A Hybrid System for the Design and Processing of C-Mn and
Microalloyed Steels
P. A. Manohar, R. J. Dippenaar and S. S. Shivathaya*
BHP Institute for Steel Processing and Products,
University of Wollongong, Northfields Avenue,
Wollongong, NSW - 2522, Australia.
*Hawker de Havilland Ltd., 361 Milperra Road,
Bankstown, NSW - 2200, Australia.
ABSTRACT
A novel hybrid approach is proposed in this paper that combines knowledge bases
along with mathematical modelling approaches to generate and evaluate the
alternative target compositions for steelmaking, which meet the customer
requirements. The methodology developed is applicable for C - Mn, Nb- and Timicroalloyed steels. The system consists of two modules. The first module uses both
mathematical (iterative) and knowledge-based approaches to generate a list of
alternative target compositions for steel making. The target compositions are then
evaluated by using the second module that consists of mathematical modelling
approach to calculate the microstructural evolution as a function of steel composition
and process route. The mechanical properties of the steel products are then computed
based on the microstructural parameters using empirical relationships contained
within the structure - property model. This method enables more realistic assessment
of the designed compositions. The system is expected to assist the product
development metallurgists in the selection of appropriate target composition for
steelmaking and for hot rolling process optimisation.
INTRODUCTION
The challenges for iron and steel industry are many fold and complex, eg. being more
flexible and responsive, less capital intensive, energy efficient and “earth friendly”.
Non-ferrous metals and non-metallic materials have provided continuous competition
to steels as alternative engineering materials. To deal with these challenges and for
efficient management of the uncertainties involved, it has become imperative to apply
artificial intelligence (AI) techniques in the manufacture of iron and steel. In the past
three decades a number of expert systems have been developed around the world for a
more efficient solution of problems in diagnostic, design, planning, scheduling,
process control, and quality control (1). Several expert systems have been developed
and utilised in almost all aspects of iron and steelmaking such as primary operations
(eg. blast furnace, sinter plants), steelmaking (eg. BOS monitoring and control),
rolling operations (eg. plate mill, hot strip mill), energy and utilities (eg. electric
power system), systems engineering, quality management (eg. defect analysis in
continuous casting), research, planning, sales price forecasting, and purchase (2).
Application of a knowledge-based approach to steel composition design has been
considered by (1, 3 - 5). However, the development of such expert systems is a
57
complex task because the material design process is ill structured, difficult to
systematise and involves a large number of rules. In addition, linear relationships do
not exist between compositional and process parameters and product properties and as
such the knowledge of steel composition design is largely intuitive and heuristic.
On the other hand, the hot rolling of steels has been investigated intensively and a
number of mathematical models and computer systems have been developed. Several
advantages gained through the use of mathematical modelling have been reported
which include the following:
 improving the efficiency of mill trials to establish optimum process conditions,
controlled rolling schedules and accelerated cooling (6),
 prediction of microstructure and mechanical properties during rolling (7),
 development of new steel grades and rolling processes (8, 9),
 increase productivity and quality, reduce manufacturing cost through the use as
an off-line prediction, on-line prediction, on-line control or off-line alloy and
process design tool (10, 11),
 ability for flexible manufacturing (12),
 control of size, shape, quality and stability of steel products, more responsive
for product development (13),
 betterment of understanding of the processes (14),
 a useful “what - if” tool which provides directions for further fundamental
research along with problem investigation, schedule development, design or
redesign of mill configuration and enhancement of understanding (15).
In the current work, a new integrated approach is proposed to build an expert system
which combines the above two approaches to generate and evaluate the alternative
compositions for steelmaking that meet the customer’s requirements. The expert
system consists of two modules. The first module uses both mathematical (iterative)
and knowledge-based approaches and utilises interview as well as non-interview
techniques for knowledge elicitation (KEL). KEL is also characterised by a threecharacter codification scheme to record customer’s special requirements. The
codification scheme is coupled with a decision table-based knowledge representation
tool “TABLEAUX” for incorporation within knowledge-based systems. The expert
system generates a list of alternative target compositions, which may meet the
property requirements. The compositions are then evaluated by using the second
module that consists of mathematical modelling approach. The module calculates the
microstructural evolution as a function of steel composition and known values of
process variables such as pass temperature, strain, strain rate, interpass time and plate
cooling rate during the hot rolling of C-Mn and Nb- and Ti- microalloyed steels. The
predicted microstructure is used as a basis for the subsequent estimation of the
mechanical properties of the steel products using empirical relationships, thus
enabling more realistic assessment of the designed compositions. The expert system is
developed in C / C++ language on an IBM PC in a windows environment. User
interface is developed utilising a commercial package, ‘PROTOGEN+’, to make the
expert system user friendly. The expert system is expected to assist the product
development metallurgists in the selection of appropriate steelmaking target
composition and for hot rolling process optimisation.
58
KNOWLEDGE ELICITATION
The process for knowledge elicitation (KEL) adopted in this work has been reported
in detail elsewhere (1, 16 - 17), however a brief summary is given here. The KEL is
characterised by a three-character codification scheme having a hybrid structure to
codify all the customer’s special requirements based on the initial structured and
unstructured interviews. The customer special requirement codes (CSRCs) are given
by the equation:
Customer Special Requirement Code = XiYjZk
The first character in the code is Xi called the major group code, which is the ith
property of a steel grade (eg. tensile strength, yield strength, elongation etc.). The
second character in the CSRC is called the subgroup code and it represents the jth type
of steel (eg. structural, pressure vessel, line pipe steel etc.). Zk is the value code which
represents the kth value of the ith property of the jth type of steel. Zk has a hierarchical
structure while Xi and Yj are chain type structures. The chain type structure facilitates
vertical (depth first) search while the hierarchical structure assists the horizontal
search. A total of 238 328 CSRCs are possible using this codification scheme. The
significance of using these CSRCs is that the knowledge representation becomes
simple, time efficient and memory efficient (i.e. requires less storage space).
Knowledge representation is a key development stage in KEL. The acquisition and
organization of knowledge for incorporation within the expert system being reported
here has been achieved through a decision table based tool ‘TABLEAUX’ developed
at BHP Steel Company, Port Kembla, Australia (18). A decision table is a collection
of rules. Each rule is represented as a row made up of conditions and actions within
the table, an example of such rows is given in Figure 1. Figure 1 illustrates the
knowledge representation in TABLEAUX along with the use of codification scheme.
Codification is achieved by combining columns 2 - 4 in Fig. 1(a) in to a three-letter
code as shown in Fig. 1(b). The conditions are to the left of bold line in Fig. 1 (a) and
Fig. 1(b) while the actions are to the right of the bold line. For any rule to be
evaluated true, it is necessary that all conditions within the rule match the values in
their corresponding cells. For example, Rule 2 in Figure 1 can be stated in natural
language as follows:
IF
the steel type is structural
the test type is reduction in area in transverse direction (RAZ)
the minimum value of RAZ is 15%
THEN
maximum Sulphur content is 0.008%
maximum Hydrogen content is 19 ppm
maximum Calcium content is 0.01%
critical caster alignment is A1
electro-magnetic stirring code is E4
sulphur print requirement is 1
59
AND
AND
AND
AND
AND
AND
AND
Rule
Rule
1
Rule
2
Steel
Type
Structural
Structural
Test
Type
RAZ
RAZ
Value
25%
Min.
15%
Min.
Max.
S
0.005%
Max. H
Alignment
A1
EMS
19 ppm
Max.
Ca
0.01%
E4
S
Print
1
0.008%
19 ppm
0.01%
A1
E4
1
(a)
Rule
SR Code
Max.
H
19 ppm
Max.
Ca
0.01%
Alignment
A1
EMS
211
Max.
S
0.005%
E4
S
Print
1
Rule
1
Rule
2
212
0.008%
19 ppm
0.01%
A1
E4
1
(b)
Figure 1: Knowledge representation (a) before codification, and (b) after codification.
Knowledge rules such as those given above have been determined through interview
as well as non-interview techniques of KEL. The knowledge rules also pertain to both
composition and steel processing. The knowledge bases developed in this work
contain a large number of such rules that elucidate complex interrelationships between
steel composition, processing variables and product properties. The details of the
knowledge bases and their interconnection in the present expert system are discussed
in the following sections.
EXPERT SYSTEM DEVELOPMENT
The expert system consists of four knowledge bases and a steel-processing module.
The inputs and outputs of the system are shown schematically in Figure 2. The
knowledge base KB I consists of information on properties and composition
corresponding to relevant material standards. The material standards include
Australian standards and other overseas standards transformed into a form, which is
similar to the Australian standards. Customer special requirements are also included in
KB I. Based on the customer special requirements, the composition and mechanical
properties from the existing steels need to be modified. This is achieved through the
knowledge rules contained in the second knowledge base, KB II.
Input information about the end use of the steel or the intended application of the steel
along with information in KB I and KB II dictates a set of rules regarding elements to
be included in the target composition and the basic process route to be followed. The
process routes could be hot rolled, controlled rolled, or normalised. These rules are
included in the third knowledge base, KBIII that calculates the upper and lower limit
values of the elements to be included in the target composition. Some values of the
elements in the target composition, in spite of being within the range of values
obtained through KB III, are not feasible due to practical difficulties faced by either
the plate mill or the slab caster. In addition, based on the end use and the mechanical
60
properties required, certain strategies need to be adopted in the design of the target
compositions. Such strategies impose further restrictions on the target composition
values. Thus the rules regarding process limitations and design strategies are
contained in the fourth knowledge base, KB IV. Some examples of such rules are:






increment in C (C)= 0.005%,
Nb = 0.001%,
  
Cu:Ni  2.0,
Ti:N  3.42,
Mn:C 3.
The output from KB IV and the process details given in KB III are combined in steel
processing module, which calculates the metallurgical structure evolution as a
function of composition and process sequence. Details of the steel processing module
are given in the following section.
Basic Inputs
Standard: AS1548-7-400NL50
Thickness: 10 mm
Size (W x L): 3000 x 9500 mm
Quantity: 20
Weight: 44.75 tons
End Use: Tankage
Customer’s Special
Requirement Codes
R22
Output: Target
Compositions for
Steelmaking
Steel Processing
Module
C
Mn
1 A11 A12
2 A21 A22
..
..
..
Expert System
KB I KB II KB III
..
..
..
KB IV
Figure 2: Input, processing and output of the expert system.
STEEL PROCESSING MODULE
The flow chart for the steel processing module is given in Figure 3. Mathematical
modelling of microstructural evolution during hot rolling of steels has received a great
deal of attention over the past two decades and a number of models which describe
metallurgical phenomena during steel processing have been published for different
steel compositions (eg. C-Mn, Nb-/Ti-/Nb-Ti/Nb-V microalloyed steels) and a variety
of steel processing routes (eg. conventional, conventional controlled rolling,
recrystallization controlled rolling, hot direct rolling etc.). These models have been
reviewed in (11, 19). The mathematical models employed in the current work for
calculating the microstructural evolution in Nb- microalloyed steels are given in
Table 1.
61
START
Initial Structure
Rolling Schedule
INPUT
Target Composition
RECRYSTALLIZATION
MODEL
PRECIPITATION
MODEL
Rolling
Finished?
No
Yes
PHASE TRANSFORMATION
MODEL
STRUCTURE - PROPERTY
MODEL
END
62
Figure 3: Flow chart for the steel processing module.
Table 1: Mathematical models describing the microstructural evolution
during the hot rolling of Nb- microalloyed steels.
Model
Reference
1.155 ln(ho/hf)
(15)
VR/ RR(ho  h f )
(15)
6.75x10-20xdo2x-4 x
exp(300000/RT)xexp{((2.75x105/
T)-185)x[Nb]}
-6
3x10 x[Nb]-1x-1x Z-0.5 x
exp(270000/RT)x
exp{(2.5x1010)/(T3(lnKs)2)}
{[Nb]+([C]+0.86[N])} /
10(2.26-6770/T)
4.92x10-17x -2x  -0.33 x do x
exp(338000/RT)
1-exp(-0.693(t/t0.5)2)
1.1xdo0.67/67 (X0
0.5xdo0.67/67 (X<0
  exp(400000 / RT )
(20)
Parameter
pass strain

pass strain rate

time for 5% recrystallization
t0.05
time for 5% precipitation of
Nb(C, N)
t0.05p
Nb supersaturation ratio
Ks
time for 50% recrystallization t0.5
volume fraction recrystallized X
recrystallized grain size
drex
Zener - Hollomon parameter
Z
time for 95% recrystallization t0.95
grain growth during
interpass time ‘t’
df
average austenite grain size when
X < 0.9
(partially recrystallized austenite)
d
(20)
(20)
(21)
(22)
(23)
(9)
(20)
7.64 x t0.05
(22)
8.75
28
- do = 2.6x10 x
(24)
exp(-437000/RT)xteff ;
teff = t - t0.95
(9)
eff = pass + 
 = const. x previousx(1-X)
const. = 1 if X < 0.1; 0.5 if X 
0.1;
1.33
(15)
d = X xdrex+(1-X)2do
ho = original slab thickness (mm), hf = final slab thickness (mm), T = pass temperature
(K), t = interpass time (s), VR = peripheral roll speed (mm/s), RR = roll radius (mm),
R = gas constant (8.31 J/mol.K), do = initial grain size (m).
df8.75
The mechanical properties for each steelmaking target composition are calculated
based on the output from KB IV and the steel processing module. Empirical models
derived from the statistical data are utilised for this process. The empirical models are
characterised by an error of about  20 MPa in the prediction of tensile strength and
upper yield strength. A factor of safety of 40 MPa is added to the required values of
tensile and upper yield strength while comparing with the corresponding computed
values. Thus the final target composition list is generated which has alternative target
compositions that are feasible for any inquiry or order.
63
SUMMARY
The new integrated expert system for steel composition design proposed in this work
combines both mathematical modelling and knowledge-based approaches.
Mathematical modelling enables iterations involving enormous computations while
the knowledge-based approach enables utilisation of the expert as well as the heuristic
knowledge from a group of experts to successfully determine the steelmaking target
compositions. The procedure involved in this approach is to identify the possible
customer requirements with regard to composition, mechanical properties and testing
requirements, then to codify them, coupled with processing schedules used in the
industry and finally to direct the KEL to acquire knowledge to deal with these special
customer requirements. The quality of the output of this expert system depends mainly
on the quality of the rules in knowledge bases and mathematical models. As the
knowledge base grows richer by experience and the mathematical models refined
further through research, it is always possible to incorporate more rules into
knowledge bases to improve the output of the expert system. The expert system is
expected to assist metallurgists to choose an existing composition or to design a new
steel composition so that the customer requirements are satisfied in an economical
way. The prototype material design expert system has been fully implemented by
developing a software module for generating alternative steelmaking target
compositions that are practically feasible for the slab caster and plate mill.
Implementation of the process optimisation module is currently under development.
ACKNOWLEDGEMENTS
The authors acknowledge the financial support extended by the Australian Research
Council and BHP Steel Company, Australia during the course of this work.
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