KE22 Final Year Project Phase 3

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


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


PROBLEM DOMAIN OVERVIEW

KEY IDEA Optimizing manufacturing asset and predictive maintenance

What is Milling? customized material of different shapes and features

What to Optimize Predict remaining lifespan of cutter

How to Optimize Implementing a Hybrid KE Model using

– Hierarchical Clustering (HC)

– Adaptive Neural Fuzzy Inferences System (ANFIS)

– Resulting in an optimal HC-ANFIS hybrid

Why Optimal determine optimal cluster size and automatically produce optimal ANFIS structure


SYSTEM DESCRIPTION

Machine sensors attached to the milling process

Cutting force sensor in x, y, z dimension

Acoustic emission sensor that measure high frequency stress wave


SYSTEM DESCRIPTION

6 cutter tools’ data given

Over 300+ samples given for each cutter

At specific interval

– Measure sensors’ readings

– Measure tool wear using electronic microscope


SYSTEM DESCRIPTION

ANFIS by itself can solve the prediction problem

– But required expert knowledge on rules determination and membership function

– Use HC to determine rules and membership function

– By providing optimal cluster size, membership function parameters

How to determine the optimal cluster size of HC

– By using cluster balance method

Improve overall learning and application performance

Coded HC module in .NET C#

Coded ANFIS module in Python


GRID PARTITION WITH HC APPROACH


GRID PARTITION WITH HC ISSUE

Complexity of the ANFIS structure is based on the product of each input’s cluster size

Given that p, q, r, s represented the cluster size of the 4 force features

ANFIS would generate (p * q * r * s) number of inferences rules

For E.g. if p = q = r = s = 10, then number of inferences rules = 10,000!

This is computationally intensive and infeasible to implement


HC-ANFIS APPROACH


HC-ANFIS APPROACH FINDINGS

Less rules produced hence a more feasible solution than the previous approach

As the features were combined, much lesser ANFIS inferences rules were created thus resulting in a much lesser intensive computation and a feasible solution to implement


RESULTS


BENEFITS BY ORGANIZATION

HC System

– Fast and customizable input selection for different application needs

– Customized output, to facilitate future seamless integration between HC and other system

– Novel cluster balance implementation to determine optimal HC cluster size

HC-ANFIS System

– Provide an alternative automated tool wear prediction method for SimTech sponsor


BENEFITS BY STUDENTS

Enforce what student learned in course

– Knowledge Modeling and Management

Use different techniques (i.e. interview, UML diagrams) and CommonKADS to gather and capture user requirements

Utilize the knowledge learned in class (i.e. Clustering, Fuzzy Inferences System and Neural Network) to come up with a Hybrid system design and final product

– Product Development

Understand the underlying principle and math of how Clustering, Fuzzy Inferences System and Neural Network works

Explore and innovate new KE techniques

Understand the importance and usage of the HC and ANFIS application in real world situation

Learned from users on the proper result testing technique

– Result must be repeatable and reliable


DEMO

Show capability of

– Grid Partition with HC

– HC-ANFIS

– Subtractive Clustering (MATLAB)


THE END

Q&A


BACKUP


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


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


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


FUZZY SYSTEM IDENTIFICATION


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)


OVERVIEW OF ANFIS

ANFIS architecture

Premise ANFIS MF(Bell) Consequence Linear Sugeno

Learning AlgorithmsFW BW

Premise Fixed Gradient Descent

Consequence LSE Fixed


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.


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


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


ANFIS ARCHITECTURES# OF LINGUISTIC VARIABLES

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


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


OPTIMAL HIERARCHICAL CLUSTERING# OF RULES

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


ANFIS ARCHITECTURES # OF RULES

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

Each cluster centre is a fuzzy rules!


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


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

KE22 Final Year Project Phase 3

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