The paper proposes a model-based approach for the design of martenzite structure steels with improved mechanical and plastic characteristics using proper composition and thermal treatment. For that purpose, artificial neural models approximating the dependence of steels strength characteristics on the percentage content of alloying components were trained. These non-linear models are further used within an optimization gradient procedure based on backpropagation of utility function through neural network structure. In order to optimizing the steel characteristics via its chemical composition, several steel brands with high values of tensile strenght, yield strenght and relative elongation were designed. A steel composition having economical alloying and proper for practical application was determined comparing several obtained decisions. The usage of that steel will lead to lightening of the hardware for automobile industry

Nikolay TONCHEV

Petia KOPRINKOVA-HRISTOVA

Sasho POPOV

Silvia POPOVA

Yavor LUKARSKI

The Annals of “Dunarea de Jos” University of Galati Fascicle IX Metallurgy and Materials Science

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FASCICLE IX. METALLURGY AND MATERIALS SCIENCE 
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A DESIGN OF NEW BRANDS OF MARTENZITE STEELS BY 
ARTIFICIAL NEURAL NETWORKS 
 
Yavor LUKARSKI1, Sasho POPOV1, Nikolay TONCHEV2,  
Petia KOPRINKOVA-HRISTOVA3, Silvia POPOVA3 
1Institute of Metal Science, Technology and Equipment “Acad. A. Balevski”, 
Bulgarian Academy of Sciences, Sofia, Bulgaria 
2 University of Transport “Todor Kableshkov”-Sofia, Bulgaria 
3 Institute of System Engineering and Robotics,  
 Bulgarian-Academy of Sciences, Sofia, Bulgaria 
e-mails: lukarski@ims.bas.bg; tontchev@vtu.bg; 
pkoprinkova@icsr.bas.bg 
 
ABSTRACT 
 
The paper proposes a model-based approach for the design of martenzite 
structure steels with improved mechanical and plastic characteristics using proper 
composition and thermal treatment. For that purpose, artificial neural models 
approximating the dependence of steels strength characteristics on the percentage 
content of alloying components were trained. These non-linear models are further 
used within an optimization gradient procedure based on backpropagation of utility 
function through neural network structure. In order to optimizing the steel 
characteristics via its chemical composition, several steel brands with high values of 
tensile strenght, yield strenght and relative elongation were designed. A steel 
composition having economical alloying and proper for practical application was 
determined comparing several obtained decisions. The usage of that steel will lead 
to lightening of the hardware for automobile industry. 
 
KEYWORDS: metal materials, high strength steels, composition optimization, 
neural networks 
 
1. Introduction 
 
Metallurgy and, in particular, steel production 
for motor industry have develop faster during the last 
decades. On the one hand, methods and technologies 
for characteristics optimization of known steel brands 
are looked for, on the other hand, a lot of research 
efforts are targeted to designing new steel brands with 
improved physical-chemical and mechanical 
characteristics.  
Especially for the steels used in motor industry, 
the research aim is to decrease the weight of the final 
product applying high strength steels. The number of 
high strength steels application increase because they 
are able to meet at the same time the contradictive 
requirements for better deformability, weldability, 
resistance to stress, fatigue, and corrosion. 
Since the motor vehicles are the biggest source 
of pollutants of the environment (producing harmful 
emissions of CO2= 0,2 kg/km), nowadays a lot of 
efforts are targeted to their weight decreasing. This 
will lead to improvement of their combustible 
efficiency and as a result, to the decrease of harmful 
emissions decrease. In [1] several innovative 
approaches related to economy of fuel, the decrease of 
harmful emissions and recycling are described. It is 
demonstrated that the use of high strength steels 
allows decreasing car carriage weight with 25%, and 
it is expected to achieve even 35 % weight decrease 
using the new generation of high strength steels that 
are still under development. 
While the strength of the most common 
application steels varies in the range of 440–590 МPа, 
a part of special components of steels have tensile 
strength about 980–1180 Мра. High strength foliate 
steels still have restricted application because of their 
restricted plasticity. In Nippon Steel Corporation, 
three brands of high strength steels (980 МРа) are 
designed and introduced into vehicle production. [2]. 
Moreover from the functional and economic 
point of view the high strength steels appear to be the 
best material for the mentioned application. The iron 
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based materials ensure such a structure and properties 
that absorb the shock energy. The quick elaboration of 
the steels applied for motor-car construction leads to 
definition of steel classes denoted by AHSS 
(Advanced High Strength Steels). In 2002 a project 
ULSAB - AVS (Ultra _ Light Steel Autobody) 
started. Fig. 3 shows that the average value of the 
tensile strength for the developed steels increases 
from 413 MPa to 758 MPa [3]. Together with the 
increasing values of the strength there is also a 
tendency towards improved deformability. The 
ULSAB - AVS project applies the high strength steels 
for parts of the entire car. In an investigation of the 
model Audi A1, one can see that the quota of the high 
strength steels is approximately two times greater than 
the conventional low carbon steels. Farahani [4] gives 
an investigation published by the International 
Council on Clean Transportation that represent 
different ways for development.  
The short-term plan foresees that in 2014 the 
mild steel will be changed with the high strength 
steels. For the analysis a Toyota Venza (model from 
2009) has been chosen. The aim is a reduction of the 
total mass with 20%. 
This study presents a new contemporary method 
for design of optimized iron-based alloy composition 
using artificial neural networks (ANN) accounting for 
economic alloying at the same time. 
 
 
 
Fig. 1. Areas of variance of steels quality characteristics according 
to the program ULSAB–AVC. 
 
Fig. 2. Change of the average value of the materials tensile strength at different 
motor-car construction projects. 
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Fig. 3. Distribution of the steel types used by the motor-car construction. 
 
2. Steel types and contemporary trends 
 
During the reduction of elements amount in the 
steels there is need to keep the design balance of the 
system “composition – treatment regime – 
characteristics” that will allow to improve its strength 
and plastic characteristics. 
Steels used in vehicle production can be 
classified based on different indications, as it is shown 
on Figure 1. 
Diphase (DP) steels structure consists of ferrite 
matrix containing hard martenzite part and of soft 
ferrite phase.  
The lather insures good conditions for shape 
changing.  
The increase in martenzite volume leads to the 
increase of steel strength. 
In TRransformation Induced Plasticity (TRIP) 
steels residual austenite of about 5% is included into 
ferrite matrix together with different amounts of hard 
phases martenzite and bainite. During deformation, 
residual austenite is converted into martenzite. 
Complex phase (CP) steels on similar to the TRIP 
steels microstructure but they do not contain residual 
austenite. 
In MART steels, the final structure is obtained 
from austenite that is converted entirely into 
martenzite matrix containing small amounts of ferrite 
and/or bainite during hot roll forging. Martenzite 
MART steels possess higher tensile strength, up to 
1700 MPa, and at the same time lower ability to 
plastic deformation. 
As can be seen in the above figure, increase in 
strength of different types of steel is related the 
decrease of its total elongation. This unfavorable 
feature of high strength steels leads to deterioration of 
their deformability but also improves their stress 
resistance without increasing product weight. 
Steels chemical composition is of crucial 
importance for product quality. The quality is 
determined by the mechanical characteristics of steel 
obtained after their plastic and thermal treatment. 
Manganese, chrome, molybdenum, and nickel added 
as alloying elements in combination or separately help 
to increase steels strength. Increasing of carbon 
amount leads the increase of martenzite. Variations in 
carbon and other alloying elements during 
optimization must improve not only steels mechanical 
characteristics but also their technological properties 
such as weldability, deformability etc. 
 
3. Design of martenzite steel with optimal 
characteristics 
 
The design of steels includes determination of 
their chemical composition, parameters of thermal 
treatment regime and obtained mechanical 
characteristisc.  
Here we propose a design procedure that relates 
the mechanical characteristics aimed at to chemical 
steels composition using multiple criteria 
optimization. Optimization criteria contain complex 
of targeted for given application steel characteristics 
defined as objective functions.  
In the present study a data base of 92 alloys 
from [5] was used. It contain alloys chemical 
compositions and their corresponding to mechanical 
characteristics. The alteration intervals of the 
considered alloying elements are given in Table 1 and 
Table 2 contains the change intervals of mechanical 
characteristics after thermal treatment (tempering and 
low temperature lukewarming). 
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Table 1. Alteration intervals of investigated steels alloying elements 
 
Element C Si Mn Ni S Р Cr Mo V 
min, % 0.12 0.27 0.27 0 0.025 0.025 0.15 0 0 
max, % 0.5 1.4 1.6 4.22 0.035 0.35 2.5 1.5 0.15 
 
Table 2. Alteration intervals of investigated steels mechanical characteristics 
 
Mechanical 
characteristic 
Rm,  
MPa 
Re,  
MPa 
A,  
% 
Z,  
% 
KCU, 
KJ/m2 
HB*10-1, 
MPa 
min 500 300 7 30 290 179 
max 1670 1375 26 70 1830 541 
 
For steel composition optimization purposes, 
developed in [6] a methodology based on ANN was 
applied. 
The first step is to train neural network model 
approximating non-linear dependence between 
alloying elements amounts and obtained after thermal 
treatment of the mechanical characteristics of the 
given steel. Our former investigations [6] showed that 
due to insufficient data it is impossible to train a 
single accurate NN model for predicting all 
investigated mechanical characteristics. Here we 
trained separate NN models for each of the 
mechanical characteristics from Table 2. Since the 
amounts of sulfur and phosphorus in all investigated 
steels are equal, they are considered as one variable. 
The NN structure as in [4] is 8:40:1. The number of 
inputs is defined by the number of alloying elements, 
i.e. eight. All NN models have single output neuron 
for the modeled one of the mechanical characteristics. 
The number of hidden units was determined by trail 
and error as in [6].  
We also had to account that the database we 
have is relatively small and it do not contain all the 
possible combinations between alloying elements. 
Because of that we divide the available data into 18 
smaller data sets used for training of 18 NN models 
excluding each time one of the data sets for testing. 
Then the best model for each of the mechanical 
characteristics was chosen based on smaller testing 
error. 
Because the investigated input/output space is 
multidimensional and the modeling dependencies are 
non-linear, in order to find global optima during 
optimization we need to explore the entire input 
space. However, due to huge number of possible 
combinations, this is a time and resource demanding 
task. That is why here we apply gradient the 
procedure proposed in [6]starting from numerous 
different initial points. Finally, the obtained optimal 
compositions are compared and the best ones were 
chosen. 
Figure 4 presents the main optimization 
procedure known as “backpropagation of objective 
function” [7]. The optimization task is the following: 
find values of input vector X that maximize 
(minimize) the objective function: 
 
 
Fig. 4. Optimization procedure. Dashed lines 
represent gradient calculation direction. 
 
( )YXJJ ,=    (1) 
 
Here Y is output variables vector that is related 
to the input one via a given function (here NN model) 
F as follows: 
 
( )pXFY ,=    (2) 
 
and p is model parameters vector. 
The gradient procedure needs calculation of 
objective function derivatives with respect to 
optimized variables as follows: 
 
X
F
Y
J
X
J
dX
dJdX
∂
∂
∂
∂
+
∂
∂
==  (3) 
 
In the cases when J do not depend explicitly on 
X, the first term in equation (3) is zero, i.e. the 
derivative depends only on function F. 
The layered structure of neural network models 
offer a convenient way for calculation of derivatives 
from (3) using backpropagation procedure. 
The next step is the iterative determination of 
the optimal values of X as follows:  
 
iXiXiX Δ±−= α1   (4) 
Model 
Y=F(X) 
X Y 
J(X, Y) 
dJ/dX 
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Here α is a parameter called learning rate and 
iXΔ  is a step change of X at i-th iteration calculated 
as follows: 
 
( )idXgiX =Δ    (5) 
Here g is a function defining the gradient. 
Usually it is proportional to dXi but in some cases it 
could depend also on previous values of iXΔ . Here 
we used simple gradient procedure with 
idXiX =Δ . The learning rate α is chosen relatively 
small and stopping criteria were very small change in 
objective function.  
In our case the input vector X consists of 
concentrations of 8 alloying elements and the output 
vector – of corresponding steel mechanical 
characteristics. 
The following optimization tasks were solved: 
A.Single criteria optimization- maximization of 
Re: 
 
maxRe1 →=J    (6) 
 
Table 3. Chosen optimal compositions,% 
 
Decision 
Compounds/ 
characteristics 
№1 №2 №3 
C 0.27 0.3 0.26 
Si 1.10 1.02 1.24 
Mn 1.06 1.2 1.18 
Ni 2.36 2.35 2.78 
S 0.02 0.02 0.02 
P 0.02 0.02 0.02 
Cr 1.04 0.96 1.26 
Mo 0.15 0.18 0.23 
V 0.0087 0.0087 0..022 
Rm, MPa 1666.9 1678. 1647.5 
Re, MPa 1370.3 1355 1370.9 
A, % 11.4 12.8 12.2 
Z, % 51.1 52.3 54.1 
HB 281.6 284.3 287.4 
 
B.Two-criteria optimization: simul-taneous 
maximization of Re and of the ratio Rm/Re: 
maxRe1 →=J , 
max
Re2
→=
RmJ   (7) 
In both cases, the restriction C≤0.3 was imposed 
aimed at obtaining low carbon steels. 
After applying the above optimization 
procedure, three optimal decisions were chosen. They 
are shown in Table 3 below. The first two of them 
maximize Re while the third one maximizes the ration 
Rm/Re. 
Finally, composition № 2 is chosen because it is 
the most economical one. 
 
Acknowledgment 
This work was supported by the Bulgarian 
National Science Fund under the Project No DDVU 
02/11 “Characteristic modeling and composition 
optimization of iron-based alloys used in machine-
building industry”. 
 
Conclusion 
 
Here we presented an approach to design 
optimal steel compositions for transport vehicles 
industry. The applied methodology on bases in 
artificial neural networks. Via optimization of 
mechanical characteristics on base of the chemical 
composition of the steel, the values of tensile strength 
and yield strength are kept high at total elongation of 
about 12%. 
 
References 
 
[1]. Krupizer, R. A - Process of Decoupling and Developing 
Optimized Body Structure for Safety Performance, 10th European 
LS-DYNA Conference, March 18, 2004. 
[2]. Development of High Strength Steels for Automobiles, Nippon 
steel technical report No.88, July, 2003. 
[3]. ULSAB-AVC, Advanced Vehicle Concepts Program Results, 
CD March 2002, www.ulsab-avc.org 
[4]. Farahani A. R. Kolleck - Hot Forming and Cold Forming—
Two Complementary Processes for Lightweight Auto Bodies, 
Proceedings from The International Conference „New 
Development in Sheet Metal. Forming Technology,” Stuttgart, 
Germany, 2004, pp. 235-244. 
[5]. http://www.splav.kharkov.com/choose_type.php. 
[6]. Koprinkova-Hristova, P., Tontchev, N., Popova, S. - Neural 
networks for mechanical characteristics modeling and 
compositions  
optimization of steel alloys, Int. Conf. Automatic and 
Informatics’10, Oct. 3-7, 2010, Sofia, Bulgaria, pp.I-49 – I-52 
[7]. Werbos, P.J. - Backpropagation through Time: What It Does 
and How to Do It. Proceedings of the IEEE vol. 78(10), pp.1550-
1560, 1990. 
 
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A Design of New Brands of Martenzite Steels by Artificial Neural Networks

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A Design of New Brands of Martenzite Steels by Artificial Neural Networks

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