KE22 FINAL YEAR PROJECTPHASE 3Modeling and Simulation of Milling ForcesSIMTech Project

Ryan Soon, Henry Woo, Yong BoonApril 9, 2011Confidential – Internal Only

2| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

AGENDA

3| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

OBJECTIVES

Understand a prognostic problem domain that enables an Hybrid implementation of Knowledge Engineering Techniques

Present research effort & implementation result of overall prognostic problem domain

Highlight novel prognostic optimization concept and model

Challenges and benefits

4| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

PROBLEM DOMAIN OVERVIEW

Predict remaining lifespan of milling cutter tool By implementing a Hybrid Knowledge Engineering Model using

– Hierarchical Clustering (HC)

– Adaptive Neural Fuzzy Inferences System (ANFIS)

5| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

SYSTEM DESCRIPTION

ANFIS by itself can solve the prediction problem

– Optimize ANFIS by HC HC-ANFIS model

– Reduce number of inferences rules to improve optimize learning time Determine the optimal cluster size of HC

– By using cluster balancing method borrowed from Subtractive Clustering

Improve overall learning and application performance

6| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

RESULTS

7| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

BENEFITS BY ORGANIZATION

H.C. System

– Fast and customizable input selection for user to select for H.C. clustering for different needs

– Customized output, to facilitate future seamless integration between H.C. and ANFIS system

– Able to use new method to deduce optimal cluster, and compare against current k-means clustering tool when clusters are fed to ANFIS used by SimTech. Result is satisfactory

ANFIS System

– SimTech is able to utilize software to make accurate tool-wear prediction or any other non-linear prediction

8| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

BENEFITS BY STUDENTS (I)

Enforced what student learned in course

– Project Management Use different techniques(i.e. interview, survey) to gather user requirements

and use CommonKads to document in proper knowledge based documentation

Utilize the knowledge learned in class(i.e. Clustering and Neural Network) to come up with the system design and final product

– Product Development Understand the underlying principle of how clustering and Neural Network

works

Able to improve new techniques to overcome current problems faced by user when using commercial products (i.e. Matlab)

9| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

BENEFITS BY STUDENTS (II)

Understand the importance and usage of the application of H.C. and ANFIS in real world situation

Learned from users on the proper testing technique for testing of final result (i.e. Result must be repeatable and reliability

10| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

PROBLEM DESCRIPTION

3 set of cutter tool data were given

– 07, 31, T12 Belong to the same family type but with differences in drill bit shape and

knife edges Problem domain requires us to build a hybrid KE system to predict the

cutter tool wear Full Microsoft .NET C# implementation of Hybrid KE system Hierarchical Clustering

– Derive number of Fuzzy linguistic values for each variable

– Derive number of Fuzzy rules ANFIS (Neural Fuzzy System) to learn and predict the tool wear

– Generic tool wear prediction model

11| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

DATA CORRELATION ANALYSIS – 1

And within each cutter tool data

– 3 sets of individual tool head data F1, F2, F3 Within each “F” data (315 records)

– Acoustic emission data (16 features)

– Force (x dimension) data (16 features)

– Force (y dimension) data (16 features)

– Force (y dimension) data (16 features) Too much features

– Use correlation coefficient method and cut down on the features

12| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

DATA CORRELATION ANALYSIS – 2

By using Pearson Correlation Coefficients, the linear dependence between the measured features values and the tool wear values can be calculated

AE data is not influencing the tool wear strongly The top influencing features are consistent between the 3 forces

AE Fx Fy Fz

13| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

FUZZY SYSTEM IDENTIFICATION

14| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

OVERVIEW OF HIERARCHICAL CLUSTERING

Agglomerative HC starts with each object describing a cluster, and then combines them into more inclusive clusters until only one cluster remains.

4 Main Steps

– Construct the finest partition

– Compute the distance matrix

– DO Find the clusters with the closest distance

Put those two clusters into one cluster

Compute the distances between the new groups and the remaining groups by recalculated distance to obtain a reduced distance matrix

– UNTIL all clusters are agglomerated into one group. Ward Methods, minimize ESS (Error Sum-Of-Square)

15| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

OVERVIEW OF ANFIS

ANFIS architecture

Premise ANFIS MF(Bell) Consequence Linear Sugeno

Learning AlgorithmsFW BW

Premise Fixed Gradient Descent

Consequence LSE Fixed

16| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

OPTIMAL HIERARCHICAL CLUSTERING

Determine the numbers of clustering using RSS with penalty.

Where,

is the penalty factor for addition # of cluster.

K’ and K = number of clusters

RSS = Residual Sum of Squares

Borrow concept from K-means using RSS as goal function.

17| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

HIERARCHICAL CLUSTERING + ANFIS

Two Different Approaches for HC + ANFIS

– Use HC to determine # of linguistic values for each input features

– Use HC to determine # of rules

18| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

OPTIMAL HIERARCHICAL CLUSTERING# OF LINGUISTIC VARIABLES

Example on SRE variables, opt # of cluster = 3

Perform HC on selected features on FXVariables Name # of Clusters

p2p 4

std_fea 4

sre 3

fstd 4

19| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

ANFIS ARCHITECTURES# OF LINGUISTIC VARIABLES

ANFIS with 4 inputs variables contains 3~4 linguistics variables generated 192 Rules!

20| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

ANFIS – RESULTS# OF LINGUISTIC VARIABLES

ANFIS Predict vs Actual

– Train Data with Avg Error 4.84

– Test Data with Avg Error 15.00

Membership Functions

– P2p

– Std_fea

– Sre

– fstd

21| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

OPTIMAL HIERARCHICAL CLUSTERING# OF RULES

Build HC on all variables, opt # of cluster = 5

22| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

ANFIS ARCHITECTURES # OF RULES

ANFIS with 4 inputs variables contains 5 linguistics variables and 5 rules.

Each cluster centre is a fuzzy rules!

23| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

ANFIS – RESULTS # OF RULES

ANFIS Predict vs Actual

– Train Data with Avg Error 5.75

– Test Data with Avg Error 15.218

Membership Functions

– P2p

– Std_fea

– Sre

– fstd

24| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

WHAT’S NEXT?

Full .NET C# Implementation Development of Hierarchical Clustering toolset with frontend GUI

– Manual range input of number cluster by user

– Optimal clustering suggesting the optimal number of cluster Make use of ANFIS model to evaluate

– GUI engine for cluster center drawing Development of ANFIS toolset with frontend GUI

– Develop the ANFIS Engine which will do the optimization

– Develop User Interface for: Display predicted tool-wear result

Evaluation of error

25| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

THE END

Demo

26| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

THE END

Q&A

27| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only

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