WO1996010220A1

WO1996010220A1 - Method of influencing cyclical processes

Info

Publication number: WO1996010220A1
Application number: PCT/DE1995/001346
Authority: WO
Inventors: Karl Hehl; Michael Manfred Gierth; Oliver Kay Wybitul
Original assignee: Arburg Gmbh & Co.
Priority date: 1994-09-28
Filing date: 1995-09-28
Publication date: 1996-04-04
Also published as: DE4434654A1; DE4434654C2

Abstract

The invention concerns a method of influencing cyclical processes. After characteristic properties of the products manufactured in the test phase have been determined by tests and a plurality of process curve shapes have simultaneously been determined, a relationship is established between process characteristic numbers characterising the process curve shapes and characteristic properties and/or raw material properties of the parts. Alternatively, this relationship can also be preselected. When given desired characteristic properties (QMsoll) and permissible tolerances are preselected, the process characteristic numbers decisive for the characteristic properties can be determined in order thus to ascertain the machine setting values which determine an optimum working point for operating the machine. The process is thus optimized in consideration of the complex relations between individual process parameters and the properties of the parts to be manufactured.

Description

DESCRIPTION

Process for influencing cyclical processes.

FIELD OF THE INVENTION

The invention relates to a method for influencing cyclically running processes according to claim 1.

The method can preferably be used in the plastics processing and metalworking industries, for example in connection with plastic injection molding machines, blow molding machines, die casting machines, aluminum die casting machines, but also in connection with presses or welding equipment, provided that cyclical processes are carried out.

STATE OF THE ART

Such a method is known from W. Michaeli, inter alia "Quality control in the injection molding of recycled material" in: PLASTVERARBEITER, 1993, No. 10, pages 96-102. In this process, a sampling plan is defined, adapted to the respective problem, in which machine setting variables when setting up the machine and raw material properties such as the proportion of regranulate are systematically varied. During this loading, the process curve profiles of the process parameters that can be determined on the machine are recorded. The products manufactured during this sampling are also examined for their characteristic properties. The process curve profiles are then broken down into individual process phases. Process indicators are formed within the individual process phases using mathematical-analytical methods. Process key figures are discrete numerical values which characterize the respective process curve course within the individual process phases (correlation coefficient, extrema, integrals, mean values, etc.). The respective process key figures can thus be correlated with the associated quality features in a mathematical-statistical form. The function determined with it QM = f (PKZ)

(Characteristic properties - function of the process key figures) allows a prediction of the characteristic properties of the manufactured product during the subsequent production, so that depending on this, for example, a switch can be controlled to separate out bad parts. The results are not optimized, possibly also taking into account non-linear dependencies between different characteristic properties.

DE-A 35 05 554 discloses a method for examining pressure profiles in order to automatically determine changes in the pressure profile which, when certain threshold values are exceeded, give a signal for a change in the switching times, e.g. between the injection phase and holding pressure phase for the following cycle. However, there is no connection to the characteristic properties of the finished product. Since only one manipulated variable is used, any properties cannot be influenced in a targeted manner;

In order to accelerate the setup of a machine on which cyclic processes take place, DE-A 4002398 relates various temperatures in the area of the machine to a temperature of a manufactured part, the temperature of the part in turn having certain properties of the Partly connected. Although this can speed up the set-up of the machine, expert knowledge is still required so that the temperature of the part actually corresponds to certain characteristic properties. This means that no analytical relationships between process variables and properties are established.

From DE-A 35 38 516 a weight regulator is also known, in which the actual weight of injection molded parts determined after the injection molding process cast parts compared to a target weight is selected as a control variable for the holding pressure in the holding pressure phase of the injection molding machine. There is no optimization.

A method is also known from EP-B 455 820, in which countermeasures are initiated on the basis of stored expert knowledge when defective parts occur. The countermeasures, however, are the stored empirical experiences of an expert, so that a partially specific change depending on the actually occurring conditions is not possible.

In W. Michaeli, et al. "Quality assured injection molding" in plastics 82 (1992) 12, pp. 1167-1171 describes a regulation of the quality characteristic "gloss" on the basis of gloss = f (injection speed, wall temperature). Here, only one target variable / quality characteristic is regulated depending on two process indicators. A quality vector cannot be described or regulated as a function of a key figure matrix, since the simultaneous regulation of several quality characteristics requires a solution to the problem of the linear dependence of individual quality characteristics on one another. There is no optimization.

In DE-A 35 45 360, a pressure-time characteristic curve determined at the spray nozzle is determined to be decisive for the quality, so that if this characteristic curve is left, it can be assessed that a bad part is now being produced. The complex relationships between different machine settings are not dealt with.

According to DE-A 40 25 221, an optimal working point is determined iteratively by simulation methods. The relationships between machine setting variables, process parameters and characteristic properties are not dealt with, instead an expert system is provided, which is usually an optimization of the working point as carried out empirically by an experienced adjuster.

Process parameters for individual cycle sections are determined in EP-A 457 230. However, there is no correlation between the individual parameters.

In US-A 5,246,644, a working space is first traversed two-dimensionally with the parameters pressure and temperature. Based on a loss weighting, the determined results determine which factor significantly influences the quality and an attempt is then made to optimize this one factor. The complex relationships between different machine setting sizes are not dealt with.

In US Pat. No. 5,225,122, the working point is updated within a given expert knowledge. Independent quality optimization, taking into account the complex relationships, does not take place here.

All previous methods have in common that no optimization is possible depending on any given characteristic properties of the part. At most, individual process variables or properties are regulated independently of other values, regardless of the complex relationships between individual process parameters and properties of the manufactured parts.

DISCLOSURE OF THE INVENTION

On the basis of this prior art, the present invention is based on the object of optimizing a method of the type mentioned at the outset, taking into account the complex relationships between individual method parameters and the properties of the parts to be produced. This object is achieved by a method having the features of claim 1.

The fact that the relationship between characteristic properties and / or raw material properties and machine setting variables can now be formed by means of interposed process key figures enables optimum process sequences to be realized in every respect. The optimization is directed towards characteristic properties, the term characteristic properties initially being understood to mean properties of the manufactured parts, that is to say any, attributive (e.g. burner) or variable (e.g. dimensions, weight) properties. However, this also includes minimizing the reject rate, which is influenced by the characteristic properties of the products. However, the characteristic properties of the manufactured products also include economic aspects, such as a stable course of the manufacturing process or a minimization of the cycle time while maintaining certain boundary conditions of the processes. The raw material properties also influence the characteristic properties, so that e.g. When using the method on a plastic injection molding machine, the regranulate content of recycled plastic material can be decisive for the properties of the end product. The indirect relationship between machine setting parameters and characteristic properties via the process key figures can be determined in the course of the method or can be specified with knowledge of this relationship, for example by previous manufacturing processes. Based on the result, an optimal operating point for the operation of the machine can then be determined.

Further advantages result from the subclaims.

The invention is explained in more detail below using an exemplary embodiment. Show it: 1 is a block diagram with the partial representation of an injection molding machine on which the inventive method is carried out,

2 shows a flow chart which clarifies the process sequence carried out,

3, 4 process curve profiles.

PREFERRED EMBODIMENTS OF THE INVENTION

The invention is explained in more detail by way of example with reference to the accompanying drawings. However, the exemplary embodiments are merely examples which are not intended to restrict the inventive principle to a specific arrangement.

The method and the device are explained below using an injection molding machine, preferably an injection molding machine for processing plastic materials, such as plastics or powder materials. However, as already explained at the beginning, the method can also be used without problems in other areas in which cyclical processes are used for the production (master shaping) or processing (shaping) of products.

2, the method is basically divided into a test phase T and a production phase P. A working point is preferably also set on the machine at the beginning of the test phase. An operating point is understood to be an adjustment of the machine setting variables, which in the first place allows the production of products at least in a reasonably reasonable manner. In the test phase, as described in the article "Quality monitoring for injection molding of recycled material", sampling is then carried out, which can also be carried out systematically according to a sampling plan. Here, for example, nine to ten different machine settings are made, in which the essential machine setting variables MG can be varied (step S1). Raw material properties RE, such as the proportion of regranulate, can also be varied in order to determine the influence of the raw materials on the characteristic properties. For each machine setting, parts are removed and examined using conventional methods, which are not dealt with here, to determine their characteristic properties (step S2). However, sampling can also take place during production by looking at individual manufactured parts. Characteristic properties are any, attributive (eg burner) or variable (eg dimensions, weight) properties, but can also be process stability, cycle time, reject rate or costs.

In addition, during these processes, the machine setting variables as well as process parameters such as e.g. Internal mold pressure, mold temperature, temperature of the processing material, injection speed and the like are continuously recorded and preferably as process curve profiles over the cycle time or e.g. the screw path is stored (step S3). Process curve profiles include not only the immediately resulting curve profiles, e.g. Understand pressure-time, pressure-path or temperature-time profiles, but also the process curve profiles that can be calculated from them, e.g. Derivatives, integrals and the like. These process curve profiles are a function of the machine setting variables, however, no conclusion can be drawn as to which machine setting variable has to be changed in order to change the process curve profile in a certain direction, let alone make it possible to make predictions as to which machine setting variable needs to be changed in order to to achieve a certain characteristic property QM.

For this reason, the process curve profiles PKV recorded in step S3 are broken down into variable process phases PPH resulting as a function of the respective process curve profiles, so that they can be described in sections (step S4). However, for some process curve profiles, such as temperature profiles, it may not be necessary to break them down into process phases, since they are largely linear, for example. The entire process curve is then viewed as one process phase. These process phases are described by process key figures PKZ, which are determined on the basis of known analysis of variance and regression, neural networks or similar analyzes (step S5). The description and determination of the characteristics of the process curve profiles PKV within individual process phases PPH is carried out using discrete individual values (process key figures PKZ), which are essentially based on the rules of differential calculation (absolute and local extremes, turning points, integrals, mean values, ...).

The formation of these process key figures PKZ is explained below by way of example with reference to FIGS. 3 and 6, in which various process curve profiles are shown. In FIG. 3, the mold internal pressure Pwi is initially plotted over time t. During the filling phase, the mold pressure sensor receives a first pressure signal at time t1, which then rises gradually until the tool is filled in the area of the lower processing limit UVG. In the subsequent compression phase, the pressure then rises continuously until the maximum mold internal pressure Max (Pwi) is reached at the upper processing limit OVG. In the subsequent holding pressure phase, the mold internal pressure then gradually drops, which can result in a different drop, as a comparison of FIGS. 3 and 4 shows.

In FIG. 3, as a further process curve profile PKV, the second derivative of the mold cavity pressure curve is plotted over time. This curve has maxima at the points of discontinuity of the internal pressure curve. If these curves are now broken down into individual process phases PPH, the machine is now able to find its range limits for sensible process phases. A correlation of different curves can certainly take place such that, for example, the process phase with in the present case its lower processing limit UVG begins where the second derivative (dPwi / dt) ^{2 has} its maximum and ends where the mold pressure curve has its maximum Max (Pwi). Different process phases PPH can be determined on the basis of different extremes, turning points, integrals and mean values. In Figure 3, for example, the following key figures could be determined:

AVG mean (dPwi / dt) ²

Maxima Max (dPwi / dt) ²

Integral INT (Pwi) of the cavity pressure.

The same can be done in FIG. 4. The mold internal pressure Pwi is also plotted there over time t. The hydraulic pressure Phy and the screw travel s are also plotted during each injection cycle. The lower processing limit UVG and upper processing limit OVG are the beginning and end of the spray cycle. Here, too, various process key figures PKZ can be determined in this area, such as:

Maxima Max (Phy) of the hydraulic pressure

Maxima Max (Pwi) of the cavity pressure

Integral INT (Pwi) of the cavity pressure

Screw path s (Pwi = 0VG), in which the cavity pressure is the upper one

Processing limit cuts.

The key figures determined in this way are in principle suitable for describing the various process curves. For this purpose, a key figure vector is generally first created for:

With:

MIN (s) minimum screw path

AVG (ds / dt) = mean value of the first derivative of the screw path over time

MAX (Phy) = maximum hydraulic pressure

MAX (Pwi) = maximum of the cavity pressure

INT (Pwi) = integral of the cavity pressure

INT (Phy) = integral of the hydraulic pressure t (Pwi = UVG) = time at which the cavity pressure is at the bottom

Processing limit is, s (Pwi = OVG) = screw path at which the cavity pressure is at the upper processing limit.

A process model vector is then determined with this key figure vector. In the simplest case, this process model vector is represented as a straight line equation as follows, which now allows a relationship between the characteristic property QM and the process characteristic number. In the simplest case, this results e.g. to the following process model vector

Example 1: Key figure vector

General: Example:

dt))))) VG) VG)

Example 2: Process model vector

Generally:

- ^p κz _n ,

The process indicators are now related to individual characteristic properties (QM ^ = f (PKZi)) and to machine setting variables (PKZ ^ = f (MG- _j )) (step S6), so that a relationship between characteristic property (s) ) and machine setting variable (n) (QM- _j = f (MG- _j )). If the relationships between machine settings and characteristic properties are known, for example in the form of previously determined process indicators or based on empirically determined relationships, these relationships can of course also be specified. These relationships can then be stored in a storage unit 16.

Neither these relationships nor the determined process indicators have to be stored in a permanent memory. The process key figures or the process curve profiles required for their creation can be saved if necessary, but the only thing that is important is the existence of the determined relationship between machine setting parameters MG and process key figures PKZ and between process key figures PKZ and characteristic properties QM.However, if the process key figures PKZ are saved, At a later point in time, a check can easily be made during production to determine whether the relationships determined are still applicable. By changes to Tool or on the injection molding machine due to wear alone, it is quite possible that the determined relationships change over time. This completes test phase T.

Now certain characteristic properties such as dimensional accuracy, weight, process stability, cycle time or the like are entered as the predetermined characteristic property Q ^M Soll (step S10). On the basis of the previously determined relationship between the process characteristic number and the characteristic property, an optimal operating point can now be determined as a function of the predefined characteristic property while observing the predefined characteristic property QMs ₀ n (step S12).

At the same time, the tolerances dQM _zu ] are specified for the characteristic properties in step S11. However, these are not tolerance bands for process curves, but tolerances of the characteristic properties QM.If an optimal working point cannot be reached because, for example, due to linear or non-linear dependencies between different characteristic properties QM or machine setting variables MG, the change to achieve an optimum at the same time if other properties deteriorate (step S13), an optimizer 19 tries to achieve a sub-optimum as a compromise solution based on the relationship between characteristic properties, process parameters and machine setting variables (step S14). The optimizer therefore tries to determine an almost optimal working point AP _su b while adhering to the tolerances dQM _for all predetermined characteristic properties Q s ₀ n using known analysis of variance, regression, neural networks or similar methods. If this almost optimal operating point can be determined, the various setting variables can be set accordingly (step S15). However, if this operating point cannot be determined, system changes are determined based on the known relationship, changes are made immediately or proposed by the control to the user, so that a working point AP can be achieved while maintaining the tolerances of all the predetermined characteristic properties (step S16).

If desired, can also be given machine setting MGs ₀ i or more machine setting MG which must be optimized _to] due to the known relationship and the DQM into account the tolerances in step S10. Limit values dMG _to ι for machine setting variables MG or permissible deviations dPKZ _to ι can also be specified in step S11, so that an optimum for operating the machine is determined taking into account multiple dependencies.

The entire process can not only be used in advance to determine an optimal working point, but can also be used successfully in ongoing production. For this purpose, an evaluation corresponding to steps S3, S4, S5 is preferably carried out cycle-synchronously, ie series process curve profiles SPKV are also continuously recorded (step S7). These series process curve profiles are preferably broken down cycle-synchronously into series process phases SPPH (step S8) and series production key figures SPKZ are determined therefrom for production, which identify the series process curve profiles and are related to certain characteristic properties (step S9). These specific characteristic properties SQM can be determined on-line, for example by calculation; but they can also be determined off-line, for example, in an analogous manner, even if they are costly. From this, the resulting calculable characteristic property (s) SQM _Der can be determined in a known manner (step S17). This can be used to determine a new optimum working point APn for each cycle (step S18), so that an adjustment of the Machine setting variables MG can be made again for each cycle. If you want to further automate this process, the machine setting variables MG can be set automatically in order to achieve the working points AP _opt , AP _SUD , AP, APn (step S 19). It is even possible to set up a closed control loop in that the machine setting variables MG are automatically returned so that, depending on the optimal operating point determined, the machine setting variables are automatically set directly by the machine.

Furthermore, based on the function determined in step S6

QMi = f (PKZ) i or QM = b • PKZ

determine which process key figure PKZ- _{j has} the greatest influence. The subset of all process key figures PKZ and all machine setting variables that significantly influence the respective properties is selected.

Since individual characteristic properties QM- _{j are} not always linearly independent of one another, machine setting variables cannot always be changed without at the same time influencing other characteristic properties. In any case, the machine setting variables can also be linearly dependent on one another, so that there is no clear solution here either. In this case, before a change, it is checked whether all the characteristic properties QM- _j lie within specified specifications or ranges. For this purpose, appropriate values for optimal characteristic properties QM _opt , optimal machine setting variables MG _opt and optimal process key figures PKZ ₀ pt were entered or determined in step S11. By means of an optimization calculation, a new operating point is calculated, which corresponds to a specific setting of the machine setting parameters, and the necessary change in the machine setting parameters is transferred to the machine. But stands the process key figure can be determined from the relevant machine setting variable (s) MG, which are manipulated variables for the control of QM and / or PKZ.

This means that the optimization is only determined during production, with several manipulated variables generally occurring due to the different requirements placed on the product.

Experiments have also shown that the decomposition of the process curve profiles takes place most numerically in the test phase T as well as in the production phase P by performing a signal analysis. On the basis of the existing analog signals for the continuous acquisition of the process parameters and / or on the basis of the digital signals which are given for switching on and off certain parts of the machine, different process phases PPH can be defined process-specifically and calculated for each production cycle. The device itself determines the process phases by considering all the signals determined during production and during the test phase and, depending on this, determines key figures for individual process phases which are characteristic of this process. These key figures are correlated with the characteristic properties and result in specific relationships between a plurality of characteristic properties and a plurality of process parameters. In the control itself, when a difference value occurs in at least one characteristic property, the machine parameter that has the most lasting influence on reducing the difference value is selected on the basis of the process characteristic number (s) without exerting a negative influence on other process parameters. The process key figures are then defined and also calculated within these process phases, which, as already explained above, allow a correlation to the characteristic properties QM. The key process indicators that can be determined with the mostly existing sensor system of the machine have been found to be those determined from the following process curve profiles, which initially represent a process indicator vector which must then be evaluated as already explained above:

Maximum and / or average pressure rise speed in the mold during the injection phase,

- maximum and / or average pressure rise speed in the tool during the compression phase,

maximum and / or average pressure drop speed in the tool during the holding pressure phase,

- mold internal pressure integral in the holding pressure phase,

- maximum mold pressure in the holding pressure phase,

- mean wall temperature in the education phase,

- average melt temperature in the education phase.

To clarify the individual phases, a cycle during the production of molded parts on an injection molding machine will first be described here by way of example. In such a cycle, material is fed continuously to the injection molding unit 30 of an injection molding machine, which material is first injected into a mold cavity 32 via injection means 31 during an injection phase. Towards the end of the injection, when the mold cavity is almost filled, the mold cavity is initially further pressurized by axial movement of the injection means during a compression phase. At a certain point, a further movement of the injection means 31 is then no longer possible and the control of the injection molding machine then usually switches over to a pressure control which regulates the holding pressure during a holding pressure phase, which is necessary for obtaining dimensionally stable molded parts. During this time, the injection molded part solidifies and then forms into the finished molded part during the formation phase. The process key figures PKZ, SPKZ of the above process key figure vector are formed within the following process areas:

Injection phase:

Start by machine digital signal: "Start injection"

End by machine digital signal: "Switch to reprint" or:

Start by threshold value analog signal "cavity pressure to e.g. 5 bar"

End by calculated signal: "Maximum acceleration tool cavity pressure"

Compression phase:

Start by machine digital signal: "Switch to reprint" End by calculated signal: "Minimum acceleration of tool cavity pressure" or:

Start by calculated signal: "Maximum acceleration tool cavity pressure"

End by calculated signal: "Minimum acceleration tool cavity pressure"

Reprint phase:

Start by machine digital signal: "Switch to reprint" End by machine digital signal: "End reprint" or:

Start by calculated signal: "Minimum acceleration internal tool pressure" End by machine digital signal: "End reprint"

Education phase:

Start by machine digital signal: "Start injection" End by machine digital signal: "End reprint" FIG. 1 shows a device which is suitable for carrying out the method. An injection molding unit 30 injects material into an mold cavity 32 of an injection mold 34 in a mold closing unit 33 via an injection means 31 such as, for example, a screw conveyor or an injection piston. The device has sensor means 11, 12 such as an internal mold pressure sensor and a temperature sensor in the area of the injection molding unit, via the sensor means a multiplicity of process parameters are determined during each process, that is to say during each cycle, and means 13 for recording process curve profiles PKV become the process parameters during of a process. In addition, the device has input means 14 which enable various values to be input into storage means 16. It is necessary, for example, to enter the characteristic properties QM of the products manufactured during this phase, determined by conventional methods during the sampling phase. However, some of these values can also be determined automatically and fed directly into the device. However, certain nominal values of predetermined characteristic properties QM _S0 -n, which are characteristic of a nominal value vector of the characteristic properties, for example, must also be entered or read in from data media.

Furthermore, the device comprises means 15 for determining a relationship between the characteristic properties QM, SQM and the process curve profiles PKV or series process curve profiles SPKV. Here, the process curves are broken down into the process phases, preferably numerically, according to the specifications made above. In the process phases, the process key figures PKZ and series process key figures SPKZ are determined, which are correlated with the characteristic properties through this relationship. The relationship determined in the test phase is stored in storage means 16. The machine setting variables MG can be influenced via adjusting devices 17, 18. In the exemplary embodiment, the setting device 18 influences Adjustment of a control valve 36, for example the pressure of a piston-cylinder unit driving the screw conveyor and thus the internal pressure of the tool, which is detected by the sensor means 11. The setting device 17 influences the heating tapes 35 supplied current and thus the temperature, which is detected by the sensor means 12.

An optimizer 19 determines an optimal working point AP _opt on the basis of the determined relationship between characteristic properties and machine setting variables and taking the specifications into account. If this optimal working point cannot be reached due to the boundary conditions, the optimizer 19 tries to determine an almost optimal working point AP ^ ₎ - ,. If this is also not possible, he suggests system changes or, if possible, makes these system changes himself. Depending on the determined operating point, the setting devices 17, 18 are then actuated.

It goes without saying that the process described here can be subjected to various modifications, changes and adaptations which move it in the area of aqualents to the appended claims.

Claims

Patent claims

1. A method for automatically influencing machine setting variables (MG) and / or raw material properties (RE) in cyclical processes, in particular in plastics processing machines and die casting machines, with the steps:

a) Setting a working point that allows the production of products, b) Test-based production of products manufactured in the processes starting from the working point with a systematic change in the machine settings (MG) and / or raw material properties (RE) and with a systematic variation of possible combinations of the machine settings (MG) and / or raw material properties (RE), c) determination of the characteristic properties (QM) of the products manufactured in step b), taking and evaluating at least one sample per combination of machine setting variables (MG) and / or raw material properties (RE) and assignment of the recorded characteristic properties (QM) to the respective combination of the machine setting variables and / or raw material properties (RE), d) determining a multiplicity of process parameters for creating process curve profiles (PKV) and recording and storing these process curve profiles (PKV) during step b) and assignment of the process curve profiles to the respective combination of the machine setting variables (MG) and / or raw material properties (RE), e) calculation of several variable process phases (PPH) resulting from the respective process curve profiles (PPP) from the process curve profiles recorded in step d) ( PKV) by determining the process key figures (PKZ) characteristic of any process curve progression, by means of which the change in the characteristic properties (QM) can be described, f) determining the relationship between the process indicators (PKZ) determined in step e) and the characteristic properties (QM) recorded in step c), g) determining the relationship between the process indicators (PKZ) determined in step e) and the process indicators (step b) ) changes made to the machine setting parameters (MG) and / or raw material properties (RE), h) specifying at least one characteristic property (QM5 ₀ ιι) for series production, i) determination of the machine setting parameter (MG) and / or raw material property (RE) based on the relationship according to step g), which results in an optimal working point while maintaining the specified characteristic property (Q 5 ₀ I1) »j) change in the machine setting variables (MG) and / or raw materials! ^'Properties (RE) to the determined in step i) values during the series production, k) repeating steps i) to j) for optimizing the operating point.

2. The method according to claim 1, characterized by the automatic dissection of the process curve profiles (PKV) in process phases according to step e) by determining the change in the signal input of the process parameters and / or in the signal input of digital signals.

3. The method according to claim 1, characterized in that steps i) to k) are replaced by the following steps:

1) Determining a large number of process parameters for creating series process curve profiles (SPKV) and recording and storing these series process curve profiles as well as assigning the series process curve profiles to the respective combination of the machine setting variables (MG),) Calculating several variable series process phases that result depending on the respective series process curve profiles (SPKV) (SPPH) from those recorded in step 1) Series process curve profiles (SPKV) while determining the series process key figures (SPKZ) which are characteristic for any series process curve profiles, by means of which the change in the characteristic properties (SQM) can be described during series production, n) calculation of the characteristic property (SQM _Der ) during production on the basis of the in step f ) determined relationship, with the series process key figures (SPKZ) determined in step m), o) determination of the machine settings (MG) and / or raw material properties (RE) based on the relationship according to step g), which for the series process key figure (SPKZ ), p) changing the machine setting variables (MG) and / or raw material properties (RE) to the values determined in step o) during series production, q) repeating steps 1) to p) to optimize the operating point.

4. The method according to claim 3, characterized by the automatic dissection of the series process curves (SPKV) in process phases according to step m) by determining the change in the signal input of the process parameters and / or in the signal input of digital signals.

5. The method according to claim 3, characterized in that on the basis of the series process key figures (SPKZ) determined during series production according to step m), the relationships determined in step f) and g) are checked and corrected in the event of deviations.

6. The method according to claim 1, characterized in the event that an optimal working point cannot be determined due to the dependencies between individual characteristic properties (QM) and / or raw material properties (RE), by the steps: - Specifying the associated tolerances (dQ _zu - _j ) of the specified characteristic property (QM $ ₀ ll) _>

- Determination of an almost-optimal working point (AP _SUD ) based on the relationships according to steps f) and g) while observing the tolerances (dQM _to - _| ) of all specified characteristic properties (QMs ₀ n)>

Setting the machine setting variables and / or raw material properties corresponding to the almost optimal working point (AP _SU ).

7. The method according to claim 6, characterized in that if, due to the relationship according to step f) and g), an operating point ( ^Ap sub) while observing the tolerances (dQM _2U - _j ) of all predetermined characteristic properties (QMsoii) cannot be determined, System changes to be made are determined so that a working point (AP) can be determined while observing the tolerances of all the specified characteristic properties.

8. The method according to claim 6, characterized in that a plurality of further machine setting (MG) are determined, on the basis of the determined in step g) relationship taking into account the tolerances (DQM _to - _j) to the predetermined characteristic property (QMS _0] 1) ^to are optimize.

9. The method according to claim 1, characterized in that in addition to the predetermined characteristic properties (QMs ₀ π) ^also limit values (MG _ZU - _| ) for the machine ^{setting variables} (MG) and / or raw materials! ^* properties (RE) can be specified.

10. The method according to claim 1, characterized in that the

Feedback of the changed machine settings to the

Machine control for setting the operating point (AP _opt , AP _perm , AP, APn) takes place automatically.

11. The method according to claim 1, characterized in that steps b) to g) are replaced by specifying the relationships according to steps f) and g).