METHOD AND COMPUTER PROGRAM PRODUCT FOR COMPARING A SIMULATION WITH THE REAL CARRIED OUT PROCESS

Abstract

A method for aligning a simulation of a process to be carried out with a shaping machine with the process really carried out, includes calculating a simulation progression of a variable characteristic of the process, measuring in the process really carried out a measurement progression of the characteristic variable, determining first distinguishing points of the curve of the simulation progression and second distinguishing points of the curve of the measurement progression, mapping the first distinguishing points and the second distinguishing points, calculating a modification parameter for the simulation and/or the process from coordinates of the first distinguishing points and second distinguishing points mapped to each other, and modifying the simulation and/or the process based on the modification parameter and carrying it out again.

Claims

1. A method for aligning a simulation of a process to be carried out with a shaping machine with the process really carried out, wherein within the framework of the simulation at least one simulation progression of a variable that is characteristic of the process, in particular a simulated pressure progression, is calculated, in the process really carried out at least one measurement progression (MV) of the characteristic variable, in particular a measured pressure progression, is measured, first distinguishing points of the curve of the at least one simulation progression and second distinguishing points of the curve of the at least one measurement progression are determined, the first distinguishing points and the second distinguishing points are at least partially mapped to each other, at least one modification parameter for the simulation and/or the process is calculated from coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other, and the simulation and/or the process is modified on the basis of the at least one modification parameter and carried out again.

2. The method according to claim 1, wherein the first distinguishing points and/or the second distinguishing points are determined using the Ramer-Douglas-Peucker algorithm, wherein at least one additional criterion is preferably used to further reduce a point set reduced using the Ramer-Douglas-Peucker algorithm in order to obtain the first distinguishing points and/or the second distinguishing points.

3. The method according to aclaim 1, wherein the first distinguishing points and/or the second distinguishing points are accordingly determined, if connecting lines to adjacent points of the simulation progression or of the measurement progression form an angle which deviates by a predefined angular amount—preferably by 5° or more, particularly preferably by 10° or more-from 180°.

4. The method according to claim 2, wherein at least one of the following conditions and/or criteria is used when determining the first distinguishing points and/or the second distinguishing points: a maximum number of reduced points and/or distinguishing points, a minimum distance between the points of the reduced point set, a maximum standardized error of the squares of the distance between the original data points of the measurement progression and/or of the simulation progression on the one hand and the points of the reduced point set on the other, exceeding and/or reaching a threshold value through the characteristic variable, excluding a predefined partial range of the process, wherein the partial range is given by absolute or relative limits.

5. The method according to claim 1, wherein the first distinguishing points and the second distinguishing points are at least partially mapped to each other, in that for all of the possible different options for mapping the first distinguishing points to the second distinguishing points, the first distinguishing points-WO and/or the second distinguishing points are scaled and/or shifted such that in each case two of the first distinguishing points and of the second distinguishing points substantially lie on top of each other, in each case at least one characteristic number for the quality of the respective mapping option is calculated on the basis of at least one of the following: scaling parameter, shifting parameter, coordinate differences between the—optionally scaled and/or shifted—first distinguishing points and the—optionally scaled and/or shifted—second distinguishing points, that mapping option is selected, at least one characteristic number of which indicates a best quality of the mapping.

6. The method according to claim 1, wherein the method is applied to results of the simulation carried out again and/or to measurements in the process carried out again, wherein this is preferably repeated until a simulation deviation between the at least one simulation progression and the at least one measurement progression is sufficiently small according to a predefined criterion.

7. The method according to claim 6, wherein the loop started by applying the method again is interrupted if: values of the at least one modification parameter reach and/or fall below a first predefined limit value, and/or differences—in particular differences in amount—from areas under the at least one simulation progression and the at least one measurement progression reach and/or fall below a second predefined limit value, and/or the at least one simulation progression at least partially—preferably completely—proceeds within a predefined first tolerance range around the at least one measurement progression, and/or the at least one measurement progression at least partially—preferably completely—proceeds within a predefined second tolerance range around the at least one simulation progression.

8. The method according to claim 1, wherein the at least one modification parameter relates to a magnitude of a time shift between the first distinguishing points and second distinguishing points mapped to each other, wherein the time shift is in particular caused by an unknown volume of the molding material present in the shaping machine.

9. The method according to claim 8, wherein the simulation is modified by modifying an injection volume predefined for the simulation and/or an injection volume flow rate predefined for the simulation on the basis of the at least one modification parameter for the magnitude of the time shift.

10. The method according to claim 1, wherein the at least one modification parameter relates to a magnitude of a scaling of those coordinates of the first distinguishing points and second distinguishing points mapped to each other which correspond to the characteristic variable.

11. The method according to claim 10, wherein the simulation is modified by modifying a material parameter predefined for the simulation on the basis of the at least one modification parameter for the magnitude of the scaling.

12. The method according to claim 1, wherein the at least one modification parameter is calculated as a statistical parameter, in particular arithmetic mean, of the coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other.

13. The method according to claim 1, wherein a Cross-WLF model and/or a 2-domain Tait pvT model is used as material model for the simulation.

14. The method according to claim 1, wherein the at least one modification parameter is stored in a database and is used when simulating and/or setting a separate process.

15. The method according to claim 1, wherein several simulation progressions and/or several measurement progressions are taken into account when determining the first distinguishing points and/or the second distinguishing points.

16. The method according to claim 1, wherein the at least one simulation progression and/or the at least one measurement progression are parameterized by means of a time index or a position index of an actuator used in the process, in particular of a plasticizing screw.

17. A shaping machine, which is set up to carry out the method according to claim 1.

18. A computer program product for aligning a simulation of a process to be carried out with a shaping machine with the process really carried out, with commands which prompt a computer executing them to calculate at least one simulation progression of at least one variable that is characteristic of the process, in particular a simulated pressure progression, within the framework of a simulation or to receive one from a separate simulation, to receive at least one measurement progression of the at least one characteristic variable, in particular a measured pressure progression, from the real process, to determine first distinguishing points of the curve of the at least one simulation progression and second distinguishing points of the curve of the at least one measurement progression, to at least partially map the first distinguishing points and the second distinguishing points to each other, to calculate at least one modification parameter for the simulation and/or the process from coordinates of the first distinguishing points and second distinguishing points at least partially mapped to each other, and to modify either the simulation and/or the process on the basis of the at least one modification parameter and to carry it out again or to output instructions which include that the simulation and/or the process is to be carried out again and what modifications are to be made to the simulation and/or the process on the basis of the at least one modification parameter.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0125] Further advantages and details of the invention are revealed by the figures as well as the associated description of the figures. There are shown in:

[0126] FIG. 1 shows an example of an injection-molded part including sprue, nozzle, measuring flange and part of the space in front of the screw, which is used as an example to illustrate the invention,

[0127] FIG. 2 shows a measurement progression and a simulation progression for the example molding process in a graph,

[0128] FIG. 3 shows the measurement progression alone in a graph,

[0129] FIGS. 4 to 6 are three graphs to illustrate the determination of the second distinguishing points,

[0130] FIG. 7 shows the simulation progression alone in a graph,

[0131] FIGS. 8 to 10 are three graphs to illustrate the determination of the first distinguishing points,

[0132] FIGS. 11 to 13 are three graphs to illustrate filling states during the filling of the cavity for molding the injection-molded part from FIG. 1,

[0133] FIGS. 14 and 15 are two graphs to illustrate a mapping of the first distinguishing points and the second distinguishing points to each other in a first example,

[0134] FIGS. 16 and 17 are two graphs to illustrate an adjustment of the injection volume flow profile used in the example simulation,

[0135] FIGS. 18 and 19 show two simulation results, in each case after an alignment according to the invention, and the associated measurement progression,

[0136] FIGS. 20 to 32 are graphs to illustrate a general algorithm for mapping the first distinguishing points and the second distinguishing points in a second example.

DETAILED DESCRIPTION OF THE INVENTION

[0137] The following embodiment examples relate to an injection process as sub-process of an injection-molding process. An injection pressure was chosen as variable that is characteristic of this process. The example simulation progression SV and the example measurement progression MV are therefore in each case a pressure progression. Of course, the invention functions analogously for other processes carried out with a shaping machine.

[0138] In all graphs (except FIGS. 1, 11 to 13 as well as 16 and 17), the “Y axis” is therefore the pressure in the real or simulated molding material (as variable that is characteristic of the molding process), denoted as coordinates p.sub.M,i or p.sub.S,i for measured and simulated pressures. The “X axis” is a time parameter (coordinates t.sub.S,i and t.sub.M,i), in order to record the development of the characteristic variable over time.

[0139] The time could, however, be parameterized just as well with the aid of equivalent volumes V.sub.m and V.sub.s. That is to say, the time could be parameterized through a (known, in the simulation progression SV optionally virtual) screw movement and converted into an equivalent volume via the known diameter of the barrel.

[0140] FIG. 1 shows an example of a molded part (in the form of a letter “F”) including sprue, nozzle, measuring flange and part of the space in front of the screw, which is to be produced in an injection-molding process according to the invention and the production of which is to be simulated at least partially according to the invention.

[0141] FIG. 2 shows a measured (measurement progression MV) as well as in addition a simulated (simulation progression SV) pressure curve, wherein values from the real injection process have been used as initial and boundary conditions for the simulation. The deviation can be easily recognized. The two curves do not correspond, since for example material parameters which are used in the simulation do not correspond with the properties of the really injected material, or because e.g. the decompression as well as the behavior of the non-return valve were not taken into account in the simulation.

[0142] It can be recognized that the simulation progression SV represented in FIG. 2 consists of a plurality of individual data points, which together represent a progression. The number of data points in the measurement progression MV is so large that this is no longer recognizable in the representation of the measurement progression MV.

[0143] FIG. 3 shows the measurement progression MV from FIG. 2 on its own. It can be seen with the naked eye that the measurement progression contains kinks, which are an example of second distinguishing points P.sub.M,i within the meaning of the invention. The kinks can be associated with a molding material front meeting obstacles in the gating system or in the molding cavity or with the volume flow that comes from the shaping machine changing rapidly for other reasons.

[0144] The reproducible and (partially) automatable finding of the first distinguishing points P.sub.S,i and of the second distinguishing points P.sub.M,i is described below, wherein i is used in each case as index for numbering the points.

[0145] Before finding the distinguishing points, the measurement progression MV and/or the simulation progression SV can first be filtered, wherein this is not absolutely necessary within the framework of the invention. The Savitzky-Golay filter, known per se, can e.g. be used as filter. A filter can be used to be able to filter out noise in the signal, which in most cases is not needed for finding distinguishing points.

[0146] A measurement progression MV (see FIG. 3), which consists e.g. of 10,000 recorded data points, can then be reduced to a smaller number of measurement points using algorithms known per se. In the present embodiment example, the Ramer-Douglas-Peucker algorithm (RDP algorithm) was used. The result is represented in FIG. 4, wherein connecting lines are drawn in between the individual points of the reduced point set.

[0147] The measurement progression MV with the high number of data points is reduced here only to the extent that the reduced point set lies within a certain tolerance range around the original measurement progression MV. Experts can choose this tolerance range and possibly subsequent further conditions (more on this later) depending on the application and at will.

[0148] Experts can, for example depending on whether a large or small number of reduced points are desired, define the conditions for the algorithm, wherein a few experiments can be carried out if very specific requirements are made of the reduced point set.

[0149] By using the algorithm a reduced point set of the measurement progression MV is therefore obtained, which consists of various kinks (the reduced point set). These kinks represent points wherein e.g. the slope could have changed significantly (which naturally depends on the reduction algorithm and the tolerance settings thereof).

[0150] In the specific embodiment example presented here, the Ramer-Douglas-Peucker algorithm was applied to the measurement signal (thus the measurement progression from FIGS. 2 and 3). Here, the measurement progression MV was in each case first standardized to 1 on the X axis as well as on the Y axis and then the RDP algorithm was applied thereto. The tolerance, how far the reduced measurement progression may deviate from the original measurement progression, can in principle be freely chosen here. However, it may be advisable for the tolerance to lie in a range between 0.1% and 5%. For this embodiment example a tolerance of 1.5% was chosen. The result of the algorithm is represented in FIG. 4, wherein the mentioned standardization to 1 of the two axes was reversed again, i.e. was scaled back. This scaling back can also be carried out after the application of additional conditions and/or criteria (see below).

[0151] The new reduced measurement progression MV—thus the reduced point set—was reduced here to a total of 9 measurement points and following this to 7 kinks (i.e. the boundary points are omitted because no kink angles can be described for them as below).

[0152] Before, during or following the application of the algorithm, still further conditions can be introduced in order to further restrict the reduced point set. Alternatively, the reduced point set obtained from the algorithm can be used directly as the second distinguishing points P.sub.M,i.

[0153] Further (secondary) conditions for reducing the point set can be, for example: [0154] a maximum number of reduced points or distinguishing points, [0155] a minimum distance between reduced points, and/or [0156] a maximum standardized error of the squares of the distance between starting points (thus the original data points of the measurement progression MV) and reduced points.

[0157] As already mentioned, following the application of the algorithm, optionally including additional (secondary) conditions, further criteria can be used for the actual selection of the second distinguishing points P.sub.M,i.

[0158] An example of a further criterion for (further) reducing the point set would be that a point from the reduced progression is only included as a distinguishing point if e.g. the angle between two straight lines (connecting lines) of a kink (of a point of the reduced point set) has a certain size.

[0159] For this purpose, the angle between the two connecting lines, which can be described by vectors vec1 and vec2, can be calculated for each of the seven kinks (points of the point set reduced by means of the RDP algorithm). The angles between the vectors can be calculated by means of the following formula.

[00002]$\alpha =\frac{\mathrm{acos}\left[\left(\stackrel{.fwdarw.}{\mathrm{vec}1}*\stackrel{.fwdarw.}{\mathrm{vec}2}\right)/\left(.Math.\stackrel{.fwdarw.}{\mathrm{vec}1}.Math.*.Math.\stackrel{.fwdarw.}{\mathrm{vec}2}.Math.\right)\right]}{2*\pi }*360°$

[0160] Here, a denotes the angle between two vectors vec1 and vec2. All angles for all kinks from the reduced progression can be calculated with this formula. For this purpose, the two vectors vec1 and vec2 which describe the kink are calculated for each kink and then the angle between the two vectors is calculated, with the aid of the above formula.

[0161] Here, the criterion can be introduced that a point resulting from the reduction is only used as one of the second distinguishing points P.sub.M,i when the angle between two vectors has a certain size, e.g. less than 170° (or when other formulae are used equivalent to more than 190°).

[0162] Before the angle calculation is carried out, the measurement progression MV should be standardized both on the X axis and the Y axis. If the angles of the kinks are subsequently calculated and the corresponding angle is plotted in the graph for each kink, the graph from FIG. 5 is obtained, wherein the calculated angle (kink angle) and the corresponding connecting lines are drawn in for each of the points from the reduced point set.

[0163] By applying the criterion addressed above that the angle between the vectors at a kink has to be less than 170°, in this example the last point is omitted as a distinguishing point.

[0164] In the present embodiment example, the standardization was reversed again, i.e. scaled back again, at this point.

[0165] Furthermore, the initial range is not to be included in the analysis for finding distinguishing points, since this is the range in which the non-return valve closes. The simulation (using current simulation software) deviates from the measurement progression MV in this initial range since it is assumed in the simulation that the non-return valve is 100% closed before the injection process, and therefore a different pressure progression compared with the measurement results.

[0166] In this respect, the criterion can be set, for example, that the pressure progression is only used for the analysis from a certain pressure threshold (e.g. from 80 bar) and/or from a certain time (e.g. 75 ms) after the start of injection. Further possible additional or alternative further criteria would be, for example, that the first 10% (relative to the time and/or screw position) after the start of injection are omitted or that, knowing the required stroke until the non-return valve is closed, for example twice the required stroke is set as criterion. Moreover, the range up to the time at which a certain adjusted injection volume flow rate was reached could be excluded, for example.

[0167] In the present embodiment example, if a pressure threshold of 80 bar is set as second criterion, the second distinguishing points P.sub.M,i represented in FIG. 6 result for the measurement progression MV.

[0168] In this embodiment example, finding the first distinguishing points P.sub.S,i from the simulation progression SV takes place completely analogously to the procedure described in connection with FIGS. 3 to 6, for which reference is also made to FIGS. 7 to 10. That is to say, all measures which were described in connection with FIGS. 3 to 6 are also provided in the embodiment example according to FIGS. 7 to 10.

[0169] Alternatively, the distinguishing points could for example also be found using slope analysis or similar analyses of derivatives.

[0170] The first distinguishing points P.sub.S,i and the second distinguishing points P.sub.M,i, which are represented together in FIG. 14, are the result. The coordinates of the first distinguishing points P.sub.S,i are denoted by (t.sub.S,i, p.sub.S,i) and those of the second distinguishing points P.sub.M,i are denoted by (t.sub.M,i, p.sub.M,i).

[0171] Within the framework of the present injection-molding process, the distinguishing points can be interpreted, for example, as those points in time at which a flow front experiences sudden changes in the resistance to propagation (like meeting obstacles). Visualizations which illustrate these situations can be produced from the simulation carried out and the calculation results thereof as well as the first distinguishing points P.sub.S,i determined above. This is represented in FIGS. 11, 12 and 13, wherein [0172] FIG. 11 represents the situation of the first distinguishing point P.sub.S,i at the point in time t.sub.S,i, wherein the flow front coming from the sprue meets the actual mold cavity, [0173] FIG. 12 represents the situation of the first distinguishing point P.sub.S,2 at the point in time t.sub.S,2, wherein the flow front meets a first end of the cavity, and [0174] FIG. 13 represents the situation of the first distinguishing point P.sub.S,3 at the point in time t.sub.S,3, wherein the flow front meets a second end of the cavity.

[0175] In this embodiment example, it is obvious, at least to human observers, how the in each case three first distinguishing points P.sub.S,i and second distinguishing points P.sub.M,i should be mapped to each other (see FIG. 15). For less obvious cases, such as will of course occur in reality, a reproducible procedure for finding the “correct” mapping is described further below.

[0176] Even if the mapping of the first distinguishing points P.sub.S,i and the second distinguishing points P.sub.M,i has taken place correctly, these points naturally do not fall on top of each other, i.e. there are deviations which can be detected by means of the (time and pressure) coordinates (t.sub.S,i, p.sub.S,i) and (t.sub.M,i, p.sub.M,i).

[0177] It should be mentioned that a Cartesian coordinate system was used in the present embodiment example. Naturally, the invention could in principle also be realized with any other desired coordinate system.

[0178] According to the invention, a modification parameter is calculated for the simulation by means of the coordinates. Two different examples of modification parameters which can be used for matching the simulation to the process really carried out are given in the following.

[0179] Firstly, the time shift which exists between the first distinguishing points P.sub.S,i and the second distinguishing points P.sub.M,i is dealt with. This can be associated with an injection volume flow rate incorrectly modelled in the simulation.

[0180] This can be quantified and compensated for by firstly calculating the arithmetic mean of the time deviations between the points of the first distinguishing points P.sub.S,i and the second distinguishing points P.sub.M,i mapped to each other:

[00003]$\Delta t=\frac{1}{N}\underset{i=1}{\overset{N}{.Math.}}{t}_{M,i}-t_{s,i}$

[0181] Instead of the arithmetic mean value, any other desired statistical parameters, such as for example the median, could naturally also be used. It has likewise already been mentioned that instead of a time index an equivalent variable, such as for example a displacement stroke or displacement volume of a plasticizing screw or another actuator, can be used.

[0182] The injection volume flow profile modelled in the simulation can be adjusted on the basis of the modification parameter □t. In this embodiment example, the original injection volume flow profile is substantially constant and is represented in FIG. 16.

[0183] With the aid of the average time shift Δt, this injection volume flow profile can be adjusted such that the time deviation between simulation progression and measurement progression is reduced by, e.g. in the simulation, not allowing the original injection volume flow profile from FIG. 16, which is plotted with the volume flow rate over time, to start from 0 s but rather only from Δt and allowing the values from the starting point to Δt from the original profile to be omitted, which is represented in FIG. 17.

[0184] Should the injection volume flow profile not be constant, but rather for example a profile with inclines, etc., it is advisable to compensate for the different injection volume flow rates through corresponding conversion factors when calculating the modification parameter.

[0185] Should the plasticizing screw be modelled in the simulation, the position of the plasticizing screw can also be correspondingly adjusted—for example via a position or speed profile.

[0186] If the simulation is carried out again with the modified injection volume flow profile represented in FIG. 17, a modified simulation progression SV2, which is represented together with the original measurement progression MV in FIG. 18, results.

[0187] As an alternative or in addition to this adjustment of the simulation, the process can also be modified. That is to say, the metering stroke could for example be modified, with the result that the injection volume during the process corresponds to that used in the simulation. Of course, mixed forms are also conceivable, wherein both the metering stroke and the injection volume flow profile modelled in the simulation are modified to a consistent extent in each case.

[0188] It can be seen in FIG. 18 that the two curves match up well in terms of time (i.e. the time deviation between the kinks or the distinguishing points from measurement progression MV and simulation progression SV has been greatly reduced), but are still scaled differently in the direction of the y axis. That is to say, although the pressures p.sub.S,i in the simulation are consistent relative to each other, they do not have the correct absolute values, which can be caused by an incorrect material model, because material parameters, e.g. used in the simulation, do not correspond to reality.

[0189] Therefore the simulation is also still modified, as described below, such that the pressure calculated in the simulation (as variable that is characteristic of the process) better models the pressure actually measured.

[0190] In the present embodiment example, for the simulation, a Cross-WLF model was used for the material simulation. The Cross-WLF model gives the melt viscosity 11 of the molding material as follows:

[00004]$\eta =\frac{{\eta }_0}{1+{\left(\frac{{\eta }_0\stackrel{.}{\gamma }}{{\tau }^{*}}\right)}^{1-n}}$

[0191] Therein: [0192] η denotes the melt viscosity in Pa*s, [0193] η.sub.0 denotes the zero shear viscosity in Pa*s, [0194] {dot over (γ)} denotes the shear velocity (unit 1/s), [0195] τ{circumflex over ( )} denotes the critical shear stress at the transition to shear thinning, and [0196] denotes an exponent which describes the shear thinning behavior at high shear rates.

[0197] The zero shear viscosity is given by the following equation:

[00005]${\eta }_0=D_1\exp \left[-\frac{A_1\left(T-T^{*}\right)}{A_2+\left(T-T^{*}\right)}\right]$

[0198] In the following, it is explained how this model is adjusted, so that the simulation can be aligned with the real process.

[0199] Firstly, a factor kp is determined from the pressure coordinates p.sub.S,i and p.sub.M,i of the first and second distinguishing points P.sub.S,i and P.sub.M,i as follows:

[00006]$\mathrm{kp}=\frac{1}{N}\underset{i=1}{\overset{N}{.Math.}}\frac{p_{M,i}}{p_{s,i}}$

[0200] N here corresponds to the number of first and second distinguishing points P.sub.S,i and P.sub.M,i and the arithmetic mean of the quotients is thus calculated from p.sub.M,i (measured pressure at the i-th second distinguishing point P.sub.M,i in the measurement progression MV) over p.sub.S,i (simulated/calculated pressure at the i-th first distinguishing point P.sub.S,i in the simulation progression SV).

[0201] It should be noted that, in contrast to WO 2016/177513 A1, in this way also the coordinates p.sub.S,i and p.sub.M,i, i.e. thus the simulated and measured pressures, or more generally the simulated and calculated characteristic variable, also actually continue to be used.

[0202] Instead, kp could naturally also be defined here via a median or any other desired statistical parameter.

[0203] With the aid of the pressure scaling parameter value kp, the simulation can be adjusted such that the pressure deviation between simulation and measurement is reduced. In this case, e.g. the material parameters in the Cross-WLF model can be adjusted on the basis of the parameter kp.

[0204] In the present embodiment example, the Cross-WLF model is adjusted by specifying new parameters D.sub.1′ and τ″ using the modification parameter value for kp and defined by

D.sub.1′=D.sub.1×kp

and

τ″=τ′×kp

[0205] If the simulation is carried out again, wherein this modified material model and the temporal adjustment of the injection volume flow profile, which was described in connection with FIG. 16 and FIG. 17, are taken into account, a simulation progression SV3 that has been modified again is obtained, which is represented together with the original measurement progression MV in FIG. 19. It is obvious that the simulation progression SV3 that has been modified again corresponds very well with the original measurement progression MV and cannot be distinguished from the measurement progression MV at all over large parts of the curves. (Of course, the pressure scaling would also be improved correspondingly if only the material model is adjusted and the injection volume flow profile were maintained).

[0206] An effective alignment between the actual measurement and the simulation was thus brought about without having to carry out a large number of simulations.

[0207] The deviating scaling of the (optionally modified) measurement progression MV and of the (optionally modified) simulation progression (see e.g. FIG. 18) could also be compensated for by modifying the process. For example by reducing the molding material temperature, the viscosity of the molding material can be increased. As a result, the pressure p.sub.M will rise more quickly, which brings the (optionally modified) measurement progression MV closer to the (optionally modified) simulation progression SV.

[0208] The time shift or the scaling of the pressure are only two examples. Naturally, more complicated calculations on the basis of the first and second distinguishing points mapped to each other are also possible. Thus, for example, the difference in the dead volume (thus the melt volume in the flange, nozzle or hot runner not accessible by the screw movement) between simulation and measurement could be calculated from these points.

[0209] Likewise, several simulations and several measurements with different boundary conditions can also be considered at the same time in order to be able to take the dependencies on these boundary conditions into account when calculating the modification parameters. Such boundary conditions can be, for example, a forming mass temperature, a tool temperature or all other parameters taken into account in the simulation.

[0210] Instead of using an arithmetic mean, the modification parameters based on the first and second distinguishing points can also be calculated for example using optimization algorithms or regression methods.

[0211] It is obvious that a thus-aligned simulation can be extremely helpful when setting the injection-molding process—or in general in processes carried out with shaping machines.

[0212] In the following it will now be discussed how the first distinguishing points P.sub.S,i and the second distinguishing points P.sub.M,i can be mapped to each other reproducibly.

[0213] In the example presented here for this purpose, we assume that four first distinguishing (simulation) points (P.sub.S for short) were found in the simulation and six second distinguishing (measurement) points (P.sub.M for short) were found in the measurement, wherein the indices i are only still noted if this is necessary for understanding, for the sake of simplicity. The points for this example are represented in the graph in FIG. 20.

[0214] Without restricting generality, the four first distinguishing points P.sub.S from the simulation progression SV are mapped onto the four “ideal” second distinguishing points P.sub.M from the possible total of six from the measurement progression MV using the procedure presented. Conversely, this means that if more first distinguishing points P.sub.S than second distinguishing points P.sub.M were present, this would naturally also be possible. Typically, however, it can be assumed that more distinguishing points will be obtained in the measurement than in the simulation, since in most real cases certain geometries, such as e.g. space in front of the screw, nozzle, etc., are not modelled in the simulation, but are reflected in the measurement progression MV.

[0215] In principle, the procedure is as follows: [0216] 1.) In the first step, it is assumed that the number k of first distinguishing points P.sub.S from the simulation progression SV is smaller than the number n of second distinguishing points P.sub.M from the measurement progression MV. Ultimately this means that k first distinguishing points P.sub.S from the simulation progression SV are mapped onto n second distinguishing points P.sub.M from the measurement progression MV (wherein in principle n>k second distinguishing points P.sub.M would be present). [0217] 2.) The k first distinguishing points P.sub.S from the simulation progression SV are always compared with n second distinguishing points P.sub.M from the measurement progression MV. All possible combinations in the selection of k first distinguishing points P.sub.S from n second distinguishing points P.sub.M from the measurement progression MV are tested. In our example, the possible combinations of four out of six points are thus tested irrespective of the sequence.

[0218] The number of possible mappings of the four P.sub.S onto the six P.sub.M can be calculated using the following known formula from combinatorics.

[00007]$\frac{n!}{\left(n-k\right)!*k!}=\frac{6!}{\left(6-4\right)!*4!}=15$

[0219] 15 possible combinations of the mapping of four P.sub.S to six present P.sub.M are possible according to this. The possible combinations of how the four P.sub.S can be mapped onto the six P.sub.M (see FIG. 20) are listed in the following table.

TABLE-US-00001 Combination P.sub.M, 1 P.sub.M, 2 P.sub.M, 3 P.sub.M, 4 P.sub.M, 5 P.sub.M, 6 1 X X X X 2 X X X X 3 X X X X 4 X X X X 5 X X X X 6 X X X X 7 X X X X 8 X X X X 9 X X X X 10 X X X X 11 X X X X 12 X X X X 13 X X X X 14 X X X X 15 X X X X

[0220] All 15 possible combinations are now tested and the combination in which the first distinguishing points P.sub.S from the simulation progression and the second distinguishing points P.sub.M from the measurement progression MV are best mapped to each other, which is the desired result, is chosen.

[0221] In order to be able to explain the procedure in an understandable manner, it is applied by way of example in the combinations 1 and 12 from the above table in the following.

[0222] In combination 1, the first four distinguishing points (P.sub.M,1, P.sub.M,2, P.sub.M,3 and P.sub.M,4) from the measurement progression MV are used.

[0223] Firstly, the first occurring distinguishing point P.sub.S,i of the simulation progression SV is shifted onto the first distinguishing point P.sub.M,i of the measurement progression MV with an offset (vector with X component o.sub.x and Y component o.sub.y) (see FIG. 21).

[0224] The remaining distinguishing points P.sub.S,i, with i equal to 2, 3 and 4, from the simulation progression SV are shifted by the same offset (see FIG. 22). The result is represented in FIG. 23, wherein the two first in each case of the first distinguishing points P.sub.S,i and of the second distinguishing points P.sub.M,i lie on top of each other. The remaining distinguishing points from the simulation progression SV were shifted by the offset.

[0225] Referring to FIG. 24, next the coordinate differences Δt.sub.M and Δp.sub.M between the second distinguishing point P.sub.M,4 (with index 4) and the corresponding second distinguishing point P.sub.M,1 (index 1) from the measurement progression MV relevant for combination 1 and the corresponding coordinate differences Δt.sub.S and Ops between the first distinguishing point P.sub.S,4 (index 4) and the first distinguishing point P.sub.S,1, with index 1, from the simulation progression SV are calculated.

[0226] Scaling parameters k.sub.x and k.sub.y are then calculated therefrom using the following formulae.

[00008]$k_x=\frac{\Delta t_M}{{\mathrm{\Delta t}}_s}\mathrm{and}{k}_y=\frac{\Delta p_M}{\Delta p_s}$

[0227] Then, for the three second distinguishing points P.sub.S,2, P.sub.S,3 and P.sub.S,4, in each case the coordinate differences in the x as well as y direction with respect to the second distinguishing point P.sub.S,1 with index 1 are calculated. In the framework of a rescaling, for each of the three points P.sub.S,2, P.sub.S,3 and P.sub.S,4, then the calculated x coordinate difference is multiplied by the scaling parameter k.sub.x as well as the calculated y coordinate difference is multiplied by the scaling parameter k.sub.y (and in each case added to the coordinates t.sub.S,1 and p.sub.S,1). These newly formed coordinates are used as coordinates for points P.sub.S,2, P.sub.S,3 and P.sub.S,4 shifted according to the rescaling. This then results in the graph from FIG. 25, wherein in each case the first and second distinguishing points P.sub.S,i and P.sub.M,i with indices 1 and 4 from the measurement progression MV and the simulation progression SV lie on top of each other. The other distinguishing points from simulation and measurement may (and will generally) differ.

[0228] Now, the differences (Δx.sub.i, Δy.sub.i) in the x as well as y direction of the distinguishing points from the simulation progression SV and the measurement progression MV, in each case occurring in the same sequence, are calculated (see FIG. 26). That is to say, the first distinguishing point P.sub.S,2 (with index 2) is compared with the second distinguishing point P.sub.M,2 (likewise index 2) from the chosen combination (combination 1 in this case) of distinguishing points from the measurement progression (for the purpose of a coordinate difference calculation) and accordingly P.sub.S,3 is also compared mit P.sub.M,3 here. If, for example, P.sub.M,2 were not contained in combination 1, P.sub.M,3 would simply be used according to the sequence if it is present in the corresponding combination.

[0229] A characteristic number f (or several characteristic numbers) can be calculated from the calculated offset (o.sub.x,o.sub.y), the scaling parameters k.sub.x and k.sub.y and the differences (Δx.sub.i,Δy.sub.i) as quality criterion for the correspondence of the points in combination 1 mapped to each other in the form of function ƒ(Δx.sub.i, Δy.sub.i,k.sub.x,k.sub.y,o.sub.x,o.sub.y). The calculation of this characteristic number can be carried out analogously for each of the 15 possible combinations. In this connection, the different parameters can be weighted differently using weighting factors.

[0230] The characteristic number could be calculated e.g. as follows:

[00009]$f\left(\Delta x_i,\Delta y_i,k_x,k_y,o_x,o_y\right)=g_1\underset{i=1}{\overset{k}{.Math.}}\left(\Delta x_i^2+\Delta y_i^2\right)+g_2\left({\left(k_x-1\right)}^2+{\left(k_y-1\right)}^2\right)+g_3\left(o_x^2+o_y^2\right)$

[0231] g.sub.1, g.sub.2 and g.sub.3 are the weighting factors. If e.g. g.sub.1=1 and g.sub.2=g.sub.3=0 are set, the following shorter formula results for the calculation of the characteristic number:

[00010]$f\left(\Delta x_i,\Delta y_i\right)\underset{i=1}{\overset{k}{.Math.}}\left(\Delta x_i^2+\Delta y_i^2\right)$

[0232] The procedure for mapping the distinguishing points is explained once again with reference to combination 12 from the above table. In this case, it involves the combination with the best correspondence during the mapping (thus the “ideal” combination with the best quality of the mapping).

[0233] In the case of this combination 12, the second distinguishing points P.sub.M,2, P.sub.M,3, P.sub.M,5 and P.sub.M,6 (i.e. with indices 2, 3, 5 and 6) from the measurement progression MV and of course all first distinguishing points P.sub.S,i with indices 1 to 4 are used for the mapping (see FIG. 20).

[0234] Firstly, the first distinguishing point P.sub.S,i with index 1 of the simulation progression SV is shifted onto the second distinguishing point P.sub.M,2 with index 2 of the measurement progression MV with an offset (see FIG. 27).

[0235] The remaining first distinguishing points P.sub.S,i from the simulation progression SV relevant for combination 12 are shifted by the same offset, which is illustrated in FIG. 28.

[0236] This results in the graph from FIG. 29, wherein P.sub.M,2 from the measurement progression MV and P.sub.S,i from the simulation progression SV lie on top of each other and the three remaining first distinguishing points P.sub.S,i from the simulation progression SV have been shifted by the offset.

[0237] As is illustrated in FIG. 30, next the coordinate differences Δt.sub.M and Δp.sub.M between the second distinguishing point P.sub.M,6 (index 6) and the second distinguishing point P.sub.M,2 (with index 2) from the measurement progression MV as well as the coordinate differences Δt.sub.S and Aps between the first distinguishing point P.sub.S,4 (index 4) and the first distinguishing point P.sub.S,1 (index 1) from the simulation progression SV are calculated (analogously to the description in connection with FIG. 24).

[0238] The scaling parameters k.sub.x and k.sub.y are then calculated using the already known following formulae.

[00011]$k_x=\frac{\Delta t_M}{\Delta t_s}\mathrm{and}{k}_y=\frac{\Delta p_M}{\Delta p_s}$

[0239] Analogously to the description in connection with FIG. 25, for the three second distinguishing points P.sub.S,2, P.sub.S,3 and P.sub.S,4, in each case the coordinate differences in the x as well as y direction with respect to the second distinguishing point P.sub.S,1 with index 1 are calculated. In the framework of a rescaling, for each of the three points P.sub.S,2, P.sub.S,3 and P.sub.S,4, then the calculated x coordinate difference is multiplied by the scaling parameter k.sub.x as well as the calculated y coordinate difference is multiplied by the scaling parameter k.sub.y (and in each case added to the coordinates t.sub.S,1 and p.sub.S,1). These newly formed coordinates are used as coordinates for points P.sub.S,2, P.sub.S,3 and P.sub.S,4 shifted according to the rescaling. This then results in the graph from FIG. 31, wherein in each case the distinguishing points P.sub.S,i and P.sub.M,2 as well as P.sub.S,4 and P.sub.M,6 lie on top of each other. The other distinguishing points from simulation and measurement may (and will generally) differ.

[0240] Now, the differences (Δx.sub.i, Δy.sub.i) in the x as well as y direction of the distinguishing points from the simulation progression SV and the measurement progression MV, in each case occurring in the same sequence, are calculated (see FIG. 32 by analogy with FIG. 26). That is to say, P.sub.S,2 is compared with point P.sub.M,3 from the chosen combination (combination 12 in this case) (for the purpose of a coordinate difference calculation) and accordingly P.sub.S,3 is also compared with P.sub.M,5. As mentioned, the parameters (Δx.sub.i, Δy.sub.i) are drawn in in the graph from FIG. 32 for better understanding.

[0241] Also in this case, the same characteristic number for the correspondence of the points mapped to each other in combination 12 can be calculated in the form of function ƒ(Δx.sub.i,Δy.sub.i,k.sub.x,k.sub.y,o.sub.x,o.sub.y) (see above). With the same weighting of the parameters g.sub.1=1 and g.sub.2=g.sub.3=0, it can easily be recognized in this example that the differences (Δx.sub.i, Δy.sub.i) turn out to be much smaller than in combination 1 described previously (compare FIG. 26 with FIG. 32).

[0242] If this procedure were used to go through all 15 combinations, it would be concluded that combination 12 generates the best correspondence/quality—i.e. the lowest characteristic number f—and can accordingly carry out the mapping of the first distinguishing points P.sub.S,i with the indices 1, 2, 3 and 4 from the simulation progression SV to the second distinguishing points P.sub.M,i with the indices 2, 3, 5 and 6 from the measurement progression MV (in this sequence, thus 1->2, 2->3, 3->5 and 4->6).

[0243] Self-evidently, instead of the first distinguishing points P.sub.S,i the second distinguishing points P.sub.M,i can also be shifted and rescaled according to the described procedure, without modifying the combination with the best quality of the mapping determined on the basis of the calculated characteristic numbers.

[0244] One advantage of the described procedure for mapping the first distinguishing points P.sub.S,i and the second distinguishing points P.sub.M,i is that it can be implemented as an algorithm e.g. as part of a computer program.

[0245] In the above-indicated formulae for the modification parameters kp and Δt, it can of course be advantageous to add up only via those indices i which actually occur in that combination (in the example here for the general procedure for working out the mapping this would be combination 12) for which the best (here lowest) characteristic number was calculated.