METHOD FOR OPTIMIZING A PROCESS OPTIMIZATION SYSTEM AND METHOD FOR SIMULATING A MOLDING PROCESS

Abstract

A method of optimizing a process optimization system for a moulding machine includes setting a setting data by a user on the actual moulding machine, obtaining first values for at least one descriptive variable of the moulding process based on the setting data set and/or on the basis of the cyclically carried out moulding process, and obtaining second values for the at least one descriptive variable based on data from the process optimization system. According to a predetermined differentiating criterion, it is checked whether the first values and the second values differ from each other. If the checking shows that the first values and the second values differ from each other, the process optimization system is modified such that, when applied to the moulding machine and/or the moulding process, the first values for the descriptive variable substantially result instead of the second values for the descriptive variable.

Claims

1. Method for optimizing a process optimization system for a moulding machine, by means of which a cyclic moulding process is carried out for the production of a moulded part, wherein (a) a setting data set is set by a user on the actual moulding machine, (b) first values for at least one descriptive variable of the moulding process are obtained on the basis of the setting data set and/or on the basis of the cyclically carried out moulding process, (c) second values for the at least one descriptive variable are obtained on the basis of data from the process optimization system, (d) according to a predetermined differentiating criterion, it is checked whether the first values and the second values differ from each other and, (e) if method step (d) shows that the first values and the second values differ from each other, the process optimization system is modified such that, when applied to the moulding machine and/or the moulding process, the first values for the at least one descriptive variable substantially result instead of the second values for the at least one descriptive variable.

2. Method according to claim 1, wherein, within the framework of carrying out method step (c) in a simulation of the moulding machine and/or of the moulding process, the process optimization system is applied and the second values are at least partially obtained from results of the simulation.

3. Method according to claim 2 using a mathematical model for the simulation, wherein parameters of the mathematical model describing the simulation are determined by minimizing error functions of measured and model variables.

4. Method according to claim 1, wherein the modified process optimization system is used in the case of the moulding machine and/or in the case of further moulding machines.

5. Method according to claim 1, wherein the modification of the process optimization system occurs by machine learning and/or numerical optimization methods and/or adaptation of an expert system.

6. Method according to claim 1, wherein, during the modification of the process optimization system, an error function between the first values and the second values is minimized for the at least one descriptive variable.

7. Method according to claim 1, wherein at least one of method steps (c), (d) and (e) is carried out on a computer which is separate from the moulding machine, wherein the first values for the at least one descriptive variable are transmitted to the computer, preferably via a remote data transmission connection.

8. Method according to claim 1, wherein at least one of the followingpreferably after transmission by means of a remote data transmission connectionis stored in a memory which is separate from the moulding machine: the first values of the at least one descriptive variable, the second values of the at least one descriptive variable, the modified process optimization system.

9. Method according to claim 1, wherein the at least one descriptive variable includes parameters of the setting data set.

10. Method according to claim 1, wherein the at least one descriptive variable includes one or more of the following: machine data which relate to a moulding machine used in the moulding process, mould data which relate to a mould used in the moulding process, material data which relate to a material used in the moulding process, process settings and measured data which relate to the moulding process, user-related data, quality parameters which describe the moulded part.

11. Method according to claim 1, wherein the process optimization system makes use of at least one of the following: neural network, mathematical model, expert system, fuzzy logic.

12. Method according to claim 1, wherein the process optimization system is used to improve setting data sets for moulding machines, wherein at least one of the following quality criteria is preferably used as criterion for an improvement: reduced waste, reduced cycle time, improved moulding quality.

13. Method according to claim 1, wherein, when method step (c) is carried out and/or when the actual moulding process is carried out, set according to method step (a), quality parameters are determined and are used in the modification of the process optimization system according to method step (e).

14. Method for simulating a moulding process, according to claim 2, wherein configuration data which relate to the moulding process to be simulated are provided on a user's computer, the configuration data are transmitted by means of a remote data transmission connection to a memory which is separate from the moulding machine and the user's computer and stored therein, a simulation program stored in the memory is executed, using the configuration data, on a computer which is connected to the memory and is separate from the moulding machine and the user's computer and results generated by means of the simulation program are output, wherein simulation parameters are automatically provided on the basis of the configuration data.

15. Method according to claim 14, wherein the generated results are transmitted by means of a remote data transmission connection to the user's computer or by means of a further remote data transmission connection to a further user's computer.

16. Method according to claim 14, wherein the configuration data include one or more of the following: machine data which relate to a moulding machine used in the moulding process to be simulated, in particular dimensions, masses, inertias, motor constants, efficiencies and/or kinematics, of the moulding machine, mould data which relate to a mould used in the moulding process to be simulated, in particular geometry, runner position and/or design of the tempering channels, of the mould, data on subcomponents, in particular drives and/or pumps, of the moulding machine and/or of the mould, material data which relate to a material used in the moulding process to be simulated, in particular viscosity, compressibility, specific volume and/or temperature constants, data on environmental influences, in particular ambient temperature and/or ambient pressure and/or disturbances.

17. Method according to claim 14, wherein, in order to provide the simulation parameters, use is made of a database, which database contains parameters collected in actual moulding processes.

18. Method according to claim 1, wherein a setting data set is provided on the user's computer, transmitted by means of the remote data transmission connection to the simulation device, and the simulation program is executed using the setting data set.

19. Method according to claim 1, wherein the setting data set includes process setting parameters relating to at least one of the following: clamping force, shot volume, injection speed, switchover point, injection cylinder temperature, mould temperature, control and/or regulating parameters, holding pressure profile, holding pressure time, screw rotation speed, back pressure profile, cooling time, injection pressure limit, decompression stroke, tempering medium flow rate.

20. Method according to claim 14, wherein the descriptive variables are at least partially obtained from the results of the simulation.

Description

[0093] Further advantages and details of the invention are to be found in the figures and the embodiment examples described below. There are shown in:

[0094] FIG. 1 a schematic drawing to illustrate the structure of the objects involved in the first embodiment example (according to the first variant of the invention),

[0095] FIG. 2 a flow diagram of a simulation method according to the state of the art,

[0096] FIG. 3 a flow diagram of an embodiment example (according to the second variant of the invention) and

[0097] FIG. 4 a moulding machine.

[0098] In the following, an embodiment example of a method according to the invention is described. In order to illustrate the structure of the various objects involved in the method, reference may be made to FIG. 1.

[0099] The following embodiment example relates to injection-moulding processes (as moulding processes). [0100] 1. There are n actual injection-moulding machines which have clamped m different moulds and are set by users, process optimization systems or a combination of the two for the injection-moulding process. [0101] 2. On the basis of the process setting, the injection-moulding process can be started (theoretically this need not happen), by means of which and also by means of possible further user inputs at least one of the following variables describing the process (descriptive variables below) is present: [0102] a. mould data (weight, geometry of the cavity, etc.) [0103] b. machine data (machine configuration=>masses, lengths, limits, etc.) [0104] c. material data (viscosity, density, etc.) [0105] d. process settings and measured data (injection profile, switchover point, injection pressure measurement, etc.) [0106] e. user-related data (user role, user level, etc.) [0107] f. quality data (moulded part dimensions, moulded part weight, etc.) [0108] 3. The data are transmitted from the injection-moulding machine to the central memory. [0109] 4. On the central computer system, simulation models are generated in an automated manner with the aid of the transmitted descriptive variables from the actual injection processes. For this, the thermodynamics of the material injected into the cavity can also be taken into account in addition to the dynamics of the injection-moulding machine.

[0110] During the creation of the corresponding systems of equations, the topological structure of the hydraulic network, different mechanisms as well as the use of different subcomponents such as motors, pumps, etc. can implicitly be taken into account depending on the component selection. To describe mechanical components, a system of differential equations in the form of

M(q){umlaut over (q)}+g(q,{dot over (q)})=Q

is applied. The degrees of freedom are represented in the vector q, the mass matrix is represented by M(q) and further parts such as Coriolis terms, friction, etc. are represented in the vector g(q,{dot over (q)}). Forces applied by the drive system are found in vector Q. The form ({dot over ()}) represents the time derivative. By solving such a system of equations, the translational motion of the screw in the injection unit, the motion of the clamping unit as well as the rotational motion of the screw are calculated.

[0111] For the translational motion of the screw, q=x.sub.s, {dot over (q)}=v.sub.s applies, whereby the volume flow into the cavity can be determined as

Q=A.sub.sv.sub.s

with the cross-sectional area of the screw A.sub.s. The volume flow forms the input variable for the fluid-dynamic consideration of the compressible polymer melt during the process of injection into the cavity. The Navier-Stokes equations, the continuity equation and the conservation of energy are taken into account to calculate the behaviour. The volume-of-fluid model is used to reproduce the multiphase flows. The phase transport is described by

[00001]$\frac{}{t}+\stackrel{.Math.}{}.Math..Math.\left(\stackrel{.Math.}{u}.Math..Math.\right)+\stackrel{.Math.}{}.Math..Math.\left(\stackrel{.Math.}{u_r}.Math.\left(1-\right)\right)=S_u+S_p$

with terms for the compressibility S.sub.u and S.sub.p. describes the phase state and u the velocity vector of the fluid. To reproduce the viscosity, the CrossWLF model is used with the zero viscosity .sub.0, the temperature T, the shear rate {dot over ()}, the pressure p and the material-specific parameters A.sub.1, A.sub.2, D.sub.1, D.sub.2, D.sub.3, D.sub.4:

[00002]$=\frac{{}_0}{1+\left(\frac{{}_0.Math.\stackrel{.}{}}{D_4}\right).Math.\left(1-n\right)}$${}_0=D_1.Math.\exp \left(\frac{\left(-A_1\right).Math.\left(T-D_2-D_3.Math.p\right)}{\left(A_2+T-D_2-D_3.Math.p\right)}\right)$

To reproduce the compressibility, the Tait model is used:

[00003]$v=\frac{1}{}$$v\left(p,T\right)=v_m\left(T\right).Math.\left[1-C.Math.\ln \left(1+\frac{p}{B_m\left(T\right)}\right)\right].Math..Math.T{T}_{\mathrm{trans}}$$v\left(p,T\right)=v_s\left(T\right).Math.\left[1-C.Math.\ln \left(1+\frac{p}{B_s\left(T\right)}\right)\right]+W_s\left(T\right).Math..Math.T{T}_{\mathrm{trans}}$

with the density , the specific volume v, and a dimensionless constant C. T.sub.trans represents the liquid-to-solid state transition temperature. The following conditions apply to both phase states:

v.sub.m,s(T)=b.sub.1m,s+b.sub.2m,s.Math.(Tb.sub.5)

B.sub.m,s(T)=b.sub.3m,s.Math.exp(b.sub.4m,s.Math.(Tb.sub.5))

T.sub.trans=b.sub.5+b.sub.6.Math.p

W.sub.s(T)=b.sub.7.Math.exp(b.sub.8.Math.(Tb.sub.5)b.sub.9.Math.p)

with material-specific parameters b.sub.1m,s, b.sub.2m,s, b.sub.3m,s, b.sub.4m,s, b.sub.5, b.sub.6, b.sub.7, b.sub.8, b.sub.9. The pressure prevailing in the polymer melt acts as an opposing force on the screw.

[0112] The dynamic description of the machine and the fluid-dynamic description can include additional terms for taking external, or unknown, disturbances into account.

[0113] For controlling the respective component, implicit dependencies are also resolved in order to select and to parameterize necessary systems such as trajectory specifications and regulating systems. These are stored in a memory on the central computer.

[0114] The simulation is now finally configured. [0115] 5. By means of a comparison of simulation and measurement (available from the descriptive variables), model and process parameters that are unknown or are not precisely known can be identified. This can be carried out e.g. by minimizing error functions (least squares, etc.). Corresponding methods are known to a person skilled in the art. From this point in time, simulation and reality are assumed to be identical. [0116] 6. Based on this, according to the invention a difference between the process settings actually set on the actual machine and the process settings suggested by the POS for the simulation, or on the actual machine, is identified. [0117] 7. By means of a machine learning method, numerical optimization methods or a similar (learning) method which is, however, suitable for the technology of the POS, the process optimization system is adapted (trained, modified) such that qualitatively it makes the same decision (setting) as the user (or a selection or statistical mean of users) who carried out (changed) the setting on the actual injection-moulding machine. Plausibility checking of the process settings input by the user as well as checking of the quality parameters can be carried out.

[0118] Using the example of the switchover point, the adaptation can have e.g. the following appearance: [0119] a. The POS determines the switchover point at V.sub.ND=80%, relative to the total volume of the cavity (e.g. on the basis of initial expert knowledge implemented in an expert system) [0120] b. The user on the actual injection-moulding machine corrects the switchover point to V.sub.ND,actual=98% [0121] c. Plausibility checking of the switchover point (between 1 and 100%) as well as user role checking (=process technician) of the actual injection-moulding machine are carried out. [0122] d. The difference is identified and the system parameter switchover point V.sub.ND is optimally adapted by means of solving the optimization problem

[00004]$\min \left(V_{\mathrm{ND}}-V_{\mathrm{ND},\mathrm{actual}}\right).Math.Q_{V_{\mathrm{ND}}}\left(V_{\mathrm{ND}}-V_{\mathrm{ND},\mathrm{actual}}\right)$

with the weighting factor Q. In this step, settings of n injection-moulding machines can be taken into account.

[0123] System parameters of the POS can be defined without restrictions, e.g. among other things as a non-linear function of material and mould parameters or as a function of machine limits such as maximum injection pressure, or the like. Moreover, system parameters need not necessarily represent process settings directly. The system parameters can also be used to evaluate quality parameters (e.g. weight) determined from the simulation and can then result in a determination of process settings (e.g. holding pressure time) by the POS.

[0124] In comparison with the state of the art, the POS in this embodiment example can be trained not only on the basis of actual data, but also through the application to a simulation adapted to reality (by measurement alignment). The data set set by the user, for example, is used in the simulation in order to evaluate quality parameters such as e.g. the flow front velocity. Here, the general correlation can be derived that a plurality of data sets optimally set by users produces an e.g. constant flow front velocity. In the case of an unknown moulded part in the future, a setting can thus be chosen such that the quality parameter flow front velocity is again constant. Thus it is not the settings that have been learned, but rather a commonality, generated therefrom, of a quality parameter (here constant flow front velocity), and for unknown moulded parts the optimal settings can thus again be determined. The learning of commonalities of quality parameters can be carried out e.g. by means of simple averaging (or median calculation, or the like) of features (here gradient of the flow front velocity) of the quality parameters determined from the simulation. The POS is then modified such that a setting results which produces the learned feature in the moulding process.

[0125] For the adaptation of the POS, a plurality of methods known from the literature can be used, such as least squares, see e.g. [1] from p. 245, numerical optimization methods (QP, NLP, etc.), see e.g. [1] from p. 448 and p. 529 respectively, supervised learning of neural networks, etc., see e.g. [2] from p. 73 and [3]. [0126] [1] J. Nocedal, S. WrightNumerical Optimization; Springer, 2006 [0127] [2] Raul RojasTheorie der neuronalen Netze: Eine systematische Einfhrung; Springer-Lehrbuch, 1993 [0128] [3] J. J. HopfieldNeural Networks and Physical Systems with Emergent Collective Computational Abilities; Proceedings of the National Academy of Sciences of the USA, Vol. 79, No. 8, 1982 [0129] 8. The POS applied to the simulation has now learned from n injection-moulding processes, and/or process settings adapted by the user, and decides in a similar optimal manner to the user. The required system parameters modified for the POS, and/or the modified POS, are stored in the memory and transmitted to all n (and optionally further) injection-moulding machines.

[0130] In FIG. 3, an embodiment example of a sequence according to the invention for configuring and carrying out a simulation is represented.

[0131] The configuration of the simulation starts with the selection of the injection-moulding machine components (A1). This overview of an injection-moulding machine includes the definition of an injection unit, a plasticizing unit, a clamping unit and an ejector system. These are selected by the local user's computer from predetermined lists of component names which are stored in a memory on the central computer and are linked to process-relevant variables (see also FIG. 4): [0132] a) injection unit: injection volume, injection pressure [0133] b) clamping unit: maximum clamping force [0134] c) plasticizing unit: plasticizing capacity [0135] d) ejector system: ejector stroke, maximum force

[0136] The selection of the respective component additionally requires the definition of the drive technology (electric/hydraulic). The selection, once made, of the components forms a first part of the configuration data which are transmitted to the central computer or memory and stored in the memory as part of the configuration.

[0137] In the next step (A2), geometric information about the mould is transmitted from the local user's computer via a remote data transmission connection to the central computer. In addition to the geometry, this includes information about the runner position and the cooling channels. Furthermore, the plastic to be injected is selected. For this, a list of material names is predetermined. The selection, once made, of the mould and of the material forms a second part of the configuration data which are also transmitted to the central computer. This completes the configuration, which is then stored in the central memory.

[0138] On the basis of the configuration, the simulation parameters (physical parameters) associated with the respectively selected component, such as e.g. lengths, masses, inertias, viscosity, compressibility, etc., are read from the database (B3) on the central computer or databases independent thereof (A3). The material parameters are obtained on the one hand from identification calculations (B2) by means of measurement processes of actual moulding processes (B1) and on the other hand from manufacturer's data (B4) or directly from databases.

[0139] On the basis of manufacturer's data, in addition further parameters of motors, ball screws, belts, etc. are determined and likewise stored in the database (B3). By means of the physical variables, systems of differential equations are generated for the mathematical description of the selected components (see also a)-d) in FIG. 4) and parameterized (A3).

[0140] For further details on model creation, reference may be made to point 4. of the embodiment example in conjunction with FIG. 1.

[0141] In the next step (A4), the simulation is created in the form of a program that can be compiled.

[0142] A setting data set can then be predetermined on the local user's computer (A5) and transmitted to the central computer. This includes process setting parameters such as clamping force, shot volume, injection speed, switchover point, injection cylinder temperature and mould temperature, etc.

[0143] On the basis of this complete configuration and parameterization, the simulation is initiated starting from the local user's computer and executed on the central computer (A6). The results are displayed on a local user's computer (A7) and used further.