EP2488973A1 - Procédé exécuté sur ordinateur pour l'optimisation d'un processus de moulage par injection destiné à la fabrication de composants à parois épaisses - Google Patents

Procédé exécuté sur ordinateur pour l'optimisation d'un processus de moulage par injection destiné à la fabrication de composants à parois épaisses

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Publication number
EP2488973A1
EP2488973A1 EP10763376A EP10763376A EP2488973A1 EP 2488973 A1 EP2488973 A1 EP 2488973A1 EP 10763376 A EP10763376 A EP 10763376A EP 10763376 A EP10763376 A EP 10763376A EP 2488973 A1 EP2488973 A1 EP 2488973A1
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EP
European Patent Office
Prior art keywords
parameters
parameter values
value
model response
model
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP10763376A
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German (de)
English (en)
Inventor
Florian Dorin
Christoph Klinkenberg
Olaf ZÖLLNER
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Covestro Deutschland AG
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Bayer MaterialScience AG
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Filing date
Publication date
Application filed by Bayer MaterialScience AG filed Critical Bayer MaterialScience AG
Priority to EP10763376A priority Critical patent/EP2488973A1/fr
Publication of EP2488973A1 publication Critical patent/EP2488973A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/22—Moulding

Definitions

  • the invention relates to a computer-implemented method for optimizing an injection-molding process for producing thick-walled components on the basis of a model parameterized on the basis of parameters to be preset. Furthermore, the present invention relates to a corresponding computer program which executes the proposed method when executed on a computing unit.
  • the inventive method is used in particular for increasing the productivity of thermoplastic injection molding processes for the production of thick-walled components, such as optical components
  • thermoplastics or other organic and inorganic plastics for example for imaging or light-forming (non-imaging) purposes, are currently produced or developed by injection molding using various injection molding special processes such as injection compression molding or dynamic temperature control of the appropriate mold.
  • Injection molding is the basis for all other injection molding processes and the most commonly used plastics processing technology.
  • a resulting frictional heat in conjunction with the heat supplied by a heated cylinder, provides a relatively homogeneous melt. This melt collects in front of a tip of the receding snail.
  • the worm of the worm-piston injection molding machine is pressurized hydraulically on the rear or by mechanical force.
  • the melt is pressed under high pressure, usually between 500 and 2000 bar, by a backflow lock e, the nozzle pressed against the injection mold, optionally a hot runner system and a runner in a shaping cavity, a so-called cavity of the tempered injection mold.
  • a reduced pressure acts as a so-called reprint on the melt until the Connection, also called sprue, solidified or frozen. This ensures that a volume shrinkage that occurs during cooling can be largely compensated. This is important for dimensional accuracy and desired surface quality.
  • a rotation of the screw begins.
  • the shot composition is prepared for the following molding. During this time, the molding in the tool can still cool until the material is solidified in the core. Then the tool opens and ejects the finished component.
  • the locking force is the force that keeps the corresponding tool against injection and repressions.
  • the cavity, the cavity, of the tool determines the shape and the surface structure of the component to be produced.
  • the screw profile also plays a role in the injection molding parameters, whereby a screw can be a catchy three-zone screw with a feed, compression and discharge zone or a barrier screw, usually for increased performance or a core-progressive PVC screw.
  • CAE programs To simulate an injection molding process, so-called computer aided engineering programs, CAE programs for short, are often used today. However, these are focused on appropriate filling processes and not on minimizing cycle times or predicting component qualities in multi-component injection molding of thick walled components. In addition to these CAE programs, there are programs that are able to parametrize simulation calculations.
  • Parametrization here means on the one hand a change of boundary conditions, such as melting temperature, tool temperature, emphasis, etc., and on the other hand a change of geometries.
  • boundary conditions such as melting temperature, tool temperature, emphasis, etc.
  • geometries for example, in a multi-layer injection molding process (multi-layer injection molding process), a corresponding component can be divided into several layers, which are injected successively or in parallel.
  • Plastics 04/2009, pages 83 to 86 Furthermore, the font by Stricker, Pillwein, Giessauf “Precision in Focus - Injection Molding of Optical Moldings", published in Kunststoffe international 04/2009, pages 30 to 34, can be used as well.
  • Rheological simulations and commercial optimizers can not be adequately combined at present, but other CAE programs that can be combined with optimizers can map temperature behavior, but only so conditionally taking into account a respective injection molding process.
  • the quantitative results can only be put into practice to a limited extent, as some parameters that have a significant influence on the cooling behavior can not be measured directly so far.
  • the wall thickness distribution, the arrangement of the respective layers, the order of the "partial shots" and different mold temperatures in the individual cavities can be achieved in multi-layer injection molding in comparison to single-lay injection molding improvements in component quality and at the same time significant shortening of the cycle times
  • a shortening of the cycle times is due, among other things, to the fact that the wall thickness of the component to be manufactured is included quadratically in the cooling time formula, whereby it must of course be taken into account that the total potential of a wall thickness reduction can not be exhausted, since for a second layer only in the direction of the tool Heat dissipation is given
  • Thickness distribution presents an engineer with special challenges. Namely, a change in the wall thickness also changes the cooling time of the pre-molded part. The pre-molded part should only be cooled until it is just so cold that it can be demoulded. The fact that there are production parameters for pre-injection and post-injection, the number of variables for optimization increases, which also still all depend on each other. These dependencies and a large number of variables increase the complexity of the model as well as the number of possible optima.
  • a computer-implemented method for optimizing a multilayer injection molding process for producing thick-walled components on the basis of a parameterized model based on parameters to be specified is proposed.
  • a thick-walled component with a component geometry is imaged in the model.
  • the proposed method has at least the following steps:
  • step e optimizing the parameter values of the individual main parameters with respect to a desired value of the model response in the respective tolerance ranges starting from the starting values from step d), f) Setting the optimized parameter values of the individual main parameters from step e) as corresponding start parameter values on an injection molding machine.
  • a model response is to be understood as a result variable which results from the simulation or the corresponding model present here.
  • a desired model response can be specified here, the value of which is then determined with the aid of the proposed method for respective values of the individual parameters and is ultimately optimized by the proposed method with regard to a desired value.
  • one parameter is a parameter which counts among those parameters which have a large effect, compared to other parameters, on the corresponding model response or on its value.
  • the number of main parameters to be determined in this case depends on an optional determination or definition of the term "maximum influence" on the respective value of a desired model response
  • a value for the given model response is first added in step a) for the individual parameters different parameter values of the respective parameter and a resulting relative influence of the individual parameters on the value for the model response is determined, wherein in step b) then set the group of parameters as the main parameters on the basis of the thus determined relative influence of the individual parameters
  • step c) a correlation of the main parameters with respect to the value for the model response for different parameter values of the individual main parameters is then determined, in which case the parameter values for the main parameters are used as starting values for the subsequent optimization in step d) n of the model and the respective tolerance ranges for the main parameters on the basis of the correlation thus determined.
  • Model response for one of the various parameter values of a first parameter this parameter value in combination with all the different parameter values of the other parameters entered into the model, and averaged from the respective resulting model response values, then the parameter value of the first parameter as the model response value is assigned.
  • This procedure is run through for all other parameters with regard to their respective different parameter values.
  • a joint consideration of the corresponding values for the model response thus obtained suggests the relative influence of the individual close the parameter to the value of the model response. Such a consideration or evaluation takes place, for example, graphically.
  • the respective relative influence of the individual parameters can be determined by simulation, with regard to the relative influence it is also possible to make use of empirical values for the individual parameters. The same applies to a determination of the parameter values of the main parameters as starting values for the subsequent optimization. Here, too, empirical values can be used.
  • the component geometry comprises, in addition to an overall shape, a variable number of layers and a respective thickness of these layers.
  • the geometric shape of these layers in the component is also variable.
  • Such a structure is, for example, in the case of the above-mentioned multilayer injection molding method of relevance or a component produced by this method generally has such a structure.
  • components to be produced in this way as already mentioned, they can be optical lenses.
  • the number of optima can be reduced and the range in which the optima can lie can be limited, so that with the aid of e.g. the optimization method mentioned in the aforementioned document XP-002573114 or other methods known from the prior art can further optimize. In this way a large number of invoices can be avoided with just a few bills.
  • the parameters can be limited and in some cases also "removed", that is, ignored, and thereby reduced. After inventive reduction of the parameters, it is then still optimized, which is valid for a component-independent multi-layer injection molding process.
  • the desired value of the model response corresponds to an extremal value, in particular a maximum or minimum of the model response.
  • suitable starting values for optimizing the model are determined not only for the main parameters but also for the remaining parameters.
  • the following steps are furthermore provided: el) Determining a correlation of the remaining parameters with respect to the value for the model response for various parameter values of the individual remaining parameters and therefrom Setting parameter values for the remaining parameters as starting values for the subsequent optimization of the model and of respective tolerance ranges for the remaining parameters,
  • step el) and step e2) after step e) is to be executed are sequentially executed.
  • the method further comprises the following step: e3) determining the value for the model response for the optimized parameter values.
  • the parameters to be predetermined are selected from a group consisting of component geometry parameters and injection molding parameters.
  • the component geometry parameters may be a layer thickness and a number of layers of the component to be produced.
  • the injection molding parameters are generally settings on a corresponding injection molding machine. These may be, for example, tool temperatures, melting temperatures, pressures, cooling times, injection profile, switching point and holding pressure profile. Further parameters can be: cooling rates on the tool wall as well as other thermal properties of the tool.
  • determining the relative impact of the parameters (with their different parameter values) on the model response in determining the value for the model response for a respective parameter according to an embodiment of the method for the individual parameters respectively 1 to 5 parameter values, in particular 2 parameter values, for example a minimum and a maximum value are indicated.
  • the value for the given model response is then determined for these different parameter values of a respective parameter, and from this a relative influence of the respective parameter on the value for the model response in comparison to the other parameters is specified.
  • the parameter values of the remaining parameters are varied such that the one parameter value of the first parameter combines with all the parameter values of the other parameter, computes a respective value of the model response for all combinations, and the ensemble the values of the model response are averaged, which is then assigned to the one parameter value of the first parameter as the value of the model response. The same is done for the other predetermined parameter values of the first parameter and each for the different parameter values of the other parameters.
  • a correlation with respect to the value for the model response can then be determined on the basis of different parameter values of the individual main parameters.
  • Such a plot is carried out in each case for different parameter values, for example parameter value 1 'and 2', of a second main parameter, such as a parameter B, whereby depending on the number of parameter values for the second main parameter, a corresponding number of plots resp Graphs whose behavior gives each other information about the correlation of the first and second main parameters. If the plots run essentially parallel to each other, this indicates a low correlation. If the plots do not run parallel, then there is a recognizable correlation. On the basis of a correlation of the main parameters with respect to the value for the model response determined in this way, parameter values for the main parameters which are used as starting values for a subsequent optimization of the model can then be defined.
  • a second main parameter such as a parameter B
  • respective tolerance ranges for the main parameters can be derived from each other.
  • the parameter values for the remaining, d. H. parameters not included in the correlation analysis are kept constant.
  • a value relevant to the practice is assumed. For example, if reprinting is not a primary characteristic, i. E. If a fixed value is to be assumed for this, then this parameter value will be chosen as low as possible both in practice and then in the model
  • the correlation of the main parameters can be determined, for example, by introducing a covariance that establishes a correlation of main parameters with respect to the value for the model response.
  • the main parameters are determined to be 1 to 5 parameters starting with the parameter having the greatest relative influence and, in the case of more than one parameter, proceeding with the parameters immediately following with regard to the relative influence.
  • the five parameters with the greatest relative influence on the value of the given model response are used as main parameters in general, and their correlation with each other is determined with respect to the value for the model response.
  • parameter values are then determined for the main parameters, which are used as starting values for a subsequent optimization. Furthermore, respective tolerance ranges for the main parameters which are also included in the subsequent optimization are determined for the respective main parameters and the specified parameter values.
  • the starting values used may be those parameter values for the main parameters which lead to a value which is as close as possible to the desired value for the given model response. While maintaining the starting values for the main characteristic quantities thus determined, further starting values for these parameters, which are then also included in a subsequent optimization, can optionally also be defined for the remaining parameters by determining their correlation with one another with respect to the value for the model response.
  • the fixed starting values are now used to optimize the parameters with respect to a desired value of the model response in the respective tolerance ranges and to determine the resulting value of the model response.
  • Such optimization is usually by taking or use of a commercial optimiser as examples play "HyperStudy ®" is carried out, wherein the parameters, in particular the main characteristics are varied within the specified tolerance ranges, and from this optimal parameter values combination for the individual main characteristics
  • the resulting results Optimized parameter values then serve as corresponding start parameter values at a corresponding injection molding machine for producing a corresponding component, in particular an optical component.
  • the model response is specified from the group consisting of maximum temperature in the component to be produced, total duration of the injection molding process, duration up to the earliest possible time for demoulding.
  • the model response is specified as a duration until the earliest possible time for demoulding, a value for the duration until the earliest possible time for demolding at different parameter values of the respective parameter is determined in step al) of the proposed method , which is carried out separately for all parameters to be specified, with the result that for the individual parameters a respective relative influence on the value for the duration until the earliest possible time for demoulding results.
  • it must be deduced from which parameters, in an independent study, a relatively high or low influence on the duration up to the earliest possible time for demoulding.
  • the parameters to be preset may be injection molding parameters, ie parameters related to the actual process of injection molding. These are, for example, the melting temperature of the material used to produce the respective component, the tool temperature of the tool used for injection molding, the cooling time, ie how long the tool is cooled together with the injection molded component, and the cooling rate.
  • the so-called switching point which is defined by injection time, injection pressure, screw position, clamping force and volume, is one of the injection-molding parameters.
  • an injection molding parameter is the hold pressure profile which is determined by the duration and height of the required holding pressure and an injection profile which results from injection time, volume flow, screw position and screw feed rate.
  • material properties are to be considered as boundary conditions, but these are essentially to be assumed to be fixed variables and as a rule are not varied.
  • Material properties are the properties of the material used for producing the component, for example the optical component, and are therefore injected into the injection molding tool.
  • Material properties include in particular heat capacity, thermal conductivity, thermal expansion and the so-called no-flow temperature.
  • This no-flow temperature is a flow limit temperature for which it is assumed that the respective material, such as, for example, a used plastic no longer flows when cooled below this temperature.
  • This flow limit temperature is an empirically determined quantity.
  • the material properties include the transition temperature and the glass transition temperature or the glass point, the solidification temperature, the melting temperature, the so-called D3 coefficient, which indicates a pressure dependence of the viscosity, and a so-called C1 / C2 coefficient, which is a so-called Juncture Loss, d. H. indicates an inlet pressure loss.
  • thermal material data heat capacity, thermal conductivity and also the heat transfer coefficient
  • the geometry of the component to be produced also flows into the parameterized model.
  • a specific geometry is desired, which in turn flows as such into the model in the form of parameters.
  • the component geometry may include, in addition to the overall shape, which is generally fixed, for example, comprise multiple layers or be divided into several layers and have variable separation planes, d. H. variable layer thicknesses. The number of layers can vary.
  • the model is first parameterized accordingly, on the basis of which the underlying injection molding process is ultimately to be optimized. After parameterizing the model, this model is first validated, i. H. its validity checked with real values. This is usually done only once.
  • a test calculation is then carried out for each geometry (in the case that different layer numbers are available for different geometries) and then subsequently recorded with a post-processor a kind of macro, with the further course of all other calculations can be evaluated in the same way.
  • the postprocessor is a program for the evaluation of simulation results. If the model is validated, ie the model reflects a real behavior, the now parameterized model is included in the proposed computer-implemented method, so that the proposed method steps can be carried out based on the parameterized model accordingly.
  • the parameters that are to be varied in the underlying parameterized model are initially specified. This can be both the component geometry, as well as the aforementioned parameters, which include the injection molding parameters, possibly also the material properties and other physical parameters.
  • model response is given with regard to which the injection molding process is to be optimized.
  • model responses are: - Maximum temperature in the part to be manufactured;
  • the duration until the demolding temperature is reached directly determines the cycle rates prevailing in the injection molding process which, for example, should be minimized or kept as low as possible when optimizing the injection molding process. This means that, for example, it may be a goal to minimize the earliest possible time for demoulding or the time to reach that point in time.
  • the component to be produced is not injected in one go, and an associated demolding time, but rather only a first layer, a so-called pre-molded part, sprayed and is cooled until its demolding temperature is reached. Only then or exactly then the tool is opened and brought the preform, for example by means of a turntable, an index plate, a sliding table or a robot in a next cavity of the same tool. There, a second layer is sprayed over the pre-molded part. Also conceivable is the use of two or more independent injection molding machines and tools.
  • Each machine can be responsible for the spraying of a layer, the transfer of injection molded parts between the machines is carried out by suitable means.
  • it is not known in advance how long it will take to reach a demolding temperature of a component to be produced. This must first be determined in a first calculation. For this purpose, a much longer cooling time than necessary and controlled, at which time the respective cooling temperature or Entginsstempe- rature has been achieved. This can be set automatically in simulations but also via a termination criterion, ie it is simulated exactly as long as the demoulding temperature has fallen below everywhere.
  • the determined value is then used again to calculate the first layer, ie the pre-molded part, namely exactly up to the time of the previously determined demolding temperature.
  • the first layer ie the pre-molded part
  • a corresponding temperature profile is determined.
  • the second layer is simulated.
  • the duration for achieving the demolding temperature for the now two layers is again calculated in combination.
  • the specific temperature profile of the preform can be used to support.
  • the cooling behavior between the individual steps can be simulated with, for example, if the preform is stored for a long time, or it takes a few minutes before the preform is over-injected, or it takes only a few seconds before the preform is injected behind.
  • the procedure described can run automatically with the help of scripts and macros, whereby only a single input is necessary.
  • the second script (to determine demolding time 1) loads a macro in which the calculation is evaluated and outputs the temperature over time as tabular value pairs.
  • the table is used to extract from when the demolding temperature has been exceeded.
  • the determination of the respective relative influence of the individual parameters on the value for the model response performed in step a1) is carried out by a graphical evaluation.
  • a graphical evaluation This means, for example, that if all the resulting values for the given model response for a parameter lie on a straight line for different values of this parameter, the slope of the line can indicate how large the relative influence of the corresponding parameter is on the value for the parameter Model answer is. For example, the greater the slope of the straight line on which the values for the model response lie at the various parameter values of the respective parameter, the greater the relative influence.
  • the graphical evaluation is done to determine the influences of the parameters. As described in the previous paragraphs, first all parameters with all parameter values are varied. Therefore, a graphical evaluation described here makes sense. The parameters can also have a curved course, then you can, for example. also read trends, minima / maxima or a course against a limit.
  • parameters are determined as the main parameters. These are preferably those parameters with the greatest relative influence. According to a possible embodiment, in a further step, their correlation with each other with respect to the value for the model response is examined. The parameter values of the parameters not identified as main parameters are kept constant.
  • the number of main parameters is usually smaller than the total number of parameters flowing into the model, the number of parameter values used to determine the correlation of the main parameters with each other, compared to the intended previous step a), according to which for each Parameters each one value for the given model response at different parameter values of the respective Parameters are determined increased.
  • step b) the parameter "cooling time"
  • step c) when determining the correlation of the main characteristics with each other with respect to the value of the model response for the parameter "Cooling time” more parameter values are used, such as values 50, 100, 200, 300, 400, 500 seconds.
  • step b) For a parameter "tool temperature", which shows only a small relative influence on the value of the given model response after carrying out step a) of step b), assuming parameter values of 60 ° C and 120 ° C for this parameter , and which is not counted in step b) to the group of parameters which are defined as main parameters, only a constant value, such as 60 ° C., is assumed when the correlation in step c) is determined Based on the results from step c) In the proposed method, it is possible to obtain information on how the main parameters depend on each other and on which constellations, ie in which parameter values of the individual main parameters, the best results can be achieved with respect to the value for the model response Optimize the model with respect to a desired value for the given model response. Furthermore, respective tolerance ranges for the main parameters can be derived therefrom.
  • the parameter values of the main parameters in the respective tolerance ranges are determined starting from the starting values and finally the resulting value for the model response. For example, if the model response is the time to reach the earliest possible demolding time, and the target value is a minimum, then one obtains a specific value for that time that is at least equal to or at least as close as possible to that minimum and secondly, optimized parameter values for the main characteristics leading to this value.
  • the parameter values thus determined for the main parameters can in turn be set as corresponding start parameter values on a real injection molding machine, so that the actual real-time injection molding process performed can be performed on the basis of these optimized parameter values.
  • boundary conditions that must be fulfilled, ie. H. can not be varied arbitrarily.
  • the model response "quality characteristic” a minimum value must first be fulfilled, which is then ensured by a corresponding boundary condition, which is optimized until all boundary conditions are met and only then is the optimization of the model response optimized or the desired value.
  • Adhering boundary conditions can furthermore be given, for example, by material properties of the material used for the injection molding.
  • material properties are in particular heat capacity, thermal conductivity, thermal expansion, glass transition point, solidification temperature, melting temperature, viscosity and density to consider.
  • the material properties relate to the material to be used for the production of the respective component.
  • the optimization, ie step c) of the proposed method is carried out.
  • Simulation models for the parameter values in the respective tolerance ranges are calculated automatically and the values for the model response are determined.
  • the obtained values for the model response are then evaluated taking into account the boundary conditions to be met and the desired value for the model response, with the results actually compared with each other and thus the underlying parameter values of the main parameters optimized. Is the optimization goal achieved or If no significant changes occur, and all boundary conditions are fulfilled, the optimization is ended and it is indicated which parameter values of the main parameters led to this optimum result.
  • These parameter values are then used to carry out a real process on a corresponding injection molding machine. In the real case, process optimizations still have to be carried out on the real injection molding machine later on, which are, however, considerably less expensive than without the previously performed optimization.
  • the proposed procedure is targeted and a "trial and error" procedure is no longer necessary.
  • a step for post-processing after first injection molding trials can be significantly reduced.
  • a number of loops are minimized by the optimized setting of parameters on an injection molding machine in advance.
  • the cycle time can be minimized based on measurable quantities.
  • Temperature measurements inside a component are not possible in the prior art. Even though such measurements are not possible by the proposed method, it is possible to simulate and thus determine a temperature distribution in a component to be produced. This temperature distribution can be adjusted by practical experiments with temperature sensors in the real tool and the accuracy of the quantitative temperature distribution can be increased. This then corresponds to a validation of the calculation models.
  • the proposed method for optimizing an injection molding process for producing thick-walled components is carried out on the basis of a parameterized model.
  • This model can in turn be based on a standard injection molding machine with, for example, a two-cavity injection molding tool.
  • a standard injection molding machine with For example, a two-cavity injection molding tool offers the possibility of spraying a plate-shaped optical component having 50 x 50 x 20 mm edge length and highly polished surfaces in three layers, the preform element optionally having wall thicknesses of 30, 50 and 70% of a total wall thickness of 20 mm having.
  • Suitable materials for optical components are all thermoplastically processable plastics, for example polycarbonate (PC), polyesters, in particular polybutylene terephthalate (PBT) or polyethylene terephthalate (PET), polyamide (PA), polyethylene (PE), polypropylene (PP), polystyrene (PS ), Poly (acrylonitrile-co-butadiene-co-styrene) (ABS), poly (acrylonitrile-co-styrene-co-acrylic ester) (ASA), poly (styrene-co-acrylonitrile) (SAN), polyoxymethylene (POM) , cyclic polyolefins (COC), polyphenylene oxide (PPO), polymethyl methacrylate (PMMA), polyphenylene sulfide (PPS), polyvinyl chloride (PVC) and their blends.
  • PC polycarbonate
  • PET polybutylene terephthalate
  • PA polyethylene terephthalate
  • PA polyethylene
  • a component geometry of an optical component to be produced can for example provide a wall thickness of more than 5 mm, preferably more than 10 mm, and edge lengths of 5 to several 100 mm.
  • the spraying of an optical component according to the injection molding process to be optimized can be carried out by a multilayer injection molding process from a thermoplastic material.
  • the injection molding process is well known in the art. Reference is made to a document by Döbler, Protte, Klinkenberg "Free travel for white light - LED optics", published in Kunststoffe international 04/2009, page 83 - 86.
  • the proposed method for optimizing an underlying injection molding process for producing thick-walled Components, in particular optical components it is achieved that, for example, demolding of an optical component to be produced no longer has to be estimated on the basis of thumb values and measurement results, but that temperature profiles and cooling processes in the interior of the component to be produced can be made visible and therefore much better understood.
  • Duration is directly related to the cycle time or cycle rate of the injection molding process, which is particularly important in mass production.
  • present disclosure also covers a computer program with program code which is suitable for carrying out a method according to the invention when the computer program runs on a corresponding arithmetic unit. Both the computer program itself and also stored on a computer-readable medium (computer program product) are claimed.
  • the software package "HyperWorks ®” by the company can be found Altair application.
  • the HyperView ® program can, for example, for analysis of temperatures and HyperStudy ® be used to control the automated optimization. with the program HyperStudy ® also preceding the optimization step the steps of the proposed method can be performed in addition to the optimization step.
  • the proposed method can also be one from the prior art software is used which is capable of a simulation for an injection molding process to include injection molding parameters, are carried out.
  • the Moldflow ® software can be used.
  • there are also other programs to Ermittl a theological behavior are suitable, such as CADMould ® . These programs are mathematically based on an Eulerian approach.
  • the software package HyperWorks ® Altair includes several independent modules that can be combined by file exchange. Accordingly, a use of individual modules is possible. As part of the proposed method while HyperView ® and HyperStudy ® and Hyper Graph ® and HyperOpt ® can in particular, as already mentioned, be used.
  • HyperView ® is a FE and MBS postprocessor.
  • HyperStudy ® is a solver-independent optimization tool.
  • HyperGraph ® is used to generate graphs from the calculations performed, while the HyperView ® module is a graphical post-processor that can reproduce the distribution of result quantities in the component such as stresses, rotations and deformations as so-called contour plots.
  • FIG. 1 shows, in a schematic representation in the form of a flow chart, the sequence of a possible embodiment of the proposed method for optimizing an injection molding process for producing thick-walled components, in particular optical components.
  • FIG. 1 shows a flow chart of an embodiment of the proposed computer-implemented method for optimizing an injection molding process for producing thick-walled components, in particular optical components, on the basis of a model parameterized on the basis of parameters to be predetermined.
  • the parameterized model is first created on the basis of which the injection molding process is optimized.
  • the parameterized model is based on predefined parameters. These parameters must first be selected from the parameters that flow into the respective injection molding process and influence the injection molding process. On the one hand, these are parameters which are incorporated in a component geometry of the component to be produced, such as, for example, a number of layers and a layer thickness of the component to be produced, which may, for example, be an optical lens.
  • injection molding parameters are to be considered, which directly or indirectly influence the injection molding process. These include, among others, melting temperatures, mold temperatures, cooling times, cooling rates, a so-called switching point, a holding pressure profile and an injection profile.
  • the so-called switching point is defined by injection time, injection pressure, screw position, closing force and volume.
  • the injection profile is defined by injection time, volume flow, screw position and screw feed rate.
  • the model is created using software of the prior art. For this one can use for example the already mentioned software Moldflow ® . It is also possible to use other programs such as CADMould ® .
  • the computational codes and mathematical approaches of these programs are mathematically based on an Eulerian approach. For a simulation for solid and liquid phases of a program called Abaqus ® may also be the company Dassault Systems used. It is an FE program where the implied solver can be used. Other programs that can be used include Ansys ® and Radioss ® . This software is preferably used because it is based on ASCII formatted data and thus can be easily combined with another used in the following optimization program called HyperWorks ®.
  • a value for a model response to be specified at different parameter values of the respective parameter is first determined for the individual parameters according to the embodiment of the method described here. From this, a relative influence of the individual parameters on the value for the model response can be determined.
  • the model response can be, for example, a maximum temperature in the component, a total duration of the injection molding process, or a period until the earliest possible time for demolding is reached.
  • a group of parameters is defined as the main parameters.
  • a correlation of the main parameters with respect to the value for the model response is then determined for different parameter values of the individual main parameters. On the basis of the thus determined correlation of the main parameters, parameter values for the main parameters are used as starting values for a following
  • the desired value of the model response may, for example, be a maximum or a minimum of the model response, depending on the model response.
  • the target value is generally a minimum of the total duration of the injection molding process. If the model response is the time to reach the earliest possible time for demoulding the component to be manufactured, reducing the cycle times at the desired value of the model response will also be a minimum of the model response, which means that the duration To achieve the earliest possible time for demoulding is as short as possible.
  • material properties of the material used for injection molding play a role with regard to the boundary conditions to be met.
  • the material properties include heat capacity, thermal conductivity, thermal expansion and a so-called no-flow temperature, which corresponds to a flow limit temperature for which it is assumed that the material used, in particular a plastic, does not flow when cooled below this temperature.
  • material properties include transition temperature, glass transition temperature, solidification temperature, melting temperature, D3 coefficient, which indicates a viscosity viscosity, Cl / C2 coefficient, viscosity, creep behavior, modulus of elasticity, density and PVT data of the material used.
  • model response corresponds, for example, to a maximum temperature in the core of the component to be produced
  • desired value of the model response may be, for example, the glass transition temperature at or below which demolding is possible.
  • the value for the model response can be determined for these optimized parameter values.
  • the calculated optimized parameter values of the main parameters are set as corresponding start parameter values on a real injection molding machine.
  • the optimization results are ultimately tested in real cases and, if necessary, fine-tuned.

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  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Injection Moulding Of Plastics Or The Like (AREA)

Abstract

L'invention concerne un procédé exécuté sur ordinateur pour l'optimisation d'un processus de moulage par injection destiné à la fabrication de composants à parois épaisses sur la base d'un modèle paramétrisé en fonction de paramètres à définir, un composant à fabriquer et sa géométrie étant reproduits dans le modèle. Le procédé comporte au moins les étapes suivantes; e) définition d'un groupe de paramètres en tant que caractéristiques principales sur la base d'une influence relative des paramètres sur une réponse modèle prédéfinie; f) définition de valeurs paramétriques pour les caractéristiques principales en tant que valeurs de départ pour une optimisation consécutive du modèle et de gammes de tolérance respectives pour les caractéristiques principales; c) optimisation des valeurs paramétriques des caractéristiques individuelles en ce qui concerne une valeur souhaitée de la réponse modèle dans les gammes de tolérance respectives à partir des valeurs de départ de l'étape a); et d) réglage des valeurs paramétriques optimisées de l'étape c) en tant que valeurs paramétriques de départ correspondantes sur une machine de moulage par injection.
EP10763376A 2009-10-16 2010-10-12 Procédé exécuté sur ordinateur pour l'optimisation d'un processus de moulage par injection destiné à la fabrication de composants à parois épaisses Withdrawn EP2488973A1 (fr)

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EP10763376A EP2488973A1 (fr) 2009-10-16 2010-10-12 Procédé exécuté sur ordinateur pour l'optimisation d'un processus de moulage par injection destiné à la fabrication de composants à parois épaisses

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EP09013073A EP2323050A1 (fr) 2009-10-16 2009-10-16 Procédé implanté par ordinateur pour optimiser un processus de moulage par injection pour la fabrication de composants à paroi large
PCT/EP2010/065279 WO2011045314A1 (fr) 2009-10-16 2010-10-12 Procédé exécuté sur ordinateur pour l'optimisation d'un processus de moulage par injection destiné à la fabrication de composants à parois épaisses
EP10763376A EP2488973A1 (fr) 2009-10-16 2010-10-12 Procédé exécuté sur ordinateur pour l'optimisation d'un processus de moulage par injection destiné à la fabrication de composants à parois épaisses

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EP10763376A Withdrawn EP2488973A1 (fr) 2009-10-16 2010-10-12 Procédé exécuté sur ordinateur pour l'optimisation d'un processus de moulage par injection destiné à la fabrication de composants à parois épaisses

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EP3010695A4 (fr) 2013-06-21 2017-04-12 Modi Consulting and Investments Pty Ltd Procédé de surmoulage ayant une étape de chauffage intermédiaire
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CN104657549B (zh) * 2015-02-06 2018-02-16 浙江大学 一种基于正交参数化ltv模型的迭代学习预测控制方法
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CN102549580B (zh) 2015-03-25
WO2011045314A1 (fr) 2011-04-21
CN102549580A (zh) 2012-07-04
EP2323050A1 (fr) 2011-05-18
US8983878B2 (en) 2015-03-17

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