Makiko Takahashi, Mohammed Maniruzzaman, Richard D. Sisson Jr. Center for Heat Treating Excellence Worcester Polytechnic Institute Worcester, MA, USA.

  2. 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)

  3. Web Interface User Output User Input Conversion Unit SQL Query SQL Result Query Processor Integrated Store (Relational Database System) Raw Data Flat Files Complex Data QuenchPAD Phase I: Query Processing • QuenchPAD on the Web for worldwide access • Integral Store for complex data, flat files, raw data • Advanced Features for queries, graphs etc.

  4. Web Interface Output to User User Scenario Semantic Analyzer Analytical Output Sample Decisions Decision-maker Background Information Data Knowledge Base Integral Store (RDBMS) Extraction Rule-building Data Miner Phase II: Decision Support System • User Case Studies and Analysis • Data Mining to acquire knowledge, build rules • Decision-making using rules and cases

  5. 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

  6. 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

  7. Geometry Thin And Long Has Sharp Corners Variable Cross Section Suspend Vertically in the Quenchant Add Rounds to the Ends Adjust CR to Thickest Section 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

  8. 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

  9. 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

  10. 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

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