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    • 专利摘要:
    Method for simulating a shaping process or a subprocess of the shaping process, wherein states of objects involved in the shaping process, in particular a forming machine, a forming tool and / or a material to be processed, are calculated in discrete and successive time steps under specification of conditions, the conditions being input parameters of the (A) after a time step which is before the end of the simulated forming process or the sub-process of the forming process, a check of the calculated states of the objects involved in the forming process is carried out on the basis of at least one quality criterion, (b) if the check after step (a) indicates that the at least one quality criterion is not fulfilled, at least one of the following is performed: (i) resumption of the simulation with repeated calculation of the time step and / or a preceding time step, (ii) continuing the simulation by calculating a time step following the time step, (c) wherein the conditions are at least partially changed when performing method step (b).
    公开号:AT519005A4
    申请号:T51182/2016
    申请日:2016-12-23
    公开日:2018-03-15
    发明作者:
    申请人:Engel Austria Gmbh；
    IPC主号:
    专利说明:

    Summary
    Method for simulating a shaping process or a sub-process of the shaping process, wherein states of objects involved in the shaping process, in particular a shaping machine, a shaping tool and / or a material to be processed, are calculated in discrete and successive time steps under conditions, the conditions being input parameters of the Represent the shaping process, wherein (a) after a time step which is before the end of the simulated shaping process or the sub-process of the shaping process, a check of the calculated states of the
    Objects involved in the shaping process are carried out on the basis of at least one quality criterion, (b) if the check after step (a) shows that the at least one quality criterion is not met, at least one of the following is carried out:
    (i) resumption of the simulation with repeated calculation of the time step and / or a previous time step, (ii) continuation of the simulation with calculation of a time step following the journal, (c) the conditions at least partially changed when method step (b) is carried out become.
    (Fig. 10)
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    80844
    The present invention relates to a method for simulating a shaping process or a sub-process of the shaping process according to the preamble of claim 1.
    In this case, states of objects involved in the shaping process, in particular a shaping machine, a shaping tool and / or a material to be processed, are calculated in discrete and successive journals under the specification of conditions, the conditions representing input parameters of the shaping process.
    Molding machines are, for example, injection molding machines, injection presses, presses and the like. Forming processes follow this terminology analogously.
    The conditions represent the input parameters in that they are the mathematical counterparts to the input parameters that are set, for example, by operators on the molding machine. For example, in an injection molding process, input parameters could be parameters related to an injection profile. In the simulation, for example, the machine elements that implement the injection profile could then be simulated in detail. The parameters relating to the injection profile then clearly define the conditions that result from the parameters for the simulation. Instead of such an identical definition of the conditions, the conditions of the simulation can also be adapted to the input parameters by modeling the conditions. In the previous example with the injection profile, this could be a time-dependent mass flow in the sprue. This is of course to be seen as an example. An analogous situation applies to all input parameters or conditions.
    The input parameters mainly relate to the movement and force profiles of drives of the shaping machine and other components of the shaping machine to be controlled. In the example of an injection molding machine, these would be drives for opening and closing a mold, building a closing force, injection, holding pressure, ejector, heaters, etc.
    The state of the art is described below using the example of
    Injection molding machines (SGM) described. The conclusions apply analogously to other shaping processes (injection molding processes are abbreviated as SG processes).
    Simulations using finite element methods are very computationally intensive and therefore require particularly powerful computing systems or a lot of time. Depending on the duration and the temporal discretization of the SG process or the size and spatial discretization of the geometries to be simulated, computing times of between a few hours and several days can be expected per filling process on commercially available computers.
    Optimization tasks usually require a large number of individual simulations to be carried out. In order to achieve a result in the optimization as quickly as possible with a given computing system, methods are necessary to reduce the computing effort as much as possible.
    In practice, methods of statistical experiment planning (Design of Experiments - DoE) have become established. With a given number of parameters to be examined, these serve to reduce the number of experiments required to such an extent that relevant interrelationships between the parameters and quality characteristics can nevertheless be determined as precisely as possible.
    Spritzqießsimulationen
    Numerical fluid mechanics (Computational fluid dynamics - CFD) can be used to simulate injection molding processes. Various types of filling simulations are in circulation (cf. Zheng R., Tanner RI, Fan X.-J. (2012): Injection Molding. Heidelberg: Springer. Pages 111-147; Zhou H. (ed.) (2013) : Computer Modeling for Injection Molding. Hoboken: Wiley. Pages 49-254). They differ fundamentally in their structure and the physics they cover. Simulations generally have spatial and temporal discretization.
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    In general, the delicacy of discretization determines the accuracy of the results and at the same time the required computing resources (size of the computing system and duration of the calculations).
    A given product, i.e. the cavity of an injection mold, is usually available as CAD data. This CAD geometry is divided into small units (spatial discretization). In 3D, the units often correspond to hexahedra or tetrahedra. For these units, various states are calculated in the simulation, piece by piece in small time units (temporal discretization). Various equations are solved in each time unit, e.g. the continuity equation or the Navier-Stokes equation. Process variables of the modeled plastic, e.g. whose temperature, pressure or density correspond.
    The full 3D model gives the most accurate results, but is also the most time and computation intensive. Simplifications to the full 3D model are made by the Hele-Shaw model (the geometry is described by a level, each location of which is assigned a wall thickness), the 2.5D model, or the dual-domain model.
    Especially in connection with full 3D models, the spatial discretization does not have to be static per se, but can change with the course of the simulation (adaptive grid creation). This may increase the computing accuracy and may shorten the computing time.
    The temporal discretization typically results from the determination of a so-called Courant number Co, which specifies the maximum number of cells a size (typically the plastic mass) moves per time step. A single time step or calculation step therefore deals with a corresponding duration in the process (for example 10 ps <Δΐ <10 ms) and this process duration is generally different for each time step. The temporal discretization is therefore dependent on the respective cell sizes, the respective flow velocities and the defined Courant number. This can be a number between 0 and 1 (0 <Co <1) or again a function 0 <Co (x) <1, where x denotes various dependencies (time, location, pressure, combinations, etc.).
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    The higher the spatial discretization (the smaller the cells), the higher the flow velocities and the lower the Courant number, the smaller the process duration treated in each calculation step. The smaller the respective process times of the respective calculation steps, the more calculation steps are required to complete a filling process. The computing resources for a calculation step are approximately independent of the respective process time. Accordingly, the total computing resources required increase with greater spatial and temporal discretization.
    Advanced injection molding simulations
    The geometry of the cavity can be expanded by that of the nozzle, the hot runner system, the distributor, the screw antechamber, etc. Some simulations also take into account the complete tool and the temperature control channels in it.
    The physics shown in the simulation can also include the crystallization of polymers, or the alignment and thermal damage of fillers. Heat transfer, shear rate and pressure-dependent viscosity, shear heating, freezing of outer layers, etc. also play a role. Subsequent or parallel to filling / injection molding simulations, it is also common to carry out temperature, cooling, warping or shrinkage analyzes.
    Process optimization simulations
    Injection molding simulations are typically used in tool design for the following tasks: determination of fiber orientation; Avoidance of weld lines; Determination of the necessary closing force; Dimensioning of wall thicknesses; Optimization of the position, number and size of gating; Minimizing delay; Determining demoldability; Avoiding hotspots
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    Calculation of filling time; Determining shrinkage; Determining sink marks; Avoidance of ventilation problems; Determination of internal stresses.
    In the meantime, the complex, but precise, 3D FEM simulations have also been used for the realistic calculation of the injection molding process in order to enable offline optimization of conditions and machine settings (input parameters). The filling speed / filling time, melt or mold temperatures or the holding pressure level should be optimized. Superordinate optimization goals are a robust injection molding process, a short cycle time or a molded part surface without defects such as e.g. Sink marks, streaks or burns.
    In practice, statistical experiment planning (Design of Experiments - DoE) is used for optimization. For a DoE, the operator of a simulation software normally (manually) defines a parameter space consisting of parameters (factors) to be varied and associated parameter values (levels). The data or results of each filling simulation are analyzed. For this purpose, suitable quality functions are necessary, which map the results of the simulations to quality variables or which make simulated quantities of quality-relevant molded part or process properties.
    In order to obtain maximum information about the influences and the relationships between the various parameters and to achieve the optimization goals in the best possible way, the parameter space should be as large as possible and any combination of parameter values should be analyzed. Obviously, the number of filling simulations required can very easily become very high. For this reason, the number of “runs” required is usually skillfully restricted at the cost of the information obtained. Nevertheless, the simulation effort required remains considerable.
    According to the state of the art, multiple simulations are carried out according to the experimental plan. The results are analyzed and the relationships between the various parameters and the
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    Quality functions modeled. Then a (local / provisional)
    Determine the optimum. If necessary or if the required quality criteria could not be met within the covered parameter space, new / different parameter spaces are used in further iterations. In this meadow you work your way up to an "optimum".
    Various supporting / alternative algorithms have been considered in the literature and were developed by Yang et al. 2015 in Figure 1 (reproduced adapted) vividly summarized (see Yi Yang, Bo Yang, Shengqiang Zhu, Xi Chen. Online quality optimization of the injection molding process via digital image Processing and modelfree optimization. Journal of Materials Processing Technology 226 (2015) 85-98). For the present invention, the one designated “offline” and especially the use of “first-principle models” is primarily relevant. However, a combination and mutual use of other methods is quite possible.
    Following the various simulations, or first-principle models, CFD, FEM calculations, etc., their results are analyzed. After carrying out and analyzing a (one-factor-at-a-time) simulation or several (DoE) simulations, it is checked whether the quality criteria are met and then the decision is made as to whether and which parameters can possibly be modified in a further iteration / should. This procedure in the prior art is illustrated in FIG. 2 from EP 1 218 163 B1.
    A flow diagram of an offline process optimization using injection molding simulations (CFD) according to the prior art is shown in FIG. 3. Expert knowledge is available in the form of operator knowledge and input. As mentioned, a simulation is evaluated after its end, followed by a check whether the results meet the quality requirements. Parameters or conditions may be modified and a further simulation carried out.
    A method which is improved over this procedure is state of the art and is shown in FIG. Instead of the parameters or conditions iteratively
    Modify 7/33 with the aim of obtaining optimal simulation results
    Test plan (DoE) created. This is processed, each of the
    Simulation results are evaluated, possibly a modeling is carried out and an optimum is identified. Optionally, this procedure can be done with the above "iterative
    Combine Optimizer ”(Figure 3).
    For example, EP 1 218 163 A1 or US 2008 0294402 discloses generic methods for simulating an injection molding process.
    The object of the invention is a method for simulating a
    To provide a shaping process that allows more effective handling of computing power.
    This object is solved by the features of claim 1.
    This is done by (a) after a time step which is before the end of the simulated shaping process or the sub-process of the shaping process, a check of the calculated states of the
    Objects involved in the shaping process are carried out on the basis of at least one quality criterion, (b) if the check after step (a) shows that the at least one quality criterion is not met, at least one of the following is carried out:
    (i) resumption of the simulation with repeated calculation of the time step and / or a previous time step, (ii) continuation of the simulation with calculation of a time step following the time step, (c) the conditions being changed at least partially when carrying out method step (b) ,
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    When resuming the simulation according to (b) (i), you can continue with the very first magazine as well as with a later magazine that has already been calculated once.
    As mentioned, the input parameters mainly relate to the movement and force profiles of drives of the shaping machine and other components of the shaping machine to be controlled. Because, for example, changing an entire molding machine or a molding tool is actually not possible for the operator when setting up the molding process.
    The invention provides for checking the quality criterion - preferably by applying a quality function to the calculated data or states. First of all, it should be noted that the quality criterion does not have to relate to the molded part produced in the molding process (dimensional accuracy, distortion, surface quality). The quality criterion can - and will in many cases - also affect certain properties of the molding process (certain pressures and temperatures, etc.).
    According to the invention, it is provided to carry out the check after method step (a) before the end of the simulated shaping process or the simulated sub-process. While “entire simulations” were carried out in the prior art, the invention does not wait for the end of the physical process under consideration.
    By the way, a “whole simulation” can be seen as “completely in time and in space”. That after the “whole simulation”, the physical process under consideration has ended.
    For example, a (virtual) plastic melt can be in a state in which it could be "removed", i.e. at least their boundary layers have solidified, or below a certain temperature.
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    There are situations in the prior art in which simulations are aborted. For example, if it turns out that the flow front is frozen before the mold cavity is full (which makes full filling impossible). The physical process or the shaping process or the sub-process thereof is also ended here. Ultimately, this is a program security measure. In terms of the further training mentioned, it is checked whether the (physical) shaping process develops as desired.
    In many cases, the invention therefore makes it possible to modify the conditions (input parameters) without going back completely to the beginning of the simulation and starting again.
    An advantage of the invention lies in the elimination of the problem that the conditions or their temporal course are defined from the beginning in a “whole simulation”. These defined conditions (in an injection molding process, for example, the injection or the holding pressure profile) accompany the simulation from its start to its end and can therefore not be modified during the simulation. This limitation is eliminated by the invention.
    The implementation of virtual / offline process optimization using injection molding simulations according to the state of the art is very time, computation and resource intensive. In contrast, the mentioned further development of the invention makes it possible to determine, before the end of the complete simulation, that various quality criteria are not met under the given conditions.
    • before the end of the complete simulation, conditions can be modified so that various quality criteria are met
    According to the invention, it can be achieved that the success of a simulation-supported optimization is independent of the skill of the operator of the respective simulation software, because he has to vary them
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    Parameters and their factors - practically no longer have to manually specify the entire test plan of a DoE, have to decide for yourself when the results of the modeling are sufficient, and possibly have to decide for yourself to carry out another different test sequence (according to DoE). It is avoided that even after carrying out N DOEs no conditions (e.g. injection profile, holding pressure profile) could be identified which deliver results of sufficient quality (in terms of aesthetics, dimensional accuracy, optics, mechanics, economy, process stability, etc.), what about processes of the prior art can certainly occur.
    Input parameters can be process parameters. However, process parameters can not only be those parameters that are entered on the shaping machine, but ultimately any physical quantities involved in the shaping process.
    The invention can be applied to casting processes, in particular to filling simulations in injection molding processes, which can then be used to find machine settings with which products of sufficient quality can be produced. The invention can be used in particular to determine the optimal internal mold pressure or flow front profiles (for example a filling front speed that is as constant as possible).
    According to the invention, this can be carried out as efficiently as possible
    Computing resources / computer resources are avoided (by reducing the necessary simulation steps and the associated time advantage).
    The invention can be used with the following simulation methods (and combinations thereof): the finite element method (FEM), the finite difference method, the finite volume method, the Hele-Shaw formalism, the 2.5D midplane analysis, the Dual domain analysis, similar simulation methods, an injection molding simulation coupled with a machine simulation, an overall injection molding simulation with the support of known optimization methods and / or pre-programmed expert knowledge.
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    The invention enables an optimization to be included within the simulation process and an incremental generation of, for example, injection and holding pressure profiles (or all the process settings required in a shaping process).
    Advantageous developments of the invention are defined in the dependent claims.
    Provision can be made for the calculated states to be checked on the basis of the at least one quality criterion by applying at least one quality function to the calculated states.
    Provision can be made for (a) to (c) to be carried out several times as part of the simulation of a shaping process or of the sub-process of the shaping process.
    It can be provided that method step (b) is carried out automatically.
    It can be provided that the simulation is divided into calculation phases, the calculation phases each comprising a plurality of time steps.
    It can be provided that the sub-process of the shaping process and / or the calculation phases are each given by a start time and an end time.
    When simulating an injection molding process (as a shaping process), it can be provided that the start time and / or the end time of the sub-process and / or the calculation phases is given by at least one of the following:
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    Beginning and / or end of a plasticization (transfer of the solid or highly viscous plastic (solid) into a liquid or low-viscosity plastic (melt))
    Beginning and / or end of a compression release (withdrawal of the screw after plasticization so that the pressure of the melt in the screw vestibule is reduced)
    Start and / or end of the flow of a plastic melt from a screw antechamber towards a tool cavity (compression of the melt in the screw antechamber, forward movement of the screw, entry of the melt into the nozzle, start of injection)
    Start and / or end of a flow of a plastic melt into the mold cavity, (first significant pressure increase)
    Reaching a predetermined degree of filling of a tool cavity (for example a degree of filling of 80%, 90%, 95% or 99%)
    Beginning and / or end of a compression phase (in the compression phase, a melt is in a state in which the plastic material is mostly only compressed)
    Reaching the sealing point (the point in time at which the gate (connection cavity - sprue system / nozzle) freezes so that hardly any plastic mass flows into the cavity)
    Reaching a changeover point
    Beginning and / or end of a reprint phase
    Boundary layers of a molded part are hardened to such an extent that the molded part is essentially a form test (also called “end of cooling time”)
    Start and / or end of a mold opening (removal of the at least partially solidified molding / molded part from the mold cavity can then take place)
    Beginning and / or end of an embossing phase
    It can be provided that the start time and / or the end time of the calculation phases by passing a fill front at predetermined spatial
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    Points of a tool cavity. (In the simulation, these
    Criteria are checked, for example, by a status function.)
    The start time and / or the end time can be set manually or automatically.
    It can be provided that at least two of the calculation phases are lined up, results of a previous calculation phase being used as the initial states of a subsequent calculation phase. The conditions of a first calculation phase can be defined automatically by an initiation system or manually by an operator (cf. FIG. 11).
    It can be provided that different process models and / or calculation methods are used for different calculation phases.
    For example, an injection and a holding pressure phase can be distinguished, which are optimized separately from one another in different ways and using different calculation methods
    It can be provided that at least one reference course is given for courses of states calculated in the simulation. The reference profiles can relate, for example, to pressures, temperatures, shear, flow rates of a plasticized plastic (in an injection molding process).
    Provision can be made for a comparison between the at least one reference curve and at least one curve calculated in the simulation to be carried out as part of the application of the quality function, preferably a difference between the at least one reference curve and the at least one curve calculated in the simulation being recorded quantitatively becomes.
    It can be provided that the reference curves are calculated in advance, preferably using an expert system. The reference courses can also be specified by an operator.
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    An expert system in the sense understood here can be understood as an intelligent database integrated into a computing system (see, for example, Krishnamoorthy, CS and S. Rajeev (1996): Artificial Intelligence and Expert Systems for Engineers, Boca Raton: CRC Press, pages 29-88 ). It contains systematized and programmed basic knowledge about the rules of the shaping process as it e.g. can be found in the relevant literature (cf. Schötz, A. (2013): Sampling of injection molding tools, Munich: Karl Hanser Verlag. Pages 31-220; Jaroschek, C. (2013): Injection molding for practitioners, 3rd edition. Munich: Karl Hanser Verlag. Pages 31-98; Fein, B. (2013): Optimization of plastic injection molding processes, 2nd edition. Berlin: Beuth Verlag GmbH. Pages 65120; Kunststoff-Institut Lüdenscheid (2013): Troubleshooting guide, 12th edition. Unna: HorschlerVerlagsgesellschaft GmbH. Pages 6-178). In addition, rules can be programmed into an expert system, which represent generalizations of procedures for machine setting, error detection or error avoidance by experienced process technicians and specialists for setting molding machines. Such a set of rules or basic knowledge can e.g. are in the form of truth value functions or implementation tables. In the case of known molded part geometries, materials, machines and quality requirements, an expert system can make rough estimates of areas of input parameters based on the programmed knowledge and the rules, which result in successful
    Machine settings open. Based on programmed
    Correlations between input parameters, machine settings, molded part properties, material properties can then be used to determine unfulfilled quality criteria with previously used input parameters, making necessary modifications to the input parameters.
    It can be provided that the following is carried out when calculating the reference curves:
    (A) Calculation of a desired filling pressure at a point of a gate based on a maximum flow path and an average flow cross section and
    15/33 (B) Determination of a linear course of a pressure from the start of injection to the calculated filling pressure when the tool cavity is completely filled.
    It can be provided that the simulation of the shaping process and / or the sub-process of the shaping process and / or the calculation phases is divided into partial calculation phases, with (a) to (c) after each
    Partial calculation phase can be carried out.
    It can be provided that the conditions are subjected to an optimization process after each partial calculation phase, the optimization process preferably being designed to
    - adapt courses of calculated states to the specified reference courses and / or
    - to change the conditions so that calculated states of objects involved in the shaping process meet at least one quality criterion.
    If several quality criteria are used, their importance for the shaping process can be weighted accordingly. In addition, an improved set of conditions for resuming or continuing the simulation can be found by means of multi-criteria optimization.
    The start and end of a calculation phase and / or one
    Partial calculation phase, or the number of calculation steps within a respective partial calculation phase, can be defined by applying “status functionals” to at least parts of previous process data or calculated states (cf. Mold geometry has happened.
    It can be provided that when changing the conditions according to (c), an expert system is used, which is preferably used to develop a statistical test plan.
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    It can be provided that the input parameters represented by the conditions of the simulation are sent to a computer after the simulation has been carried out
    Injection molding machine transferred and used in a real molding process.
    Provision can be made for the results of the simulation to be stored in a database after the simulation of the shaping process has been carried out, the database preferably additionally containing corresponding process data from real shaping processes and the real process data being correlated with the results of the simulation.
    In the following two exemplary embodiments for injection molding processes, reference is made in part to the figures. Show:
    1 to 4 and 7 flowcharts for simulation methods according to the
    State of the art and
    5, 6 and 8-12 flowcharts for examples of further methods according to the invention.
    Embodiment 1: Optimizer based on FEM simulations using evaluations of individual simulations even before these have ended
    A simulation to be carried out is not evaluated based on the quality criteria only after the end of the simulated shaping process (or sub-process), but already at regular intervals during the same. This allows problems in the process to be identified before the simulation is completed, which suggests that the conditions used in the simulation are unsuccessful. The simulation can therefore be stopped early and another can be started.
    Due to the temporal discretization, any steps can be jumped backwards in the simulation at a time when it is expected that under a
    17/33 determined change of the conditions and simulating again from this point in time the problem encountered can be solved (see FIG. 10 and FIG. 12). If not, it could be attempted to vary other conditions in another iteration or to go back in time and try again.
    Of course, new problems can arise and the process can be terminated according to programmed rules, whereupon a completely new simulation is started within a previously defined parameter space. If the problem is resolved, a complete simulation with the changed conditions is then carried out.
    The general idea is that in addition to the actual simulation program, an optimization program “manages” and continuously learns the simulations, or generates information about the filling, pressure and temperature behavior of the SG process in each step, which serves the optimization program to take new steps in the simulation to meet all quality criteria in the best possible way and to deliver a corresponding machine setting set (input parameters).
    As an additional measure, an optimizer based on FEM simulations using test planning and restriction of the parameter space using programmed expert knowledge (expert system) can be used.
    In order to further reduce the necessary computing resources, it is advisable to limit the number of simulations again based on expert knowledge. For example, it makes sense not to carry out some experiments because it is known in advance that they will fail. For example, it may be useful to vary the temperature of the plastic melt and the injection speed in certain areas, but realizing that the combination of a low temperature and a low injection speed leads to an incomplete filling. Experience gained in experiments could thus be in the optimization program
    18/33 can be implemented so that simulations with few chances of success are automatically skipped.
    As a further additional measure, an optimizer based on FEM simulations using test planning and programmed expert knowledge (expert system) can be used, parameter areas with little prospect being excluded during the processing of a parameter space.
    A test plan can also be carried out in such an order that a certain parameter space can be excluded at an early stage. To do this, the individual simulations must be evaluated directly on the basis of quality criteria. On the basis of pre-programmed expert knowledge, each simulation is only followed by those that have not been judged to be unsuccessful by previous simulations and the applied expert knowledge.
    Goals of the optimization process:
    • Achieve full filling at minimum speeds and minimum temperatures • Achieve a minimum range of temperature distribution (at the end of filling) • Achieve an ideal pressure curve for the individual volume elements • Achieve a constant filling speed • Achieve a maximum injection speed profile that is steady / smooth and not leads to material damage or does not exceed similar limits • Adjustment of the positions and diameters of the temperature control channels so that a uniform demolding temperature is achieved • Adjustment of the flow rates and media temperatures in the individual temperature control channels so that a uniform demolding temperature is achieved
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    Figure 5: A calculation in the sense of the invention deals with a shaping process or physical sequence (for example a sub-process of the shaping process), which is characterized by a start and an end. Accordingly, a certain duration t Pr0Z ess can be assigned to the process to be simulated, which is not known per se at the start of the simulation, but generally results from the point in time at which the process to be simulated has ended. For example, the start corresponds to the penetration of a plastic melt into a cavity and the end of a complete filling of the cavity, which results in the duration of the so-called filling time. For an iterative calculation, such as for a simulation according to the finite element method, the process duration is calculated step by step. A sum of N calculation steps (time steps) therefore results in the entire calculation. Each time step deals with a partial duration At, which can also be variable.
    Figure 6: The number N of necessary time steps and their respective duration generally result from the course of the results of the individual time steps and the Courant number Co. The Courant number need not necessarily be constant. In general, the respective time steps can be of different lengths.
    Figure 7: Simulation method according to the prior art. A simulation is generally carried out from the start to the end of the physical process that is being simulated, with results being analyzed only at the end of the simulation and the overall calculation possibly being repeated with (mostly manually) modified conditions.
    Figure 8: Simulation method according to the invention, according to which an overall calculation is divided into M calculation phases, each of which consists of N time steps. The number M is not necessarily defined at the start of the simulation, but generally results from the course of the results of the individual time steps. Analogously, the process time t Pr0Z ess, m and the number of time steps N m of the calculation phase m at the beginning are not defined,
    20/33 but generally results from the course of the results of the individual time steps. The start and end of the respective calculation phases, except for the start of the first calculation phase M = 1, are also not defined in advance.
    Figure 9: The start and end of a respective calculation phase or
    According to the invention, partial calculation phase can result, for example, as follows: before / during / after each time step, at regular intervals or on the basis of a defined function, the status of the process is analyzed. For this, e.g. a (simple) so-called status functional can be applied to at least parts of the resulting states or it can be checked whether the flow has already passed certain spatial points P of the cavity. Depending on this, the next time step is then simulated ("Next", the calculation phase is "extended") or the calculation phase is ended and the check is carried out
    Process step (a) carried out, e.g. by determining the quality of at least part of the previous process ("analysis", the calculation phase is "ended"). For this, e.g. a quality function can be applied to at least parts of the previously calculated states.
    Figure 10: Has a calculation phase ended according to the invention, e.g. due to the application of a status function to at least some of the previous states, at least four different actions can be triggered according to the invention: the simulation is ended, the calculation phase is repeated (that is, the current calculation phase is started again, “repetition”), a previous one The calculation phase is repeated (that is, it goes back to the start of a previous calculation phase and continues to calculate from there, "backwards"), or a subsequent calculation phase is started (usually the immediately following one; that is, the calculation is simply continued, "forward").
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    In the case of a repetition, the number N of time steps and their respective durations Δΐ can generally, but not necessarily, vary from the repeated calculation phase. In general, the conditions and the process parameters (including the input parameters) can also change, but only to the extent that a process that corresponds to the original requirements results continuously when the method is completed.
    Which of the various actions is actually triggered is decided by an optimization system that analyzes at least parts of previous conditions. Quality functions, quality criteria, experts know, optimization procedures, modeling procedures, databases or reference courses can be used for this. The optimization system also decides on any changes to various conditions (input parameters).
    Figure 11: According to the invention, several simulations consisting of several calculation phases consisting of several time steps can (automatically) be linked together. For example, a first calculation phase can represent a plasticization, a second
    Calculation phase the injection process and a third calculation phase the holding pressure process. Each individual calculation phase can be based on a different process model, which describes the relevant decisive physical phenomena in the best possible way, while neglecting insignificant phenomena and using the available computing resources in the best possible way.
    The respectively necessary conditions of the process and the corresponding states of the process are passed on from one calculation phase to the next calculation phase. Initially, the conditions and the necessary conditions are provided by an operator and / or an initiation system. To
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    At the end of the simulation, input parameters to a
    Transfer machine control to operate an injection molding machine and store various data in a database for future use in the same or in other calculation cases.
    Figure 12. Inline optimizer according to the inventive method.
    Innsbruck, December 23, 2016
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    权利要求:
    Claims (19)
    [1]
    claims
    1. A method for simulating a shaping process or a sub-process of the shaping process, wherein states of objects involved in the shaping process, in particular a shaping machine, a shaping tool and / or a material to be processed, are calculated in discrete and successive time steps under the specification of conditions, the conditions being calculated Represent input parameters of the shaping process, characterized in that (a) after a time step which is before the end of the simulated shaping process or the sub-process of the shaping process, a check of the calculated states of the
    Objects involved in the shaping process are carried out on the basis of at least one quality criterion, (b) if the check after step (a) shows that the at least one quality criterion is not met, at least one of the following is carried out:
    (i) resumption of the simulation with repeated calculation of the time step and / or a previous time step, (ii) continuation of the simulation with calculation of a time step following the time step, (c) the conditions at least partially changed when method step (b) is carried out become.
    [2]
    2. The method according to claim 1, characterized in that the checking of the calculated states is carried out on the basis of the at least one quality criterion by applying at least one quality function to the calculated states.
    [3]
    3. The method according to any one of the preceding claims, characterized in that (a) to (c) are carried out several times as part of the simulation of a shaping process or the sub-process of the shaping process.
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    [4]
    4. The method according to any one of the preceding claims, characterized in that process step (b) is carried out automatically.
    [5]
    5. The method according to any one of the preceding claims, characterized in that the simulation is divided into calculation phases, the calculation phases each comprising a plurality of magazines.
    [6]
    6. The method according to any one of the preceding claims, characterized in that the sub-process of the shaping process and / or the calculation phases are each given by a start time and an end time.
    [7]
    7. The method according to claim 6 for simulating an injection molding process, characterized in that the start time and / or the end time of the sub-process and / or the calculation phases is given by at least one of the following:
    - Start and / or end of plasticization
    - Beginning and / or end of a compression relief
    - Start and / or end of the flow of a plastic melt from an antechamber in the direction of a tool cavity
    - Start and / or end of a flow of a plastic melt into the tool cavity
    - Reaching a predetermined degree of filling of a tool cavity
    - Beginning and / or end of a compression phase
    - reaching a sealing point
    - Reaching a changeover point
    - Beginning and / or end of a reprint phase
    - Edge layers of a molded part are hardened to such an extent that the molded part is essentially dimensionally stable
    - Start and / or end of a tool opening
    - Beginning and / or end of an embossing phase
    [8]
    8. The method according to claim 6 or 7, characterized in that the start time and / or the end time of the calculation phases by
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    Passing a filling front at predetermined spatial points of a tool cavity is determined.
    [9]
    9. The method according to any one of claims 6 to 8, characterized in that at least two of the calculation phases are strung together, results of a previous calculation phase are used as the starting data of a subsequent calculation phase.
    [10]
    10. The method according to claim 9, characterized in that different process models and / or calculation methods are used for different calculation phases.
    [11]
    11. The method according to any one of the preceding claims, characterized in that at least one reference course is given for courses of states calculated in the simulation.
    [12]
    12. The method according to claim 2 and claim 11, characterized in that within the scope of the application of the quality function, a comparison is carried out between the at least one reference curve and at least one curve calculated in the simulation, preferably a difference between the at least one reference curve and the at least one a course calculated in the simulation is recorded quantitatively.
    [13]
    13. The method according to claim 12, characterized in that the reference curves are calculated in advance - preferably using an expert system.
    [14]
    14. The method according to claim 13, characterized in that the following is carried out in the calculation of the reference curves:
    (A) Calculation of a desired filling pressure at a point of a gate based on a maximum flow path and an average flow cross-section and (B) Determination of a linear course of a pressure from the start of injection to the calculated filling pressure when the tool cavity is completely filled.
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    [15]
    15. The method according to any one of the preceding claims, characterized in that the simulation of the shaping process and / or the sub-process of the shaping process and / or the calculation phases is divided into partial calculation phases, with (a) to (c) being carried out after each partial calculation phase.
    [16]
    16. The method according to claim 15, characterized in that the conditions are subjected to an optimization process after each partial calculation phase, the optimization process preferably being designed to
    - Adapt courses of calculated states to the given reference courses from claim 11 and / or
    - to change the conditions so that calculated states of objects involved in the shaping process meet at least one quality criterion.
    [17]
    17. The method according to any one of the preceding claims, characterized in that when changing the conditions according to (c), an expert system is used, which is preferably used to develop a statistical test plan.
    [18]
    18. The method according to any one of the preceding claims, characterized in that the input parameters represented by the conditions of the simulation are transmitted to an injection molding machine after the simulation has been carried out and are used in a real shaping process.
    [19]
    19. The method as claimed in one of the preceding claims, characterized in that after the simulation of the shaping process has been carried out, the results of the simulation are stored in a database, the database preferably additionally containing corresponding process data from real shaping processes and the real process data correlating with the results of the simulation become.
    Innsbruck, December 23, 2016
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    ENGEL AUSTRIA GmbH
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    同族专利:
    公开号 | 公开日
    US20180203431A1|2018-07-19|
    US10520917B2|2019-12-31|
    CN108241774A|2018-07-03|
    AT519005B1|2018-03-15|
    CN108241774B|2021-11-09|
    DE102017130997A1|2018-06-28|
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    法律状态:
    优先权:
    申请号 | 申请日 | 专利标题
    ATA51182/2016A|AT519005B1|2016-12-23|2016-12-23|Method for simulating a shaping process|ATA51182/2016A| AT519005B1|2016-12-23|2016-12-23|Method for simulating a shaping process|
    US15/841,710| US10520917B2|2016-12-23|2017-12-14|Method of simulating a shaping process|
    DE102017130997.7A| DE102017130997A1|2016-12-23|2017-12-21|Method for simulating a shaping process|
    CN201711401641.8A| CN108241774B|2016-12-23|2017-12-22|Method for simulating a forming process|
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