US 10,949,582 B2 · Classification optimization method for steel grade family of steel rolling model

Abstract

Provided is a classification optimization method for steel grade family of a steel rolling model, including: step 1: calculating the deformation rate and deformation resistance of a rolled piece according to rolling process data; step 2: reversing the “equivalently measured” deformation resistance according to the measured roll force; step 3: drawing a scatter plot of data on a graph, and judging whether layer division is reasonable according to distribution characteristics; and step 4: for the unreasonable division of steel grade family, optimizing the existing classification of steel grade family using a new algorithm called line clustering, determining steel grade codes that need to be divided into new family, searching for the shortest distance from a sample point to the curve of each cluster centre based on a Nelder-Mead algorithm, dividing all data points into k subsets according to the principle of proximity, fitting a power curve to each data subset using a least square method, repeating the above steps, and stopping iterating when cluster centrelines obtained by two successive iterations are consistent. The present invention improves the precision of the division of steel grade family of a hot rolled model.

A classification optimization method for steel grade family of a steel rolling model, characterized by comprising:

step 1:
for each coil of strip in steel grade family, calculating a deformation rate {dot over (ε)} and deformation resistance KmPOS of a rolled piece according to latest rolling process measured data obtained during model adaptive post-calculation process,
calculation formulas for the deformation rate being as follows:
ɛ
=
ln
⁡
(
1
1
-
r
)
=
ln
⁡
(
H
h
)
ɛ
.
=
ɛ
·
v
R
R
′
·
(
H
-
h
)
where ε is the deformation degree of the rolled piece, {dot over (ε)} is the deformation rate of the rolled piece, r is a reduction rate of a stand, H and h are the entry thickness and exit thickness of the rolled piece on a stand respectively, vR is a measured speed of rolls, and R′ is a roll flattening radius,
a calculation formula for the deformation resistance being as follows:
Km
POS
=
(
a
0
+
∑
i
=
1
n
⁢
a
i
,
x
i
)
·
exp
⁡
(
b
1
T
+
273
)
·
ɛ
b
⁢
⁢
2
·
ɛ
.
b
⁢
⁢
3
where KmPOS is a calculated value of the deformation resistance of the rolled piece, T is a rolling deformation temperature, xi is the content of each chemical component of strip, and ai and bi are model parameters to be determined;
step 2:
for each coil of hot rolled strip, substituting a measurement value of roll force according to roll force model calculation formulas, and reversely obtaining the deformation resistance KmACT of the rolled piece, the calculation formulas being as follows:
Km
ACT
=
F
ACT
W
ACT
·
L
d
ACT
·
Q
p
ACT
+
α
b
·
T
b
ACT
+
α
f
·
T
f
ACT
L
d
ACT
=
R
′
⁢
Δ
⁢
⁢
h
Q
p
ACT
=
(
a
F
⁢
⁢
1
+
a
F
⁢
⁢
2
·
r
)
+
(
a
F
⁢
⁢
3
+
a
F
⁢
⁢
4
·
r
+
a
F
⁢
⁢
5
·
r
2
)
·
R
′
h
R
′
=
(
1
+
16
·
(
1
-
v
2
)
π
·
E
·
F
act
·
1000
W
ACT
·
Δ
⁢
⁢
h
)
·
R
where KmACT is a “measurement” value of the deformation resistance of the rolled piece, FACT is a measured roll force, WACT is a measured strip width, LdACT is a roll flattening arc length, QpCT is a stress state coefficient, TbACT is a measured backward tension, TfACT is a measured forward tension, αb and αf are tension influence coefficients, αFi is a model parameter, v is a Poisson's coefficient, E is an elasticity modulus, Δh is a rolling reduction, and R is a roll original radius;
step 3:
drawing a scatter plot of data ({dot over (ε)}, KmPOS) and ({dot over (ε)}, KmACT) on the same graph, according to strip distribution characteristics of data scatters:
if the data scatters are concentrated within a strip-shaped range, layer division is more reasonable;
if they are scattered into several strip-shaped distributions segmented obviously, the layer division is unreasonable, and the layer needs to be further subdivided;
and
step 4:
for the unreasonable division of steel grade family, optimizing the existing classification of steel grade family using a new algorithm called line clustering, and determining steel grade code that need to be divided into a new family;
(1) randomly initializing k curves as cluster centres within the range of a data set, wherein randomly initializing k curves refers to randomly selecting k groups of C and m coefficient values according to a relationship Km=C·{dot over (ε)}m existed in a deformation resistance data set;
(2) calculating the distance from all sample points to the k cluster centrelines Kmcenj=Cj·{dot over (ε)}mj, for each sample point, finding a cluster centreline closest to the sample point, adding the sample point into a cluster corresponding to the cluster centreline closest to the sample point, and dividing the whole data set into k clusters after traversing all the sample points;
and
(3) fitting power curves to data subsets of the k clusters using a least square method respectively, updating the C and m coefficient values to update all the cluster centrelines, repeating step (2), and stopping iterating when the cluster centrelines obtained by two successive iterations are consistent to complete clustering.

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Classification optimization method for steel grade family of steel rolling model

Life of this patent

Classification optimization method for steel grade family of steel rolling model

Life of this patent

Abstract

Claims2