ke22 final year project phase 3
DESCRIPTION
KE22 Final Year Project Phase 3. Modeling and Simulation of Milling Forces SIMTech Project. Ryan Soon, Henry Woo, Yong Boon April 9, 2011 Confidential – Internal Only. Agenda. Objectives Problem Domain Overview System Description Models and Results - PowerPoint PPT PresentationTRANSCRIPT
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
3| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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 | Jan 14 2012 | 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 | Jan 14 2012 | 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 | Jan 14 2012 | 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 | Jan 14 2012 | Confidential – Internal Only
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
8| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | Confidential – Internal Only
GRID PARTITION WITH HC APPROACH
9| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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 | Jan 14 2012 | Confidential – Internal Only
HC-ANFIS APPROACH
11| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | Confidential – Internal Only
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
12| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | Confidential – Internal Only
HIERARCHICAL CLUSTERING CLUSTER BALANCE
13| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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
14| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | Confidential – Internal Only
BELL MEMBERSHIP FUNCTION
C = Cluster Centroid
a = Standard Deviation
15| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | Confidential – Internal Only
HC AND ANFIS ARCHITECTURES
16| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | Confidential – Internal Only
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
17| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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
18| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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
19| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | Confidential – Internal Only
DEMO
Show capability of
– .NET C# HC program
– Grid Partition with HC using Python
– HC-ANFIS using Python
– Subtractive Clustering (MATLAB)
20| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | Confidential – Internal Only
THE END
Q&A
21| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | Confidential – Internal Only
BACKUP
22| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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
23| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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
24| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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
25| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | Confidential – Internal Only
FUZZY SYSTEM IDENTIFICATION
26| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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)
27| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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.
28| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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
29| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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
30| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | Confidential – Internal Only
ANFIS ARCHITECTURES# OF LINGUISTIC VARIABLES
ANFIS with 4 inputs variables contains 3~4 linguistics variables generated 192 Rules!
31| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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
32| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | Confidential – Internal Only
OPTIMAL HIERARCHICAL CLUSTERING# OF RULES
Build HC on all variables, opt # of cluster = 5
33| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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!
34| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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
35| KE22 FYP, Modeling and Simulation of Milling Forces | Jan 14 2012 | 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