Institute of System Science
National University of Singapore
KE22 Final Year Project
Modeling and Simulation of Milling Forces
SIMTech Project
Phase 2 Report
ISS Supervisors
Charles, Pang
Fang Ming, Zhu
SimTech Sponsors
Xiang, Li
KiahMok, Goh
Junhong, Zhou
KE22 Students
Yong Boon, Lim
A0065796
Kai Jie Ryan, Soon A0065972
Meng Chew, Woo
A0065935
Table of Contents
1.
Introduction ......................................................................................................... 4
1.1
Problem Description ...................................................................................... 5
1.1.1
1.2
Project Scope and Objectives ....................................................................... 7
1.3
Project Plan ................................................................................................... 9
1.3.1
Milestones .............................................................................................. 9
1.3.2
Timeline .................................................................................................. 9
1.3.3
Project Team Member Role .................................................................. 10
1.4
2.
Milling Process Setup ............................................................................. 5
KE Technique Selection Outline.................................................................. 11
1.4.1
Fuzzy Logic .......................................................................................... 11
1.4.2
Neural Network ..................................................................................... 11
1.4.3
Adaptive Neural Fuzzy Inference System (ANFIS) ............................... 11
1.4.4
Rule-based Reasoning System ............................................................ 11
1.4.5
Genetic Algorithm ................................................................................. 11
1.4.6
Heuristic Search ................................................................................... 12
1.4.7
Case-based Reasoning System ........................................................... 12
Solution Description .......................................................................................... 13
2.1
Operational Context .................................................................................... 13
2.1.1
Problems & Opportunities Worksheet OM-1 ......................................... 13
2.1.2
Variant Aspects Worksheet OM-2 ........................................................ 14
2.1.3
Process Breakdown Worksheet OM-3 .................................................. 14
2.1.4
Knowledge Assets Worksheet OM-4 .................................................... 14
2.1.5
Feasibility Checklist Worksheet OM-5 .................................................. 15
2.2
Functional Description ................................................................................. 15
2.3
System Modeling and Design...................................................................... 19
2.3.1
Overview of ANFIS ............................................................................... 19
2.3.2
ANFIS Learning Algorithm .................................................................... 21
2.3.3
Fuzzy System Modeling Method ........................................................... 21
2.3.3.1
Input Variable Identification ............................................................... 22
2.3.3.2
Rule Identification .............................................................................. 23
2.3.4
System Modeling with Matlab HC/ANFIS ............................................. 24
2.3.4.1
Input Variable Identification ............................................................... 24
2.3.4.2
Rule Identification with Hierarchical Clustering ................................. 25
2.3.5
Training ANFIS using Matlab ................................................................ 26
2.4
User Interface Design ................................................................................. 28
2.5
Knowledge Structure and Representation ................................................... 31
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3.
Hardware and Software .................................................................................... 38
4.
APPENDIX A - DRAFT PROJECT PROPOSAL ............................................... 39
5.
APPENDIX B - FORMAL PROJECT PROPOSAL AND PROJECT PLAN ........ 42
6.
5.1
Problem Description & Overview................................................................. 43
5.2
Scope .......................................................................................................... 44
5.3
Sources ....................................................................................................... 45
5.4
Project Plan ................................................................................................. 45
5.5
Final Comments .......................................................................................... 46
APPENDIX C – References .............................................................................. 48
Figures
Figure 1-1: Generic CNC Milling Machine .................................................................. 4
Figure 1-2: High-Level Architecture Milling Process to Facilitate Intelligent Prognostic
Monitoring .................................................................................................................. 6
Figure 1-3: Setup of the Milling Machine with its various Sensors ............................. 7
Figure 1-4: Selected Given Data from Sponsor .......................................................... 8
Figure 2-1: Usecase Diagram .................................................................................. 16
Figure 2-2: Generate Co-Relation Model Sequence Diagram .................................. 17
Figure 2-3: Analyze Model Error Sequence Diagram ............................................... 18
Figure 2-4: Prediction Sequence Diagram ............................................................... 19
Figure 2-5: Co-Relation Module Diagram ................................................................. 29
Figure 2-6: Analyze Error Module Diagram .............................................................. 30
Figure 2-7: Generate Prediction Module Diagram .................................................... 31
Figure 2-8: Class Diagram of the Milling Process .................................................... 32
Figure 2-9: Concept Map Diagram ........................................................................... 33
Figure 2-10: Cause Tree Diagram ............................................................................ 34
Figure 2-11: Decision Tree Diagram ........................................................................ 35
Figure 2-12: Milling Setup Process Tree Diagram .................................................... 36
Figure 2-13: Measurement of Cutter Process Tree Diagram .................................... 37
Tables
Table 1-1: Selected Given Features from Sponsor ................................................................................ 8
Table 1-2: Project Milestones.................................................................................................................. 9
Table 1-3: Phase 1 Domain Familiarization, Knowledge Acquisition & Project Planning ....................... 9
Table 1-4: Phase 2 Problem Modeling & System Design ..................................................................... 10
Table 1-5: Phase 3 Development, testing and completion ................................................................... 10
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1.
Introduction
Milling is a common machining process used in material manufacturing where
customized solid material can be designed and created. Milling is typically
used to produce parts that are not axially symmetric and have many features,
such as holes, slots, pockets, and even 3 dimensional surface contours.
Unwanted material is cut away to create the customized features.
The milling process requires a milling machine, workpiece, fixture, and cutter.
The workpiece is a piece of pre-shaped material that is secured to the fixture,
which itself is attached to a platform inside the milling machine. The cutter is a
cutting tool with sharp teeth that is also secured in the milling machine and
rotates at high speeds. By feeding the workpiece into the rotating cutter,
material is cut away from this workpiece in the form of small chips to create
the customized shape.
Milling machines may be manually operated, mechanically automated or
digitally automated via Computer Numerical Control (CNC). The project scope
is limited to CNC type of milling machine and its processes.
The figure below shows a high level generic CNC milling machine.
Figure 1-1: Generic CNC Milling Machine
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1.1
Problem Description
In the modern competitive era of manufacturing, one of the challenges of the
high precision milling process is the prediction of cutter tool wear. As the highspeed cutter works around the material, wear and tear effect cause the cutter
head to get worn out, until certain extent that it might not cut properly. This
aging cutter damages the surface of the expensive work piece material. The
milling company in this case is liable for this damage and hence increasing its
production cost.
Currently in the industry, conventional methods are used to mitigate this kind
of damage:
1. Upon reaching a predefined cutting usage threshold, the operator replaces
the existing cutter with a new one. Example, replace the cutter after every
100 cuts. This practice, although pre-empts and protects the expensive
workpiece material from damage, it introduces wastage on the part of the
cutter tool. The cutter tool might not have reached its full usage lifespan
and can still be used for further cutting.
2. The operator of the milling machine would periodically stop the milling
process, take the cutter tool out and inspect it under a powerful electronic
microscope for wear and tear. The operator would rely on years of
experience and determine whether the cutter tool has reached its usage
threshold. If the cutter tool crosses this threshold, the operator would
replace this cutter tool with a new one, else continue to use the current
cutter tool. Again, this practice introduces productive and efficiency waste
as the milling process needs to be stopped to inspect the cutter tool and
later re-calibrated and setup to continue the milling process.
Both conventional methods require downtime of the milling machine. In
today’s LEAN advocated manufacturing environment, machine downtime and
human operator intervention should be minimized. Customer should not be
paying for wastage due to the milling process productivity and efficiency loss.
But on the other hand, failure to replace the worn cutter tool can lead to
damage to the workpiece or worst, damage to the milling equipment itself.
Therefore a prognostic monitoring system that can predict and diagnose tool
wear and failure can be researched and developed upon.
1.1.1 Milling Process Setup
Currently at the sponsor site, a high speed milling process is setup to facilitate
the tool wear prognostic monitoring system. The aim is to produce a
systematic, efficient and robust approach to the tool wear prognostic problem
and produce new intelligent learning tools that can monitor and alert operators
for pre-emptive and preventive maintenance actions.
In general, the approach in setting up a prognostic system is as follow:
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1.
2.
3.
4.
5.
6.
7.
8.
Sensor system setup of the milling machine
Perform data acquisition
Perform data pre-processing
Extract the identified features
Tool wear measurement via high power electronic microscope
Build correlation model for prognostic monitoring and accuracy evaluation
Store accurate model output into knowledge base
GUI dashboard for visualization and reporting
The high-level architecture milling process to facilitate the intelligent
prognostic monitoring is shown in Figure 1-1 below.
Figure 1-2: High-Level Architecture Milling Process to Facilitate Intelligent Prognostic Monitoring
Figure 1.2 below displays the setup of the milling machine setup with its
various sensors.
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Figure 1-3: Setup of the Milling Machine with its various Sensors
1.2
Project Scope and Objectives
An intelligent tool wear and failure prognostic model is proposed to solve the
conventional methods’ inefficiency discussed above.
The sensor setup, data acquisition system, tool measurement system and
pre-processing system are not within the scope of the project.
The selected features and its cleansed data are given by the sponsor for our
KE model research and creation. They are based on the following two
sensors.
1. A Kistler quartz 3-component platform dynamometer was mounted
between the workpiece and machining table to measure the cutting forces
in the form of Newton, and converted them to voltages by the Kistler
charge amplifier.
2. A Kistler acoustic emission (AE) sensor was mounted on the workpiece to
monitor the high frequency stress wave generated by the cutting process.
Table 1.1 below gives an overview of the given selected Force and AE
features
No
1
2
3
Force Feature
Maximum Force Level
Total Amplitude of Cutting Force
Amplitude Ratio
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AE Features
Peak to peak
Skewness
Kurtosis
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4 Average Force
Mean of band power
Table 1-1: Selected Given Features from Sponsor
Figure 1-3 gives a high-level overview of the various attributes given to the
team by the sponsor.
Figure 1-4: Selected Given Data from Sponsor
A 3rd sensor (accelerometer) was setup in the milling process but its features
and data were not given at this point given that the sponsor wants the team to
focus first on the force and AE feature.
The current scope and objectives of this KE project encompass:
1) Using the KE technique taught, derive suitable KE models for prediction of
the milling cutter tool wear
2) Provide accuracy measurement and comparison for derived models
3) Generate knowledge base for suitable model for tool wear prediction
4) Design and develop KE model engines using Microsoft .NET C#
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5) Provide GUI for model accuracy simulation and prediction
1.3
Project Plan
The timeline/milestones and the team member roles are discussed in the
sections below.
1.3.1 Milestones
Table 1-2: Project Milestones
1.3.2 Timeline
Table 1-3: Phase 1 Domain Familiarization, Knowledge Acquisition & Project Planning
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Table 1-4: Phase 2 Problem Modeling & System Design
Table 1-5: Phase 3 Development, testing and completion
1.3.3 Project Team Member Role
The various roles of the project team member are discussed in this section.
Ryan Soon
- Project Manager
- KE Technique Modeler
- Project Technical Writer
- C# Developer
Henry Woo
- Chief Software Architect
- KE Technique Modeler
- Quality Tester
- C# Developer
Yong Boon
- Chief Knowledge Engineer and Database Architect
- KE Technique Modeler
- Functional Requirement Specialist
- C# Developer
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1.4
KE Technique Selection Outline
The aim of the project is to build the predictive model for the lifespan of the
cutter to optimize the production efficiency and cutter utilization. Several KE
techniques have been evaluated to build the model. The data are acquired
through the sensors, which contains the force and acoustic emission features.
In the project, the Neural Fuzzy Network (ANFIS) technique will be selected to
build the model as it would able to extract the fuzzy rules through Neural
Network from the input/output data. These rules could be referenced/used to
build a more generic model for the cutter tool wear monitoring system.
These are the initial evaluation of the other KE techniques for the predictive
model.
1.4.1 Fuzzy Logic
Fuzzy logic inference system allow ambiguous and imprecision of the
input/output, and it is presented in the forms understandable by the domain
expert. The challenge of the technique lie on the definition of the proper
membership functions and the fuzzy logic inference rules.
1.4.2 Neural Network
Neural Network is used on the problem domain which the model does not
exist/clear with domain expert, but the input/output data are available. The
challenge faced on the Neural Network is to extract useful rules from the
Neural Network Structure.
1.4.3 Adaptive Neural Fuzzy Inference System (ANFIS)
ANFIS allows the neural network to realize the fuzzy rules through the
input/output data. This method is particular useful to extract the rules, and the
membership functions out of the data set which it’s not cleared to the domain
expert.
1.4.4 Rule-based Reasoning System
Rule-based system could only be used if the domain knowledge could be
elicited from the domain expert. In the existing problem domain, only the
guideline instead of clear rules exist, and each cutter has different
characteristic which make Rule-based system difficult to solve the problem.
1.4.5 Genetic Algorithm
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Genetic Algorithm is preference method if the fitness function and proper
chromosome are defined. However, in the existing problem domain, the
fitness function is unclear for the domain expert for prediction.
1.4.6 Heuristic Search
Heuristic search could only be used with the proper model existing. In the
problem domain, the accurate model to predict the lifespan of different cutter
does not exist.
1.4.7 Case-based Reasoning System
The Case-based Reasoning system is effective solution when the model does
not exist. This requires numerous cases being built to cover the different
cutters on different scenario.
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2.
Solution Description
2.1
Operational Context
The project adopts CommonKADS for the operational context which covers
the context model of the existing operation.
2.1.1 Problems & Opportunities Worksheet OM-1
Problems and Opportunities
One of the challenges on the milling process is the
prediction of cutter tool wear. The aging cutter damages
the surface of work piece material, in case of high
precision cutting, the damage work piece is no longer
usable.
The current practices for preventing the failure caused
by the aging cutter are,
1. Replacing the existing cutter with the new one after
reaching the predefined threshold
2. Inspect the cutter under Olympic Microscope, if the
tool wear exceed the threshold, replace it with the
new one, else put the cutter back to the milling
machine.
Both methods require the downtime of the milling
machine. The first method might cause the waste of the
cutter before its end of life. The second method requires
addition downtime when the cutter is scrutinized under
microscope, on top of the cutter replacing time.
Organizational Context
If the cutter tool wear could be predicted, this will
minimize the downtime, and reduce the cutter waste, at
the same time, avoid the cutter causing damage to the
work piece.
Mission:
The Singapore Institute of Manufacturing Technology
(SIMTech) develops high value manufacturing
technology and human capital to enhance the
competitiveness of Singapore's manufacturing industry.
Goal:
- To create intellectual capital through the generation,
application and commercialization of advanced
manufacturing science and technology
- To nurture Research Scientists and Engineers by
providing opportunities to do use-inspired research
for industry
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-
To contribute to Singapore's industrial capital by
collaborating in projects and sharing research
expertise and infrastructure with industry
Develop a predictive model using data mining technique
for the cutter life span. This will increase the utilization of
the cutter, and reduce the downtime for replacement
and inspection.
Solutions
2.1.2 Variant Aspects Worksheet OM-2
Process
1. Insert the work piece.
2. Insert CNC machine parameters.
3. Start Milling Process.
4. Inspect/Replace Cutter.
5. Finish Milling Process.
6. Replace work piece.
Operator/Researcher
Machine Sensor to measure the output of the CNC.
Database to store the sensor output.
CNC Input Parameters
Tool Wear Rules of Thumbs/Material Microstructure
knowledge.
People
Resource
Knowledge
2.1.3 Process Breakdown Worksheet OM-3
No
1
2
3
4
5
6
Task
Performed
By
Operator
Where
Operator
CNC
Machine
CNC
Machine
Operator
CNC
Machine
CNC
Machine/
Lab
Finish Milling Operator
Process
Remove
Operator
work piece
CNC
Machine
CNC
Machine
Insert the
work
piece/cutter
Insert CNC
machine
parameters
Start Milling
Process
Inspect/
Replace
cutter
Knowledge
Asset
CNC
Machine
CNC Input
Parameters
Tool Wear
Rules of
thumbs/
Olympic
Microscope
Intensive
Significance
N
1
N
2
N
2
Y
3
N
1
N
1
2.1.4 Knowledge Assets Worksheet OM-4
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Knowledge
Asset
Possessed Used in
By
Task
CNC Input
Parameters
Tool Wear
Rules of
thumbs/
Olympic
Microscope
Operator
Operator
Insert CNC
parameters
Inspect
/Replace
cutter
Right
Form
?
Y
Right
Right Time?
Place?
Right
Quality?
Y
Y
Y
Y
Y
N (The
inspection might
be too frequent
to increase the
production time
or too less and
result the cutter
to damage the
work piece.)
Y
2.1.5 Feasibility Checklist Worksheet OM-5
Business
Feasibility
Technical
Feasibility
Project Feasibility
Proposed Actions
2.2
Develop a prediction model to reduce the number of cutter
inspection/replacement, and at the same time, protect the work
piece damaged by the Wear cutter.
This will have the advantages of
1. Reduce the number of cutters needed for work piece.
2. Reduce the production time by reducing the frequency of cutter
inspection/replacement.
3. Reduce the risk of Wear cutter damage the work piece.
The established data mining algorithm for prediction are available,
and the data are available for analysis.
Milling Domain expert and 3 KE engineers will be involved in the
project.
1. Setup the project team.
2. Domain /Data Familiarization.
3. Build/Evaluate the Prediction Model.
4. Develop software for the Prediction Model.
Functional Description
The usecase and sequence diagrams are shown in the diagrams below.
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Figure 2-1: Usecase Diagram
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Figure 2-2: Generate Co-Relation Model Sequence Diagram
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Figure 2-3: Analyze Model Error Sequence Diagram
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Figure 2-4: Prediction Sequence Diagram
2.3
System Modeling and Design
The ANFIS is used as an universal approximator to map the non-linear input
spaces (acoustic and force input) and the output space (tool wear) in the form
of fuzzy IF-THEN rule form without employing precious quantitative analysis.
2.3.1 Overview of ANFIS
This section will give a brief description of the ANFIS based on First order
Sugeno Fuzzy Model with two fuzzy IF-THEN rules [8],
Rule 1 - 𝐼𝐹 𝑋 𝐼𝑆 𝐴1 𝐴𝑁𝐷 𝑌 𝐼𝑆 𝐵1 𝑇𝐻𝐸𝑁 𝑓1 = 𝑝1 𝑥 + 𝑞1 𝑦 + 𝑟1
Rule 2 - 𝐼𝐹 𝑋 𝐼𝑆 𝐴2 𝐴𝑁𝐷 𝑌 𝐼𝑆 𝐵2 𝑇𝐻𝐸𝑁 𝑓2 = 𝑝2 𝑥 + 𝑞2 𝑦 + 𝑟2
ANFIS architecture is shown in Figure 2-5 with each of the i th node at layer l
denoted as 𝑂𝑙,𝑖
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Figure 2-5 : ANFIS Architecture
Layer 1
Each node in this layer describe the membership function of the
linguistics variables,
𝑂1,𝑖 = 𝜇𝐴𝑖 (𝑥), 𝑓𝑜𝑟 𝑖 = 1, 2
𝑂1,𝑖 = 𝜇𝐵𝑖−2 (𝑥), 𝑓𝑜𝑟 𝑖 = 3, 4
A generalized bell MF (or bell MF) is selected due to the smoothness
and concise notation.
Figure 2-6 : Generalized Bell Function [7]
The bell membership function is specified by three parameters a, b, c.
These parameters are referred to as premise parameters
1
𝜇𝐴 (𝑥) =
𝑐 2𝑏
(1 + [(𝑥 − 𝑎)] ]
Layer 2
Each node in the layer 2 is the product of membership value from layer
1. T-norm operators (min, production) are used to perform the fuzzy
AND logic.
𝑂2,𝑖 = 𝜔𝑖 = 𝜇𝐴𝑖 (𝑥)𝜇𝐵𝑖 (𝑦), 𝑖 = 1, 2
Layer 3
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Each node in this layer calculate the ratio of ith rule’s firing strength to
the sum of all rules’ firing strength (normalized firing strengths) :
𝜔𝑖
𝑂3,𝑖 = 𝜔
̅𝑖 =
, 𝑖 = 1, 2
𝜔1 + 𝜔2
Layer 4
Each node in this layer represents the consequence of the fuzzy IFTHEN rule in the Sugeno model,
𝑂4,𝑖 = 𝜔
̅𝑖 𝑓𝑖 = 𝜔
̅𝑖 (𝑝𝑖 𝑥 + 𝑞𝑖 𝑦 + 𝑟𝑖 ), 𝑖 = 1, 2
The p, q, r parameters in this layer is referred to consequence
parameters.
Layer 5
The single node in this layer represents the overall output which is,
∑𝑖 𝜔𝑖 𝑓𝑖
𝑂5,𝑖 = ∑ 𝜔
̅𝑖 𝑓𝑖 =
∑𝑖 𝜔 𝑖
𝑖
2.3.2 ANFIS Learning Algorithm
The learning algorithms in the ANFIS could be separated into two passes as
Forward Pass
Backward Pass
Premise Parameters (a, b, c)
Fixed
Gradient Descent
Consequent Parameters (p, q, r)
Least Squares Estimate
Fixed
Signals
Node Outputs
Error Rates
Table 2-1 Hybrid Learning Procedures in ANFIS
2.3.3 Fuzzy System Modeling Method
Fuzzy System Modeling in the board sense could be separated into structure
identification (type of membership function, number of linguistic value, and
number of rules) and parameters identification (membership function
parameters and first order Sugeno output parameters) which shown in
diagram on Figure 2-6.
The parameter identifications (both premises and consequent parameters) are
optimized through offline ANFIS hybrid learning (Least Square Estimate &
Gradient Descent).
Hierarchical Clustering is used to cluster the data and determine the number
of Fuzzy Rules in the dataset. Last, the rule types (membership function &
number of linguistic values) are determined heuristically.
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Figure 2-7 : Fuzzy Model Identification [1]
2.3.3.1
Input Variable Identification
Three Data sets for different cutters are given, and each of them contains
2 main categories of data,
No
Force Feature
AE Features
1
Maximum Force Level
Peak to peak
2
Total Amplitude of Cutting Force
Skewness
3
Amplitude Ratio
Kurtosis
4
Average Force
Mean of band power
Table 2-2 : Features from Sponsors
Some of the features in each category are highly correlated which only
one set of them will be kept to avoid the unnecessary repeating
enhancement effect (dimension reduction) of the input space and to
improve the performance of the ANFIS (less rules).
Force Features
AE Features
Maximum Force Level
Peak to peak
Total Amplitude of Cutting Force
Mean of band power
Average Force
Table 2-3 : Highly Correlated Features
Therefore, the following features from each category are used for the
HC/ANFIS
Force Features
AE Features
Maximum Force Level
Peak to peak
Amplitude Ratio
Skewness
Kurtosis
Table 2-4 : Selected Features for HC/ANFIS
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2.3.3.2
Rule Identification
2.3.3.2.1 Rule Structure
Several flat clustering methods [2] [3] have been used to construct the
fuzzy model through identify the number of clusters for the training data
which represent the number of fuzzy IF-THEN rules. However, some
researchers claim that the hierarchical clustering produce better clustering
than flat clustering [4].
Various monotonic HC algorithms (single-link, complete-link, and Ward’s
method) are available for clustering, and Ward’s methods (based on the
minimum variance cluster) is chosen for HC based on the comparative
studies [4][5] indicates it outperforms others methods.
The following equation are used to select the suitable number of cluster
𝐾=
𝑎𝑟𝑔𝑚𝑖𝑛
′
𝐾′[𝑅𝑆𝑆(𝐾 )
+ 𝛾𝐾 ′ ]
Where K’ refers to the cut of the hierarchy that result in K’ clusters, and
RSS is the residual sum of squares and 𝛾 is the penalty for each additional
cluster.[6].
𝑅𝑆𝑆(𝐾) = ∑𝑖∈𝐶 𝐷𝐼𝑆𝑇(𝐶, 𝑥𝑖 )2
And 𝛾is the penalty factor of the number of cluster, and DIST is the
distance between Cluster Centre C and the data point in the same Cluster.
2.3.3.2.2 Rule Type
The bell membership function, T-norm operator are used by First Order
Sugeno Model to describe the fuzzy IF-THEN rules.
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2.3.4 System Modeling with Matlab HC/ANFIS
As mentioned in the section 2.3.3 in Fuzzy System Modeling, we’ll follow the
same methodology to construct the ANFIS with the data in this section using
MATLAB on Acoustics Emission Data Set.
2.3.4.1
Input Variable Identification
The Acoustics Emission data set contains 5 features as shown earlier in table
2-2.
AE Features
Peak to peak
Skewness
Kurtosis
Mean of band power
Tool Wear
Table 2-5 AE Features
The time-variant of each AE feature of one of the cutter is shown in the
diagram below,
peak
0.4
0.2
0
0
50
100
150
sk
200
250
300
350
0
50
100
150
ku
200
250
300
350
0
50
100
150
MeanBP
200
250
300
350
0
50
100
150
200
250
300
350
200
250
300
350
1
0
-1
5
0
0.02
0.01
0
toolwear
150
100
50
0
50
100
150
Figure 2-8 : Time-variant of Acoustics Features
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The correlation coefficient of each pair of features are calculated,
Peak
1
sk
0.022
0.065
1
toolwear
0.102
sk
0.022
1
-0.293
0.028
-0.685
ku
0.065
-0.293
1
0.062
0.185
meanbp
1
0.028
0.062
1
0.099
toolwear
0.102
-0.685
0.185
0.099
1
Peak
ku
Meanbp
Table 2-6 Correlation Coefficient of Each Pair of AE Features
The Mean of Band Power feature are highly correlated to the Peak to peak
feature (highlighted in Yellow in table 2-6), so it is discard to remove the
redundancy on the dimension.
AE Features
Peak to peak
Skewness
Kurtosis
Mean of band power
Tool Wear
Type
Selected
Input
Yes
Input
Yes
Input
Yes
Input
No
Output
Yes
Table 2-7 AE Features Selected
2.3.4.2
Rule Identification with Hierarchical Clustering
Ward Method Hierarchical clustering is used to identify the number of
clusters of the Acoustics emission data set as the dendrogram shown
below.
8
7
6
5
4
3
2
1
9
10
11
5
8
29
3
1
6
16
2
4
7
12
18
19
21
22
15
23
25
13
20
14
17
24
26
27
28
30
Figure 2-9 : Dendrogram of Ward Hierarchical Clustering on AE Data.
With the penalty factor of 1, the relation between number of cluster and the
RSS with penalty factor are shown in the diagram below. The RSS + λK
reaches its minimum when number of cluster is 7.
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RSS + λK (# of Cluster)
45
40
35
30
25
20
15
10
5
0
RSS
RSS +λK
0
10
20
30
40
50
# of Clusters
Figure 2-10 : # of Cluster with RSS (with Penalty Factor = 1)
Therefore ANFIS contains 8 rules (approximated from 7) with 2 membership
functions assign to 3 input variables.
2.3.5 Training ANFIS using Matlab
Matlab ANFIS is used to construct the prototype with the configurations
Description
Value
# of Input
3
# of Membership Functions per Input
2
Membership Function
Bell Functions
Output
First Order Sugeno Model
Learning Algorithm
Hybrid
Table 2-8 : Matlab ANFIS Configuration
Based on the configuration in Table 2-8, Matlab generates the 8 rules ANFIS
structure.
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Figure 2-11 : ANFIS Model Structure generated by Matlab.
After the ANFIS is trained, three set of data (trained data, checking data, and
test data) are feed into the ANFIS, and predict output (in Green) is compared
with the actual output (in Blue)
Actual Training Data and ANFIS Prediction
140
130
120
110
100
90
80
70
0
50
100
150
200
250
300
350
250
300
350
Seq
Prediction Errors on Training Data
40
20
0
-20
-40
-60
0
50
100
150
200
Seq
Figure 2-11 : Comparison on Predict Output vs Training Output
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Actual Checking Data and ANFIS Prediction
250
200
150
100
50
0
0
50
100
150
200
250
300
350
250
300
350
250
300
350
250
300
350
Seq
Prediction Errors on Checking Data
50
0
-50
-100
-150
-200
0
50
100
150
200
Seq
Figure 2-12 : Comparison on Predict Output vs Checking Output
Actual Testing Data and ANFIS Prediction
250
200
150
100
50
0
0
50
100
150
200
Seq
Prediction Errors on Testing Data
50
0
-50
-100
-150
-200
0
50
100
150
200
Seq
Figure 2-13 : Comparison on Predict Output vs Testing Output
2.4
User Interface Design
The initial mock up GUI designs are shown below.
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Figure 2-5: Co-Relation Module Diagram
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Figure 2-6: Analyze Error Module Diagram
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Figure 2-7: Generate Prediction Module Diagram
2.5
Knowledge Structure and Representation
The class and the various context diagrams are shown in the diagrams below.
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Figure 2-8: Class Diagram of the Milling Process
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Figure 2-9: Concept Map Diagram
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Figure 2-10: Cause Tree Diagram
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Figure 2-11: Decision Tree Diagram
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Figure 2-12: Milling Setup Process Tree Diagram
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Figure 2-13: Measurement of Cutter Process Tree Diagram
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3.
Hardware and Software
Hardware: Computer for server software installation
Software:
 Windows Server 2008
 MS SQL Server 2008
 MS Visual Studio 2010
 ASP.NET v4.0
 Microsoft Office 2010
 Matlab 2010
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4.
APPENDIX A - DRAFT PROJECT PROPOSAL
KE-PROJECT PROPOSAL
Project Title:
Knowledge Engineering for In-Situ Quality Control in Manufacturing Process
Proposer(s):
Soon Kai Jie Ryan, Woo Meng Chew(Henry), Lim Young Boon
Sponsor/Client: (Name, Address, Telephone No. and Contact Name)
Dr Li Xiang, Dr Goh Kiah Mok, Ms Zhou Junhong
Singapore Institute of Manufacturing Technology (SIMTech)
71 Nanyang Drive, Singapore 638075
Tel: 67938264, 67938420
Aims/Objectives:
Many manufacturing companies have actively adopted QA, QC, SPC, track and trace
technology to prevent costly errors, due to drifts and degradations, from occurring.
Some of the more proactive semiconductor companies in Singapore have set up
advanced process control teams to understand and develop knowledge-base, adaptive
control, fault detection and classification systems to improve their operational
effectiveness and enhance their product quality. However, in the pursuit of these
intelligence-based systems, one of the major gaps is the lack of expertise to develop
representative, reliable and robust methods for the generation of reference models
which can be effectively adopted as baselines for monitoring trends or deviations and
for production yield and quality enhancement.
This project is proposed to develop an intelligent monitoring system (IMS) using
knowledge engineering techniques to build up predictive modeling algorithms for the
development of reference models which in turn help engineers in milling process to
prevent costly errors, characterize the performance, detect degradation and improve
production yield and quality. A real CNC high speed milling machine data will be used
as a test-bed for the IMS development.
1.
2.
3.
4.
5.
Requirements Overview:
Scope of the project:
Explore manufacturing milling process problems
Pre-process the data collected from dynamometer, accelerator and AE sensors, which
will be provided by SIMTech
Build Hierarchical Clustering (HC) for data classification
Establish an ANFIS fuzzy neural network for performance prediction
Develop and implement an Intelligent Monitoring System (IMS) with HC and ANFIS
models for data classification and tool life prediction in high speed milling process with
C# in MS .NET development environment:
a. User interface
b. MS SQL Database schema design
c. HC and ANFIS models
d. The system integration and testing
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6. Project report
Risk:
1. Highly challenging project where in-depth knowledge of the various KE techniques is
required to code the engine
2. Given the amount of research involved, substantial amount of time needs to be spend
with the domain experts on understanding the domain
-
Timeline:
1st 3 months: Domain understanding and modeling
2nd 3 months: Development and implementation
Last 3 months: Integration and testing
Resource Requirements
Hardware: Computer for server software installation
Software: Windows Server 2003, MS SQL Server 2008, MS Visual Studio 2010,
ASP.NET v4.0Microsoft Office 2007
1.
2.
3.
4.
5.
Methods and Standards:
Techniques:
CommonKADS methodology
Hierarchical Clustering
ANFIS (Adaptive-Network-based Fuzzy Inference Systems) fuzzy neural network
OOD and C# programming
ASP.NET framework
1.
2.
3.
4.
5.
Requirements and Standards
User dashboard for monitoring and visualization reporting
HC algorithm engine
ANFIS algorithm engine
Integration of HC and ANFIS engine
Reference model of the various engine
Programme Name: MTech KE
For ISS Use Only
Project No:
KE-DIP
Student Batch: KE22
Accepted/Rejected/KIV:
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Students Assigned:
Advisor Assigned:
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5.
APPENDIX B - FORMAL PROJECT PROPOSAL AND
PROJECT PLAN
To Charles and Fang Ming
Formal Project Proposal
And
Project Plan
(Phase 0)
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Master of Technology KE Project Proposal
PROJECT TITLE: Modeling and Simulation of Milling Forces
TEAM MEMBERS: Henry Woo, Ryan Soon, Lim Yong Boon
DATE SUBMITTED: 28 Feb 2011
5.1
Problem Description & Overview
Milling is a common form of machining and material removal process. It can create a
variety of features on a raw work-piece by cutting away the unwanted material. This
is done by feeding the work-piece into a rotating cutter. Material is cut away from this
work-piece in the form of small chips to create the desired shape.
For any milling company, their main goal is to prolong the lifespan of the cutter, while
minimizing waste by cutting the work-piece to the required specification at the least
cost. As the cutter is rotating at high speeds, wear/tear effect will cause the cutter
head to get Wear out, until certain extent that it might not cut properly. This Wear out
cutter head would damage the expensive work-piece and the company is liable for
this damage and compensation. Therefore the operator has to use more
conventional preventive measures, like premature changing of the cutter head even
before it starts to wear out. Clearly this kind technique leads to lower productivity,
increased wastage and ultimately hurt the bottom-line profit for the company.
Currently, the operator needs to periodically take out the cutter, and place it under
the microscope to examine the extent of wear/tear. They would then manually
determine if the cutter is still within the acceptable wear/tear range for further usage.
Some company would set a time limit on when to replace the cutter. Example, each
cutter head will be proactively replaced after every 30 minutes of usage. This leads
to high wastage and definitely not very cost effective.
In order to correctly predict the effective lifespan of the cutter head during a milling
process, we need to consider factors like:
1. Cutting feed – The distance that the cutting tool or work-piece advances
during one revolution of the spindle and tool, measured in inches per
revolution (IPR)
2. Cutting speed – The speed of the work-piece surface relative to the edge of
the cutting tool during a cut, measured in surface feet per minute (SFM)
3. Spindle speed – The rotational speed of the spindle and tool in revolutions per
minute (RPM). The spindle speed is equal to the cutting speed divided by the
circumference of the tool
4. Feed rate – The speed of the cutting tool's movement relative to the workpiece as the tool makes a cut. The feed rate is measured in inches per minute
(IPM) and is the product of the cutting feed (IPR) and the spindle speed
(RPM)
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As the milling machine is continuously running, all the above measures would be
captured by special equipments, namely:
1. Dynamometer – a device for measuring force, moment of force (torque), or
power
2. Accelerator – a device that measures the speed, normally in revolutions per
minute (RPM) in which the cutter is spinning during the the milling process
3. AE sensors – a device that uses ultrasonic (acoustic emission) technology
which can check the noise emitted during the milling process.
The 3 equipments will provide a continuous stream of sensory information
regarding the cutter condition during the milling process.
It’s a very tedious and complex process for the engineers to utilize the
information to determine the lifespan of the cutter, and determine when to
replace the cutter in-order to fully optimize the cutter.
Therefore a monitoring system can be developed based on the inputs from
the above mentioned devices to correctly predict the Wear out time of the
cutter, thus eliminating waste and improved profit margin.
5.2
Scope
The goal of this project is to develop an intelligent monitoring system (IMS) using
knowledge engineering techniques to build up predictive modeling algorithms for the
development of reference models which in turn help engineers in milling process to
prevent costly errors, characterize the performance, detect degradation and improve
production yield and quality.
Initial proposed scope will be focused on mainly on
1. Knowledge Engineering Models using
o Hierarchical Clustering: responsible for classifying data which are used
by the Adaptive Neural Fuzzy Inference System later
o Adaptive Neural Fuzzy Inference System: responsible for rule
extraction which helps to predict the tool life span
2. User Interface: A reporting and visualization dashboard that can be used to
supply live milling machine status and alarm information for necessary actions
3. RDBMS system: Microsoft SQL Server DB for data storage
The system developed will be benchmarked against sponsor’s previous
research findings.
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5.3
Sources
Dr Li Xiang (xli@SIMTech.a-star.edu.sg), from SIMTech is the domain expect on the
Tool Condition Monitoring System from SIMTech. She has lead SIMTech project of
Predictive Intellifence for Tool and Surface Integrity Monitoring joint ventured with
NUS, NTU and APS. She will provide the real CNC high speed milling machine data
as well as the domain knowledge needed by the team. She will be the project
champion from SIMTech and team members are required to provide her monthly
project progress reports. During the domain familiarization and knowledge
acquisition phase (1st Phase), the project team will go to the SIMTech campus to
meet with Dr Li. For the modeling and system design phase (2nd Phase) and the
System Development phase (3rd phase), the project team will work with Dr Li Xiang
through E-mail, and meet up in SIMTech campus if necessary.
Dr. Goh Kiah Mok, a Research Scientist, joined SIMTech in July 1996, and has since
assumed various responsibilities of in-house/industrial project leader, research team
leader, centre manager, group manager. He was project leader for many successful
completed research and industrial projects. For example, Smart Box for Tata
Consultancy services (TCS), Yokogawa's SEMI interface intelligent code generators,
SATS Terminal 5 Legacy system upgrade. He played lead roles in architecting
system design proposals for numerous industrial projects including equipment
controller for local equipment maker IC Equipment, MIT and STI, Middleware to link
the robot and controller system for Turbine overhaul system, SEMI's Recipe
Management System (RMS) for Lucent (formally AT&T), Shop Floor Integration
study for Philips DAP, Semicon equipment integration Study for UTAC, AAA project
for Infineon and ESEC etc.
Ms. Zhou Junhong, a Senior Research Engineer in the Manufacturing Execution and
Control Group is involved in the research and development of process adaptive
control and monitoring; process monitoring & control, sensing and advanced signal
processing; SCADA system, sensing & measurement for machine tooling condition,
and intelligent systems for diagnostics and prognostics of manufacturing equipment.
Many publicly available papers on the Internet describing the milling and KE
techniques can be sourced and research accordingly.
5.4
Project Plan
The table below shows the initial project plan; the time/task might change depending
on the actual progress of the project.
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5.5
Final Comments
Various different KE techniques other than the one proposed can be used. Genetic
algorithm can be used in place of Hierarchical Clustering to obtain a set of optimized
parameters which can be used as inputs to a Neural Fuzzy network.
The MATLAB application can be used in the modeling stage to aid in the model
development. This would greatly reduce the development cycle of the various
models.
The sponsor has indicated that an end product consists of the model + frontend GUI
needs to be delivered. This is requested such the model developed can be feedback
and output visually to the end user. The focus is on an end system that can improve
a company’s bottom-line by reducing waste and optimizing resources.
Huge amount of research effort needs to be put in by the team to understand the
more advanced KE techniques that are not covered in-detail in the syllabus. The
literature available from the public domain needs to be carefully analyzed and
studied so that the correct techniques can be applied throughout. ISS supervisor can
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provided intelligent research guides to the team in-order to shorten the information
searching and filtering process.
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6.
APPENDIX C – References
[1] O. Nelles, S. Sinsel, R. Isermann, Local basis function networks for identification of a
turbocharger, IEE UKACC Control'96, UK, September 1996.
[2] Yager RR, Filev DP (1993): “Learning of fuzzy rules by mountain clustering” Proc. SPIE
Conf. on Application of Fuzzy Logic Technology, Boston, MA, pp. 245 – 254.
[3] S. L. Chiu. Fuzzy model identification based on cluster estimation. Journal of Intelligent
and Fuzzy Systems, 2(3), 1994
[4] Jain, Anil K., and Richard C. Dubes. 1988. Algorithms for Clustering Data. Prentice Hall.
[5] “On the Use of Hierarchical Clustering in Fuzzy Modeling” M. Delgado Dept. Ciencias de la
Computaci6n e Inteligencia Artificial, Universidad de Granada, Granada, Spain
[6] Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to
Information Retrieval, Cambridge University Press. 2008. P380.
[7] J.-S. R. Jang, `` ANFIS: Adaptive-Network-based Fuzzy Inference Systems,'' IEEE Trans.
on Systems, Man, and Cybernetics, vol. 23, pp. 665-685, May 1993
[8] J.-S. R. Jang, C.-T. Sun, and E. Mizutani, `` Neuro-Fuzzy and Soft Computing: A
Computational Approach to Learning and Machine Intelligence,'' Prentice Hall, 1996.
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