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 | Jan 14 2012 | Confidential – Internal Only

AGENDA

Objectives Problem Domain Overview System Description Models and Results Benefits to both Organization and Students Demo Q&A


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 (Universal Approximator)

– But required expert knowledge on rules determination and membership functions

– Use HC to determine ANFIS structure and membership 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

Lesser rules produced 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 practical solution to implement


HIERARCHICAL CLUSTERING CLUSTER BALANCE


OVERVIEW OF ANFIS

ANFIS architecture

Premise ANFIS MF(Bell) Consequence Linear Sugeno

Learning AlgorithmsFW BW

Premise Fixed Gradient Descent

Consequence LSE Fixed


BELL MEMBERSHIP FUNCTION

C = Cluster Centroid

a = Standard Deviation


HC AND ANFIS ARCHITECTURES


COMPARISON OF DIFFERENT METHODS

Self Training (Single Cutter Tool Training Data)Methods Accuracy RMSE # of Rules

Grid Partition 0.9592 8.628 72

HC-ANFIS 0.9638 7.577 8

SC-ANFIS 0.9796 7.6894 4

Generalized Training (Two Cutter Tool Training Data)Methods Accuracy RMSE # of Rules

Grid Partition 0.9042 14.908 72

HC-ANFIS 0.9313 11.341 5

SC-ANFIS 0.9318 14.348 3

Testing (with 3rd Cutter Tool Production Data)Methods Accuracy RMSE # of Rules

Grid Partition 0.83 20 72

HC-ANFIS 0.87 14.22 5

SC-ANFIS 0.84 15.15 3


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

– .NET C# HC program

– Grid Partition with HC using Python

– HC-ANFIS using Python

– 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)


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

Documents

KE22 Final Year Project Phase 3