Method for setting up a forming machine

Abstract

Method for setting up a forming machine by means of which a cyclically running forming process is carried out, by finding values ​​for setting parameters which setting parameters at least partially determine the control of controllable components of the forming machine during the forming process, by carrying out several simulations of the forming process based on at least one first parameter and at least one second parameter, wherein (a) which describes at least one first parameter of the physical conditions of the shaping process, (b) which is suitable as a basis for at least one of the setting parameters of the forming machine, (c) the simulations are carried out based on different combinations of values ​​of at least one first parameter and at least one second parameter, (d) from the results of the simulations for the different combinations of values ​​of at least one first parameter and at least one second parameter, values ​​of at least one quality parameter are calculated, and (e) the forming machine is provided, characterized in that (f) a value of at least one first parameter realized on the forming machine is determined by measurement, (g) from the value of the at least one first parameter measured according to (f) a value of the at least one second parameter is determined such that an substantially optimal value of the at least one quality parameter is obtained, and (h) for which at least one setting parameter a setting value is set on the forming machine, which setting value is the value of the at least one second parameter determined according to (g) and / or is determined from the value of the at least one second parameter determined according to (g).

Description

[0001] The present invention relates to methods for setting up a forming machine by means of which a cyclically running forming process is carried out.

[0002] In particular, the invention relates to methods in which values ​​for setting parameters, which setting parameters at least partially determine the control of controllable components, such as drives of the forming machine, during the forming process, are found by carrying out several simulations of the forming process based on at least a first parameter and at least a second parameter, wherein (a) which describes at least one first parameter of the physical conditions of the shaping process, (b) which is suitable as a basis for at least one of the setting parameters of the forming machine, (c) the simulations are carried out based on different combinations of values ​​of at least one first parameter and at least one second parameter, (d) from the results of the simulations for the different combinations of values ​​of at least one first parameter and at least one second parameter, values ​​of at least one quality parameter are calculated, and (e) the forming machine is provided.

[0003] Forming machines can include, for example, injection molding machines, injection presses, presses, and the like. Forming processes follow this terminology analogously.

[0004] The following outlines the state of the art with regard to injection molding machines (IMM) and injection molding processes (IM processes). However, the conclusions apply more generally to forming machines and processes. Experimental pre-sewing for machine setting

[0005] The setup of injection molding machines is still performed today, just as it was 30 years ago, by manually adjusting individual parameters in the machine control system. Some assistance systems are used to support the operator during the setup process. Generally, a step-by-step approach, based on trial and failure or one-parameter-at-a-time, is still common practice. The success of this approach depends heavily on the experience and skill of the operator. Expert knowledge, available in software or printed manuals, can be helpful in finding a working point that results in plastic products of sufficient quality. Optimizing the injection molding process can also be supported by the art of statistical design of experiments. A purely experimental approach leads to acceptable results.However, these are often not particularly robust against disturbances and at best represent one of many local optima in a relatively large parameter space. Machine setup simulations

[0006] Simulating injection molding processes and conducting process optimizations using experimental design, followed by transferring the resulting parameters to the injection molding control system, are state of the art. It is also state of the art to use simulations to identify the ranges of parameters (process windows) within which a process produces products with properties within specified tolerances.

[0007] Terms like "Virtual Moulding" or "Virtual Tool Sampling" are in circulation.

[0008] Due to the associated effort, the availability of suitable software, and trained personnel, simulations for the purpose of and in the run-up to tool sampling do not yet appear to have become established in practice. However, software providers promote the advantages of a holistic approach, from component design and tool construction to series production. In addition to the cavities themselves, the rest of the molding process, including the gating system and temperature control, is also captured. This should at least allow the necessary machine capability to be determined, a rough operating point to be estimated, and the influences and trends of various parameters to be analyzed in advance. The actual machine behavior, including play and response times, is generally not considered; however, in some cases, various parameters are varied only within certain narrow limits of a given molding model.

[0009] Due to the ongoing development of powerful computers, improvements in computational methods such as the finite element method, and refinements of the underlying process models, process simulations can now, in principle, deliver realistic results, provided the boundary conditions in the simulation have been chosen correctly. In reality, however, the results obtained through simulation and the resulting "offline" optimizations regularly deviate significantly and decisively from reality.

[0010] The non-ideal nature of the environment, machine, tool, control system or material usually makes it difficult to determine a robust process window for the injection molding process or even a corresponding optimal operating point.

[0011] EP 1 253 492 A2 discloses a method for determining the properties of an injection-molded part, using neural networks and mathematical analytical methods.

[0012] EP 2 539 785 A1 discloses a method for controlling an injection molding process, whereby data from previous cycles are used to change process parameters in such a way as to result in improved quality characteristics of the produced parts.

[0013] US Patent 2006 282 186 A1 discloses a method for optimizing a process, wherein several solutions for the process are calculated by simulations and, in a separate process step, such solutions are chosen that are closest to an optimal value for certain parameters.

[0014] EP 0 747 198 A2 deals with the optimization of an injection molding process, whereby closing-side and injection-side settings are optimized separately with the help of databases.

[0015] US patent 2002 188 375 A1 discloses a method that uses computer-assisted engineering (CAE) to iteratively optimize process settings.

[0016] EP 0 368 300 A2 discloses a method in which a simulation of a forming process and a real forming process are carried out alternately, the simulation being adjusted in each case using results from the real test.

[0017] DE 10 2015 107 024 B3 discloses a method in which an injection process of an injection molding process is simulated and virtual events are compared with real measured event patterns.

[0018] US patent 4,816,197 A discloses a method for controlling an injection molding process, in which a so-called PVT optimization is carried out while observing the viscosity.

[0019] DE 10 2013 008 245 A1 and WO 2014 / 183 863 A1 disclose a method for operating an injection molding machine with a control system in which expert knowledge about the operation of the injection molding machine and its possible peripheral devices as well as about the production of injection-molded parts is stored.

[0020] EP 2 679 376 discloses a method in which simulations of injection molding processes are carried out in a cloud server and stored in a cloud storage.

[0021] Further state of the art can be found in EP 1 166 994 A1.

[0022] Therefore, methods are needed that allow the information obtained through simulation to be used in the real shaping process.

[0023] This problem is solved by a method having the features of claim 1.

[0024] This is done by (a) a value of at least one first parameter realized on the forming machine is determined by measurement, (b) from the value of the at least one first parameter measured according to (f) a value of the at least one second parameter is determined such that an substantially optimal value of the at least one quality parameter is obtained, and (c) for which at least one setting parameter a setting value is set on the forming machine, which setting value is the value of the at least one second parameter determined according to (g) and / or is determined from the value of the at least one second parameter determined according to (g).

[0025] This can therefore solve the problem that simulation results often do not match reality, because simulations sometimes depend crucially on the assumed boundary conditions, the measured material data, the underlying physical models and calculation methods.

[0026] Ultimately, this translates into improved control of the forming machine and a more economical and robust forming process. The result is also good parts with sufficient and consistent quality.

[0027] Differences between theory / idealization and practice / reality can arise from differing material properties, material variations, batch variations, environmental influences, dimensions of machine components, dimensions of tool components, and machine behavior. All these sources of simulation results that deviate from reality can be addressed by the present invention.

[0028] The invention can be implemented as a procedure based on FEM simulations, which, taking into account boundary conditions that are not exactly known, provides characteristic curve fields consisting of machine and quality parameters, and based on this characteristic curve field together with actually determined boundary conditions on the injection molding machine, a machine setting is found with which products of sufficient quality can be produced.

[0029] To determine the second value, from which a substantially optimal value of a quality parameter can be derived, the criterion can be that the actual value is fitted as closely as possible to a target value or target range. The target value can be the extremum of a calculated relationship or a specification provided by an operator.

[0030] The adjustment in question may involve the optimization of a single quality parameter or a so-called multi-criteria optimization (i.e., when several quality parameters are processed).

[0031] Both the first parameter and the second parameter can be functions, e.g., time- and / or path-dependent.

[0032] When measuring at least one initial parameter, the measurement can be direct or indirect. Indirect measurement means, for example, that a measured value can be transformed through arithmetic operations so that the actual measured value corresponds to at least one initial parameter.

[0033] When determining the quality parameters according to process step (d), in which values ​​of at least one quality parameter are calculated from the simulation results, at least one value of at least one quality parameter can be calculated for essentially every combination of values ​​of the at least one first parameter and the at least one second parameter. For certain combinations of values ​​of the at least one first parameter and the at least one second parameter, the calculation of the values ​​of the at least one quality parameter can be omitted if, for example, it is clear from the course of the simulation that no usable setting parameters / values ​​will result in these cases.

[0034] The quality parameters can be the same, similar, or different physical quantities for the various combinations of values ​​of at least one first parameter and at least one second parameter. Adjustment of characteristic curve fields

[0035] A characteristic curve field generated by simulations (quality parameters vs. at least one first parameter and at least one second parameter) can be modified based on material-related boundary conditions identified at the forming machine, so that a theoretical optimum derived from the simulations is transformed into a real optimum. This means that the simulation results can be adapted to the real boundary conditions. Specifically, this can involve adjusting the characteristic curve fields themselves, as well as (slightly) varying values ​​for compressibility and viscosity, i.e., pressure transmission and flow properties.

[0036] The necessary modification, i.e., the method of modification (e.g., linear offset, multiplication) or its specific form (in which direction and how much), can be known in advance. The necessary modification can be determined, for example, by simulation using varying material-related boundary conditions.

[0037] The necessary modifications can be similar for different types of plastics and for different component groups. Therefore, it would not be necessary to calculate the required modifications for each individual case; instead, an existing database containing the necessary modifications for the respective plastic / component combinations could be used. Multi-stage optimization

[0038] A multi-stage application and adaptation of the characteristic curve field can be achieved through an interplay between simulations and real-world experiments. This means that process steps (d) and (e) can be repeated after process steps (f), (g), and (h) have already been performed. Naturally, different parameters or values ​​can be chosen for at least one of the first and at least one of the second parameters.

[0039] The general idea behind multi-stage optimization is to feed experimentally determined data (related to the material, machine behavior, process, and product) back into the simulation. The purpose of this feedback is to adapt the simulation to the real-world boundary conditions. Based on n iterations of alternating simulations, measurements, feedback, and adjustments, a machine setting should be found that results in satisfactory products.

[0040] First, simulations are performed using material-related boundary conditions as specified in the literature, databases, or measured in the laboratory. Process and machine parameters (setting parameters) are varied in each simulation, thus covering a predetermined parameter space. From these stepwise calculated data points, characteristic curve fields are generated using regression analysis. These curve fields relate product and overall process-related quality parameters to the process and machine parameters (setting parameters).

[0041] A preliminary process optimum can be determined from the characteristic curve fields, which, however (as explained in more detail above), does not necessarily correspond to a real optimum. Nevertheless, information specific to the respective injection mold regarding the products or cavities, the gating system, etc., has been obtained.

[0042] Trends or dependencies within this information remain valid even if the material-related boundary conditions change.

[0043] The preliminary optimum is used to obtain initial process information in the form of products or measurement data. The products are evaluated for quality through visual inspection, weighing, or measurement. Process measurement data can, for example, correspond to the actual machine behavior, such as specific injection pressure or actual injection speed. Additionally, material-related properties (viscosity, compressibility, etc.) can be determined using appropriate tests.

[0044] The experimentally determined information is then used to perform new simulations with boundary conditions adapted to reality. These new simulations generate a new set of characteristic curves or modify the existing one in order to subsequently provide a machine setting that results in products with properties within specified tolerances.

[0045] By applying the invention to injection molding processes, a digital injection molding machine (D-SGM) can be realized which makes the injection molding process simulateable, taking into account the machine behavior, the tool and the plastic.

[0046] The invention provides a tool for finding a functional machine setting as independently as possible from a real forming machine. Ideally, a machine setting determined in this way would come as close as possible to a real optimum, or could be relatively easily adjusted to an optimum on the actual forming machine. The advantages for operators would be, firstly, time savings during sampling; secondly, additional process understanding, which would also be available, for example, directly at the forming machine; thirdly, a process well within a robust process window; and fourthly, an efficient process in terms of energy and time. The data obtained during the simulation could be used for adjustments during the forming process, thus guaranteeing stability in subsequent production.

[0047] An important aspect of the invention is the subsequent comparison of simulation and experiment, i.e., an adaptation of the setting determined under idealized assumptions to the actual boundary conditions of the injection molding process. Computer-aided simulations can also take place in parallel, alternately, or following an actual setting process or production.

[0048] The goals are always a successful, stable, and efficient forming process. Numerical calculations can aid in process understanding by visualizing the forming process and its various process variables in a time-discrete manner, i.e., in the form of virtual and interactive filling studies that adapt to the respective inputs in the machine. This provides process engineers with additional information to help them solve problems in the forming process, the forming tool, the forming machine, or the product itself.

[0049] With increasing sensor technology, computing power, networking, and data storage, new possibilities arise. For example, collected information can be used to continuously improve the models underlying the simulation. Boundary conditions in simulations

[0050] Numerical simulations deliver results based on models and boundary conditions. These boundary conditions include material properties such as melt viscosity or pvT behavior in plastics. Other boundary conditions concern machine parameters such as cylinder and nozzle temperatures, tool-related parameters such as mold wall temperature, and gate-related parameters such as channel diameter and temperature. It is also conceivable that the exact geometry of cavities may vary, for example, due to minor modifications during sampling or wear during production. Significant process variations can occur, particularly with regard to the total cavity volume, gate dimensions, or the precise surface texture of the cavities. Material-related boundary conditions

[0051] Material-related boundary conditions include, for example, viscosity, compressibility, pvT behavior, specific heat capacity, heat transfer coefficients, thermal conductivity, crystallization behavior, a critical temperature below which no material flow occurs (freezing point, no-flow temperature), or the specific heat capacity. In general, material properties can depend on physical quantities such as temperature, pressure, shear, etc.

[0052] Material-related boundary conditions can be measured in advance in the laboratory using various methods or obtained from the manufacturer. It is well known that material properties depend on the specific operating point and therefore vary with prevailing pressure, temperature, or shear rate. The behavior of a plastic measured in the laboratory does not necessarily correspond to its behavior under real-world conditions in the molding machine. The latter is due, for example, to the comparatively high pressures and flow rates in the injection molding process. It is also known that there are batch-dependent variations in material properties, which are attributable to the original manufacturing process of the plastic granules. Furthermore, the subsequent addition of additives such as colorants (masterbatch) can significantly influence the flow behavior of plastics.

[0053] Material-related boundary conditions also depend on the specific processing conditions, such as the various temperatures occurring during the process. While values ​​for tool or nozzle temperatures can be specified and regulated in the SGM control system, these values ​​often do not correspond to the actually relevant values ​​of the temperature or temperature distribution of the melt or the surface temperatures of the tool cavities. Rather, certain offsets or shifts must be assumed, which are caused by the design of the controllers and the integrated sensors.

[0054] The invention makes it possible to transfer the process sequences obtained and optimized from simulations as setting sets into the real forming machine, whereby variations in the boundary conditions can be taken into account and corrections can possibly be made to the real forming process.

[0055] The invention can be used in particular for the most optimal possible adjustment of a plastics processing machine.

[0056] (Further) advantageous embodiments of the invention are defined in the dependent claims.

[0057] It may be provided that at least one first parameter includes at least one of the following: - Parameters relating to kinematics, dynamics, controllers of all types, wear condition and / or - Parameters relating to a mold, in particular cavity geometry, gate geometry, hot runner geometry, nozzle geometry, mold material properties, cavity texture, venting, wear, heat capacity, and / or - Parameters relating to the forming machine, in particular injection unit, dynamics, kinematics, controller, cylinder diameter, behavior of the non-return valve, screw geometry, nozzle geometry, closing side, stiffness, friction, and / or - Parameters relating to a material used in the forming process, in particular filler composition, filler content, masterbatch, additives, moisture, viscosity, pvT behavior, thermal conductivity, heat capacity, coefficient of expansion, modulus of elasticity, shear modulus, coefficient of thermal expansion, and / or - Parameters relating to peripheral equipment used in the forming process, in particular pre-drying, material supply, temperature control, circulation pumps, material mixers, and / or - Parameters relating to environmental influences, in particular humidity and ambient temperature.

[0058] It may be provided that at least one second parameter relates to at least one of the following: metering speed profile, back pressure profile, cylinder temperature profile, mold opening and closing profile, clamping force profile, metering volume, hot runner temperatures, injection speed profile, holding pressure profile, holding pressure time, switchover point, injection pressure limit, compression relief strokes, ejector movement profile, mold core movements, temperature control fluid temperatures, cooling time, removal device movement profile.

[0059] It may be provided that an expert system is used for selecting the at least one first parameter and / or the at least one second parameter, preferably specifying value ranges for the at least one first parameter and / or the at least one second parameter. (Of course, multiple first parameters and multiple second parameters can also be selected and used in the method according to the invention.)

[0060] An expert system, as understood here, can be understood as an intelligent database integrated into a computing system (see, for example, Krishnamoorthy, CS and S. Rajeev (1996): Artificial Intelligence and Expert Systems for Engineers, Boca Raton: CRC Press, pages 29-88). It contains systematized and pre-programmed basic knowledge about the rules of the forming process, as can be found, for example, in relevant literature (see Schätz, A. (2013): Sample Selection of Injection Molds, Munich: Karl Hanser Verlag. Pages 31-220; Jaroschek, C. (2013): Injection Molding for Practitioners, 3rd edition. Munich: Karl Hanser Verlag. Pages 31-98; Fein, B. (2013): Optimization of Plastic Injection Molding Processes, 2nd edition. Berlin: Beuth Verlag GmbH. Pages 65-120; Plastics Institute Lüdenscheid (2013): Troubleshooting Guide, 12th edition. Unna: Horschler Verlagsgesellschaft GmbH. Pages 6-178).Furthermore, an expert system can contain pre-programmed rules that represent generalizations of procedures for machine setup, fault detection, or fault prevention used by experienced process engineers and specialists for setting up forming machines. Such a set of rules or fundamental knowledge can be in the form of truth functions or conversion tables, for example. Given known part geometries, materials, machines, and quality requirements, an expert system can use this pre-programmed knowledge and these rules to make rough estimates of process parameter ranges, leading to successful machine settings.

[0061] It may be provided that, as part of the selection of at least one first parameter and at least one second parameter, known data on at least one of the following are taken into account: forming machine, molding tool, gating system, material processed in the forming process, quality criteria, previous machine settings.

[0062] It can be provided that the data is supplied via a database. A database allows the information obtained using the method according to the invention to be centrally managed and made available.

[0063] It may be stipulated that at least one quality parameter relates to at least one of the following: - Process characteristics, in particular individual process times, total cycle time, robustness, tool stress, energy consumption, required clamping force, melt temperature, maximum injection pressure, environmental impact, temperature control requirements, economic efficiency, machine stress, required machine size, and / or - Component properties, in particular sink marks, dimensional accuracy, color streaks, air streaks, grooves, weld lines, burrs, shrinkage, dimensions, demolding temperature, frozen surface layer thickness, tempering requirements, material homogeneity, burns, warpage, material damage, color homogeneity, mass, mechanical stability, thermal stability.

[0064] It can be provided that, for each combination of values ​​from at least one first parameter and at least one second parameter, values ​​from at least two quality parameters are calculated, whereby a weighting of the at least two quality parameters is used. Such weighting allows more important quality parameters to have a greater influence on the settings of the forming machine.

[0065] It may be possible to choose the weighting of individual quality parameters based on a global quality criterion (multi-criteria optimization). Examples of quality criteria that can be used include: reduced warpage or greater dimensional accuracy of the manufactured molded parts, greater robustness of the molding process, smaller quantities of scrap, fewer or smaller surface defects, and shorter cycle times.

[0066] It may be provided that at least one of the following process variables is calculated from the results of the simulations and is used at least partially as a basis for calculating the values ​​of at least one quality parameter: process variables, in particular melt temperature, shear rate, shear stress, fill level, mold wall temperature, density, pressure, viscosity, velocity, volume shrinkage, filler distribution and orientation, mass homogeneity.

[0067] The simulations may include at least one – preferably all – of the following: - Process simulation according to a process model, in particular a simulation of the plasticization, filling process, holding pressure phase, cooling phase, demolding, and / or - Material simulation according to a material model, in particular a simulation of the flow behavior, the thermal behavior, the elastic behavior, and / or - Control simulation according to a control model, in particular a simulation of the control, the controllers of individual controllable components, the machine sequence, and / or - Machine simulation according to a machine model, in particular a simulation of the injection unit, the clamping mechanism, the robotics, and / or - Temperature control simulation according to a temperature control model, in particular a simulation of heat transport, heat transfer, and flow properties.

[0068] It may be necessary to use a mathematical analytical model and / or a numerical model of the forming machine and / or the forming process within the simulations. The simulations / numerical models can be performed using known methods (for example, finite element method, finite volume method, finite difference method).

[0069] It may be planned that a selection of values ​​for at least one first parameter and at least one second parameter is carried out using statistical experimental design. By cleverly omitting certain parameter combinations, simulations (and the corresponding resources) can be saved (see Montgomery, DC (2013): Design and Analysis of Experiments, 8th edition. Wiley. Pages 1-23).

[0070] It can be provided that the results of the simulations and / or relationships derived from the simulation results between the at least one first parameter, the at least one second parameter, and the at least one quality parameter are transferred to the forming machine and preferably stored in a central machine control system. Because the relationships thus determined are known at the forming machine, it is possible to react quickly and easily to changes in the environment (of the at least one first parameter).

[0071] It may be provided that, as part of the execution of process step (f), at least one of the following is carried out: measuring at least one length (for example, of machine components, for example, using calipers), performing a viscosity measurement (for example, using a rheometer nozzle or a rheometer tool), determining the value of at least one parameter realized on the forming machine by means of signals present on the forming machine, in particular force curves and / or pressure curves, manual inputs by an operator, ultrasonic analysis methods, mass spectrometry, X-ray spectroscopy, computed tomography, optical profilometry, use of a coordinate measuring machine, moisture meter, performing a temperature measurement and other laboratory equipment or measuring instruments.

[0072] It can be provided that the value of at least one parameter determined in process step (f) is transferred to a separate computing unit, and that the value of at least one second parameter determined according to (g) – preferably derived on the basis of relationships modeled from the results of the simulations – is transferred from the separate computing unit to the central machine control of the forming machine. This saves computing resources that would otherwise be required at the forming machine. More importantly, this measure allows certain parts of the calculations within the simulations to be carried out independently of the forming machine itself.

[0073] It may be necessary to verify the simulation results for quality and feasibility before carrying out process step (h), i.e., before using the determined value of at least one second parameter. This verification can include, for example, checking machine capabilities, achievable material throughput (e.g., of a plasticizing unit), and material stress limits. This is particularly useful if certain aspects of the molding process were simplified in the simulations. For instance, if maximum temperatures that a processed plastic can withstand were not considered in the simulation, it can be subsequently verified whether these temperatures were met in the actual solution. The same applies to machine capabilities and achievable material throughput. Visualization in the control system

[0074] Previously, during sampling, it was standard practice for an operator to conduct injection molding trials and assess the products in the usual way (regarding fill level, warpage, etc.). Normally, the operator would adjust machine parameters until the resulting molded parts met the specified quality tolerances. Visual inspection of the components can be supplemented by analyzing signal curves from existing sensors, measuring critical component dimensions, or weighing the component mass.

[0075] It has been common practice for operators to primarily rely on their experience with forming machines (e.g., SGMs) to resolve any problems that arise. Consulting technical literature and guides can be helpful in this process. These resources typically list frequently occurring errors, their possible causes, and suggest solutions.

[0076] It would often be helpful to be able to trace the process or even see inside the mold during the filling process. Due to the nature of the process, this is generally not possible. Only finished products outside the mold can be inspected. Pressure and temperature sensors can only provide localized information about the process. Measurements are often distorted due to the methodology used (systematic errors). Partial fillings and so-called filling studies could be performed to address filling problems, but these can be time-consuming and expensive (especially with large components). Partial fillings are also often associated with problems, such as demolding issues.

[0077] Within the scope of the present invention, it is possible to display the derived relationships on the forming machine, preferably in the form of characteristic curve fields. The process information previously obtained through simulation can thus be further processed and, for example, visualized in a central control system of the forming machine. This facilitates further manual adjustment, optimization, and fine-tuning of the machine.

[0078] Alternatively or in addition to a characteristic curve field, it may be provided that a value of at least one quality parameter belonging to a combination of values ​​of at least one first and at least one second parameter is displayed on the forming machine.

[0079] It may be provided that operators are allowed to select which dependent and independent parameters from the set of at least one first parameter and at least one second parameter are displayed.

[0080] An automatic restriction of the display areas of the characteristic curve fields can be provided based on predefined criteria.

[0081] A display of a current operating point / working point and / or a predicted operating point / working point based on a provisional change in setting parameters in the characteristic curve fields may also be provided. A display of quality forecasts may also be provided.

[0082] Furthermore, at least a partial representation of the CAD data of the tool, gating system, nozzles and injection unit can be provided.

[0083] This can also include a representation of a fill image and time-dependent process data.

[0084] Overall, the operator of the forming machine should be able to graphically illustrate the process flow through time-resolved display ("scrolling in time").

[0085] This can, for example, relate to process states such as local material densities, pressures, or temperatures, which can be displayed for discrete time steps according to the invention. Virtual cross-sections through the volume can be made, thus providing insights into the "interior" of the component. An improved understanding of the process could therefore aid in troubleshooting.

[0086] Simulations are only valid for a specific process setting. Therefore, it can be helpful to update the simulation simultaneously with changes to the machine settings and to make its results visually apparent (multi-stage optimization).

[0087] At the same time, it can be helpful for the operator to have a clear illustration of where the process is currently located in the parameter space and what consequences changes in the machine settings have or would have.

[0088] During these simulations, large amounts of data are generated. A setup assistant can analyze this data (modeling) and generate characteristic curve fields that display quality characteristics as a function of machine parameters. For example, an optimum can be determined using regression methods, which is then transferred to the forming machine or the operator in the form of a machine setup set. Optionally, characteristic curve fields can be transferred to the forming machine, and adjustments to the actual material- or machine-related boundary conditions can be made using experimental methods.

[0089] In order to benefit as much as possible from the generated data during the actual setup process, this data can be processed appropriately, transferred to the forming machine and made accessible to the operator in a convenient and clear manner.

[0090] The display methods described here primarily refer to a screen located on the forming machine and usually connected to the machine's central control system. However, it is also possible to perform these displays not directly on the forming machine, but, for example, via a data connection (LAN, Internet, etc.) to another computer.

[0091] An example of a corresponding visualization is in Fig.Figure 1 shows a screen of the control system displaying a characteristic curve field. Quality parameters QP are represented by the setting parameters EP1 and EP2. Depending on the current values ​​selected for EP1 and EP2, an operating point is plotted in the characteristic curve field. Its numerical value can also be displayed separately. A slider is also shown, which can be used to change the value of a parameter. This value can affect the machine's function directly or subsequently via a separate command. Optionally, depending on the input value for a first setting parameter, the value of a second parameter can change, so that the value of a quality parameter is maximized according to the characteristic curve fields.

Claims

[1] Method for adjusting a forming machine by means of which a cyclically running forming process is carried out, by finding values ​​for setting parameters which setting parameters at least partially determine the control of controllable components of the forming machine during the forming process, by carrying out several simulations of the forming process based on at least one first parameter and at least one second parameter, wherein (a) which describes at least one first parameter of the physical conditions of the shaping process, (b) which is suitable as a basis for at least one of the setting parameters of the forming machine, (c) the simulations are carried out based on different combinations of values ​​of at least one first parameter and at least one second parameter, (d) from the results of the simulations for the different combinations of values ​​of at least one first parameter and at least one second parameter, values ​​of at least one quality parameter are calculated, and (e) the forming machine is provided, characterized by , that (f) a value of at least one first parameter realized on the forming machine is determined by measurement, (g) from the value of the at least one first parameter measured according to (f) a value of the at least one second parameter is determined such that an substantially optimal value of the at least one quality parameter is obtained, and (h) for which at least one setting parameter a setting value is set on the forming machine, which setting value is the value of the at least one second parameter determined according to (g) and / or is determined from the value of the at least one second parameter determined according to (g). [2] Method according to claim 1, characterized by , that at least one first parameter includes at least one of the following: - Parameters relating to kinematics, dynamics, controllers of all types, wear condition and / or - Parameters relating to a mold, in particular cavity geometry, gate geometry, hot runner geometry, nozzle geometry, mold material properties, cavity texture, venting, wear, heat capacity, and / or - Parameters relating to the forming machine, in particular injection unit, dynamics, kinematics, controller, cylinder diameter, behavior of the non-return valve, screw geometry, nozzle geometry, closing side, stiffness, friction, and / or - Parameters relating to a material used in the forming process, in particular filler composition, filler content, masterbatch, additives, moisture, viscosity, pvT behavior, thermal conductivity, heat capacity, coefficient of expansion, modulus of elasticity, shear modulus, coefficient of thermal expansion, and / or - Parameters relating to peripheral equipment used in the forming process, in particular pre-drying, material supply, temperature control, circulation pumps, material mixers, and / or - Parameters relating to environmental influences, in particular humidity and ambient temperature. [3] Method according to any one of the preceding claims, characterized by, that at least one second parameter concerns at least one of the following: metering speed profile, back pressure profile, cylinder temperature profile, mold opening and closing profile, clamping force profile, metering volume, hot runner temperatures, injection speed profile, holding pressure profile, holding pressure time, switchover point, injection pressure limit, compression relief strokes, ejector movement profile, mold core movements, temperature control fluid temperatures, cooling time, removal device movement profile. [4] Method according to any one of the preceding claims, characterized by , that an expert system is used when selecting the at least one first parameter and / or the at least one second parameter, preferably specifying value ranges for the at least one first parameter and / or the at least one second parameter. [5] Method according to claim 4, characterized by, that in the selection of at least one first parameter and at least one second parameter, known data on at least one of the following are taken into account: forming machine, molding tool, gating system, material processed in the forming process, quality criteria, previous machine settings. [6] Method according to claim 5, characterized by that the data is provided through a database. [7] Method according to any one of the preceding claims, characterized by that at least one quality parameter affects at least one of the following: - Process characteristics, in particular individual process times, total cycle time, robustness, tool stress, energy consumption, required clamping force, melt temperature, maximum injection pressure, environmental impact, temperature control requirements, economic efficiency, machine stress, required machine size, and / or - Component properties, in particular sink marks, dimensional accuracy, color streaks, air streaks, grooves, weld lines, burrs, shrinkage, dimensions, demolding temperature, frozen surface layer thickness, material homogeneity, burns, warpage, material damage, color homogeneity, mass, mechanical stability, thermal stability. [8] Method according to any one of the preceding claims, characterized by , that for each combination of values ​​of at least one first parameter and at least one second parameter, values ​​of at least two quality parameters are calculated, using a weighting of the at least two quality parameters. [9] Method according to claim 8, characterized by that the weighting of at least two quality parameters is chosen based on a global quality criterion. [10] Method according to any one of the preceding claims, characterized by, that at least one of the following process variables is calculated from the results of the simulations and is used at least partially as a basis for calculating the values ​​of at least one quality parameter: process variables, in particular melt temperature, shear rate, shear stress, fill level, tool wall temperature, density, pressure, viscosity, velocity, volume shrinkage, filler distribution and orientation, mass homogeneity. [11] Method according to any one of the preceding claims, characterized by that the simulations include at least one - preferably all - of the following: - Process simulation, in particular a simulation of the plasticization, filling process, holding pressure phase, cooling phase, demolding, and / or - Material simulation, in particular a simulation of the flow behavior, the thermal behavior, the mechanical behavior, the elastic behavior, and / or - Control simulation, in particular a simulation of the component controllers, the machine sequence, and / or - Machine simulation, in particular a simulation of the injection unit, the clamping mechanism, the robotics, and / or - Temperature simulation, in particular a simulation of heat transport, heat transfer, and flow properties. [12] Method according to any one of the preceding claims, characterized by , that a mathematical analytical model and / or a numerical model of the forming machine and / or the forming process is used within the framework of the simulations. [13] Method according to any one of the preceding claims, characterized by , that a selection of values ​​for at least one first parameter and at least one second parameter is carried out using statistical experimental design. [14] Method according to any one of the preceding claims, characterized by, that results of the simulations and / or relationships derived from the results of the simulations between the at least one first parameter, the at least one second parameter and the at least one quality parameter are transferred to the forming machine and preferably stored in a central machine control. [15] Method according to claim 14, characterized by that the derived relationships are represented on the forming machine, preferably in the form of characteristic curve fields. [16] Method according to claim 15, characterized by , enabling operators to select which dependent and independent parameters from the set of at least one first parameter and at least one second parameter are displayed. [17] Method according to any one of the preceding claims, characterized by, that a value of at least one quality parameter belonging to a combination of values ​​of at least one first and at least one second parameter is displayed on the forming machine. [18] Method according to any one of the preceding claims, characterized by , that in the course of carrying out procedure step (f) at least one of the following is carried out: Measuring at least one length, performing a viscosity measurement, determining the value of at least one parameter realized on the forming machine by means of signals present on the forming machine, in particular force curves and / or pressure curves, manual inputs by an operator, ultrasonic analysis methods, mass spectrometry, X-ray spectroscopy, computed tomography, optical profilometry, use of a coordinate measuring machine, moisture measurement, performing a temperature measurement. [19] Method according to any one of the preceding claims, characterized by , that the value of the at least one first parameter determined in process step (f) is transferred to a separate computing unit and the value of the at least one second parameter determined according to (g), preferably derived on the basis of relationships modeled by means of results of the simulations, is transferred from the separate computing unit to the central machine control of the forming machine.

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