The present invention relates to the field of design optimization
and production planning in order to obtain cost effective and/or resource effective
production of parts, in particular in production processes related to the production
of cast products such as metallic castings and injection molded plastic parts. The
present invention also relates to methods or systems which provide for coupling
quantitative casting property predictions and casting design optimization loops
with the optimization of local material requirements during component design, thereby
improving the final design.
Since the 1960's the digital computer has steadily been
used more and more intensively to simulate different procedural aspects and phenomena
in manufacturing processes. This development has intensively been supported by the
continuous increase in computer power over the years. Today enough computer power
is available at low costs to be able to carry out exceedingly complex and comprehensive
What has been known and used thus far through the use of
simulation software has resulted in sub-optimization of different technical casting
process production aspects.
For example, the textbook entitled: "Numerical Simulation
and Modeling of Casting and Solidification Processes for Foundry and Cast-House",
authored by Peter R. Sahm and Preben N. Hansen, published by Comité International
des Associations Techniques de Fonderie, International Committee for Foundry Technical
Associations, CIATF 1984 (hereafter referred to as "Hansen et al."); long ago described
how to carry out simulation of casting processes to be able to design a casting
layout and a casting process which would lead to castings with what was then a sufficient
particular quality level for a particular characteristic of the casting. Further,
Marek Dariusz Lipinski, disclosed a substantially very similar methodology, though
providing greater detail, especially for the mold filling process, in a Ph.D. dissertation
"Mold Filling Simulation for Casting Processes", Technical University, Aachen, November
4, 1996 (hereafter, "Lipinski").
Over the ensuing years many articles have been published
on how to simulate and attempt to optimize, i.e., endeavoring but only ultimately
sub-optimizing, different process aspects in casting processes. A series of conferences
with proceedings have been held, including: "Modeling of Casting, Welding and Advanced
Solidification Processes" held by The Engineering Foundation, U.S.A.
These ideas from 1984 forward have been commercialized
in software products like MAGMASOFT® software for metal casting processes,
where it is possible to simulate the filling process to be able to design a proper
gating system and a correct way to fill a casting. Further, the solidification process
can be simulated to be able to check if the quality is satisfactory, as for example,
in checking to determine whether the microstructure and the mechanical properties
are acceptable and fulfilling the specifications as well as to check if any faults
are to be expected in the casting process. Further it is possible to simulate the
stresses which are generated in the different steps in casting and heat treatment
processes to check whether these are acceptable or not.
Also in the field of plastic injection molding simulation,
activities have been undertaken with software products like SIGMASOFT® software,
a software product like MAGMASOFT® software having a plastics database and
a user interface, and the Moldflow software product, inter alia. Peter Kennedy
has also published some specific ideas for plastics in the book "
Mold Filling of Injection Molds," Hanser Publishers, 1995
It may be fair to summarize the main aim of using simulation
prior to the advent of the present invention in that it had been to improve the
quality of cast metallic and plastic parts to be able to meet a particular one or
more quality specifications; noting even so that such specifications may generally
have been steadily increasing. Then when a particular pre-defined quality level
had been sufficiently obtained in the resulting end-product, the goal had been reached
and the simulation was not taken any further.
DISCLOSURE OF THE INVENTION
On this background, it is an object of the present invention
to provide a method for optimizing a process for producing a cast or molded product
made by a number of production steps in respect of a predetermined parameter towards
an optimal value. This object is achieved in accordance with claim 1 by providing
a method for optimizing a process for producing a cast or molded product made by
a number of production steps in respect of a predetermined parameter towards an
optimal value, the process comprising the steps of defining one or a number of technical
requirements for the cast or molded product; providing computer implemented processes
that each reflect one of said production steps; generating with each of the computer
implemented processes a number of solutions for the production step concerned that
will lead to a cast product that will fulfill the technical requirements; optionally
selecting a set of production steps, and determining in a further computer implemented
process which combination of the individual solutions results in an actual process
or in a selected set of production steps in which the predetermined parameter is
closest to the optimal value.
This method allows the overall process to be optimized
towards a given parameter, such as the lowest manufacturing cost, the overall energy
consumption, the lowest material consumption, or the lowest environmental load,
shortest lead time inter alia. The process takes the interrelation between
the process steps into account and can thus deduce the optimal solution towards
a given parameter.
The optimized method can than be fed back into the respective
overall production process to thereby provide input to the manufacturing machinery
for the respective manufacturing sub-steps.
Further objects, features, advantages and properties of
optimizing a process or a method for producing a cast product according to the invention
will become apparent from the following detailed description and the drawings appended
BRIEF DESCRIPTION OF THE DRAWINGS
In the following detailed portion of the present description,
the invention will be explained in more detail with reference to the exemplary embodiments
shown in the drawings, in which:
- Fig. 1 is a flow chart diagram illustrating a method for optimizing a process
for producing a cast product,
- Fig. 2 is a flow chart diagram illustrating a casting process simulation procedure
and phenomena to be simulated,
- Fig. 3 is a general routing diagram illustrating a manufacturing optimization
- Fig. 4 is a manufacturing routing diagram for a casting manufacturing process
illustrating an optimization methodology,
- Fig. 5 is a flow chart for the collection of data for an optimization method
and software hereof.
- Fig. 6, which includes the sub-part Figs. 6a, 6b, 6c and 6d, provides respective
isometric and cross-sectional views of a first design of a casting part according
to the present invention,
- Fig. 7 is a cross section of a second design of a casting and core hereof,
- Fig. 8 is a cross section of a third design of a casting (with no core used)
- Fig. 9 is a cross section of a fourth design of a casting and 2 cores hereof,
- Fig. 10 is a cross section of a casting and 3 cores of a fifth design hereof,
- Fig. 11 is a first layout with a horizontal parting for the first design (see
Fig. 6) hereof,
- Fig. 12 is a second layout with a horizontal parting also for the first design
(see Fig. 6) hereof,
- Fig. 13 is a first layout with a vertical parting for the first design (see
Fig. 6) hereof,
- Fig. 14 is a second layout with a vertical parting for the first design (see
Fig. 6) hereof,
- Fig. 15 is a process diagram of an alternative embodiment of a design process
hereof incorporating casting analyses therewith,
- Fig. 16 is an alternative view of a process diagram for an alternative embodiment
incorporating casting analysis with a design process,
- Fig. 17 is a cast component, the design of which may be optimized hereby,
- Fig. 18 is a load diagram depicting loading of a cast component,
- Fig. 19 is a mould filling simulation,
- Fig. 20 is a solidification simulation,
- Fig. 21 is a simulation of a distribution of microstructures in a cast part,
- Fig. 22 is a simulation of defects such as porosities,
- Fig. 23 is a simulation of yield strength,
- Fig. 24 is a simulation of elongation,
- Fig. 25 is a simulation of residual stresses,
- Fig. 26 is a simulation of risering,
- Fig. 27 is a simulation of gating,
- Fig. 28 is a methodological pathway alternative of the present invention,
- Fig. 29 is an alternative methodological pathway alternative of the present
- Fig. 30 is a further alternative methodological pathway alternative of the present
With reference to Figs. 1 and 2 a review of the different
elements necessary to carry out a numerical simulation of casting processes is illustrated.
The main steps of a simulation identified generally using the reference numeral
20 (see Figs. 1 and 2) are the following:
- A digital geometrical representation of the geometry of the simulation domain,
see box 22;
- Enmeshment, which is subdivision of the calculation domain into many small elements,
which are the bases for discretizing the differential equations (utilizing different
solution algorithms) and in this way finding the solutions to the physical phenomena
to be simulated, see box 24;
- Specifying the boundary conditions for the simulation project, see box 26;
- Attaching the necessary physical data for the different materials domains into
the simulation model (data base or data bank), see box 28;
- Solving the differential equations for heat flow, fluid flow and stresses and
strains using numerical algorithms, see box 30; and
- Displaying the results, see box 32.
As further shown in Fig. 1, various other elements may
contribute to the overall process; as for example, first having technical drawing
data, see box 34, particularly in electronic form, this data (and/or graphics, etc.)
can be fed into the process, particularly at the first step 22, providing a geometrical
representation of the ultimate part to be cast.
Similarly, other information may be fed, particularly in
digital form, into the process. Such is shown for example by the feeding of measurement
data, via an A/D converter, see box 36, into the boundary condition definition.
Or, other analyses can be performed with the results fed into the overall process;
examples here being shown by boxes 38 and 40 representing respectively a thermal
analysis of solidifying alloys or a thermophysical and/or mechanical analysis of
mold materials. Moreover, still further technical and/or administrative (e.g., managerial,
financial and/or economic) data could be incorporated into the overall process as
is indicated generally by the boxes 42, 44, in Fig. 1.
Still furthermore, several aspects and related models can
be coupled to and/or expanded from within one or more of the otherwise discrete
steps of this main simulation structure 20 as shown in a further detailed example
of a casting simulation process 200 in Fig. 2. See e.g.:
- Criteria functions, which may combine in different formulas the parameters,
which are the result of the simulation to be able to predict different phenomena,
as for example the three solidification/thermal/cooling calculations 38a, 38b and
38c broken out (figuratively and/or actually) from a single set of calculations
38 in Fig. 1; and/or as for a further example, the additional microstructure calculations
such as porosity formation, shrinkage formation, crack formation, erosion phenomena,
cold shots, etc., all generally indicated by the box 48 in Fig. 2;
- Micro modeling of the formation of the microstructure during solidification
of castings and during heat treatment of castings, box 38a, e.g.; and
- Models for simulation of stresses and strains, box 38b, e.g.
Additional and/or further broken out detailed calculations
related to the overall process may also be included as is suggested generally by
the box 50, which here may include the functionality of the box 40 of Fig. 1. Note
also that all reference identifications (numerals) are intended to show similarity,
not necessary identity of functionality(ies) throughout the discrete figures, as
e.g., between Fig. 1 and Fig. 2. Thus, for example, the three boxes identified with
a 32 in Fig. 2 are intended to demonstrate different display functionalities, not
unlike, but in no way limitative of the functionalities more generally indicated
in Fig. 1. Similarly, note that the calculations of box 30 in Fig. 1 may be understood
to be distributed throughout numerous of the boxes of Fig. 2, and are thus not separately
identified herein. Other add-on modules with more specific functionalities may also
or alternatively be added into/onto an overall process 20 or 200 as well. Non-limitative
examples, such as iron, steel, HPDC, LPDC and/or plastic injection molding add-on
modules may be used, details of exemplary such modules being set forth hereinbelow.
In any case, in both figures, Figs. 1 and 2, it can be
seen that a way to find the wanted technical process solution may include using
human iteration where new simulation runs may be carried out after changing either
the geometry, and/or the boundary conditions, and/or the thermophysical data, etc.
Such a procedure can then be iteratively carried out any number of times until the
user decides that a satisfactory solution is found.
However, as in other areas of leading edge development
today, a further preferable means may include having a software program using an
algorithm (in some instances, a substantially generic iteration algorithm) to carry
out these iteration loops where a criterion is selected for the optimal solution
(e.g., component or process cost or a particular material or a particular end quality
criterion), so that it is no longer human iterations but automatic iterations on
the computer, which finds the best technical solution for a given phenomena.
Then, as a further developed option to support the user
in deciding for and selecting the best solution for him, one or a plurality of criteria
may be selected and run in parallel so that the results from the one or a plurality
of technical simulation runs can be presented to a human operator at the same time.
Then, it may be easier for the human user to see which one of the potential one
or more solutions may provide the best solution in a particular case. One means
for achieving such an operation may include working in a scheme referred to as VTOS®
(i.e., a "Virtual Try Out Space®" a sort of "Virtual Reality"), the results
from all the technical simulation runs being presented on a computer display (e.g.,
on a screen) at the same time. The human user may then select which one of the potential
one or more solutions may provide the best solution in a particular case.
Examples of methodologies running in such a parallel or
substantially simultaneous fashion are shown in Figs. 3 and 4 (note, the "parallel"
or "simultaneous" phraseology is intended for descriptive purposes of preferred
embodiments only; these terms are not intended to limit the methodologies to absolute
parallelism or simultaneity as the relative timings of the individual sub-processes
may not be concurrent or may occur in staggered or piecemeal fashions as well).
Figs. 3 and 4 show substantially the same methodological structure; however, where
Fig. 3 retains a generalized or generic scheme, Fig. 4 has taken on sub-processes
useful in metal or plastic castings as a specific example of the generalized process.
In looking first more specifically at the generalized form
of Fig. 3, the overall process, identified here with the numeral 2000 has one or
a plurality of sub-processes, generally identified as sub-processes 70. As shown,
these sub-processes may include specified sub-processes 71, 72 and 73, for example;
and/or may further include any number of sub-processes 70i and/or 70i+1. Moreover,
the completion of the finished product may be identified as well as the "step" 70fp
even if such a step is not entire "sub-process" itself, marking instead merely the
completion of the overall process 2000. Similarly, any one or more of the sub-processes
70 may also not necessarily include entire sub-processes; rather only including
a single step, or mere transition to another step or separate sub-process.
Then, still in Fig. 3, it can be noted that separate channels
for data input, see e.g., technical data input line 75 and economic/material/energy
data input line 76, may be connected to each of the sub-processes 70. These may
actually be separate data input lines, or they may actually be a common line, as
in a common serial bus connection, or other common data input/output connection,
or there may be one or more further separate lines providing the data connections
to the appropriate sub-processes. Similarly, it may be that the data lines are not
connected to each and every sub-process, rather only those to and/or from which
data is to be communicated (e.g., it may be that not all sub-processes will have
an economic data tie). In any case, it may also be noted that the two way arrows
connected herebetween are intended to show the preferability that data be provided
in either direction so that input may be taken in for the sub-process to calculate
an output value which may then be communicated back, e.g., via the technical data
line 75 to another sub-process which may then use that calculated data to reach
another particular result. Note, arrows directly between sub-processes are shown
also to indicate the possibility that sequential transition between any two or more
sub-processes directly may be used herein as well; however, such is not necessary,
where indeed, more substantial parallel and/or piecemeal back-and-forth transactions
may be performed.
In any case, the ultimate conclusions of any one or more
sub-processing will preferably be communicated to the optimization sub-process 80,
wherein the results may individually be evaluated and/or displayed for ultimate
use/decision by the human user. In a VTOS model, one or a preferable plurality of
sub-processing results may be displayed substantially simultaneously (even if not
necessarily achieved simultaneously), or sequentially, for review and comparison
by the human operator and ultimate decision-making thereby. Thus, step 80 may include
one or more display steps. This iteration may be performed by the operator or by
a computer implemented algorithm.
The more specific example 2001 in Fig. 4 of such a generalized
model 2000, includes example sub-processes 70a - 70g for a metal or plastic casting
process. More particularly, these example sub-processes include, without limitation
hereto, a design sub-process 70a, a mold/pattern/box making sub-process 70b, a casting
simulation sub-process 70c, fettling sub-process 70d, machining 70e, heat treatment
70f and a surface treatment sub-process 70g. These are described in further detail
herein below. This is but one example of a collection of sub-processes. Desirably
then, based upon one or more certain input data, as for example, certain preferred
technical and/or economic data, one or more conclusions are reached and communicated
to the sub-step 80 for optimization. Note, a plurality of alternative input data
sets may be used to generate a corresponding plurality of output conclusion sets
for ultimate use in comparison by the user/operator as for example in a VTOS or
virtual reality operation at box 80, e.g.
Note, the optimization methodology, which so far in the
figures 3 and 4 has been illustrated by example with the box 80 "Collection of data
for optimization + method and software," may take various forms, as for example
the display function suggest above. However, such a sub-process 80 may take a form
such as is shown by the sub-process 800 in Figure 5 including more elaborate functionality,
i.e., may include more sub-steps. The Fig. 5 example methodology 800 may thus start
at substep 81 with data input from the production sub-steps for different possible
ways of manufacturing in every substep (or a selected sub-set thereof).
After this, in steps 82 and 83, one or more or all possible
combinations of the different ways of manufacturing in the different sub-steps/sub-processes
70 (sub-steps and sub-processes are used interchangeably herein) are analyzed related
to the optimization criterion, which can be the lowest manufacturing cost, the lowest
materials consumption, the lowest environmental load, etc., or any combination hereof.
Doing this the most optimal manufacturing route can be found.
This can be followed in steps 84 and 85 by giving this
information back to the different manufacturing sub-steps/sub-processes (step 84)
to be used (via the simulation results) to give input to the actual manufacturing
machinery (step 85) for the different manufacturing sub-steps.
A result may be a desirable methodology for design optimization
and production planning setting the frame for cost effective and/or resource effective
production of parts, especially metallic castings and injection molded plastic parts.
As is described herein, this numerical simulation technique
may thus be commercialized to be a turnkey solution, which is very user friendly,
fast, comprehensive and accurate, which may include features like:
- Completely menu driven user interface;
- Project management module;
- Pre-processor including solid geometry modeling, CAD data transfer to and from
other and conventional CAD-systems and automatic enmeshment;
- Simulation modules for process description (fluid flow, solidification and heat
transfer, stresses and strain, microstructure development in solidification and
heat treatment processes) and solution algorithms to solve the physical equations
and provide (via simulation runs) the simulation results;
- Post-processor for 3D visualization and evaluation of results;
- Thermo-physical data base;
- Software releases for single processor computers, dual processor computers and
multi processor computers (Cluster technologies);
- Special add-on modules for specific casting processes: HPDC-module for simulating
the high pressure die casting process, LPDC-module for simulating the low pressure
die casting process, DISA®-module for simulating the DISAMATIC® casting
process, Iron-module for simulating the solidification of iron casting processes,
Steelmodule for simulating the specifics of steel casting processes, Stress-module
for simulating stresses and distortions in casting processes, Cosworth-module for
simulating the specifics of the Cosworth casting process, etc.; and
- Software for automatic initiation of the simulation iteration loops using generic
algorithms and criterions to automatically select the optimal technical solution.
The pre-processor can include solid geometry modeling,
CAD data transfer and automatic enmeshment. This enables an easy and fast geometry
description of complex shaped castings and mould geometries. All parts of the geometry
can be manipulated. This allows rapid modifications of the gating and feeding system
based on the results of previous simulation runs. To facilitate the design of cooling
channels in permanent molds, feeding sleeves and chills and the easy construction
of a multiple cavity permanent mold (or die), standard components can be saved and
loaded from a database. Optionally available general and direct interfaces like
STL allow external generated geometries constructed within different CAD systems
to be read into the pre-processor. An automatic enmeshment functionality allows
for creation of a sound enmeshment in minutes. The user defining global control
parameters can adjust accuracy and coarseness of the mesh.
Simulating mould filling allows the investigation of the
filling pattern in both dies, permanent moulds, sand moulds and around cores for
all shape casting processes; mainly by solving the Navier-Stokes equations coupled
with the energy equation in a way, which was described in above-cited literature
references of Hansen et Al., Lipinski and Kennedy. The following information may
thus be obtained:
- Mould filling pattern;
- Metal/plastic velocities in the die or mould cavity
- Loss of superheat and temperature distribution during mould filling; and
- Potentials for cold shots, cold laps, flow lines and possible sand erosion during
filling and the likelihood of gas entrapment and entrainment.
This type of information assists the foundryman in the
- Optimizing the gating system;
- Prediction of sand erosion and penetration due to critical velocities in the
- Determination of filling times dependent on the gating system, pouring/filling
rates or pressure in bottom stopped ladles
- The optimal placements of overflows in die casting
- Investigation of turbulences, splashes and "hammer effects" within the melt
causing entrapment of slag, air inclusions and droplet generation at the flowing
- The use of filters.
Simulating heat flow and solidification is a powerful tool
for the investigation of casting solidification and cooling. It takes into account
liquid and solid contraction and shows the feeding of the casting and porosity formation
and provides information about:
- Solidification patterns and feeding paths;
- Solidification times, temperature gradients and cooling rates at all points
of the casting;
- Critical regions in the casting;
- Thermal loading of dies, cores and moulds (to be used as load input to the following
calculations of stresses, strains and distortions such as segregations and inclusions);
- Cooling curves at any location within the casting, mould or die.
This kind of information assists the foundryman in the
- The optimal methoding of castings and the layout of permanent moulds and dies,
and the pattern- and core box design for sand casting processes
- The use of feeders and feeding aids, minimization and effective use of chills
- Investigation of process conditions such as optimal time to remove the casting,
cooling needs of the mould material, deterioration of molding sand and related gas
evolution, effect of chilling, heat impact to cores resulting in deterioration and
- Provides quantitative feeding needs for any feeder
- Provides insight to methoding changes seeking to remove porosities and shrinkages.
The batch functionality provides the capability of modeling
multi-cycle casting processes in permanent moulds and provides information about:
- The temperature distribution within the casting and permanent moulds at any
- The number of casting cycles needed to reach the "steady state" production conditions
in the start up production phase, where the permanent moulds are heated to the "steady
state" thermal production balance. The associated change in the quality of the cast
part can also be observed.
- Optimal casting removal time (for a given removal temperature).
This batch functionality supports the following objectives:
- Optimal manufacturing conditions for permanent mould and die casting processes;
- Optimal layout of dies and cooling and heating channels;
- The minimization of cycle times by the identification of the earliest casting
- Pin-pointing of critical areas in the die where; thermal loading is critical
and could reduce die life;
- The achievement of constant casting quality; and
- General process understanding.
In post-processing, there can be obtained a 3D visualization
and evaluation of results (see Fig. 2, and Figs. 6-14 e.g.). The 3D post-processor
allows the user to view the results from any direction and slice through the results
from any direction and to slice through certain areas, thereby identifying critical
areas of the casting. Temperatures in the casting and mould can be viewed at any
stage of the casting process. Porosity levels, the filling pattern as well as the
thermal history can be pinpointed and viewed as an X-ray film too. Various criteria
(criteria functions) help the user to condense the information from filling, solidification
or feeding with one comprehensive picture. Cooling curves, velocity, pressure, stresses
and strains can be shown at any location.
The data base module may provide the user with the necessary
thermophysical data to perform the simulation runs. The user may then have the option
to add or make changes to the thermophysical data sets for different alloy compositions
or materials and to assign them to different databases. Process conditions and the
geometry of different objects (feeders, feeder sleeves, filters, gating systems,
etc. can be stores as required).
Figs. 6-14 show graphic examples of optimization variables
relative to a particular cast product as discussed herein. In particular, Fig. 6
initially provides views of a cast product 100 which could be of any shape, but
for our purposes is a shape which includes one or more intentionally formed voids
as defined by cores 98 in Fig. 6. Note, as shown in Fig. 6c, four cores 98 are used
to form corresponding voids in the cast product 100. Further description of the
product of Fig. 6 will be addressed below.
However, first several alternative cast products will first
be described beginning with the product 100a of Fig. 7 which has a single multi-armed
core 98a used to form similar voids in the end product 100a. A similar, though voidless
product 100b is shown in Fig. 8 (the four voids are to be machined in a machining
process). The primary point in showing such alternative structures is to highlight
various forms providing similar end-products recognizing that different internal
or other constraints may be used in their formation, noting even so that the different
forms, including the different cores can an will have various impacts on the resulting
products. Some will be easier to make. Others will use more or alternatively less
material (in some cases more will be better, as for example to make a stronger product;
whereas in other cases, more material will seem a waste in that the strength will
not be advantageous).
Still further alternatives in the same fashion are shown
in Figs. 9 and 10, where for example, an end-product 100c is shown in Fig. 9 with
two cores 98b and 98c (these perhaps simplifying one aspect of production, while
perhaps simultaneously increasing complexity of another aspect); and where an alternative
end-product 100d is shown in Fig. 10, this example including three cores, represented
by cores 98d and 98e. The Fig. 10 example may thus simplify the Fig. 6 example in
requiring fewer parts (three cores versus four cores); however, there may be increased
complexity involved in removing such an elongated core 98e, or perhaps a trade-off
in reduced end-product mass, thus off-setting strength, inter alia.
Moving to the examples of Figs. 11-14; four alternative
molding processes are shown; each of these having various trade-offs in either functionality
or end quality, inter alia. In Fig. 11, a first molding process is shown for formation
of an end-product 100 (as in the first design 100 of Fig. 6 (with four cores)).
Here, a horizontal parting is shown with a central feeder 101 and an underside inlet
102. An alternative second horizontal parting is shown in Fig. 12 with again a central
feeder 101, but with a lateral/side inlet 102. Then, in Figs 13 and 14, alternative
vertical partings are shown for a substantially similar end-product 100; in these
cases, a topside feeder 101 is shown in Fig. 13 with a bottomside inlet 102; whereas
a side feeder 101 and inlet 102 are shown in Fig. 14. These four examples are also
intended to demonstrate the various alternatives, here merely in the formation process,
which may have particular impact on the ultimate calculations for a preferred outcome.
Certain desirable outcome characteristics may make one or more of these alternatives
more attractive in one scenario, and vice versa, depending upon which criteria may
be more important in one or another application.
As mentioned above, various other alternative add-on modules
may be found usable herewith, each such module taking into account particularly
preferential characteristics or phenomena for the particular end-product, or the
particularly desired procedure, inter alia. Examples hereof will be addressed hereafter.
Iron add-on module
Cast iron quality may be determined decisively by the applied
melting practice, melt treatment and metallurgy. The prediction of feeding and micro
structure formation during solidification and solid state transformation of cast
irons may require an accurate consideration of the microscopic phase formation,
the foundries actual melt analysis and the type and effect of melt treatment and
inoculation. Microscopic kinetic growth models, which predict the type and amount
of graphite formed, give an accurate simulation of competing graphite expansion
and austenite contraction forces through which shrinkage or porosity are determined.
This procedure also allows the prediction of the final microstructure and mechanical
properties in the casting.
The output from coupling such an add-on iron module to
the general simulation methodology is the information, which will be gained about:
- The amount of grey and white iron in the different locations in a casting;
- Eutectic cell size and lamella spacing for grey iron
- Fraction of Austenite, primary graphite, eutectic and white iron phase;
- Graphite nodule count for ductile iron;
- Nodularity for CGI;
- Cooling curves for any point of the casting showing the degree of undercooling,
thermal recalescence and growth temperature as function of cooling rate and time;
- The fraction of liquid at different stages of solidification;
- Locates hot spots in the casting and the last areas to solidify;
- Thermal modulus at any location in the casting;
- Shrinkage and porosity formation in ductile iron, CGI and gray iron castings;
- Pearlite and ferrite distribution in the casting;
- Distribution of hardness and mechanical properties (yield- and tensile strength,
elongation at fracture, Young's modulus).
These data can via a link to conventional Finite-Element-load
simulation software programs be used to improve the results of the load calculations.
In this way the local variation in these data can be used instead of the conventional
way of using homogeneous non varying data for the casting part domain as the initial
and boundary conditions for the load calculation simulations
Steel add-on module
The concept is to provide extended capabilities to support
the technical simulation of the manufacturing route for steel castings, form layout
through the casting and heat treatment processes. The simulation capabilities are
extended with numerical models to calculate velocities and pressure of the metal
in both the liquid melt in the casting as well as in the mushy zone driven by thermal
and solutal natural convection forces. The effect of this flow on the thermal map
in the solidifying casting is taken into account. The calculated velocities are
also coupled with a micro segregation model for the dendritic scale, to track the
redistribution of the alloying elements in the alloy and predict macro segregation.
To model the final microstructure and related mechanical
properties in the heat treatment process the heat transfer during the heat treatment
process based on the changing conditions during austenitization, quenching and tempering
is taken into account. This information, together with the alloy composition, is
coupled through a regression analysis based on transformation diagrams for hundreds
of steel grates. The analysis provides a prediction of the local microstructures
and mechanical properties throughout the casting.
In the pre-production planning and methoding the foundryman
may be supported with information like:
- Volume and mass of the casting, machining allowances, gating system;
- Riser system, mould sand, core sand and chills;
- Fettling areas of the gating and feeding system;
- Key quantities such as the sand/metal ratio and yield;
- Modulus values for the complete casting and user specified feeding zones;
- Ladle discharge rate and pouring time as a function of ladle geometry.
Additional information from the add-on module may include:
- Velocity field in the casting due to both thermal and solutal natural convection;
- Visualization of the movement of tracer particles through the melt during solidification;
- Distribution of the alloy and trace elements throughout the casting (macro segregation).
Simulating the heat treatment process may give information
Add-on module for HPDC (high pressure die casting) process
- Temperature distribution in the casting after each heat treatment step;
- Heating/cooling curves in the casting throughout heat treating cycles;
- Alloy and austenitization condition dependent CCT diagram linked with quench
- Martensite, bainite and ferrite/pearlite distributions in the quenched casting;
- Hardness distribution in the quenched and tempered casting;
- Yield strength, tensile strength end elongation distributions in the tempered
The concept is to define and incorporate many if not all
of the specific process parameters for this specific casting process, e.g.:
- Ejection time (controlled by time or casting temperature) ;
- Die opening sequence;
- Delay time (simulating the effect of cycle interruptions on the thermal balance
in the die);
- Die closing sequence;
- Lead time until beginning of the next cycle;
- Individual control of each cooling or tempering channel (or channel loop);
- Definition of die spraying procedure;
- By the help of a shot curve calculator the die casting machine configuration
for a specific casting geometry can be determined to give the optimal boundary conditions
for the different shot phases to be used in the mould filling simulation.
The accurate filling simulation of the mould allows the
identification of critical flow velocities, flow patterns, cold shots and critical
areas for air entrapment. This information can be used to optimize the position
and dimensions of runners, gates overflows and vents. The solidification and cooling
off simulation can be used to establish the best cooling channel positioning and
Add-on module for LPDC (low pressure die casting) process
The concept is to define and incorporate many if not all
of the specific process parameters for this specific casting process to be able
to make comprehensive simulation of fluid flow and casting quality, e.g.:
- The die filling based on furnace temperature;
- The feeding conditions in the casting during solidification taking into account
the applied pressure in the furnace and resultant static pressure in the liquid
phase in the casting and the influence of gravity as well;
- The effect of individual cooling or heating channels as well as their control
by time or a control thermocouple within the die or casting;
- The effect of die spraying or coatings;
- The influence of cores or inserts;
- The effect of the sequence of die opening and closing through the individual
control of each die section as a function of temperature or time.
Simulation with this add-on functionality may enable the
foundryman in the following objectives:
Plastic injection molding
- Optimized filling of the mould cavity based on the applied pressure;
- Optimal applied feeding pressure and its removal time;
- Minimized die opening times;
- The best cooling channel positioning and layout as a function of time or a thermocouple
within the die;
- Provide support for reduction of lead times and optimization of the entire processing
- The reduction in the thermal loading of cores, inserts and die sections to maximize
This process is very similar to the HPDC process for metals,
which was described above, so most of the technical issues are the same or similar.
Again special one or more add-on functionalities to the general simulation systematic
may be put in use to have the possibility to simulate this process with the production
parameters, which are specific for this process. The viscosity for plastics (thermo-plastics)
is much higher than for metals and another difference is that metals can be treated
as incompressible in the casting process, but plastics on the other hand are compressible.
This means that during the mould-filling phase of the casting process, it is possible,
after the mould is filled with plastics, to inject, i.e., "fill in" more plastics
by compressing the plastics already in the mould; a phenomenon, which may be simulated
by the module hereof as well. In the cooling down process of thermo-plastics a specific
phenomenon can take place; namely, surface shrinkage close to mass centers, a phenomenon
which should be modeled with a special model. A peculiarity is often found with
elastomers, wherein a hardening process takes place some time after the mould is
filled with elastomer melt. This is a similar process to the solidification process
for metals; however, even so, still other models have to be used to describe this
process. It is more a chemical reaction where the solidification of metals is more
of a physical process.
Add-on module for stress/strain and distortion simulations
Utilizing such a module, the stresses and strains developing
during solidification, cooling off and heat treatment of castings can be calculated
predicting the residual stresses and distortions generated in the final casting
product. Machining allowances can be defined too so the redistribution of the stresses/strains
and distortions after machining can be simulated as well.
These results can be linked to conventional Finite-Element-load
calculation programs, and may thus be used to provide the starting condition (initial
conditions) for the load calculation simulations for the specific casting. In this
way more accurate load calculations can be carried out.
Other casting processes
Many different casting processes exist. For every process
add-on modules may be implemented and used to specify the specific process parameters
to simulate what is of specific technical interest for a given process. For investment
casting processes, special attention has to be paid to radiation heat exchange.
In the DISAMATIC casting process the heat transfer in the mould is of special interest,
since the moulds are stacked together, so a heat exchange between the moulds may
take place, which is incorporated, as well as the thermal deterioration of the sand
to give input to the recycling process of the sand (sand plant). New developments
in this process are "active up-hill bottom filling" of the moulds using a pump or
a pressurized furnace and "active feeding", activating the feeders using compressed
air. Again functionalities which can be specified in the add-on module may be taken
into account in the simulations. For the Cosworth process, metal is pumped uphill
into the moulds, which has to be described to set the boundary conditions for simulating
the mould filling process. Further in one variation of the process the moulds are
rotated after filling to enable the feeders to function by the use of gravity. Tilt
casting processes and roll over processes rotate the moulds during the filling process,
which is taken account in the add-on module, so it is possible to carry out simulations
of the filling process under such circumstances.
Still further simulation alternatives may be included.
Hereafter for example, simulations of processes which are related to and/or supporting
the casting processes are described.
Manufacturing of core boxes, metallic moulds and pattern plates.
Based on the geometrical data for the geometry to be manufactured
it can be modeled how to program the machinery, which are used for the manufacturing
and in this way estimate the manufacturing effort needed. An aspect here is to use
the simulated distortion results from the add-on module for stress/strain simulations
to modify the geometry of the pattern plates, metallic moulds and core boxes, so
the final geometry of the cast product can be distortion free.
This information is used as a basis for the cost calculations,
since in this way it will be know how much effort will be needed.
Core shooting process.
Models may be used herein/herewith for simulation of the
flow of sand filling a core box, using equations similar to the equations used to
simulate mould filling for the metallic melts and the plastic melts. Special attention
though has to be to the fact that the core sands are particulate materials, which
has to be reflected in the models. The results of such simulations provide information
on where to place vents in the core boxes and also core box life time can be estimated
using models to describe the wear by the sand flowing into the core boxes dependent
on the core shooter process and the inlets for the sand.
Further the curing process can be simulated to find the
way to carry out curing to have the shortest cycle time possible for producing cores.
Sand mould making process.
The filling process for the sand filling and building up
the mould is simulated the same way as for the core shooting process. Again the
interest is the similar in finding the right position of the vents on a DISAMATIC
pattern plate and to estimate the life time of a pattern plate, which can be related
to the way the molding machine is producing the mould and in this way finding the
different possible technical possibilities, which are the basis information necessary
for the cost calculations.
Permanent moulds and dies.
The lifetime is here estimated by modeling the oscillating
stresses and strains during the casting cycles, generated by the oscillating temperature
fields due to the heat input from the castings, which has to be cooled away by typically
cooling pipes. So during the lifetime fatigue will take place and destroy the mould/die
determining the lifetime (faults like heat checkings). This information is used
for the cost calculations.
This process is cleaning the casting after being removed
from the mould/die and removing the ingate system and risers. Different ways of
doing this independent of the casting layout is the input for the economical calculations.
After fettling the casting is now ready for machining.
The technical implication of the different designs has to be considered/modeled
to be able to make the cost calculation.
The way to simulate the technical aspects of this process
has already been described in the section "Steel add-on module" above. These technical
data are used to make the cost calculations.
The technical implication of the different designs has
to be considered/modeled to be able to make the cost calculation.
Couplinq design analyses with casting analyses.
Also disclosed herein are methods or systems for optimising
local casting properties to meet the required local demands on component performance.
Such may be accomplished using an integrated virtual optimization tool for casting
process and design optimization. These methods or systems may be provided via a
computer implemented process that couples quantitative casting property predictions
resulting from different in-situ processes and casting design optimization loops
to the optimization of local material requirements during component design, thereby
generating a casting or component design which adjusts the local casting properties
to the component's performance demands.
Previously, the optimization of casting component design
has neglected consideration of local material property variations that may result
from casting processes. On the other hand, casting process optimization has not
considered the local requirements on the cast component's performance in terms of
static or dynamic loads. Computer implemented analyses of both casting processes
and component design have been carried out independently of one another. This will
often have resulted in a design or conditions optimised for either the casting process
or for the component's design only. Neglecting interactions between both optimisation
loops may result in either conflicting solutions or in inferior use of the material's
According to an embodiment hereof, both the casting process
and the design will be integrated to produce superior component designs. With reference
to Figures 15 and 16, different elements for carrying out a numerical optimization
of an entire casting design process chain according hereto is illustrated. Fig.
15 generally includes a combination 110 of a casting process 111 with a design process
115; whereas more particularly, the main steps of such an optimisation are the following:
- Use of physical based simulation methods to predict the entire casting process
including consideration of melt quality, mould filling, solidification, cooling
and subsequent processes such as heat treatment or machining, process 111.
- Use of simulation methods to quantitatively predict local casting properties,
such as dendrite arm spacing, structures, porosities, and residual stresses as a
function of casting design and casting process conditions, analysis parts 112, 113.
- Use of simulation methods to quantitatively predict local mechanical properties
as a function of the above casting properties, analysis part 114.
Further included are:
- Use of in-situ optimisation loops for casting design and casting process conditions
to be able to predict and adjust local casting properties, Fig. 16, sub-process
- Use of simulation methods to design casting components Figure 15, process 115,
as a function of performance requirements, such as static loads, analysis part 116,
and dynamic loads, analysis part 117.
- Use of design optimization methods to optimise the component's shape based on
the determined local load conditions, Fig. 16, sub-process 122.
- Method to link the information of local casting properties with performance
simulation analyses, Fig. 16, part 123.
- Use of optimisation methods to adjust one or more of component shape, casting
process conditions or casting design in order to achieve a match between the local
components material requirements and the local casting properties, Fig. 16, part
In an example hereof, a cylinder head 125, see Figure 17,
may be used as a cast component to be evaluated. Such a component may typically
be produced in Aluminium alloys by sand or permanent mould casting processes. Due
to its function, the component is subject to static and alternating loads of different
kinds during operation. Several exemplar steps of a process hereof follow hereafter.
- 1. The requirements for the design of a cylinder head 125 may first typically
be identified through a complex load analysis of static, thermal or alternating
loads, which represent the actual conditions during operation of the engine, see
e.g., Figure 18.
- 2. The design of a cylinder head 125 may then be subjected to a systematic and
at times complex analysis in order to determine a component geometry which fulfils
the strength and endurance demands as identified in step 1. Note, geometrical modifications
are typically used as the sole degree of freedom to meet the required strength of
the component. In other words, the component geometry may be iteratively changed
(in this step 2) until the stress levels and durability meet the requirements (identified
in step 1). In this iterative optimisation process, the material performance resulting
from its internal structure or the applied manufacturing process may be considered
to be isotropic and uniform.
- 3. The manufacturing process of cylinder heads may include multiple stages in
which the structure and resulting performance of the material may be influenced
in particular by the preparation of the melt, the casting process, the local conditions
during solidification and cooling as well as a subsequent heat treatment often applied
to establish improved mechanical properties. Thus, during the design of the casting
process, the requirements for mould design, gating and risering, as well as process
conditions may be identified through a complex analysis of the entire process. Main
steps and conditions to be considered may be melt quality, mould filling (Figure
19), solidification and cooling (Figure 20), subsequent heat treatment and machining.
This analysis delivers information about the distribution of the micro-structures
(Figure 21) and defects (Figure 22) such as porosities in the cast part.
- 4. This information (micro-structures, Figure 21 and defects, Figure 22) may
be used to influence the final local mechanical properties (tensile, yield strength
and elongation) in the component as illustrated in Figs. 23 and 24. In parallel,
the inhomogeneous cooling of the cast component during either casting or heat treatment
may result in a thermally induced stress distribution (shown in Fig. 25), which
contributes to the total component load during operation. Note the residual stresses
as well as local defect and structure distribution contributes to the total durability
of the component, as these are shown and described in Fig. 15, process flow 118.
- 5. During the optimisation of the production process, the foundry expert has
the flexibility to iteratively change mould geometry, gating and risering design
and process conditions for the casting and heat treatment stages, see figures 26
and 27. In this iterative optimisation process as described in figure 16, part 121,
the casting material's performance may be designed to meet the minimum and uniform
material standard specifications in all areas throughout the component.
Through the innovative coupling of these two design optimization
methodologies, two new optimisation pathways are possible:
- A) From the component design analysis and optimization, see step 2, the local
material requirements in all parts of the cylinder head are known. This information
can be used as the objective to be matched by the local material properties to be
achieved through the casting process design optimization loop of pathway 180, see
- B) From the casting process design analysis and optimization, see step 4, the
local casting properties in all parts of the cylinder head are known. This information
can be used in place of the isotropic and uniform properties as described in step
2 resulting either in potential component weight reduction or performance improvement,
see pathway 190 in Figure 29. Further to this option B, an innovative global optimization
methodology is proposed where the casting's design as well as the component's design
is integrated into an iterative optimization loop in order to simultaneously determine
the optimal component design (based on local casting properties) and the optimal
casting process (based on the local material requirements), see pathway 195; Figure
Although the present invention has been described in detail
for purpose of illustration, it is understood that such detail is solely for that
purpose, and variations can be made therein by those skilled in the art without
departing from the scope of the invention.