Computer-implemented method for controlling and / or monitoring at least one injection molding process

Abstract

A computer-implemented method for controlling and / or monitoring at least one injection molding process in at least one injection molding machine (110) is proposed, said injection molding process being based on a plurality of process parameters, said method comprising the following steps: a) providing a set of input parameters by at least one external processing unit (118), the set of input parameters including at least one simulation model, material-specific parameters, and injection molding machine parameters; b) the external processing unit (118) simulates an injection molding process based on the set of input parameters and determines at least one predicted process parameter of the simulated injection molding process by applying an optimization algorithm with respect to at least one optimization objective of the simulation model, and the predicted process parameter is provided to the injection molding machine via at least one interface; c) performing at least one injection molding process using the injection molding machine (110) based on the predicted process parameters to generate at least one workpiece (114), determine at least one characteristic of the generated workpiece (114), and compare the characteristic to the optimization target, wherein if the characteristic of the generated workpiece (114) deviates from the optimization target, adapting at least one process parameter of the injection molding machine (110) in response to the comparison, and repeating the injection molding process, determining the characteristic of the generated workpiece (114), and comparing the characteristic to the optimization target using the adapted process parameters until the characteristic of the generated workpiece (114) is within at least a predefined tolerance according to the optimization target; d) determining at least one actual process parameter of the injection molding process, comparing the actual process parameter with the predicted process parameter, and adapting the simulation model based on the comparison; Includes:

Description

[Technical Field]

[0001] The present invention relates to a computer-implemented method, a computer program product, a computer-readable storage medium, and an automatic control system for controlling and / or monitoring at least one injection molding process. Such methods, systems, and devices may generally be used for engineering design or configuration purposes, for example, during the development or production phase of an injection molding process. However, further applications are possible. [Background technology]

[0002] The injection molding process is a common manufacturing method in modern small- and large-scale manufacturing industries. In a typical injection molding process, a plastic material, such as a thermoplastic material, a thermosetting material, or an elastomeric material, is typically melted in a heating process and then injected into an empty die, for example, under pressure. The plastic material is then typically hardened in a cooling or curing process to maintain the shape imparted by the die and thereby become a product. This allows the product formed by the die to be reproduced in large quantities. Due to the high cost of die design and construction, dies cannot be easily modified even if any problems occur during injection molding. Therefore, to minimize production costs and waste, the filling process of the die or mold cavity is typically simulated in advance using common simulation methods.

[0003] Today, injection molding simulations from, for example, Moldflow, can be used to optimize the tool and filling process for a given part. Moldflow has two flagship products: Moldflow Adviser, which provides manufacturability guidance and directional feedback for standard part and mold designs, and Moldflow Insight, which provides definitive results for flow, cooling, and warpage, along with support for specialized molding processes (see en.wikipedia.org / wiki / Moldflow).

[0004] It is known, for example from DE 10 20 13 11 1 257 B3, DE 10 20 18 107 233 A1 or EP 3 294 519 B1, that optimization procedures can be carried out in the injection molding machine itself.

[0005] Although recent injection molding process optimization and simulation methods have advantages, several technical challenges remain. Thus, simulation and optimization of injection molding processes can still be very time-consuming and complex, and the required computing power can still be excessively high and may not be feasible within the injection molding machine itself due to the fact that the injection molding machine must produce workpieces and not simulation results. Furthermore, it would be desirable to further improve known simulation and optimization methods for injection molding with respect to the efficiency and accuracy of the simulation and optimization process.

[0006] Further optimization methods are known in other technical fields, such as chemical processes, as described in WO2019 / 138118, WO2019 / 138120, WO2019 / 138122.

[0007] US5900259A describes a molding condition optimization system for an injection molding machine, including a plastic flow condition optimization unit and an operating condition determination unit. The plastic flow condition optimization unit performs plastic flow analysis on a molded product model and determines optimal flow conditions for the filling and packing stages of the injection molding process of the injection molding machine by repeatedly performing automatic calculations using the plastic flow analysis results and the plastic flow analysis itself. The operating condition determination unit includes an injection side condition determination unit that determines optimal injection side conditions for the injection molding machine using the optimal flow conditions obtained by the plastic flow condition optimization means and a knowledge database related to injection conditions, and a mold clamping side condition determination unit that determines optimal mold clamping side conditions using molded product shape data generated by the plastic flow condition optimization means, the plastic flow analysis results, mold design data, and a knowledge database related to mold clamping conditions.

[0008] US 2018 / 181694 A1 describes a method for optimizing a process optimization system for a molding machine, including: setting configuration data for an actual molding machine by a user; obtaining a first value for at least one descriptive variable of the molding process based on the setting data and / or based on a molding process periodically performed; and obtaining a second value of the at least one descriptive variable based on data from the process optimization system. Whether the first value and the second value differ from each other is checked according to a predetermined differentiation criterion. If the check indicates that the first value and the second value differ from each other, the process optimization system is modified so that, when applied to the molding machine and / or the molding process, the first value of the descriptive variable substantially becomes the result instead of the second value of the descriptive variable.

[0009] WO 2019 / 106499 A1 describes a method for processing molding parameters of an injection molding machine obtained by CAE. A CAE simulation generates simulation results, first machine parameters are generated by electronically processing the simulation results, and second machine parameters, different from the first machine parameters, are obtained from running another molding process for the same object; and the first and second machine parameters are associated and stored in a common collection in an electronic database accessible to a user. In a further variant, the last method step is replaced by processing the first and second machine parameters with software and modifying the machine parameters calculated in the subsequent CAE simulation as a function of the processing generated by the software.

[0010] US2006 / 224540A1 describes test molding and mass production molding (using a neural network) performed by an injection molding machine including a control device. A quality prediction function determined based on the test molding is modified as necessary for mass production molding.

[0011] EP0368300A2 describes a system for setting optimal molding conditions for an injection molding machine. This system includes a molten material flow analysis means that uses a designed model mold to analyze resin flow, resin cooling, and the structure / strength of the molded product, and an analysis result evaluation means that determines initial molding conditions and their allowable ranges based on the analysis results. The initial molding conditions are set in the injection molding machine, and a test shot is performed to check for defects in the molded product. If a defect is found in the molded product, data on the defect is input to a molding defect elimination means. [Prior art documents] [Patent documents]

[0012] [Patent Document 1] DE102013111257B3 [Patent Document 2] DE102018107233A1 [Patent Document 3] EP3294519B1 [Patent Document 4] WO2019 / 138118 [Patent Document 5] WO2019 / 138120 [Patent Document 6] WO2019 / 138122 [Patent Document 7] US5900259A [Patent Document 8] US2018 / 181694A1 [Patent Document 9] WO2019 / 106499A1 [Patent Document 10] US2006 / 224540A1 [Patent Document 11] EP0368300A2 Summary of the Invention [Problem to be solved by the invention]

[0013] It is therefore desirable to provide means and methods that address the above-mentioned technical problems, in particular to propose methods, systems, programs and databases for further improving the performance of injection molding process simulation and optimization, particularly in terms of efficiency and accuracy, compared to devices, methods and systems known in the art. [Means for solving the problem]

[0014] This problem is addressed by a method, a system, a program and a database with the features of the independent claims. Advantageous embodiments, which may be realized independently or in any combination, are set out in the dependent claims.

[0015] As used below, the terms "have," "comprise," or "include," or any grammatical variations thereof, are used in a non-exclusive manner. Thus, these terms can refer both to a situation in which, besides the features introduced by these terms, no further features are present in the entity described in this context, and to a situation in which one or more further features are present. As an example, the expressions "A has B," "A comprises B," and "A includes B" can refer both to a situation in which no other elements are present in A besides B (i.e., a situation in which A solely and exclusively consists of B), and to a situation in which, in addition to B, one or more elements are present in entity A, such as element C, elements C and D, or further elements.

[0016] Furthermore, it should be noted that the terms "at least one," "one or more," or similar expressions indicating that a feature or element may be present more than one time are typically used only once when introducing each feature or element. Note that in most cases hereinafter, when referring to each feature or element, the expressions "at least one" or "one or more" will not be repeated, despite the fact that the feature or element may appear more than one time.

[0017] Furthermore, when used hereinafter, the terms "preferably," "more preferably," "particularly," "more particularly," "particularly," "more particularly," or similar terms are used in connection with any feature without limiting the possibility of substitution. Features introduced by these terms are therefore optional features and are not intended to limit the scope of the claims in any way. The present invention can be practiced using alternative features, as will be recognized by those skilled in the art. Similarly, features introduced by "in one embodiment of the present invention" or similar expressions are intended to be optional features, without any limitation regarding alternative embodiments of the invention, without any limitation regarding the scope of the invention, and without any limitation regarding the possibility of combining features introduced in this way with other optional or non-optional features of the invention.

[0018] In a first aspect of the present invention, a computer-implemented method for controlling and / or monitoring at least one injection molding process in at least one injection molding machine is disclosed.

[0019] The term "computer-implemented" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to any special or customized meaning. This term may specifically refer to processing that is fully or partially performed using a data processing means, such as, but not limited to, a data processing means including at least one processor. Thus, the term "computer" may generally refer to a device or a combination or network of devices having at least one data processing means, such as, but not limited to, at least one processor. A computer may further include one or more additional components, such as, for example, at least one data storage device, electronic interface, or human-machine interface. The term "processor" or "processing unit" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to any special or customized meaning. This term may specifically refer, but not limited to, any logic circuitry configured to perform the basic operations of a computer or system, and / or generally to a device configured to perform calculations or logical operations. In particular, a processor may be configured to process the basic instructions that drive a computer or system. As an example, a processor may include at least one arithmetic logic unit (ALU), at least one floating-point unit (FPU), such as a math coprocessor or numeric coprocessor, a plurality of registers, particularly registers configured to provide operands to the ALU and store operation results, and memory, such as L1 and L2 cache memories. In particular, the processor may be a multi-core processor. In particular, the processor may be or include a central processing unit (CPU). Additionally or alternatively, the processor may be or comprise a microprocessor, and thus in particular, elements of the processor may be included on an integrated circuit (IC) chip.Additionally or alternatively, the processor may be or include one or more application specific integrated circuits (ASICs) and / or one or more field programmable gate arrays (FPGAs), or the like.

[0020] The term "molding process" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. This term may specifically, without limitation, refer to a process or procedure for molding at least one material into any form or shape. The term "injection molding process" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. This term may specifically, without limitation, refer to a type of molding process performed by injecting molten material into a mold.

[0021] The term "mold" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or customized meaning. This term may specifically refer, without limitation, to a die or form, such as a form that provides a matrix or a frame. In particular, as used herein, a mold may refer to any die and / or form having at least one cavity, such as at least one form that provides a structure and / or cutout. A mold may be used, in particular, in an injection molding process, in which at least one molten mass of material can be injected into at least one cavity of the mold. For simplicity, the terms "mold" and "mold cavity" may be used interchangeably herein. As an example, a mold having at least one cavity may be used in a molding process to form a material. In particular, the molten mass of material injected into the mold cavity may be given the negative shape and / or geometry of the cavity. Specifically, the mold may be used to manufacture at least one workpiece, also referred to as a component, and the manufactured workpiece may have a negative form and / or shape of the mold cavity.

[0022] The molding process may be configured to produce at least one workpiece. As used herein, the term "workpiece" is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. The term may specifically refer to any part or element without limitation. In particular, a workpiece may be or comprise a component of any machine or device. A workpiece may, for example, at least partially have the negative shape of a mold or mold cavity used in the molding process to produce the part. Thus, an "injection molding process" may be or refer to a shaping procedure to create a workpiece.

[0023] The term "injection molding machine" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. The term may specifically refer, without limitation, to any device or machine configured to perform an injection molding process. An injection molding machine may include at least one injection unit and at least one clamping unit.

[0024] The injection molding process is based on multiple process parameters. As used herein, the term "process parameter" is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. This term may specifically refer, without limitation, to at least one settable, selectable, adjustable, and / or configurable parameter that affects the injection molding process. The process parameter may relate to the operating conditions of the injection molding machine. In particular, the process parameter may be an injection molding machine parameter. For example, the process parameter may include at least one cooling or hardening parameter, such as polymer melt temperature, barrel temperature, injection unit temperature, screw speed, injection speed, hold pressure, hold time, cooling or hardening time, cooling or hardening medium throughput, or one or more cooling or hardening medium temperatures. The injection molding machine parameters may further include machine dimensions, such as clamping force, tie bar gap, injection unit dimensions, machine equipment, such as cylinder diameter or maximum cylinder temperature, etc.

[0025] The term "control" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. This term may specifically, without limitation, refer to determining and / or adjusting at least one process parameter. The term "monitoring" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. This term may specifically, without limitation, refer to quantitatively and / or qualitatively determining at least one process parameter.

[0026] The computer-implemented method includes the following steps, which may be performed in a given order, although different orders are possible. Furthermore, one, more than one, or all of the method steps may be performed once or repeatedly. Furthermore, the method steps may be performed overlapping in time or in parallel. The method may further include additional method steps not listed.

[0027] The method comprises the following steps: a) providing a set of input parameters by at least one external processing unit, the set of input parameters including at least one simulation model, material-specific parameters, and injection molding machine parameters; b) the external processing unit simulates an injection molding process based on the set of input parameters and determines at least one predicted process parameter of the simulated injection molding process by applying an optimization algorithm with respect to at least one optimization target of the simulation model, and the predicted process parameter is provided to the injection molding machine via at least one interface; c) performing at least one injection molding process using the injection molding machine based on the predicted process parameters to generate at least one workpiece, determine at least one characteristic of the generated workpiece, and compare the characteristic with the optimization target, wherein if the characteristic of the generated workpiece deviates from the optimization target, at least one process parameter of the injection molding machine is adapted in response to the comparison, and the injection molding process, determination of the characteristic of the generated workpiece, and comparison of the characteristic with the optimization target are repeated using the adapted process parameters until the characteristic of the generated workpiece is within at least a predefined tolerance according to the optimization target; d) determining at least one actual process parameter of the injection molding process, comparing the actual process parameter with the predicted process parameters, and adapting the simulation model based on the comparison; Includes:

[0028] The term "external processing unit" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. This term may specifically refer, without limitation, to at least one processing unit designed separately from the injection molding machine. The injection molding machine may include an internal processing unit configured, among other things, to control and monitor machine parameters. The external processing unit may be configured to transfer and / or receive data from the internal processing unit via at least one communication interface. The internal processing unit may be configured to transfer and / or receive data from the external processing unit via at least one communication interface. The external processing unit may include multiple processors. The external processing unit may be and / or comprise a cloud computing system.

[0029] The external processing unit may include at least one database. The term "database" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. The term may specifically refer to any collection of information, without limitation. The database may be stored in at least one data storage device. In particular, the database may include any collection of information. The data storage device may be or may comprise at least one element selected from the group consisting of at least one server, at least one server system including multiple servers, at least one cloud server, or a cloud computing infrastructure.

[0030] The term "communications interface" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. The term may specifically refer, without limitation, to an item or element forming a boundary configured to transfer information. In particular, a communications interface may be configured to transfer information, such as to send or output information from a computing device, e.g., a computer, to another device. Additionally or alternatively, a communications interface may be configured to transfer information to a computing device, e.g., a computer, to receive information. A communications interface may, among other things, provide a means for transferring or exchanging information. In particular, a communications interface may provide a data transfer connection, e.g., Bluetooth, NFC, inductive coupling, etc. By way of example, a communications interface may be or include at least one port, including one or more of a network or internet port, a USB port, and a disk drive. A communications interface may be at least one web interface.

[0031] The term "providing" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. This term may specifically refer, without limitation, to searching and / or selecting a set of input parameters. The term "searching" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. This term may specifically refer, without limitation, to a system, specifically a computer system process, that generates and / or obtains data from any data source, for example, from data storage, from a network, or from an additional computer or computer system. Searching may specifically be performed through at least one computer interface, such as through a port, such as a serial port or a parallel port. Searching may include several substeps, such as utilizing primary information to obtain one or more items of primary information, for example, by applying one or more algorithms to the primary information using a processor, and generating secondary information.

[0032] The term "set of input parameters" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. Specifically, the term may refer, without limitation, to information regarding a simulation model, material-specific parameters, and injection molding machine parameters.

[0033] The term "injection molding machine parameters" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. This term may specifically refer to, but is not limited to, parameters that affect the operating conditions of an injection molding machine. The injection molding machine parameters may include settings of mechanical components of the injection molding machine. The injection molding machine parameters may include specific values ​​and / or parameter profiles. The injection molding machine parameters may include at least one parameter selected from the group consisting of polymer melt temperature, barrel temperature, injection unit temperature, screw speed, injection speed, hold pressure, hold time, cooling or hardening time, cooling or hardening medium throughput, and cooling or hardening medium temperature, among other cooling or hardening parameters.

[0034] The term "material-specific parameters" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. This term may specifically refer, without limitation, to information about the material used in the injection molding process. Material-specific parameters may be provided by material suppliers and / or downloaded from websites or other databases. Material suppliers may have a wealth of product-specific data, such as rheological data, viscosity, and lot-specific data for all materials produced. Material-specific parameters may include at least one parameter selected from the group consisting of compressibility, flow characteristics, and temperature characteristics.

[0035] Materials, particularly materials used in molding processes, e.g., in the manufacture of workpieces, may be or include, for example, plastic materials. The term "plastic material" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. The term may specifically refer to, without limitation, any thermoplastic, thermosetting, or elastomeric material. In particular, a plastic material may be a mixture of substances including monomers and / or polymers. In particular, a plastic material may be or include a thermoplastic material. Additionally or alternatively, a plastic material may be or include a thermosetting material. Additionally or alternatively, a plastic material may include an elastomeric material. The material may specifically be in a molten state during the manufacture of the workpiece.

[0036] The terms "simulation" or "simulating" as used herein are broad terms and should be given their ordinary and customary meaning to those skilled in the art, and should not be limited to any special or customized meaning. The terms may specifically refer, without limitation, to a process for roughly mimicking an actual injection molding process. The terms "simulation model" as used herein are broad terms and should be given their ordinary and customary meaning to those skilled in the art, and should not be limited to any special or customized meaning. The terms may specifically refer, without limitation, to at least one model on which a simulation is performed. The simulation model may be generated by software on an external processing unit, or the simulation model may be a data set within the software.

[0037] The simulation model may include at least one trained and trainable model. As used herein, the term "trained model" is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or customized meaning. This term may specifically, without limitation, refer to a mathematical model trained with at least one training data set. As used herein, the term "trainable model" is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or customized meaning. This term may specifically, without limitation, refer to a simulation model that can be further trained and / or updated based on additional training data. Specifically, the simulation model is trained with a training data set. The simulation model may be trained using machine learning. The simulation model may be at least partially data-driven by being trained with data from past production runs. As used herein, the term "data-driven" is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or customized meaning. The term may specifically, but is not limited to, refer to the model being an empirical predictive model. Specifically, a data-driven model is derived from an analysis of experimental data of past injection molding processes. The term "past production runs" refers to injection molding processes at past or earlier points in time. Specifically, for further training of the simulation model, a training data set can be generated from comparison data of the actual and predicted process parameters determined in step d). As used herein, the term "at least partially data-driven model" is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or customized meaning.The term may specifically, but not be limited to, refer to the fact that a trained model includes a data-driven model portion, where the model may include additional or other model portions. The term "machine learning," as used herein, is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. The term may specifically, but not be limited to, refer to a method of using artificial intelligence (AI) for automatic model building of machine learning models, particularly predictive models. The external processing unit may be configured to execute and / or implement at least one machine learning algorithm. The simulation model may be based on the results of at least one machine learning algorithm. The machine learning algorithm may include a decision tree, a naive Bayes classifier, a nearest neighbor, a neural network, a convolutional neural network, a backpropagation generative network, a support vector machine, a linear regression, a logistic regression, a random forest, and / or a gradient boosting algorithm. Preferably, the machine learning algorithm is configured to process a high-dimensional input into a much lower-dimensional output. Such machine learning algorithms are called "intelligent" because they are capable of "learning." The algorithm may be trained using records of training data. A training data record may include training input data and corresponding training output data. The training output data of a training data record may be the result predicted to be produced by a machine learning algorithm when given the training input data of the same training data record as input. The deviation between this predicted result and the actual result produced by the algorithm may be observed and evaluated by a "loss function." This loss function may be used as feedback to adjust parameters of the machine learning algorithm's internal processing chain. For example, the parameters may be adjusted with an optimization goal of minimizing the value of the loss function that occurs when all training input data is given to the machine learning algorithm and the results are compared with the corresponding training output data.The result of this training can be given a relatively small number of training data records as "ground truth," allowing the machine learning algorithm to perform its job well with an order of magnitude higher number of input data records. Thus, the simulation model can include at least one algorithm and model parameters. The parameters of the simulation model may be generated by using at least one artificial neural network. The simulation model, and in particular the model parameters, can be trained and therefore further adapted in step d).

[0038] The simulation model can include a digital twin of an injection molding process. The simulation model is configured to simulate an injection molding process. The simulation model can include a filling simulation. Specifically, the simulation model can be configured to simulate filling of a mold cavity with a molten mass of at least one material. The simulation model can be configured to simulate manufacturing of a workpiece. The simulation model can be configured to simulate the geometry and / or shape of the workpiece. The simulation model can include strength analysis.

[0039] The simulation model may use geometric data of the workpiece to be manufactured. As used herein, the term "geometrical data" is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. The term may specifically, but without limitation, refer to information about the three-dimensional form or shape of any object or element. Specifically, geometrical data, such as information about three-dimensional shape, may exist in a computer-readable form, such as a computer-compatible data set, specifically a digital data set. As an example, the geometrical data may be or include computer-aided design data (CAD data). Specifically, the three-dimensional geometrical data may be or include CAD data describing the form or shape of an object or element.

[0040] The simulation model can be configured to take into account material-specific properties. The simulation model can include a digital twin of the material. The simulation model can be configured to take into account batch properties of the raw material batch, such as the viscosity of the material batch. The simulation process is not performed on the injection molding machine itself, but rather by an external processing unit, such as at least one cloud computing system. This allows additional parameters affecting the injection molding process to be taken into account in addition to the machine parameters and / or sensor parameters provided by the injection molding machine and / or its at least one sensor and / or available at the injection molding machine. These additional parameters may relate to external knowledge, such as material supplier knowledge, product-specific data such as rheological data, viscosity, and / or algorithms, and / or specific data of the produced material.

[0041] Cloud-based use of simulation data, process data, and product-related data can enable optimization of the injection molding process. As outlined above, material suppliers may have a lot of product-specific data, such as rheological data, viscosity, and lot-specific data for all materials produced. The present invention proposes a closed loop between simulation and the injection molding process, so that parameters from the simulation can be used directly in the injection molding process. Furthermore, conversely, process data can be used to optimize the modeling process using machine learning models. Material lot-specific information can be further linked to the simulation of the manufacturing process by using a cloud-based digital twin of materials and the injection molding process, further improving the efficiency of the injection molding process.

[0042] As used herein, the term "predicted process parameters of a simulated injection molding process" is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. This term may specifically refer, without limitation, to expected values ​​of process parameters, particularly to reach optimal manufacturing results and / or optimal use of resources. Predicted process parameters may be parameters that affect the injection molding process. Predicted process parameters may be determined to optimize the injection molding process. In known systems and apparatus, such as those described in U.S. Pat. No. 5,900,259 A, optimization is performed in terms of workpiece optimization. In contrast, the present invention refers to process optimization. Process optimization can consider optimal use of resources in addition to optimal manufacturing results.

[0043] Step b) may include at least one optimization step. The term "optimization" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to any special or customized meaning. This term may specifically refer, without limitation, to a process of selecting an optimal parameter set with respect to an optimization goal from a parameter space of possible parameters. The term "optimization goal" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to any special or customized meaning. This term may specifically refer, without limitation, to at least one criterion under which the optimization is performed. The optimization goal may include at least one optimization objective and precision and / or accuracy. The optimization goal may be at least one characteristic of the workpiece. The workpiece characteristic may be at least one element selected from the group consisting of workpiece mass, workpiece dimensions, and warpage. The optimization goal may be pre-specified, for example, by at least one customer and / or at least one user of the injection molding machine. The optimization goal may be at least one user specification. The user can select the optimization goal and the desired accuracy and / or precision. The predicted process parameters are provided to the injection molding machine via at least one interface, in particular a communication interface. In known systems and devices, such as those described in US Pat. No. 5,900,259 A, parameters defining the injection molding process are stored in the injection molding machine. Therefore, the parameters are typically static. In contrast, the present invention proposes a self-learning method, and in particular a continuous improvement of the performance of the injection molding process, by adapting a simulation model in step d) taking into account the newly determined predicted process parameters in step c) and running at least one injection molding process in step c) using the improved simulation model to predict the improved process parameters. Thus, a cycle or loop is proposed by performing steps a) to d).

[0044] The method includes performing at least one injection molding process using an injection molding machine based on predicted process parameters to produce at least one workpiece. Using the predicted process parameters to perform the injection molding process may refer to not only relying on machine parameters and / or sensor parameters provided by the injection molding machine and / or at least one sensor available in the injection molding machine, but also considering external knowledge, such as material supplier knowledge, product-specific data such as rheological data and viscosity, and / or algorithms, and / or specific data of the produced material. Using the predicted process parameters allows for continuous improvement of the injection molding process. The manufactured workpiece may be measured, for example, by using optical or tactile measurement techniques, such as scanning. The term "scanning" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or customized meaning. The term may specifically refer, without limitation, to any process or procedure for inspecting any object or data. Scanning may include determining the shape and dimensions of the workpiece. Scanning may specifically be performed automatically. The scanning may be performed autonomously by a computer or computer network.

[0045] The determined characteristics of the workpiece can be compared to optimization targets. The comparison may include determining deviations from a target shape and / or target dimensions (also referred to as target size). If the difference between the determined characteristics and the optimization targets exceeds a tolerance limit, the produced workpiece is deemed to deviate from the target shape and / or target dimensions. The tolerance limit may depend on the accuracy of the characteristics and / or the determination, such as by customer requirements.

[0046] If the properties of the produced workpiece deviate from the optimization target, at least one process parameter of the injection molding machine is adapted in response to the comparison.

[0047] For example, a comparison of the determined workpiece characteristics with the optimization targets may reveal that the workpiece deviates from the desired shape, particularly with twists, warps, wavy surfaces, and angular deviations. This may be due to different shrinkage tendencies (shrinkage potential) in different areas of the workpiece. The difference in shrinkage may be caused by different degrees of filling in different areas of the workpiece and by differences in the orientation of the fibers and polymer chains. Further possible causes include an unfavorable selected mold temperature, different wall thicknesses of the molded workpiece, a pressure gradient along the flow path in the workpiece that is too high, a cooling time that is too short, resulting in the workpiece being removed from the mold at too high a temperature and deformation of the workpiece after removal from the mold, the use of unfavorable materials, or glass fibers in glass-fiber-reinforced thermoplastics that are primarily oriented in the flow direction. In the latter case, deviations may occur if the orientation of the glass fibers varies from place to place. These causes may be, for example, flow deflections, orientation effects at the ends of the flow paths, weld lines, and gates. At least one of the following process parameters of the injection molding machine can be adapted in response to the comparison: changing the temperature of the mold halves and slide core, increasing the cooling time, adapting the process so that the molded part is not stuck or held in a negative draft, changing the holding pressure, and changing the holding time. Furthermore, the material used can be changed in terms of the comparison. Specifically, a material with less warpage, such as a blend with an amorphous phase, can be used. The workpiece design can also be changed. The process parameters of the injection molding machine can be adapted according to a predetermined hierarchy. For example, the mold temperature can be adapted first, followed by the cooling time. Further process parameters can then be adapted.

[0048] For example, a comparison of the determined characteristics of the workpiece with the optimization goal may reveal that the workpiece contains at least one sink mark. The term "sink mark" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. The term may specifically refer, without limitation, to an indentation in the surface of a molded workpiece. Sink marks may occur primarily in locations where there is an increase in wall thickness. This causes a local increase in volumetric shrinkage, which may pull the surface layers inward. Sink marks may only occur after removal from the mold when the center of the polymer heats the already-cooled edge layers, causing them to yield. Sink marks may also be recognized only by a difference in gloss compared to the periphery. Sink marks can occur for several reasons, such as when volumetric shrinkage is not sufficiently compensated for by the holding pressure during the cooling phase, when the workpiece design is not suitable for processing this plastic (e.g., material sections with increased wall thickness, abrupt changes in wall thickness along the flow path), when there is no melt cushion, when large pressure losses occur in the machine nozzle and / or gating system, or when the workpiece is gated with thin walls. At least one of the following process parameters of the injection molding machine can be adapted depending on the comparison: increasing the holding pressure, increasing the holding time, decreasing the melt temperature, decreasing the mold temperature, changing the pressure transmission along each flow path by changing the wall thickness of the molded workpiece, lengthening the metering stroke and adjusting the switching point if necessary, adapting the sealing function of the check valve, adapting the barrel wear to increase the flow cross-section of the runner and gating system, adapting the workpiece position in the area with the largest wall, etc. Furthermore, the design of the workpiece can be changed. The process parameters of the injection molding machine can be adapted according to a predetermined hierarchy. For example, first the hold pressure may be adapted, then the hold time, then the melt temperature, and then further process parameters may be adapted.

[0049] The injection molding process, determining the properties of the produced workpiece, and comparing the properties with the optimization goals are repeated with the adapted process parameters until the properties of the produced workpiece are within at least a predefined tolerance according to the optimization goals.

[0050] Step d) includes determining at least one actual process parameter of the injection molding process. The injection molding machine may be configured to measure and / or monitor at least one process parameter of the process during the injection molding process. The at least one actual process parameter may be at least one process parameter measurable and / or monitorable during the injection molding process, for example, by using at least one sensor. The term "during the injection molding process" may refer to a time interval between the start and end of the injection molding process and / or a time interval during which process conditions are expected to be essentially equivalent to those during the injection molding process. The injection molding machine may be configured to measure the process parameter in real time and adapt the process parameter at run time. The injection molding machine may be configured to measure at least one actual process parameter in real time. The injection molding machine may be configured to adapt the at least one actual process parameter at run time. When step c) includes determining a plurality of predicted process parameters, step d) may include determining a plurality of process parameters, such as a set of process parameters defining the injection molding process. The injection molding machine may include at least one sensor. The measured parameters of the injection molding machine can be registered and transferred to an external processing unit. The injection molding machine can include at least one element selected from the group consisting of a temperature sensor, a pressure sensor, and a clock. For example, the at least one actual process parameter can be at least one parameter selected from the group consisting of a polymer melt temperature, a barrel temperature, an injection unit temperature, a screw speed, an injection speed, a holding pressure, a holding time, a cooling or hardening time, a cooling or hardening medium throughput, or at least one cooling or hardening parameter such as a cooling or hardening medium temperature. Step d) can include determining a set of actual process parameters to be optimized, in particular actual process parameters corresponding to the process parameters predicted in step c).Thus, rather than just a single process parameter being used during an optimization cycle, multiple process parameters may be used, particularly a set of process parameters that define an injection molding process.

[0051] Step d) may further include comparing the actual process parameters with the predicted process parameters and adapting the simulation model based on the comparison. If multiple predicted process parameters are determined in step c), step d) may further include comparing each actual process parameter with each predicted process parameter and adapting the simulation model based on the comparison. The comparison may include determining a deviation of the predicted process parameter from the actual process parameter, or vice versa. If the difference exceeds a tolerance limit, the actual process parameter is considered to deviate from the predicted process parameter. The tolerance limit may depend on the measurement accuracy. The comparison may be performed by an internal processing unit of the injection molding machine. Information about the deviation and / or the actual process parameter may be transferred to an external processing unit. The external processing unit may be configured to adapt the simulation model, in particular the model parameters, based on the information about the deviation and / or the actual process parameter.

[0052] The method may further include outputting the predicted process parameters and / or the results of the comparison of the actual process parameters to the predicted process parameters via at least one output interface or port. The output may include a set of predicted process parameters and / or the results of the comparison of the actual process parameters to the predicted process parameters. As used herein, the term "output" is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or customized meaning. The term may specifically refer, without limitation, to a process that makes information available to another system, data storage, or natural or legal person. By way of example, the output may be provided via one or more interfaces, such as a computer interface or a human-machine interface. By way of example, the output may be provided in one or more of a computer-readable format, a visual format, or an audible format. For example, the output may be provided via at least one display, at least one microphone, etc.

[0053] Method steps a) to d) can be repeated and the adapted simulation model can be used in step a).

[0054] In a further aspect of the invention, a computer program comprises instructions that, when the program is executed by a computer or a computer system, cause the computer or the computer system to carry out the method according to the invention, in particular steps a) to d).For possible definitions of most of the terms used herein, reference can be made to the description of the computer-implemented method set out above or in more detail below.

[0055] In particular, the computer program may be stored on a computer-readable data carrier and / or a computer-readable storage medium. As used herein, the terms "computer-readable data carrier" and "computer-readable storage medium" may in particular refer to non-transitory data storage means such as a hardware storage medium having computer-executable instructions stored thereon. A computer-readable data carrier or storage medium may in particular be or include a storage medium such as a random access memory (RAM) and / or a read-only memory (ROM).

[0056] Further disclosed and proposed herein is a computer program product comprising instructions which, when the program is executed by a computer or computer system, cause the computer or computer system to perform a computer-implemented method, as described above or in more detail below. Therefore, for most possible definitions of the terms used herein, reference may also be made to the description of the method disclosed in the first aspect of the invention.

[0057] In particular, a computer program product may include program code means stored on a computer-readable data carrier for performing the methods according to one or more embodiments disclosed herein when the program is run on a computer or a computer network. As used herein, a computer program product refers to a program as a tradeable product. The product may generally exist in any form, such as a paper medium or a computer-readable data carrier. In particular, a computer program product may be distributed over a data network.

[0058] Also disclosed and suggested herein is a computer-readable storage medium containing instructions which, when executed by a computer or computer system, cause the computer or computer system to perform a computer-implemented method, as described above or in more detail below. Accordingly, for most possible definitions of the terms used herein, reference may also be made to the description of the method disclosed in the first aspect of the present invention.

[0059] In a further aspect, a system for automatically controlling an injection molding process of at least one injection molding machine is disclosed, wherein the injection molding process is based on a plurality of process parameters.

[0060] The control system comprises at least one external processing unit configured to simulate an injection molding process based on an input parameter set including at least one simulation model, material-specific parameters, and injection molding machine parameters by applying an optimization algorithm with respect to at least one optimization objective of the simulation model.

[0061] The control system includes at least one interface configured to provide the predicted process parameters to the injection molding machine. The control system is configured to perform at least one injection molding process using the injection molding machine based on the predicted process parameters to produce at least one workpiece. The control system is configured to determine at least one characteristic of the produced workpiece, compare the characteristic to an optimization target, and adapt at least one process parameter of the injection molding machine in response to the comparison. The control system is configured to repeat the injection molding process, determining the characteristic, comparing the characteristic to the optimization target, and adapting the process parameter until the characteristic of the produced workpiece is within at least a predefined tolerance according to the optimization target.

[0062] The control system is configured to determine at least one actual process parameter of the injection molding process, compare the actual process parameter to the predicted process parameter, and adapt the simulation model based on the comparison.

[0063] An automatic control system may be configured to carry out the method according to the invention. Therefore, for most possible definitions of the terms used herein, reference may also be made to the description of the method disclosed in the first aspect of the invention.

[0064] The methods, systems, and programs of the present invention have numerous advantages over methods, systems, and programs known in the art. In particular, the methods, systems, and programs disclosed herein can improve the performance of injection molding processes compared to devices, methods, and systems known in the art. Simulations can be performed on cloud solutions. The present invention proposes that a simulation model be run in the cloud to identify optimal parameters (to be process), and that this information can be linked to the actual parameters of the process so that a fast and efficient estimation loop can be performed. Digital matching allows the simulation model to take into account material-specific properties, further improving the simulation.

[0065] In summary, and without excluding further possible embodiments, the following embodiments can be envisaged: Embodiment 1: A computer-implemented method for controlling and / or monitoring at least one injection molding process in at least one injection molding machine, said injection molding process being based on a plurality of process parameters, said method comprising the steps of: a) providing a set of input parameters by at least one external processing unit, the set of input parameters including at least one simulation model, material-specific parameters, and injection molding machine parameters; b) the external processing unit simulates an injection molding process based on the set of input parameters and determines at least one predicted process parameter of the simulated injection molding process by applying an optimization algorithm with respect to at least one optimization objective of the simulation model, and the predicted process parameter is provided to the injection molding machine via at least one interface; c) performing at least one injection molding process using the injection molding machine based on the predicted process parameters to generate at least one workpiece, determine at least one characteristic of the generated workpiece, and compare the characteristic with the optimization target, wherein if the characteristic of the generated workpiece deviates from the optimization target, adapting at least one process parameter of the injection molding machine in response to the comparison, and repeating the injection molding process, determining the characteristic of the generated workpiece, and comparing the characteristic with the optimization target using the adapted process parameters until the characteristic of the generated workpiece is within at least a predefined tolerance according to the optimization target; d) determining at least one actual process parameter of the injection molding process, comparing the actual process parameter with the predicted process parameters, and adapting the simulation model based on the comparison; A method comprising:

[0066] Embodiment 2: A method according to the preceding embodiment, wherein method steps a) to d) are repeated and the adapted simulation model is used in step a).

[0067] Embodiment 3: The method according to any one of the preceding embodiments, wherein the injection molding machine parameters include at least one parameter selected from the group consisting of polymer melt temperature, barrel temperature, injection unit temperature, screw speed, injection speed, hold pressure, hold time, cooling or curing time, and cooling or curing parameters.

[0068] Embodiment 4: A method according to any one of the preceding embodiments, wherein the measured parameters of the injection molding machine are registered and transferred to an external processing unit, and the injection molding machine includes at least one element selected from the group consisting of a temperature sensor; a pressure sensor; and a clock.

[0069] Embodiment 5: The method according to any one of the preceding embodiments, wherein the simulation model includes a filling simulation.

[0070] Embodiment 6: A method according to any one of the preceding embodiments, wherein the simulation model is configured to simulate filling of a mold cavity with a molten mass of at least one material.

[0071] Embodiment 7: The method according to any one of the preceding embodiments, wherein the simulation model is configured to simulate the geometry and / or shape of the workpiece.

[0072] Embodiment 8: The method according to any one of the preceding embodiments, wherein the simulation model includes a strength analysis.

[0073] Embodiment 9: The method according to any one of the preceding embodiments, wherein the material-specific parameters include at least one parameter selected from the group consisting of compressibility, flow properties, and temperature properties.

[0074] Embodiment 10: A method according to any one of the preceding embodiments, wherein the simulation model is configured to take into account material-specific properties.

[0075] Embodiment 11: The method according to the preceding embodiment, wherein the simulation model is configured to take into account batch characteristics of raw material batches.

[0076] Embodiment 12: The method according to any one of the preceding embodiments, wherein the property of the workpiece is at least one element selected from the group consisting of: mass of the workpiece, dimensions of the workpiece, and warpage.

[0077] Embodiment 13: The method according to any one of the preceding embodiments, wherein the optimization goal is at least one characteristic of the workpiece.

[0078] Embodiment 14: The method according to any one of the preceding embodiments, wherein the method further comprises outputting the predicted process parameters and / or results of the comparison of the actual process parameters with the predicted process parameters via at least one output interface or port.

[0079] Embodiment 15: The method according to any one of the preceding embodiments, wherein the parameters of the simulation model are generated by using at least one artificial neural network.

[0080] Embodiment 16: The method according to any one of the preceding embodiments, wherein the external processing unit is and / or includes a cloud computing system.

[0081] Embodiment 17: A computer program comprising instructions that, when the program is executed by a computer or computer system, cause said computer or computer system to carry out a method according to any one of the preceding embodiments.

[0082] Embodiment 18: A computer-readable storage medium comprising instructions that, when executed by a computer or computer system, cause the method according to any one of the preceding embodiments referring to a method to be performed.

[0083] Embodiment 19: An automated control system for an injection molding process in at least one injection molding machine, the injection molding process being based on a plurality of process parameters, the control system comprising at least one external processing unit configured to simulate the injection molding process based on an input parameter set comprising at least one simulation model, material-specific parameters, and injection molding machine parameters by applying an optimization algorithm with respect to at least one optimization objective of the simulation model, the control system comprising at least one interface configured to provide predicted process parameters to the injection molding machine, the control system performing at least one injection molding process using the injection molding machine based on the predicted process parameters to produce at least one workpiece. an injection molding process, the control system configured to: determine at least one characteristic of the generated workpiece, compare the characteristic to the optimization goal, and adapt at least one process parameter of the injection molding machine in response to the comparison; the control system configured to repeat the injection molding process, determine the characteristic, compare the characteristic to the optimization goal, and adapt the process parameter until the characteristic of the generated workpiece is within at least a predefined tolerance in accordance with the optimization goal; the control system configured to determine at least one actual process parameter of the injection molding process; and the control system configured to compare the actual process parameter with the predicted process parameter and adapt the simulation model based on the comparison.

[0084] Embodiment 20: An automatic control system according to the preceding embodiment, wherein the automatic control system is configured to perform a method according to any one of the preceding embodiments referring to a method. [Brief explanation of the drawings]

[0085] Further optional features and embodiments are disclosed in more detail in the description of the following embodiments, preferably in connection with the dependent claims, where each optional feature may be realized separately and in any possible combination, as understood by a person skilled in the art. The scope of the present invention is not limited by the preferred embodiments. The embodiments are illustrated schematically in the figures, in which identical reference numbers in these figures indicate identical or functionally interchangeable elements. [Figure 1] FIG. 1 illustrates an exemplary embodiment of a computer-implemented method and automatic control system for controlling and / or monitoring at least one injection molding process in at least one injection molding machine. [Figure 2] 2A to 2D are diagrams showing the experimental results. DETAILED DESCRIPTION OF THE INVENTION

[0086] Detailed Description of the Embodiments FIG. 1 illustrates an exemplary embodiment of a computer-implemented method and automatic control system 112 for controlling and / or monitoring at least one injection molding process in at least one injection molding machine 110 .

[0087] The injection molding machine 110 is configured to perform at least one injection molding process. An injection molding process may include at least one process or procedure for molding at least one material into any form or shape. An injection molding process may be a molding process performed by injecting molten material into a mold. A mold may be a die or a form, such as a form that provides a matrix or a frame. In particular, as used herein, a mold may refer to any die and / or form having at least one cavity, such as at least one form that provides a structure and / or cutout. A mold may be used in particular in an injection molding process, in which at least one molten mass of material can be injected into at least one cavity of the mold. As an example, a mold having at least one cavity can be used in a molding process to form a material. In particular, the molten mass of material injected into the mold cavity may be given the negative shape and / or geometry of the cavity. In particular, the mold may be used to manufacture at least one workpiece 114, and the manufactured workpiece may have the negative shape and / or dimensions of the mold cavity.

[0088] The molding process may be configured to produce at least one workpiece 114. The workpiece 114 may be any part or element. In particular, the workpiece 114 may be or comprise a component of any machine or device. The workpiece 114 may, for example, at least partially have the negative shape of a mold or mold cavity used in the molding process to produce the part. Thus, the injection molding process may be or may refer to a shaping procedure for creating the workpiece 114.

[0089] The injection molding process is based on a plurality of process parameters. The process parameters may be settable and / or selectable and / or adjustable and / or configurable parameters that affect the injection molding process. The process parameters may relate to the operating conditions of the injection molding machine 110. In particular, the process parameters may be injection molding machine parameters. For example, the process parameters may include one or more of the polymer melt temperature, barrel temperature, injection unit temperature, screw speed, injection rate, hold pressure, hold time, cooling or hardening time, cooling or hardening medium throughput, or at least one cooling or hardening parameter such as cooling or hardening medium temperature.

[0090] The method comprises the following steps: a) providing a set of input parameters by at least one external processing unit 118 (denoted by reference numeral 116), the set of input parameters including at least one simulation model, material-specific parameters, and injection molding machine parameters; b) the external processing unit 118 (denoted by reference numeral 120) simulates an injection molding process based on the set of input parameters and determines at least one predicted process parameter of the simulated injection molding process 122 by applying an optimization algorithm with respect to at least one optimization objective of the simulation model, and the predicted process parameter is provided to the injection molding machine 110 via at least one interface 126 (denoted by reference numeral 124); c) performing at least one injection molding process (denoted by reference numeral 130) using the injection molding machine 110 based on the predicted process parameters to generate at least one workpiece 114, determine at least one characteristic of the generated workpiece 114, and compare the characteristic with the optimization target (denoted by reference numeral 132); if the characteristic of the generated workpiece 114 deviates from the optimization target, adapting at least one process parameter of the injection molding machine 110 in response to the comparison, and repeating the injection molding process, the determination of the characteristic of the generated workpiece 114, and the comparison of the characteristic with the optimization target using the adapted process parameters until the characteristic of the generated workpiece 114 is within at least a predefined tolerance according to the optimization target; d) determining at least one actual process parameter of the injection molding process (indicated by reference numeral 134), comparing the actual process parameter with the predicted process parameters, and adapting the simulation model based on the comparison (indicated by reference numeral 136); Includes:

[0091] The external processing unit 118 may be at least one processing unit designed separately from the injection molding machine 110. The injection molding machine 110 may include an internal processing unit (not shown here) configured, among other things, to control and monitor machine parameters. The external processing unit 118 may be configured to transfer and / or receive data to the internal processing unit via at least one communication interface. The internal processing unit may be configured to transfer and / or receive data to the external processing unit via at least one communication interface. The external processing unit 118 may include multiple processors. The external processing unit 118 may be and / or comprise a cloud computing system.

[0092] The external processing unit 118 may include at least one database. A database may be any collection of information. The database may be stored in at least one data storage device. The external processing unit 118 may comprise at least one data storage device having information stored therein. In particular, the database may include any collection of information. The data storage device may be or may comprise at least one element selected from the group consisting of at least one server, at least one server system including multiple servers, at least one cloud server, or a cloud computing infrastructure.

[0093] Providing 116 the set of input parameters can include searching and / or selecting the set of input parameters. Searching can refer to a system, particularly a computer system, process that generates and / or obtains data from any data source, e.g., from data storage, from a network, or from an additional computer or computer system. Searching can particularly occur through at least one computer interface, e.g., through a port, e.g., a serial port or a parallel port. Searching can include several substeps, such as utilizing primary information to obtain one or more items of primary information, e.g., by applying one or more algorithms to the primary information using a processor, and generating secondary information.

[0094] The set of input parameters may include information about a simulation model, material-specific parameters, and injection molding machine parameters. The injection molding machine parameters may be parameters that affect the operating conditions of the injection molding machine. The injection molding machine parameters may include settings of machine components of the injection molding machine 110. The injection molding machine parameters may include specific values ​​and / or parameter profiles. The injection molding machine parameters may include at least one parameter selected from the group consisting of cooling or hardening parameters such as polymer melt temperature, barrel temperature, injection unit temperature, screw speed, injection speed, hold pressure, hold time, cooling or hardening time, cooling or hardening medium throughput, and cooling or hardening medium temperature. The injection molding machine parameters may further include machine dimensions such as clamping force, tie bar gap, injection unit dimensions, machine equipment such as cylinder diameter or maximum cylinder temperature, etc.

[0095] The material-specific parameters may be information about the material used in the injection molding process. The material-specific parameters may be provided by the material supplier and / or downloaded from a website or other database. The material supplier may have a lot of product-specific data, such as rheological data, viscosity, and lot-specific data for all materials produced. The material-specific parameters may include at least one parameter selected from the group consisting of compressibility, flow characteristics, and temperature characteristics. The material, specifically the material used in the molding process, e.g., the material used to manufacture the workpiece, may be or include, for example, a plastic material. Specifically, the plastic material may be or include a thermoplastic material. Additionally or alternatively, the plastic material may be or include a thermosetting material. Additionally or alternatively, the plastic material may include an elastomeric material. The material may be in a molten state during the manufacture of the workpiece 114.

[0096] The simulation model may be generated by software on the external processing unit 118, or may be a dataset within the software. The simulation model may include at least one trained and trainable model. The external processing unit 118 may be configured to execute and / or implement at least one machine learning algorithm. The simulation model may be based on the results of at least one machine learning algorithm. The machine learning algorithm may include a decision tree, a naive Bayes classifier, a nearest neighbor, a neural network, a convolutional neural network, a backpropagation generative network, a support vector machine, a linear regression, a logistic regression, a random forest, and / or a gradient boosting algorithm. Preferably, the machine learning algorithm is configured to process a high-dimensional input into a much lower-dimensional output. The algorithm may be trained using training data records. The simulation model may include at least one algorithm and model parameters. The parameters of the simulation model may be generated by using at least one artificial neural network. The simulation model, and in particular the model parameters, may be adapted in step d) and thus further trained.

[0097] The simulation model can include a digital twin of an injection molding process. The simulation model is configured to simulate an injection molding process. The simulation model can include a filling simulation. Specifically, the simulation model can be configured to simulate filling of a mold cavity with a molten mass of at least one material. The simulation model can be configured to simulate manufacturing of a workpiece. The simulation model can be configured to simulate the geometry and / or shape of the workpiece. The simulation model can include strength analysis.

[0098] The simulation model can be configured to account for material-specific properties. The simulation model can include a digital twin of the material. The simulation model can be configured to account for batch properties of the raw material batch, such as the viscosity of the material batch.

[0099] Cloud-based use of simulation data, process data, and product-related data can enable optimization of the injection molding process. As outlined above, material suppliers may have a lot of product-specific data, such as rheological data, viscosity, and lot-specific data for all materials produced. The present invention proposes a closed loop between simulation and the injection molding process, so that parameters from the simulation can be used directly in the injection molding process. Furthermore, conversely, process data can be used to optimize the modeling process using machine learning models. Lot-specific information for materials can be further linked to the simulation of the manufacturing process by using a cloud-based digital twin of the material and injection molding process to further improve the efficiency of the injection molding process.

[0100] The predicted process parameters of the simulated injection molding process 122 may be expected values ​​of the process parameters, particularly to reach an optimal manufacturing result and / or optimal use of resources.

[0101] Step b) may include at least one optimization step. Optimization may be a process of selecting an optimal parameter set with respect to an optimization goal from a parameter space of possible parameters. The optimization goal may be at least one criterion against which the optimization is performed. The optimization goal may include at least one optimization objective and precision and / or accuracy. The optimization goal may be at least one characteristic of the workpiece 114. The characteristic of the workpiece 114 may be at least one factor selected from the group consisting of the mass of the workpiece 114, the dimensions of the workpiece 114, and warpage. The optimization goal may be pre-specified, for example, by at least one customer and / or at least one user of the injection molding machine 110. The optimization goal may be at least one user specification. The user may select the optimization goal and the desired precision and / or accuracy. The predicted process parameters are provided to the injection molding machine 110 via at least one interface, in particular a communication interface.

[0102] In step c), the manufactured workpiece 114 may be measured, for example, by using optical or tactile measurement techniques, such as scanning. The scanning may include determining the shape and dimensions of the workpiece 114. The scanning may in particular be performed automatically. The scanning may be performed autonomously by a computer or a computer network. The determined characteristics of the workpiece 114 can be compared to optimization targets. The comparison may include determining deviations from the target shape and / or target dimensions. If the difference between the determined characteristics and the optimization targets exceeds a tolerance limit, the produced workpiece is deemed to deviate from the target shape and / or target dimensions. The tolerance limit may depend on the accuracy of the characteristics and / or the determination, such as by customer requirements.

[0103] If the characteristics of the produced workpiece 114 deviate from the optimization target, at least one process parameter of the injection molding machine 110 is adapted in response to the comparison. The injection molding process, determining the characteristics of the produced workpiece 114, and comparing the characteristics to the optimization target are repeated with the adapted process parameters until the characteristics of the produced workpiece 114 are within at least a predefined tolerance according to the optimization target.

[0104] Step d) 134 includes determining at least one actual process parameter of the injection molding process. The injection molding machine 110 may be configured to measure and / or monitor at least one process parameter of the process during the injection molding process. The injection molding machine 110 may be configured to measure the process parameter in real time and adapt the process parameter on the fly. The injection molding machine 110 may include at least one sensor. The measured parameters of the injection molding machine 110 may be registered and transferred to an external processing unit. The injection molding machine 110 may include at least one element selected from the group consisting of a temperature sensor; a pressure sensor; and a clock.

[0105] Step d) 134 further comprises comparing the actual process parameters with the predicted process parameters and adapting the simulation model based on the comparison. The comparison may comprise determining the deviation of the predicted process parameters from the actual process parameters, or vice versa. If the difference exceeds a tolerance limit, the actual process parameters are deemed to deviate from the predicted process parameters. The tolerance limit may depend on the measurement accuracy. The comparison may be performed by an internal processing unit of the injection molding machine. Information about the deviation and / or the actual process parameters may be transferred to an external processing unit. The external processing unit may be configured to adapt the simulation model, in particular the model parameters, based on the information about the deviation and / or the actual process parameters.

[0106] The method may further include outputting the predicted process parameters and / or results of the comparison of the actual process parameters to the predicted process parameters via at least one output interface or port. The output may include a process that makes the information available to another system, data storage, or a natural or legal person. By way of example, the output may be via one or more interfaces, such as a computer interface or a human-machine interface. By way of example, the output may be in one or more of a computer-readable format, a visual format, or an audible format.

[0107] Method steps a) to d) can be repeated and the adapted simulation model can be used in step a).

[0108] FIG. 1 also illustrates an automated control system 112. The injection molding process is based on a plurality of process parameters. The control system 112 includes at least one external processing unit 118. The external processing unit 118 is configured to simulate the injection molding process based on an input parameter set including at least one simulation model, material-specific parameters, and injection molding machine parameters by applying an optimization algorithm with respect to at least one optimization goal of the simulation model. The control system 112 includes at least one interface, represented by arrow 138, configured to provide predicted process parameters to the injection molding machine 110. The control system 112 is configured to perform at least one injection molding process using the injection molding machine 110 based on the predicted process parameters to generate at least one workpiece 114. The control system 112 is configured to determine at least one characteristic of the generated workpiece 114, compare the characteristic with the optimization goal, and adapt at least one process parameter of the injection molding machine 110 in response to the comparison. The control system 112 is configured to iterate the injection molding process, determine the properties, compare the properties to the optimization goals, and adapt the process parameters until the properties of the produced workpiece are at least within a predefined tolerance according to the optimization goals. The control system 112 is configured to determine at least one actual process parameter of the injection molding process. The control system 112 is configured to compare the actual process parameter with the predicted process parameter and adapt the simulation model based on the comparison.

[0109] The automatic control system 112 may be configured to carry out the method according to the invention, therefore see the description of the method for possible implementations.

[0110] For example, a comparison of the determined workpiece 114 characteristics with the optimization goals may reveal that the workpiece 114 deviates from the desired shape, particularly warpage, e.g., twists, bows, ripples, and angular deviations. This may be due to different shrinkage tendencies (shrinkage potential) in different areas of the workpiece 114. The difference in shrinkage may be caused by different degrees of filling in different areas of the workpiece 114 and by different orientations of the fibers and polymer chains. Further causes may include an unfavorable selected mold temperature, different wall thicknesses of the molded workpiece 114, a pressure gradient along the flow path of the workpiece 114 that is too high, a selected cooling time that causes the workpiece 114 to be removed from the mold at too high a temperature and thus deform after removal from the mold, the use of unfavorable materials, or glass fibers in glass fiber-reinforced thermoplastics that are primarily oriented in the flow direction. In the latter case, deviations may occur if the orientation of the glass fibers varies from place to place. These factors include, for example, flow deflection, channel termination, weld line, and gate orientation effects. At least one of the following process parameters of the injection molding machine 110 can be adapted for comparison: changing the mold half and slide core temperatures, increasing the cooling time, adapting the process so that the molded part is not stuck or held in a negative draft, changing the holding pressure, and changing the holding time. Furthermore, the materials used can be changed in terms of comparison. Specifically, materials with less warpage, such as blends with an amorphous phase, can be used. The workpiece design can also be changed. The process parameters of the injection molding machine can be adapted according to a predetermined hierarchy. For example, the mold temperature can be adapted first, followed by the cooling time. Subsequently, further process parameters can be adapted. Figures 2A-2C show the effect of mold temperature on the warpage of an Ultraform® holding mandrel. In Figures 2A-2C, the mold temperature of the cavity was 80°C.In Figure 2A, the core mold temperature was 80°C, in Figure 2B it was 30°C, and in Figure 2C it was 50°C. The gap between the elements of the holding mandrel varies from figure to figure: in Figure 2A it is 1.0 mm, in Figure 2B it is 5.0 mm, and in Figure 2C it is 2.4 mm. Figure 2D shows a further example of an insulating panel made from glass-fiber reinforced Ultradur®. The top part of Figure 2D shows the geometry of the molded part optimized by simulation, while the bottom part shows the original state. [Explanation of symbols]

[0111] 110 Injection molding machine 112 Automatic Control System 114 workpieces 116 Providing input parameters 118 External Processing Unit 120 Simulation 122 Predicted process parameters of a simulated injection molding process 124 Providing predicted process parameters 126 Interface 130 Performance 132 Comparison 134 Determining at least one actual process parameter 136 Adaptation 138 Interface

Claims

1. 1. A computer-implemented method for controlling and / or monitoring at least one injection molding process in at least one injection molding machine (110), said method comprising: a) providing a set of input parameters by at least one external processing unit (118), said set of input parameters including at least one simulation model, material specific parameters, and injection molding machine parameters; b) simulating, by the external processing unit (118), an injection molding process based on the set of input parameters and determining at least one predicted process parameter of the simulated injection molding process by applying an optimization algorithm with respect to at least one optimization objective of the simulation model, wherein the predicted process parameter is provided to the injection molding machine via at least one interface; c) manufacturing at least one workpiece (114), measuring the manufactured workpiece (114) by using optical or tactile measurement techniques, determining a mass and / or dimensions and / or warpage of the manufactured workpiece (114), and performing at least one injection molding process using the injection molding machine (110) based on the predicted process parameters by the external processing unit (118) to compare the mass and / or dimensions and / or warpage with the optimization targets, wherein if the mass and / or dimensions and / or warpage of the manufactured workpiece (114) deviate from the optimization targets, adapting parameters of the injection molding machine in response to the comparison so that the mass and / or dimensions and / or warpage of the manufactured workpiece (114) are at least within a predefined tolerance range with respect to the optimization targets; d) determining, by the external processing unit (118), at least one actual process parameter of the injection molding process, comparing the actual process parameter with the predicted process parameters, and adapting the simulation model based on the comparison, wherein the actual process parameter is at least one parameter of the injection molding machine measured during the injection molding process by using at least one sensor; Including, Steps a) to d) are repeated by the external processing unit (118), and the adapted injection molding machine parameters and the adapted simulation model are used in step a).

2. The method described in claim 1, wherein the parameters of the injection molding machine include at least one parameter selected from the group consisting of polymer melt temperature, barrel temperature, injection unit temperature, screw speed, injection speed, holding pressure, holding time, cooling or hardening time, and cooling or hardening parameters.

3. A method as described in claim 1 or 2, wherein the measured parameters of the injection molding machine are registered and transferred to the external processing unit (118), and the injection molding machine (110) includes at least one element selected from the group consisting of a temperature sensor; a pressure sensor; and a clock.

4. The method according to any one of claims 1 to 3, wherein the simulation model comprises a filling simulation.

5. The method according to any one of claims 1 to 4, wherein the simulation model is configured to simulate the filling of a mould cavity with a molten mass of at least one material.

6. The method of any one of claims 1 to 5, wherein the simulation model is configured to simulate the geometry and / or shape of the workpiece.

7. The method according to any one of claims 1 to 6, wherein the simulation model includes a strength analysis.

8. The method according to any one of claims 1 to 7, wherein the material-specific parameters include at least one parameter selected from the group consisting of compressibility, flow properties, and temperature properties.

9. The method according to any one of claims 1 to 8, wherein the simulation model is configured to take into account material-specific properties.

10. The method of claim 9 , wherein the simulation model is configured to take into account characteristics of raw materials.

11. 11. The method of claim 1, further comprising outputting the predicted process parameters and / or results of the comparison of the actual process parameters with the predicted process parameters via at least one output interface or port.

12. The method of any one of claims 1 to 11, wherein the external processing unit (118) is and / or comprises a cloud computing system.

13. A computer program comprising instructions which, when the program is executed by a computer or computer system, cause said computer or computer system to carry out a method according to any one of claims 1 to 12.

14. An automatic control system (112) for an injection molding process in at least one injection molding machine (110), comprising: The automatic control system (112) comprises at least one external processing unit (118), the external processing unit (118) comprising: a) providing a set of input parameters, the set of input parameters including at least one of a simulation model, material specific parameters, and injection molding machine parameters; b) simulating an injection molding process based on the set of input parameters and determining at least one predicted process parameter of the simulated injection molding process by applying an optimization algorithm with respect to at least one optimization objective of the simulation model, wherein the predicted process parameter is provided to the injection molding machine via at least one interface; c) manufacturing at least one workpiece (114), measuring the manufactured workpiece (114) by using optical or tactile measurement techniques, determining a mass and / or dimensions and / or warpage of the manufactured workpiece (114), and performing at least one injection molding process using the injection molding machine (110) based on the predicted process parameters in order to compare the mass and / or dimensions and / or warpage with the optimization targets, and if the mass and / or dimensions and / or warpage of the manufactured workpiece (114) deviate from the optimization targets, adapting parameters of the injection molding machine in response to the comparison so that the mass and / or dimensions and / or warpage of the manufactured workpiece (114) are at least within a predefined tolerance range with respect to the optimization targets; d) determining at least one actual process parameter of the injection molding process, comparing the actual process parameter with the predicted process parameters, and adapting the simulation model based on the comparison, wherein the actual process parameter is at least one parameter of the injection molding machine measured during the injection molding process by using at least one sensor; is configured to run The external processing unit (118) is configured to repeat steps a) to d), and the adapted injection molding machine parameters and the adapted simulation model are used in step a).

Images