o The aim of this research study is to perform data mining for the improvement of strip thickness control on a cold reduction mill using data analytics. This project is done for Cogent Power (a subsidiary company of Tata Steel) located at Newport, UK. Further to this, a software was developed in python to perform data mining to avoid developing codes for future purposes

1. PROJECT UPDATE FOR IMPROVEMENT OF ‘FINAL
STRIP THICKNESS CONTROL’ IN HIGH ‘SI
ELECTRICAL STEEL’ THROUGH THE USE OF ‘DATA
ANALYTICS’
Presentation by:
Srinivasa Raghavan Ramanujam
F E A T U R E
S E L E C T I O N
M O D E L
B U I L D I N G
P R E -
P R O C E S S I N G R E S U L T S
R E C O M M E N D
A T I O N S
O V E R V I E W

2. Objective
Current Progress
Feature Selection
Dimensionality Reduction
Model Building
Results and Discussion
Recommendation
Future Work
F E A T U R E
S E L E C T I O N
M O D E L
B U I L D I N G
P R E -
P R O C E S S I N G R E S U L T S
R E C O M M E N D
A T I O N S
O V E R V I E W

3. Aim
To analyse the cold reduction of high Si steel for reducing the thickness by
90% to achieve sharp magnetic losses which are proportional to the final
strip thickness through the use of data analytics.
F E A T U R E
S E L E C T I O N
M O D E L
B U I L D I N G
P R E -
P R O C E S S I N G R E S U L T S
R E C O M M E N D
A T I O N S
O V E R V I E W

4. Dissertation Plan
F E A T U R E
S E L E C T I O N
M O D E L
B U I L D I N G
P R E -
P R O C E S S I N G R E S U L T S
R E C O M M E N D
A T I O N S
O V E R V I E W
Topic
Weeks
1 2 3 4 5 6 7 8 9 10 11 12
Initial Proposal
Feature Selection in Weka
Pre-processing Dataset
Model Building in Python
Analysing Results Current Position
Compiling
Report Writing

5. Project Approach
F E A T U R E
S E L E C T I O N
M O D E L
B U I L D I N G
P R E -
P R O C E S S I N G R E S U L T S
R E C O M M E N D
A T I O N S
O V E R V I E W
•Analyzing the generalized data
•Identifying the base of the problem
Top down
approach
•Dividing into test and train sets
•Feature Scaling
Data pre-
processing
•Identify the relation between different
attribute
Feature
Selection
•Dimensionality ReductionPCA
•Build the correct algorithm using python
•Predict the results
Model Building
Visualize Results

6. Current Update
F E A T U R E
S E L E C T I O N
M O D E L
B U I L D I N G
P R E -
P R O C E S S I N G R E S U L T S
R E C O M M E N D
A T I O N S
O V E R V I E W
Update Check
Exporting the IBA data into a csv
format
Trying to find a relationship between
different attribute
Develop various mathematical
model to predict the snap
Working on a GUI to perform data
mining using python

7. Feature
Selection
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S

8. Problem: Unclean Data
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S
Fluctuations when
velocity is zero

9. Solution: Removing Zeros
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S

10. Feature Selection: Weka
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S
It is the process of selecting a subset of
relevant features (variables, predictors) for
use in model construction. Feature
selection techniques are used for four
reasons:
Simplification of models to make them
easier to interpret by researchers/users,
Shorter training times,
To avoid the curse of dimensionality,
Enhanced generalization by reducing
overfitting
Point
where
snap
happens

11. Feature Selection: Weka
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S
Performed for snap and non snap
coils
727 Attributes
30 coils
Six Different Models
BestFirst+ Cfs Subset Eval
Greedy StepWise+ Cfs Subset Eval
Greedy StepWise + WrapperSubset
Eval
Ranker + Principal Components
Ranker+ ReliefAttribute Eval
Ranker + CorrelationAttribute Eval

12. Feature Selection Result: Entire
Coil
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S
Entry Strip Speed
Exit Strip Speed
Entry Gauge
Deviation
Exit Gauge Deviation
Total Force Feedback
Mill Speed
Right gauge coarse
deviation
Feedback Error
Single Shot Gauge Deviation
Delayed Raw Mass flow Gauge Error
Right Gauge Deviation Component
Fit
Force Error RMS
Back Capsule Force
Servo Front Load
Servo Back Load
Measured Slip
Back_Capsule_Titt
Calc mill speed
WR circ

13. Feature Selection Result: Snap
Time Data
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S
Entry Strip Speed
Exit Strip Speed
Entry Gauge
Deviation
Exit Gauge Deviation
Total Force
Feedback
Mill Speed
Feedback Error
Single Shot Gauge Deviation
Right Gauge Deviation
Component Fit
Mass Flow Exit Gauge
Force Error RMS
Back Capsule Force
Servo Front Load
Servo Back Load
Measured Slip
Back_Capsule_Titt
Calc mill speed
WR circ

14. Feature Selection Result: Non Snap
Selection Coil
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S
Entry Strip Speed
Exit Strip Speed
Entry Gauge
Deviation
Exit Gauge Deviation
Total Force Feedback
Mill Speed
Right gauge coarse
deviation
Feedback Error
Single Shot Gauge Deviation
Delayed Raw Mass flow Gauge Error
Left Gauge Deviation Component Fit
Right Gauge Deviation Component
Fit
Force Error RMS
Back Capsule Force
Servo Front Load
Servo Back Load
Measured Slip
Back Capsule Titt
Calc mill speed
WR circ

15. Data Pre-processing
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S

16. Data Pre-processing
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S
Four steps
Importing the Libraries
Importing the datasets
Splitting into test and train sets
Feature Scaling
Dimensionality Reduction

17. Dimensionality Reduction
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S
Principal Component Analysis
Unsupervised Mode of Dimensionality
Reduction
Statistical procedure that uses an
orthogonal transformation to convert a
set of observations of possibly correlated
variables into a set of values of linearly
uncorrelated variables called principal
componentsSplitting into test and train
sets

18. Model Building
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S

19. Model: Multiple Linear Regression
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S
Multiple linear regression attempts
to model the relationship between
two or more explanatory variables
and a response variable by fitting a
linear equation to observed data.
Every value of the independent
variable x is associated with a value
of the dependent variable y

20. Model: Random Forest Regression
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S
It is unexcelled in accuracy among
current algorithms.
It runs efficiently on large databases.
It can deal with a large number of
information factors without variable
erasure

21. Model: Decision Tree Regression
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S
Decision tree builds regression or
classification models in the form of a
tree structure.
It breaks down a dataset into
smaller and smaller subsets while at
the same time an associated
decision tree is incrementally
developed.
The final result is a tree with
decision nodes and leaf nodes

22. Model Results
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G
R E S U L T S
R E C O M M E N D
A T I O N S

23. O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S
PCA Components Model Accuracy mean
1 7.58%
2 37.62%
3 38.54%
4 40.98%
5 75.14%
6 80.61%
7 80.81%
8 82.66%
PCA Components Model Accuracy mean
9 82.76%
10 82.76%
11 82.76%
12 82.76%
13 82.76%
14 82.76%
15 82.76%
Model: Multiple Linear Regression

24. Model: Decision Tree Regression
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S
PCA Components Model Accuracy mean
1 92.95%
2 99.42%
3 99.88%
4 99.952%
5 99.96%
6 99.97%
7 99.97%
8 99.97%
PCA Components Model Accuracy mean
9 99.98%
10 99.98%
11 99.983%
12 99.985%
13 99.986%
14 99.986%
15 99.986%

25. Model: Random Forest Regression
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S
PCA Components Model Accuracy mean
1 94.77%
2 96.82%
3 98.42%
4 99.23%
5 99.45%
6 99.54%
7 99.72%
8 99.77%
PCA Components Model Accuracy mean
9 99.89%
10 99.98%
11 99.987%
12 99.998%
13 99.998%
14 99.998%
15 99.998%

26. Snap Identification
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S
Three primary snap identification
parameter
Total Force Feedback
Force Error RMS
Measured Slip

27. Snap Results
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S
7/8/2017-27/04/2018
2294 coils
K-means clustering algorithm
Parameter Value Percentage
No Snap 1279 56%
Snap 1015 44%

28. Snap Classification
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S
Year: 2018
837 coils
Hierarchical clustering algorithm
Parameter Value Percentage
No Snap 599 71.5%
Edge Split 76 9.08%
Pinch 14 1.06%
Scab 22 2.6%
Stress 10 1.19%
Straight Line 12 1.4%
Unknown 83 9.91%
Mixed Snap 21 2.5%

29. Snap Relationship Analysis
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S

30. O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S
Snap Coil
Coil ID Speed Total Force Feedback
B02595 320 1250
B02776 310 1550
B02825 240 1230
220 1250
B02833 230 1220
B02838
300 1450
405 1250
200 1500
240 1500
B02846
400 1250
370 1260
190 1500
B02856 310 1550
B99514 310 1550
B99659
320 1550
320 1550
300 1500
300 1500
B99680
320 1550
320 1550
B99690
450 1250
250 1500
B99727 350 1250
Non Snap Coil
Coil ID Speed Total Force Feedback
B02595 200 1200
B02825
210 1300
125 1250
B02786
110 1250
150 1250
B02774
200 1400
220 1250
B02776
170 1250
190 1450
B02786
110 1100
250 1250
B02833
125 1150
175 1250
B02838
190 1300
260 1550
B02843
220 1250
400 1550
B02846
210 1350
175 1150
B02856
125 1250
150 1450
B99514 150 1250
B99727 190 1150

31. Recommendatio
ns
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S

32. Snap Improvement Methods:
Mechanical System
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S
Stress
Lubrication
Increased force value
Roll wear
Straight Line Snap
Rolling Force
Bending Force
Lubrication
Pinch
Lubrication
Load Values
The gauge is too heavy
Material defect
Scab
Material defect
Edge Split
Guide vane
Roll position
Lubrication

33. Thickness Control Improvement
Methods: Control System
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S
Too much noise in the data
Control System
Kalman Filter
Fuzzy logic
Convolutional Neural Network
Pictures of different snaps and put
them into model to predict the snap.

34. GUI Data Mining
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S

35. Version 2.0
O V E R V I E W
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S

36. Future
F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S
Trying to find more optimization
methods
Complete the GUI
Prepare and compile the report
O V E R V I E W

37. F E A T U R E
S E L E C T I O N
P R E -
P R O C E S S I N G
M O D E L
B U I L D I N G R E S U L T S
R E C O M M E N D
A T I O N S
O V E R V I E W
Thank you for your time and
patience!