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
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
5| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only
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
6| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only
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
7| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only
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
8| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only
GRID PARTITION WITH HC APPROACH
9| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only
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
10| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only
HC-ANFIS APPROACH
11| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only
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
12| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only
RESULTS
13| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only
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
14| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only
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
15| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only
DEMO
Show capability of
– Grid Partition with HC
– HC-ANFIS
– Subtractive Clustering (MATLAB)
16| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only
THE END
Q&A
17| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only
BACKUP
18| 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
19| 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
20| 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
21| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only
FUZZY SYSTEM IDENTIFICATION
22| 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)
23| 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
24| 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.
25| 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
26| 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
27| 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!
28| 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
29| 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
30| 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!
31| 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
32| 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