optimization of rebar production process

Metallurgical and Materials Engineering

IIT Kharagpur

Summer Internship Presentation

Developing correlation for product properties

as a function of operating conditions

in Rebar Mill

Objective• Explore the feasibility of a predictive model for property estimation

in NBM.

After Implementation:A machine learning model trained with all possible data can be used for online prediction of controlling parameters.

Current status:Theoretical model based on heat transfer calculations which is highly inaccurate.The operator uses his experience.

YS distribution

NBM LAYOUT

WB1 WB2

FRT

A

B

Billet Temperature Pyrometer

Chemistry (C, Mn)

Testing(YS,UTS,C,Mn

)

Approach

UTS/Y

S>

1.1

550

0D

CS=16 mm

YSUTS

FRT<0

WB1flow rate

WB2 flow rate

Billet Temperature

Mill Speed

Carbon Equivalent

>0

Carbon

Manganese

CS=12 mm

CS=10 mm

Relationship between individual variables

CE was found to be insignificant in predicting YS.Multiple Regression did not give any satisfactory results.Problem with YS: Taken from any random point of rebar.

500D,16mm

YS vs FRT

The operator operates at atleast 2 different ranges of water flow

rates.Towards Clustering

• Fuzzy clustering• K-means clustering

Cluster Analysis

• Satisfactory relation was obtained only for cluster which contained low values of water box flow rate implying negligible leakage.

• The results of Cluster Analysis varied for different data periods.

K means clustering:• 3 clusters created by taking into

consideration WB1, WB2, FRT• All the clusters had similar range of

FRT and Billet Temperature but differed in WB1 and WB2 ranges.

Multiple Linear Regression

LeakageSplit nozzle design

Fuzzy Approach

• Takes into account the error in taking readings.

• Inputs and outputs are assigned membership functions.

• Membership functions create fuzzy sets.

• Results:• RMSE in FRT ~ 11.6C• Predicted values are

concentrated around the mean

BLT

WB1

WB2

FRT

1 23

WB1 values

WB1 Membership Fcn

ANN Model

ANN Architecture: ANN with 1 hidden layer having 10 neurons.

Tansig transfer function=-1

WB1WB2Billet TempSpeed

Backpropagation Learning Algorithm: Works by minimizing error with respect to weights.

FRT

Linear transfer function

Training Data=70%Validation Data=15%Test Data=15%

ANN Results

r2=0.88Results for data on which network was trained

• ANN model predicted extremely well (r2>0.84) for unseen test data on which it was trained.

• Predicted well (r2>0.60) for untrained data which followed a recognizable pattern. • Failed to predict data which didn’t follow any trained pattern. • This showed that data varied considerably with time. • Nozzle wear out increases with time.

Rebar Diameter

Nozzle Bore Diameter

Sufficient water pressure keeps the rebar floating in the water channel

and do uniform circumferential cooling

Insufficient pressure built up could not hold the bar in place

and thus uniform circumferential cooling is not

possible

Cooling in Nozzles

Nozzle wear out

Conclusions

• Efficiency of water quenching decreases with time due to nozzle wear-out.

A dynamic fuzzy based ANN model with sufficient training can be developed for online prediction of product properties within a range of +-5 MPa.

Plant Recommendations :• Leakage minimization.• Identification of sample taken for tensile testing to

correctly map YS with corresponding FRT.• Bringing uniformity among operators in different shifts to

reduce data variation with time.

Thank You