Title of Invention

METHOD AND DEVICE FOR CONTROLLING AN INSTALLATION FOR PRODUCING STEEL

Abstract The invention relates to a method for controlling an installation for producing steel having specific material properties, which depend on the structure and which can be influenced by operating parameters of the installation. Said operating parameters are determined by a computing program for structure optimization, whereby the set values of the chemical composition of the steel are simultaneously determined by computing program for structure optimization
Full Text Description
Method and device for controlling an installation for producing
steel
The invention relates to a method for controlling an
installation for the production of steel which has defined
materials properties that are dependent on the microstructure
and can be influenced by operating parameters of the
installation, which operating parameters are defined by means
of a computation program for microstructure optimization.
Steel has to be produced with the materials properties
requested by the customer. Important materials properties of
steel include, for example, the tensile strength, the yield
strength and the elongation at break. These materials
properties play an important role if the steel is intended for
deep-drawing, for example for the production of automobile body
panels or drinks cans.
The materials properties of the steel result from its
microstructure, which is turn influenced and defined by the
production guidelines for the production process.
During production, it is necessary to ensure that the chemical
composition of the steel corresponds to the respective setpoint
values. These setpoint values correspond to quantitative
fractions which can be checked by a chemical laboratory
analysis.
To produce a steel which complies with the stipulations, it is
also necessary to maintain defined operating parameters. These
operating parameters include setpoint values for the melting,
casting, heating, rolling and cooling phase. They may, for
example, be temperatures, pressures, velocities or changes of
these parameters over the course of time.

A setpoint value for a defined quantitative fraction for an
element or a setpoint value for an operating parameter always
comprises a target value and a lower limit value and an upper
limit value, between which the tolerance range lies.
In steelworks or rolling mills, it is often difficult to define
suitable setpoint values for production in such a way that the
desired materials properties in the end product are produced
under production conditions that are as low-cost as possible
and involve the minimum possible risk. The demands of the
customer, who for example orders a standardized steel grade for
a specific application area, have to be implemented taking into
the production processes used in the steelworks. These
production processes include the works standards for the steel
grade and also the target values and tolerance ranges for the
individual production steps.
In practice, however, the actual values from production always
deviate from the setpoint values for the production process.
The deviations in the actual values in a production step have
to be counteracted in the subsequent steps in such a way that
the required materials properties, for example the minimum
value for the tensile strain, are achieved. If this value can
no longer be achieved, the corresponding steel material (the
melt, slab or coil) can no longer be used for the intended
order. In this case, the processing of this part of the order
has to be restarted at the first production step, which leads
to financial losses and possibly also to delivery delays.
The defining of suitable setpoint values for production is
usually carried out by a planning department as part of
technical order planning. The operator makes use of various
technical tables, which are stored in a data processing
installation, to convert the customer's order into a suitable
production process for the plant,

this production process being linked to defined setpoint
values.
For the steel, the quantitative fractions of the individual
elements are defined and can be checked by a chemical
laboratory analysis. This set of setpoint values is also
referred to as the steel grade. The setpoint values for the
operating parameters of the individual production steps are
also defined. In the steelworks, the melt is produced and the
slabs are cast from this melt. In the furnace, the slabs are
heated for the rolling operation. In the hot-rolling mill, the
slabs are rolled to form coils, and if appropriate further
rolling is carried out in a cold-rolling mill, and/or
qualitative heat treatment of the coils and if appropriate
division into smaller formats are carried out.
The situation often arises whereby a certain order can be
produced by a plurality of alternative sets of setpoint values
which result from the tables. To achieve the desired
properties, the operator has to decide on one of these possible
sets of setpoint values, since the production order to the
production department only permits one set of setpoint values.
The operator decides on a defined set of setpoint values based
on his production experience. The entire know-how of the
company relating to production operations is held within these
technical tables, which are looked after by the quality
division. New entries and changes in these tables result from
long years of production experience and from test productions
using sets of setpoint values which have been altered in steps.
In recent years, mathematical methods have been developed for
calculating the materials properties of steel. These methods
include a physico-metallurgical model of the production process

and determine the changes in the microstructure of the steel
for each production step and then calculate the materials
properties from this information.

Individual production steps can be simulated on the computer in
this way. For standard steel grades, it has been ascertained
that the materials properties which are calculated are well
matched to the values which are actually measured.
EP 0 901 016 A2 has disclosed a mathematical method for
determining properties of a steel. Typical variables, such as
yield strength, tensile strength, elongation at break,
hardness, etc. are supposed to be determined by means of a
neural network.
WO 99/24182 has disclosed a method for controlling a
metallurgical installation for the production of steel or
aluminum, in which steel is produced with defined materials
properties that are dependent on the microstructure, the
materials properties being dependent on operating parameters
with which the installation is operated. The operating
parameters are supposed to be defined by means of a
microstructure optimizer as a function of the desired materials
properties of the steel. The microstructure optimizer is a
computation program for microstructure optimization which makes
use of neural networks.
However, a drawback is that the production planning is often
unable to produce a successful production program, i.e. an
appropriate sequence, since it is restricted to one set of
setpoint values per production order. One such example is melt
planning, which has to combine a large number of production
orders with different steel grades and dimensions in the form
of melts and sequences, i.e. technically compatible sequences
of melts. Only orders of one steel grade can be processed in
one melt, and only melts with compatible steel grades can
follow one another in one sequence. The stock of orders often
comprises a large number of qualitatively similar orders of
small quantities which, however, have different steel grades.

This situation often leads to poor production programs, since
the number of possible melts in the program is small and the
individual melts

often have to be filled with what are known as store orders,
since there is an absence of customer orders for suitable
qualities .
Another drawback is that the quality department has to spend
considerable money and time on optimizing sets of setpoint
values with regard to the demands on the materials properties
and with regard to production costs. The large number of
production tests required, with modified sets of setpoint
values, and the testing of the materials properties using
laboratory specimens which in each case ensues, lead to high
levels of outlay. Therefore, many sets of setpoint values are
compromised in terms of reliability with regard to the desired
target values and tolerance ranges since they have had to be
derived from an inadequate number of production tests. Larger
tolerance ranges and more cost-effective target values for
certain materials properties can only be implemented after a
steel grade has been produced for a number of years.
In principle, the control systems of the individual production
units attempt to maintain their setpoint values for the output
variables even if the input values, i.e. the actual values from
the preceding step, deviate. If a slab arrives at the rolling
train from the furnace at a temperature which is too cold, the
control system of the rolling train nevertheless attempts to
reach its setpoint value for the output temperature of the
rolled strip by suitably changing the control variables of the
control system. This only works for setpoint values which can
still be influenced, for example incorrect temperatures or
dimensions which can be changed to a certain extent. However,
it is not possible for a steel with an incorrect chemical
analysis, i.e. with incorrect quantitative fractions of the
alloying elements, to be altered in the subsequent rolling
mill.

If the deviations cannot be tolerated, for example because
individual elements in the chemical analysis infringe the limit
values, the current intermediate product is qiven up for this.

order. Production planning then has to produce further material
with a high priority, but this leads to time delays. The
current intermediate material is either switched to other
suitable orders, i.e. reassigned, or is if necessary switched
to store orders without any associated customer request, or is
removed from production and placed in an intermediate store.
The final option is for the material to be designated scrap and
return to the resource circuit.
Therefore, the invention is based on the problem of providing a
method for controlling an installation for producing steel
which can plan orders more flexibly and can react more flexibly
to deviations from the setpoint values.
To solve this problem, it is provided, in a method of the type
described in the introduction, that at the same time the
setpoint values for the chemical composition of the steel are
defined by means of the computation program for microstructure
optimization.
Unlike in known methods, the computation program for
microstructure optimization defines not only the operating
parameters, which have an influence on the materials
properties, but also at the same time the setpoint values for
the chemical composition of the steel. The term "at the same
time" also encompasses methods in which, for example, in a
first computation operation the setpoint values for the
chemical composition are determined, and then in a second
computation operation the operating parameters are determined,
or vice versa. The individual computation operations can take
place in succession, but the setpoint values for the chemical
composition and the operating parameters are always taken into
account. Accordingly, the solution has significantly more
degrees of freedom available to it, since for each order it is
not just a single, predefined set of setpoint values which can

be used, but rather a plurality of sets of setpoint values. It
was not hitherto possible to correct a set of setpoint values,
since the operator of a production unit

was not able to determine or stipulate the necessary effects on
the downstream production steps. However, the computation
program for microstructure optimization makes it possible for
the required adjustments to setpoint values for subsequent
production steps to be calculated on-line from the previous
production results, taking account of the required materials
properties.
The invention is based on the discovery that when defining the
setpoint values, not only the individual production step, but
rather the entire possible solution range for variations in
setpoint values can be taken into account in order to achieve
the required materials properties. This solution range with
setpoint values which are dependent on one another even across
production steps contains many more degrees of freedom and
accordingly many more possible solutions than have hitherto
been used.
As a result of the computation program for microstructure
optimization being used to define the setpoint values for the
chemical composition of the steel and the operating parameters
at the same time, production planning can be made significantly
more flexible. Moreover, continuous on-line quality control can
be carried out during the individual production steps. This
improved production planning allows all possible variations in
the setpoint values to be included in the calculation, so that
better production programs can be produced from the available
stock of orders, i.e. longer sequences of melts and fewer
filler orders in the melts.
The method according to the invention ensures that during
production deviations in individual actual values from the
setpoint values are compensated for by calculating the changes
required to subsequent setpoint values, so that even deviations
which have not hitherto been tolerable can now be tolerated.

Consequently, the method according to the invention means that
the tolerance

ranges for the quantitative fractions of the elements contained
in the steel can be widened.
It is expedient if the setpoint values for the chemical
composition and if appropriate correspondingly calculated
operating parameters for a defined steel application are in
each case combined in a data record. As a result of an
optimization calculation, a data record for a specific steel
application is selected by the computation program and output
as a result of an optimum combination of data records for the
respective steel application of the orders to be planned being
selected during production planning.
In the method according to the invention, it can be provided
that the actual values for the chemical composition of the
steel and the actual values for the operating parameters are
compared with the setpoint values, and if deviations are
present the computation program for microstructure optimization
is run through again. The result of this calculation can result
in the selection of a different set of setpoint values which is
better matched to the actual values. As a result, the number of
possible combinations is considerably increased, and in the
event of a deviation between actual values and setpoint values,
the steel product can still be used with a high degree of
probability for the intended purpose, as a result of the
further production steps being carried out with a modified set
of setpoint values.
According to one configuration of the method according to the
invention, it can be provided that a setpoint value is assigned
a tolerance range, which lies between an upper limit and a
lower limit, and the computation program for microstructure
optimization is run through again in order to define setpoint
values for the chemical composition of the steel and/or for the
operating parameters in the event of values which lie outside
the tolerance range.

It can be provided that the computation program for
microstructure optimization optimizes the setpoint values with
a view to one or more

optimization variables. It may be expedient for the
optimization calculations to be carried out with a view to
minimizing materials costs and/or production costs. By way of
example, it is possible to save on certain expensive alloying
elements by carrying out certain treatment steps at altered
temperatures.
A particular advantage of the method according to the invention
is that during and/or after a production step, a quality check
is carried out by comparing the setpoint values with the actual
values by means of the computation program for microstructure
optimization. As a result, it is possible to dispense with
relatively slow and expensive laboratory tests, the validity of
which is however only limited in many cases. The computation
program for microstructure optimization continuously checks all
the steel products, such as melt, slab or coil, in terms of
their materials properties and compliance with the setpoint
values, so that true material tests, for example in the form of
tensile tests, can be reduced to occasional quality control
tests.
In a hot-rolling mill, it is recommended for the quality check
to be carried out at various points during the production
sequence. The linking of the slabs to the production order in
the slab yard, the exit of the heating furnace, the exit of the
roughing stand and the exit of the coiler at the end of the
production line are particularly suitable for this purpose. At
each checkpoint, the computation program for microstructure
optimization calculates the expected materials properties from
the actual values from production which are known by that stage
and the subsequent process with the associated setpoint values.
If the required materials properties cannot be achieved in the
subsequent steps with the current setpoint values, the
microstructure optimizer automatically attempts to adapt the
setpoint values. If the calculation gives a positive result,
the quality check enables the adjustment of the setpoint

values. If the calculation does not give a result, the qualityt

check, with the aid of the computation program for
microstructure optimization, searches the setpoint value lists
of the stock of orders for an inexpensive, appropriate set of
setpoint values and switches the intermediate material to this
order. As a result, new setpoint values are also defined for
the subsequent production steps.
It is possible to provide that the method according to the
invention be coupled to the production planning of the
installation for steel production. The production planning and
the quality check are modules of the production control system
(manufacturing execution system). Accordingly, the method
according to the invention can also be directly coupled to the
production control system of the installation for steel
production.
The invention also relates to a device for carrying out the
method described for controlling an installation for producing
steel. According to the invention, it is provided that the
computation program for microstructure optimization is designed
to define the setpoint values for the chemical composition of
the steel and the associated operating parameters at the same
time.
Further advantages and details of the invention will emerge
from the exemplary embodiment described below and with
reference to the drawings in which:
Fig. 1 diagrammatically depicts the way in which the
microstructure optimizer operates;
Fig. 2 shows the change in the mechanical properties of a
steel as a function of the temperature and the element
content;

Fig. 3 shows the interaction of the microstructure optimizer
with a production control system;

Fig. 4 shows a flow chart illustrating the individual method
steps;
Fig. 5 shows the execution of the quality check during
production; and
Fig. 6 shows the determination of optimum setpoint values for
the entire production path.
Fig. 1 diagrammatically depicts the way in which the
microstructure optimizer operates for optimizing alloying
costs. The computation program for microstructure optimization,
referred to below as microstructure optimizer 1, receives
setpoint values 2 for the chemical composition and setpoint
values 3 for the operating parameters of the desired process.
The setpoint values 2, 3 together result in what is known as a
steel grade, which is based on experience and takes into
account all the information required for production of the
respective order. The materials properties of the steel
produced can be influenced by operating parameters of the
installation, including treatment temperatures and pressures,
and if appropriate also the time derivatives thereof.
Mathematical models, which simulate the behavior of the
material and determine the changes in the microstructure of the
steel for each production step, are implemented in the
microstructure optimizer 1. The microstructure optimizer 1 can
also operate using the neural network method. As a result, the
microstructure optimizer 1, after the execution of optimization
calculations, delivers modified setpoint values 4 for the
chemical composition of the steel, which allow production costs
to be reduced compared to the setpoint values 2. By way of
example, it is possible to aim to reduce the levels of
relatively expensive alloying materials, such as niobium,
manganese or titanium, without any deterioration in the

materials properties. Modified setpoint values 5 for the
production processes are coupled to the modified setpoint
values for the chemical composition. The setpoint values 4, 5
together form a set of setpoint values which forms the basis
for production.

Fig. 2 shows the change in the mechanical properties of an
element as a function of the temperature and the element
content.
The microstructure optimizer 1 is intended to optimize, i.e.
minimize, production costs. One of the ways of reducing costs
consists in reducing the element content of a specific chemical
element, in particular an alloying element. The element content
of a specific element is plotted on the horizontal axis in
Fig. 2. The temperature is plotted on the vertical axis. The
lines 6 shown in Fig. 2 indicate combinations of the element
content and the temperature having the same mechanical .
property. A specific steel has hitherto been produced using the
element content indicated by point 7. However, it is known that
the property shown in Fig. 2, which may for example be the
strength, remains constant if point 7 is shifted in the
direction of the arrow toward point 8. In^practice, this means
that the content of the respective alloying element can be
reduced, while at the same time the setpoint value for the
temperature for a specific production step has to be increased.
Reducing the quantitative fractions of this element results in
a reduction in the costs, while the mechanical properties of
the steel remain the same.
Fig. 3 shows the interaction of the microstructure optimizer 1
with a production control system. The production control system
9, also known as the manufacturing execution system (MES),
comprises, inter alia, the production planning and the quality
check. The microstructure monitor is a calculation module which
calculates materials properties, such as the strength or yield
strength, on the basis of input data. As a result, time-
consuming laboratory tests can be substantially replaced. If it
is established, after execution of a calculation by the
microstructure monitor, that a mechanical property cannot be
complied with, the microstructure optimizer 1

analyzes whether the steel product in question, by changing the
setpoint values, can still be treated in such a way that it
lies within the appropriate tolerances. The actual values 10,
which are input variables for the microstructure monitor or the
microstructure optimizer 1, comprise the quantitative fractions
of the individual chemical elements, process temperatures, etc.
Setpoint values 11 output are operating parameters and process
parameters which form the basis for the further production
steps.
The coupling of the microstructure optimizer 1 with the level
of the production control system 9 allows costs to be reduced,
since optimized steel grades are used for production. Time can
be saved during the quality checking of coils, since the
microstructure monitor substantially replaces laboratory tests.
Nevertheless, materials properties are calculated for each
coil, so that the probability of defects is reduced. A further
advantage is that there are more degrees of freedom available
for production planning. Hitherto, there has been a single
standardized steel grade with predetermined setpoint values for
rolling and cooling for each order. By contrast, with the
method according to the invention, there are a number of
presets available per order which each satisfy the required
materials properties. Each preset comprises the works standard,
which indicates the steel grade including the setpoint values
for rolling and cooling. From the plurality of presets, the
microstructure optimizer 1 can calculate and select the one
which entails the lowest production costs.
The order planning supplies melt planning with a list of
alternative works standards that can achieve the materials
properties and, in addition, suitable setpoint values
calculated by means of the microstructure optimizer, for each
order. This results in very many more degrees of freedom for
the planning of the melts. It is possible to form larger
sequences

and the number of steel grade changes is reduced. It is also
possible to reduce the number of filler orders in the melts.
The production

can be carried out using a smaller number of steel grades by-
virtue of order-specific properties being compensated for by
calculated rolling and cooling conditions. Order planning
supplies melt planning with more orders using the same grade of
steel in order to achieve better production programs. The
microstructure optimizer calculates suitable setpoint values
for the order-specific materials properties.
Fig. 4 shows a flow chart illustrating the individual method
steps based on the example of a hot-rolling mill.
Prior to the beginning of production, the costs relating to the
chemical analysis are optimized off-line. The result of this
optimization can be utilized in method step 12 in order to
modify the source data for the production control system. Order
planning 13 can offer melt planning a wide range of steel
grades for planning for each order. In the production plan,
melt planning optimizes the costs with regard to the materials
properties. An on-line quality check 15 is carried out, in
which the linking of a slab to an order is either saved or the
slab is reassigned on-line. A further on-line quality check 16
is carried out following the heating in the furnace or the
rolling in the roughing stand. After a further on-line quality
check 17 downstream of the coiler, the coil can be released
immediately, thereby terminating the method.
Fig. 5 shows the execution of the quality check during the
production.
The current process data pass from the furnace or the roughing
stand 18 to the production control system (MES) 9. In step 19,
it is checked whether the required materials properties can be
achieved. If the result is "yes", production is continued with
the existing setpoint values. If

the result is "no", the setpoint values for the order have to
be adapted in step 20. For this purpose, the production control
system 9 interacts with the microstructure monitor and
microstructure optimizer 1 and receives modified setpoint
values. On the basis of the modified setpoint values, method
step 21 checks whether the strength can be achieved using the
modified setpoint values. If the strength can be achieved,
production is continued using the modified setpoint values. If
the strength cannot be achieved, a reassignment request is
output in order to link the slab to a different order. The
number of reassignments is reduced, and the modification to the
setpoint values in many cases allows the slab to be saved for
the existing order. The slabs or coils which are not suitable
for the existing order are immediately reassigned without
further production steps being carried out.
Fig. 6 shows, in a diagram, the determination of optimum
setpoint values for the entire production sequence.
The method determines optimum setpoint values for the entire
production sequence, with the individual operations and
treatment steps of the manufacturing sequence being modeled and
simulated by computation. The basis for the optimization
calculation is the chemical analysis 22, the results of which
form input variables for the continuous - casting 23. This is
followed by hot-rolling 24 and cold-rolling 25. To improve the
microstructure, the cold-rolling is followed by an annealing
operation 26. All the individual treatment steps are input into
the mathematical models of the microstructure optimizer 1, so
that the latter can determine optimum setpoint values taking
account of the entire production sequence. The microstructure
optimizer 1 is in turn linked to the production control system
(MES) 9, and accordingly the production can be controlled as a
function of the results of the microstructure optimizer 1.
Overall, the method allows considerable energy and

materials costs to be saved. In addition, expert knowledge is
built up about the overall process.

Patent Claims
1. A method for controlling an installation for the
production of steel which has defined materials properties that
are dependent on the microstructure and can be influenced by
operating parameters of the installation, which operating
parameters are defined by means of a computation program for
microstructure optimization, characterized in that at the same
time the setpoint values for the chemical composition of the
steel are defined by means of the computation program for
microstructure optimization.
2. The method as claimed in claim 1, characterized in that
the setpoint values for the chemical composition and if
appropriate correspondingly calculated operating parameters for
a defined steel application are in each case combined in a data
record.
3. The method as claimed in claim 2, characterized in that
the computation program for microstructure optimization, during
production planning, selects an optimum combination of data
records for the respective steel application of the orders to
be planned.
4. The method as claimed in one of the preceding claims,
characterized in that the actual values for the chemical
composition of the steel and the actual values for the
operating parameters are compared with the setpoint values, and
if deviations are present the computation program for
microstructure optimization is run through again.
5. The method as claimed in claim 4, characterized in that a
setpoint value is assigned a tolerance range, which lies
between an upper limit and a lower limit, and the computation
program for microstructure optimization is run through again in
order to define setpoint values for the chemical composition

of the steel and/or for the operating parameters in the event
of values which lie outside the tolerance range.

6. The method as claimed in one of the preceding claims,
characterized in that the computation program for
microstructure optimization defines the setpoint values with
the objective of minimizing materials costs and/or production
costs.
7. The method as claimed in one of the preceding claims,
characterized in that during and/or after a production step, a
quality check is carried out by comparing the setpoint values
with the actual values by means of the computation program for
microstructure optimization.
8. The method as claimed in claim 7, characterized in that
the quality check is carried out in a hot-rolling mill during
the linking of slabs to the production order in the slab yard
and/or at the exit of a heating furnace and/or at the exit of
the roughing stand and/or of the coiler at the end of a
production line.
9. The method as claimed in one of the preceding claims,
characterized in that the computation program for
microstructure optimization checks whether the required
materials properties can be achieved with the current setpoint
values in subsequent production steps.
10. The method as claimed in claim 9, characterized in that
the computation program adapts the setpoint values if the
required materials properties in subsequent production steps
cannot be achieved with the current setpoint values.
11. The method as claimed in one of the preceding claims,
characterized in that it is coupled to the production planning
of the steel production installation.

12. The method as claimed in one of the preceding claims,
characterized in that it is coupled to the production control
system of the steel production installation.
13. The method as claimed in one o£ the preceding claims,
characterized in that the number of steel grades in a plant is
reduced by calculated variations in the operating parameters
when defining a steel grade for an order.
14. The method as claimed in one of the preceding claims,
characterized in that the tolerance ranges for the chemical
elements in steel grades are increased by varying the operating
parameters.
15. A device for carrying out the method as claimed in one of
the preceding claims for controlling an installation for
producing steel, which has defined materials properties that
are dependent on the microstructure and can be influenced by
operating parameters of the installation, which operating
parameters can be defined by means of a computation program for
microstructure optimization, characterized in that the
computation program for microstructure optimization is also
designed to define the setpoint values for the chemical
composition of the steel.

The invention relates to a method for controlling an installation for
producing steel having specific material properties, which depend on the
structure and which can be influenced by operating parameters of the
installation. Said operating parameters are determined by a computing
program for structure optimization, whereby the set values of the
chemical composition of the steel are simultaneously determined by
computing program for structure optimization

Documents:

417-KOLNP-2006-CORRESPONDENCE.pdf

417-KOLNP-2006-FORM-27.pdf

417-kolnp-2006-granted-abstract.pdf

417-kolnp-2006-granted-claims.pdf

417-kolnp-2006-granted-correspondence.pdf

417-kolnp-2006-granted-description (complete).pdf

417-kolnp-2006-granted-drawings.pdf

417-kolnp-2006-granted-examination report.pdf

417-kolnp-2006-granted-form 1.pdf

417-kolnp-2006-granted-form 18.pdf

417-kolnp-2006-granted-form 2.pdf

417-kolnp-2006-granted-form 3.pdf

417-kolnp-2006-granted-form 5.pdf

417-kolnp-2006-granted-gpa.pdf

417-kolnp-2006-granted-reply to examination report.pdf

417-kolnp-2006-granted-specification.pdf

417-kolnp-2006-granted-translated copy of priority document.pdf

417-KOLNP-2006-PA.pdf


Patent Number 228776
Indian Patent Application Number 417/KOLNP/2006
PG Journal Number 07/2009
Publication Date 13-Feb-2009
Grant Date 10-Feb-2009
Date of Filing 23-Feb-2006
Name of Patentee SIEMENS AKTIENGESELLSCHAFT
Applicant Address WITTELSBACHERPLATZ 2, 80333 MUNCHEN
Inventors:
# Inventor's Name Inventor's Address
1 HANS-PETER BURVENICH KILLINGER STR. 81 91056 ERLANGEN
2 GERHARD DACHTLER J.S. BACH-STR.2, 91083 BAIERSDORF
3 GUNTER SORGEL ZAUNKONIGWEG 8 90455 NURNBERG
PCT International Classification Number G05B 13/02
PCT International Application Number PCT/EP2004/008347
PCT International Filing date 2004-07-26
PCT Conventions:
# PCT Application Number Date of Convention Priority Country
1 10339466.3 2003-08-27 Germany