KE22 FINAL YEAR PROJECTPHASE 2Modeling 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

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

3| 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

4| 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

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

FUZZY SYSTEM IDENTIFICATION

6| 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)

7| 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

8| 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.

9| 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

10| 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

11| 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!

12| 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

13| 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

14| 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!

15| 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

16| 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

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

THE END

Demo

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

THE END

Q&A

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

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