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

Abstract

A computer-implemented method is proposed for controlling and / or monitoring at least one injection molding process in at least one injection molding machine (110). The injection molding process is based on multiple process parameters. The method includes the following steps: a) providing an input parameter set by at least one external processing unit (118), wherein the input parameter set includes at least one simulation model, material-specific parameters, and injection molding machine parameters; b) the external processing unit (118) simulating the injection molding process based on the input parameter set, and determining at least one predicted process parameter of the simulated injection molding process by applying an optimization algorithm on at least one aspect of the optimization objective to the simulation model, wherein 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 parameter to generate at least one workpiece (114). d) Determine at least one attribute of the generated workpiece (114), compare the attribute with an optimization target, wherein if the attribute of the generated workpiece (114) deviates from the optimization target, adapt at least one process parameter of the injection molding machine (110) according to the comparison, repeat the injection molding process with the adapted process parameter, determine the attribute of the generated workpiece (114) and compare the attribute with the optimization target until the attribute of the generated workpiece (114) is consistent with the optimization target at least within a predetermined tolerance; d) Determine at least one actual process parameter of the injection molding process, compare the actual process parameter with the predicted process parameter, and adapt the simulation model based on the comparison.

Description

Technical Field

[0001] This invention relates to a computer-implemented method, computer program, computer-readable storage medium, and automatic control system for controlling and / or monitoring at least one injection molding process. Typically, this method, system, and apparatus can be used for technical design or configuration purposes, for example, in the development or production phase of an injection molding process. However, further applications are also possible. Background Technology

[0002] Injection molding is a common manufacturing process in both small and large-scale manufacturing industries. In a typical injection molding process, plastic material (such as thermoplastic, thermosetting, or elastomer materials) is typically melted during heating and then injected into an empty mold, for example, under pressure. The plastic material is then typically hardened during cooling or curing to maintain the shape given by the mold, thus becoming the manufactured product. It allows for the mass reproduction of products formed by the mold. Because the design and configuration of molds are costly, it is not easy to modify the mold if any problems occur during injection molding. Therefore, to minimize production costs and waste, the filling process of the mold or mold cavity has previously typically been simulated using common simulation methods.

[0003] Today, injection molding simulation (e.g., simulations from Moldflow) can be used to optimize tooling and filling processes for a given part. Moldflow has two core products: Moldflow Adviser, which provides manufacturability guidance and directional feedback for standard part and mold design; and Moldflow Insight, which provides final results for flow, cooling, and warpage, and supports specialized molding processes (see en.wikipedia.org / wiki / Moldflow).

[0004] As is well known, optimization programs can be implemented in the injection molding machine itself, such as those from DE 10 2013 111257 B3, DE 10 2018 107 233 A1, or EP3294519B1.

[0005] Despite the advantages of recent methods for optimizing and simulating injection molding processes, several technical challenges remain. Therefore, simulating and optimizing injection molding processes can still be very time-consuming and complex, requiring substantial computational power, which may not be feasible within the injection molding machine itself since it must produce workpieces without simulation results. Furthermore, it is desirable to improve upon known simulation and optimization methods for injection molding in terms of both efficiency and accuracy.

[0006] In other technical fields (such as for chemical processes), further optimization methods are known, as described in WO 2019 / 138118, WO 2019 / 138120, and WO 2019 / 138122.

[0007] US 5,900,259 A describes a molding condition optimization system for an injection molding machine, comprising a plastic flow condition optimization section and an operating condition determination section. The plastic flow condition optimization section performs plastic flow analysis on a molded part model and determines the optimal flow conditions for the injection molding process of the injection molding machine during the filling and packaging stages by repeatedly performing automatic calculations using the results of the plastic flow analysis and the plastic flow analysis itself. The operating condition determination section includes: an injection-side condition determination section for determining the optimal injection-side conditions of the injection molding machine based on the optimal flow conditions obtained from the plastic flow condition optimization device and a knowledge database of injection conditions; and a mold-closing-side condition determination section for determining the optimal mold-closing-side conditions based on the molded part shape data generated by the plastic flow condition optimization device, the results of the plastic flow analysis, mold design data, and a knowledge database of mold-closing conditions.

[0008] US 2018 / 181694 A1 describes a method for optimizing a process optimization system for a molding machine. The method includes having a user set configuration data on an actual molding machine, obtaining a first value for at least one descriptive variable of the molding process based on the configuration dataset and / or based on a cyclically executed molding process, and obtaining a second value for at least one descriptive variable based on data from the process optimization system. The first and second values ​​are checked against each other according to a predetermined distinguishing criterion. If the check shows that the first and second values ​​are different, the process optimization system is modified such that, when applied to the molding machine and / or the molding process, the first value of the descriptive variable substantially replaces 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. CAE simulation generates simulation results; first machine parameters are generated by electronically processing the simulation results; second machine parameters are obtained from the execution of another molding process on the same object and are different from the first machine parameters; the first and second machine parameters are stored together in a common set in a user-accessible electronic database. In a further variation, the last method step is replaced by processing the first and second machine parameters with software and modifying the machine parameters calculated by subsequent CAE simulations as a function of the processing generated by the software.

[0010] US 2006 / 224540 A1 describes test molding and mass production molding performed by an injection molding machine that includes a control unit in which a neural network is used. The quality prediction function determined based on the test molding is modified as needed during mass production molding.

[0011] EP 0 368 300 A2 describes an optimal molding condition setting system for an injection molding machine. The system includes a melt flow analysis device for analyzing resin flow, resin cooling, and the structure / strength of the molded product using a designed mold, and also includes an analysis result evaluation device for determining 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 injection is performed to check for defects in the molded product. If defects are found in the molded product, the defect data is input into a molding defect elimination device.

[0012] Problems to be solved

[0013] Therefore, it is desirable to provide apparatus and methods that can address the aforementioned technical challenges. Specifically, methods, systems, procedures, and databases should be proposed to further improve the performance of simulation and optimization of injection molding processes, particularly in terms of efficiency and accuracy, compared to devices, methods, and systems known in the art. Summary of the Invention

[0014] This problem is solved by the methods, systems, programs, and databases proposed in this invention. Advantageous embodiments that can be implemented individually or in any arbitrary combination are listed herein.

[0015] As used below, the terms “have,” “include,” or “contain,” or any of their grammatical variations, are used in a non-exclusive manner. Thus, these terms can refer either to a situation where no further features exist in the entity described in the context, in addition to the features introduced by these terms, or to a situation where one or more further features exist. As an example, the expressions “A has B,” “A includes B,” and “A contains B” can refer either to a situation where no other elements exist in A besides B (i.e., A consists solely and exclusively of B), or to a situation where entity A contains one or more further elements besides B, such as elements C, C and D, or even more elements.

[0016] Furthermore, it should be noted that when the terms "at least one," "one or more," or similar expressions (indicating that a feature or element may exist once or more) are used to introduce a corresponding feature or element, these expressions will typically be used only once. In the following text, in most cases, the expressions "at least one" or "one or more" will not be repeated when referring to a corresponding feature or element, even though it is true that the corresponding feature or element may exist once or more.

[0017] Furthermore, as used herein, the terms “preferably,” “more preferably,” “particularly,” “more particularly,” “specifically,” “more specifically,” or similar terms are used together with optional features but do not limit the possibility of substitution. Therefore, features introduced by these terms are optional features and are not intended to limit the scope of the claims in any way. As those skilled in the art will recognize, the invention can be carried out by using alternative features. Similarly, features introduced by phrases such as “in one embodiment of the invention” or similar expressions are intended as optional features, without limiting alternative embodiments of the invention, without limiting the scope of the invention, and without limiting 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 invention, a computer-implemented method is disclosed for controlling and / or monitoring at least one injection molding process in at least one injection molding machine.

[0019] As used herein, the term "computer-implemented" is a broad term and is given its common and customary meaning to those skilled in the art, without limitation a specific or customized meaning. The term may specifically refer to, but is not limited to, a process implemented wholly or partially using a data processing apparatus, such as a data processing apparatus including at least one processor. Therefore, the term "computer" can generally refer to a device or combination or network of devices having at least one data processing apparatus (such as at least one processor). Furthermore, a computer may include one or more further components, such as at least one of a data storage device, an electronic interface, or a human-machine interface. As used herein, the terms "processor" or "processing unit" are broad terms and are given their common and customary meaning to those skilled in the art, without limitation a specific or customized meaning. The term may specifically refer to, but is not limited to, any logic circuit configured to perform basic operations of a computer or system, and / or, generally, a device configured to perform computational or logical operations. In particular, a processor may be configured to process the basic instructions that drive a computer or system. As one embodiment, the processor may include at least one arithmetic logic unit (ALU), at least one floating-point unit (FPU), such as a math coprocessor or a digital coprocessor, multiple 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. Specifically, the processor may be or may include a central processing unit (CPU). Additionally or alternatively, the processor may be or may include a microprocessor, and thus, specifically, the elements of the processor may be contained in a single integrated circuit (IC) chip. Additionally or alternatively, the processor may be or may include one or more application-specific integrated circuits (ASICs) and / or one or more field-programmable gate arrays (FPGAs) or the like.

[0020] As used herein, the term "molding process" is a broad term and is given its common and customary meaning to those skilled in the art, but not limited to a specific or customized meaning. The term may specifically refer to, but is not limited to, a process or procedure for shaping at least one material into any form or shape. As used herein, the term "injection molding process" is a broad term and is given its common and customary meaning to those skilled in the art, but not limited to a specific or customized meaning. The term may specifically refer to, but is not limited to, a type of molding process performed by injecting molten material into a mold.

[0021] As used herein, the term "mold" is a broad term and is given its common and customary meaning to those skilled in the art, without limitation a specific or customary meaning. The term may specifically refer to, but is not limited to, a mold or molded part, such as a molded part giving a casting or frame. In particular, as used herein, a mold may refer to any mold and / or molded part including at least one cavity, such as at least one molded part giving a structure and / or cutouts. The mold may specifically be used in injection molding processes, wherein at least one block of molten material may be injected into at least one cavity of the mold. For simplicity, the terms "mold" and "mold cavity" are used interchangeably herein. As an example, a mold having at least one cavity can be used in a molding process for forming material. In particular, the block of molten material injected into the mold cavity may be given a negative form and / or geometry of the cavity. Specifically, the mold can be used to manufacture at least one workpiece, also referred to as a part, wherein the manufactured workpiece may have a negative form and / or shape of the mold cavity.

[0022] This molding process can be configured to manufacture at least one workpiece. As used herein, the term "workpiece" is a broad term and is given its common and customary meaning to those skilled in the art, without limitation a specific or customary meaning. The term can specifically refer to, but is not limited to, any part or component. In particular, a workpiece can be or can include any component of a machine or device. For example, the workpiece may at least partially have a negative shape of a mold or mold cavity in the molding process used to manufacture the part. Therefore, injection molding process can be or can refer to a shape-assigning procedure used to manufacture a workpiece.

[0023] As used herein, the term "injection molding machine" is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or customary meaning. The term may specifically refer to, but is not limited 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 mold clamping unit.

[0024] Injection molding is based on multiple process parameters. As used herein, the term "process parameter" is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or custom meaning. The term may specifically refer to, but is not limited to, at least one settable and / or selectable and / or adjustable and / or configurable parameter that affects the injection molding process. This 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, process parameters may include one or more of the following: polymer melt temperature, barrel temperature, injection unit temperature, screw speed, injection speed, holding pressure, holding time, cooling or curing time, and at least one cooling or curing parameter (such as the yield of the cooling or curing medium, or the temperature of the cooling or curing medium). Injection molding machine parameters may further include machine dimensions such as clamping force, tie rod clearance, injection unit, and machine equipment dimensions such as barrel diameter or maximum barrel temperature.

[0025] As used herein, the term "control" is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or customized meaning. The term may specifically refer to, but is not limited to, determining and / or adjusting at least one process parameter. Similarly, as used herein, the term "monitoring" is a broad term and is given its common and customary meaning to those skilled in the art, but should not be limited to a specific or customized meaning. The term may specifically refer to, but is not limited to, quantitatively and / or qualitatively determining at least one process parameter.

[0026] A computer-implemented method includes the following steps, which may be performed in a given order. However, a different order is also possible. Furthermore, one or more, or even all, of the steps may be performed once or repeatedly. Additionally, the method steps may be performed in an overlapping manner or even in parallel. The method may further include additional method steps not listed.

[0027] The method includes the following steps:

[0028] a) Provides an input parameter set via at least one external processing unit, wherein the input parameter set includes at least one simulation model, material-specific parameters, and injection molding machine parameters;

[0029] b) An external processing unit that simulates the injection molding process based on the input parameter set and determines at least one predicted process parameter of the simulated injection molding process by applying an optimization algorithm on at least one optimization objective to the simulation model, wherein the predicted process parameter is provided to the injection molding machine via at least one interface.

[0030] c) Based on predicted process parameters, perform at least one injection molding process using an injection molding machine to generate at least one workpiece, determine at least one attribute of the generated workpiece, and compare the attribute with an optimization target, wherein if the attribute of the generated workpiece deviates from the optimization target, adapt at least one process parameter of the injection molding machine according to the comparison, repeat the injection molding process, determine the attribute of the generated workpiece, and compare the attribute with the optimization target with the adapted process parameters until the attribute of the generated workpiece is at least within a predetermined tolerance and consistent with the optimization target;

[0031] d) Determine at least one actual process parameter for the injection molding process, compare the actual process parameter with the predicted process parameter, and adapt the simulation model based on the comparison.

[0032] As used herein, the term "external processing unit" is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or customized meaning. The term may specifically refer to, but is not limited to, at least one processing unit designed separately from the injection molding machine. The injection molding machine may include an internal processing unit, particularly one configured to control and monitor machine parameters. The external processing unit may be configured to transmit data to and / or receive data from the internal processing unit via at least one communication interface. The internal processing unit may be configured to transmit data to 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 include a cloud computing system.

[0033] The external processing unit may include at least one database. As used herein, the term "database" is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or customized meaning. The term may specifically refer to, but is not limited to, any collection of information. The database may be stored in at least one data storage device. In particular, the database may contain any collection of information. The data storage device may be or may include at least one element selected from the group consisting of: at least one server, at least one server system comprising multiple servers, at least one cloud server, or cloud computing infrastructure.

[0034] As used herein, the term "communication interface" is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or customized meaning. The term may specifically refer to, but is not limited to, items or elements configured to form boundaries for transmitting information. In particular, a communication interface may be configured to transmit information from a computing device (e.g., a computer), such as sending or outputting information, for example, to another device. Additionally or alternatively, a communication interface may be configured to transmit information to a computing device, such as to a computer, for example, to receive information. A communication interface may specifically provide means for transmitting or exchanging information. In particular, a communication interface may provide data transmission connectivity, such as Bluetooth, NFC, inductive coupling, etc. As one embodiment, a communication interface may be or may include at least one port, including one or more network or internet ports, USB ports, and disk drives. The communication interface may be at least one network interface.

[0035] As used herein, the term "provide" is a broad term and is given its common and customary meaning to those skilled in the art, without limitation a specific or customized meaning. The term may specifically refer to, but is not limited to, retrieving and / or selecting a set of input parameters. As used herein, the term "retrieve" is a broad term and is given its common and customary meaning to those skilled in the art, without limitation a specific or customized meaning. The term may specifically refer to, but is not limited to, the processing by which a system (particularly a computer system) generates and / or obtains data from any data source (such as from a data storage device, from a network, or from a further computer or computer system). In particular, retrieval may be performed via at least one computer interface (such as via a port, such as a serial or parallel port). Retrieval may include several sub-steps, such as obtaining one or more primary information items and generating secondary information by utilizing the primary information (such as by applying one or more algorithms to the primary information, for example, by using a processor).

[0036] As used herein, the term "input parameter set" is a broad term and is given its common and customary meaning to those skilled in the art, without limitation a specific or customary meaning. The term may specifically refer to, but is not limited to, information concerning simulation models, material-specific parameters, and injection molding machine parameters.

[0037] As used herein, the term "injection molding machine parameter" is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or customary meaning. The term may specifically refer to, but is not limited to, parameters that affect the operating conditions of an injection molding machine. Injection molding machine parameters may include settings of the machine components of the injection molding machine. Injection molding machine parameters may include specific values ​​and / or parameter curves. 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, holding pressure, holding time, cooling or curing time, and cooling or curing parameters (such as the yield of the cooling or curing medium, the temperature of the cooling or curing medium).

[0038] As used herein, the term "material-specific parameter" is a broad term and is given its common and customary meaning to those skilled in the art, but not limited to a specific or customary meaning. The term may specifically refer to, but is not limited to, information about the material used in the injection molding process. Material-specific parameters may be provided by the material supplier and / or downloaded from websites or other databases. Material suppliers may have a great deal of product-specific data, such as rheological data, viscosity, and batch-specific data for each material produced. Material-specific parameters include at least one parameter selected from the group consisting of: compressibility, flow properties, and temperature properties.

[0039] Materials (particularly, materials used in molding processes, such as those for manufacturing workpieces) may be, or may include, plastic materials. As used herein, the term "plastic material" is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or customary meaning. The term may specifically refer to, but is not limited to, any thermoplastic, thermosetting, or elastomeric material. In particular, a plastic material may be a mixture of substances composed of monomers and / or polymers. Specifically, a plastic material may be, or may include, thermoplastic materials. Additionally or alternatively, a plastic material may be, or may include, thermosetting materials. Additionally or alternatively, a plastic material may include elastomeric materials. During the manufacture of the workpiece, the material may specifically be in a molten state.

[0040] As used herein, the term "simulation" or "performing a simulation" is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or customized meaning. The term may specifically refer to, but is not limited to, a process used to specifically approximate a real injection molding process. As used herein, the term "simulation model" is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or customized meaning. The term may specifically refer to, but is not limited to, at least one model upon 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 dataset within software.

[0041] A simulation model may include at least one trained and trainable model. As used herein, the term "trained model" is a broad term and is given its common and conventional meaning to those skilled in the art, without limitation a specific or customized meaning. The term may specifically refer to, but is not limited to, a mathematical model trained on at least one training dataset. As used herein, the term "trainable model" is a broad term and is given its common and conventional meaning to those skilled in the art, without limitation a specific or customized meaning. The term may specifically refer to, but is not limited to, the fact that the simulation model can be further trained and / or updated based on additional training data. Specifically, the simulation model is trained on a training dataset. The simulation model can be trained using machine learning. The simulation model can be at least partially data-driven by training on data from historical production runs. As used herein, the term "data-driven" is a broad term and is given its common and conventional meaning to those skilled in the art, without limitation a specific or customized meaning. The term may specifically refer to, but is not limited to, the fact that the model is an empirical, predictive model. Specifically, the data-driven model is derived from the analysis of experimental data from previous injection molding processes. The term "historical production run" refers to an injection molding process at a past or earlier point in time. Specifically, to further train the simulation model, the training dataset can be generated from comparative 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 is given its common and customary meaning to those skilled in the art, without limitation a specific or customized meaning. The term may specifically refer to, but is not limited to, the fact that the trained model includes a data-driven model component, where the model may include further or other model components. As used herein, the term "machine learning" is a broad term and is given its common and customary meaning to those skilled in the art, without limitation a specific or customized meaning. The term may specifically refer to, but is not limited to, the method of automatically modeling a machine learning model (particularly a predictive model) using artificial intelligence (AI). An external processing unit may be configured to perform and / or execute at least one machine learning algorithm. The simulation model may be based on the results of at least one machine learning algorithm. Machine learning algorithms can include decision trees, Naive Bayes classification, nearest neighbor, neural networks, convolutional neural networks, generative adversarial networks, support vector machines, linear regression, logistic regression, random forests, and / or gradient boosting algorithms. Preferably, the machine learning algorithm is organized to process inputs with high dimensionality into outputs with much lower dimensionality. Such machine learning algorithms are called "intelligent" because they can be "trained." The algorithm can be trained using a training data record. The training data record can include training input data and corresponding training output data.The training output data of the training data record can be the expected result of a machine learning algorithm when given training input data from the same training data record. The deviation between this expected result and the actual result produced by the algorithm can be observed and evaluated through a "loss function". This loss function can be used as feedback to adjust the parameters of the internal processing chain of the machine learning algorithm. For example, the parameters can be adjusted by an optimization objective that minimizes the value of the loss function, which is generated when all training input data is fed into the machine learning algorithm and the results are compared with the corresponding training output data. The result of this training may be that, given a relatively small number of training data records as "base facts", the machine learning algorithm can perform its work well on many orders of magnitude larger numbers of input data records. Therefore, the simulation model can include at least one algorithm and model parameters. The simulation model parameters can be generated using at least one artificial neural network. The simulation model (especially the model parameters) can be adapted in step d) so that it can be further trained.

[0042] The simulation model may include a digital twin of the injection molding process. The simulation model is configured to simulate the injection molding process. The simulation model may include filling simulation. Specifically, the simulation model may be configured to simulate filling a mold cavity with at least one molten material. The simulation model may be configured to simulate the manufacturing of the workpiece. The simulation model may be configured to simulate the geometry and / or shape of the workpiece. The simulation model may include strength analysis.

[0043] The simulation model can use the geometric data of the workpiece to be manufactured. As used herein, the term "geometric data" is a broad term and is given its common and customary meaning to those skilled in the art, without limitation a specific or customary meaning. The term can specifically refer to, but is not limited to, information about the three-dimensional form or shape of any object or element. Specifically, geometric data (such as information about three-dimensional shapes) can exist in a computer-readable form, such as computer-compatible datasets, particularly digital datasets. As an example, geometric data can be or can include computer-aided design data (CAD data). Specifically, three-dimensional geometric data can be or can include CAD data describing the form or shape of an object or element.

[0044] The simulation model can be configured to consider material-specific properties. The simulation model may include a digital twin of the material. The simulation model can be configured to consider batch attributes 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 by an external processing unit, such as at least one cloud computing system. This allows for the consideration of additional parameters affecting the injection molding process, in addition to those provided by the injection molding machine and / or at least one of its sensors and / or available within the injection molding machine. These additional parameters may involve external knowledge, such as knowledge from the material supplier, product-specific data such as rheological data, viscosity, and / or algorithms, and / or material-specific data.

[0045] It is possible to use simulation data, process data, and product-related data in the process optimization of cloud-based injection molding processes. As mentioned above, material suppliers may possess a wealth of product-specific data, such as rheological data, viscosity, and batch-specific data for each material produced. This invention proposes establishing a closed loop between simulation and the injection molding process, allowing simulation parameters to be directly applied to the injection molding process. Furthermore, conversely, process data can be used to optimize the modeling process using machine learning models. By using cloud-based digital twins of materials and injection molding processes, batch-specific information of the materials can be further linked to the simulated manufacturing process, thereby further improving the efficiency of the injection molding process.

[0046] As used herein, the term "predicted process parameters for simulated injection molding" is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or customized meaning. The term may specifically refer to, but is not limited to, the expected values ​​of process parameters, particularly for achieving optimal manufacturing results and / or optimal use of resources. Predicted process parameters can be parameters that affect the injection molding process. Predicted process parameters can be determined for optimizing the injection molding process. In known systems and apparatus, such as those described in US 5,900,259 A, optimization is performed in view of workpiece optimization. In contrast, this invention refers to process optimization. Process optimization takes into account not only optimal manufacturing results but also optimal use of resources.

[0047] Step b) may include at least one optimization step. As used herein, the term “optimization” is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or customized meaning. The term may specifically refer to, but is not limited to, the process of selecting the optimal set of parameters from a parameter space of possible parameters in relation to the optimization objective. As used herein, the term “optimization objective” is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or customized meaning. The term may specifically refer to, but is not limited to, at least one criterion under which optimization is performed. The optimization objective may include at least one optimization objective and accuracy and / or precision. The optimization objective may be at least one attribute of the workpiece. The attribute of the workpiece may be at least one element selected from the group consisting of: workpiece weight, workpiece dimensions, warpage. The optimization objective may be pre-specified, such as by at least one customer and / or at least one user of the injection molding machine. The optimization objective may be a specification of at least one user. The user can select the optimization objective and the desired accuracy and / or precision. Predicted process parameters are provided to the injection molding machine via at least one interface (particularly via a communication interface). In known systems and devices, such as those described in US 5,900,259A, parameters defining the injection molding process are stored in the injection molding machine. Therefore, these parameters are typically static. In contrast, the present invention proposes a self-learning method, specifically by adapting a simulation model in step d) to take into account the newly determined predicted process parameters in step c), and using the improved simulation model in step c) to predict improved process parameters for performing at least one injection molding process, thereby continuously improving the performance of the injection molding process. Thus, a loop or cycle is proposed by performing steps a) through d).

[0048] This method involves performing at least one injection molding process using an injection molding machine based on predicted process parameters for generating at least one workpiece. Using predicted process parameters to perform the injection molding process means not only relying on machine parameters and / or sensor parameters provided by the injection molding machine and / or at least one of its sensors, and / or available in the injection molding machine, but also taking into account external knowledge, such as knowledge from material suppliers (e.g., product-specific data, such as rheological data, viscosity, and / or algorithms) and / or specific data on the production materials. Using predicted process parameters allows for continuous improvement of the injection molding process. The manufactured workpiece can be measured, for example, by using optical or tactile measurement techniques, such as scanning. As used herein, the term "scanning" is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or customized meaning. The term can specifically refer to, but is not limited to, any process or procedure for examining any object or data. The scan can include determining the shape and dimensions of the workpiece. The scan can be specifically performed automatically. The scan can be performed autonomously by a computer or computer network.

[0049] The determined workpiece properties can be compared with the optimization target. This comparison may include determining the deviation from the target shape and / or target size (which may also be expressed as target size). If the difference between the determined properties and the optimization target is higher than the tolerance limit, the resulting workpiece is considered to deviate from the target shape and / or target size. This tolerance limit may depend on the accuracy of factors such as the determination of the properties and / or customer requirements.

[0050] If the properties of the generated workpiece deviate from the optimization target, at least one process parameter of the injection molding machine is adapted based on comparison.

[0051] For example, comparing the determined properties of the workpiece with the optimization target may reveal that the workpiece deviates from the desired shape, particularly exhibiting distortion, warping, wavy surfaces, and angular deviations. This could be caused by different shrinkage tendencies (shrinkage potential) in different regions of the workpiece. These shrinkage differences may be due to varying degrees of packing in different regions of the workpiece, as well as differences in the orientation of the fiber and polymer chains. Further causes could include unfavorable mold temperatures, molded workpieces with varying wall thicknesses, excessively high pressure gradients along the flow path, excessively short cooling times leading to the workpiece being removed from the mold at excessively high temperatures and deforming after removal, the use of unsuitable materials, or the glass fibers in glass fiber reinforced thermoplastics being primarily oriented in the flow direction. In the latter case, deviations occur if the orientation of the glass fibers varies at different locations. For example, this could be caused by flow offsets, directional effects at the ends of the flow path, weld lines, and gates. Based on the comparison, at least one of the following process parameters of the injection molding machine can be adjusted: changing the temperature of the mold half and the sliding core, increasing the cooling time, adapting the process to prevent seams or negative draft holding, changing the holding pressure, and changing the holding time. Furthermore, considering the comparison, the material used can be changed. In particular, materials with low warpage, such as mixtures with amorphous phases, can be used. Additionally, the workpiece design can be changed. The process parameters of the injection molding machine can be adapted relative to a predetermined hierarchy. For example, the mold temperature can be adapted first, then the cooling time. Subsequently, further process parameters can be adapted.

[0052] For example, comparing the identified workpiece properties with optimization objectives may reveal that the workpiece includes at least one dent mark. As used herein, the term "dent mark" is a broad term and is given its common and customary meaning to those skilled in the art, without limitation a specific or customary meaning. The term may specifically refer to, but is not limited to, indentations in the surface of a molded workpiece. Dent marks may occur primarily where the wall cross-section increases. This can lead to a localized increase in volumetric shrinkage, thereby pulling the surface layer inward. Sometimes, dent marks appear after injection from the mold when the thermal centers of the polymer heat the already cooled edge layers, causing them to yield. Sometimes they can only be identified by a difference in gloss compared to the surrounding area. Dent marks can have a variety of causes, such as if the volumetric shrinkage during the cooling phase is not adequately compensated by the holding pressure, or the workpiece design is unsuitable for processing this plastic (e.g., thicker sections of material, abrupt changes in wall thickness along the flow path), or there is no melt buffer, or there is a large pressure loss in the machine nozzle and / or gating system, or the workpiece is cast as a thin-walled piece. Based on the comparison, at least one of the following process parameters of the injection molding machine can be adapted: increasing holding pressure, increasing holding time, decreasing melt temperature, decreasing mold temperature, altering pressure transmission in each flow path by changing the wall thickness of the molded part, extending the metering stroke and adjusting the switching point as needed, adapting the sealing function of the check valve, adapting to barrel wear and expanding the flow cross-section of the flow path and gating system, and adapting the position of the part (e.g., in the area with the largest wall thickness). Furthermore, the design of the part can be changed. The process parameters of the injection molding machine can be adapted relative to a predetermined hierarchy. For example, holding pressure can be adapted first, followed by holding time, then melt temperature, and subsequently, further process parameters.

[0053] Using adapted process parameters, repeat the injection molding process, determine the properties of the generated workpiece, and compare these properties with the optimization target until the properties of the generated workpiece are at least within the predetermined tolerance and consistent with the optimization target.

[0054] Step d) includes determining at least one actual process parameter of the injection molding process. The injection molding machine can be configured to measure and / or monitor at least one process parameter during the injection molding process. The at least one actual process parameter can be, for example, measurable and / or monitorable during the injection molding process using at least one sensor. The term "during the injection molding process" can refer to the time span between the start and end of the injection molding process and / or the time span during which the expected process conditions are substantially equivalent to the process conditions in the injection molding process. The injection molding machine can be configured to measure the process parameter in real time and adapt the process parameter during operation. The injection molding machine can be configured to measure at least one actual process parameter in real time. The injection molding machine can be configured to adapt at least one actual process parameter during operation. If step c) includes determining multiple predicted process parameters, then step d) can include determining multiple process parameters, such as defining a set of process parameters for the injection molding process. The injection molding machine can include at least one sensor. The measurement parameters of the injection molding machine can be registered and transmitted to an external processing unit. An injection molding machine may include at least one element selected from the group consisting of: a temperature sensor; a pressure sensor; and a clock. For example, at least one actual process parameter may be 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 curing time, and at least one cooling or curing parameter (e.g., the yield of the cooling or curing medium, or the temperature of the cooling or curing medium). Step d) may include determining the set of actual process parameters to be optimized, particularly the actual process parameters corresponding to the predicted process parameters in step c). Therefore, in the optimization cycle, not only a single process parameter but also multiple process parameters can be used, particularly the set of process parameters defining the injection molding process.

[0055] Step d) further includes comparing actual process parameters and 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 the corresponding actual process parameters and the corresponding predicted process parameters, and adapting the simulation model based on the comparison. This comparison may include determining the deviation between the predicted and actual process parameters, or vice versa. If the difference is higher than a tolerance limit, the actual process parameter is considered to deviate from the predicted process parameter. This tolerance limit may depend on the measurement accuracy. The comparison can be performed by the internal processing unit of the injection molding machine. Information regarding the deviation and / or actual process parameters can be transmitted to an external processing unit. The external processing unit can be configured to adjust the simulation model, particularly the model parameters, based on information regarding the deviation and / or actual process parameters.

[0056] The method may further include outputting predicted process parameters and / or comparison results of actual process parameters with predicted process parameters via at least one output interface or port. The output may include a set of predicted process parameters and / or a set of comparison results of actual process parameters with predicted process parameters. As used herein, the term "output" is a broad term and is given its common and customary meaning to those skilled in the art, but is not limited to a specific or customized meaning. The term may specifically refer to, but is not limited to, the process of providing information to another system, data storage, person, or entity. As one embodiment, the output may be delivered via one or more interfaces (such as a computer interface or a human-machine interface). As one embodiment, the output may be delivered in one or more of a computer-readable, visible, or audible format. For example, the output may be delivered via at least one display, at least one microphone, etc.

[0057] Steps a) through d) can be repeated, wherein the adapted simulation model can be used in step a).

[0058] In another aspect of the invention, a computer program includes instructions that, when executed by a computer or computer system, cause the computer or computer system to perform the method according to the invention, particularly steps a) to d). For possible definitions of most terms used herein, reference may be made to the foregoing description of the computer-implemented method, or as further detailed below.

[0059] Specifically, computer programs may be stored on computer-readable data carriers and / or computer-readable storage media. As used herein, the terms "computer-readable data carrier" and "computer-readable storage media" may specifically refer to non-transitory data storage devices, such as hardware storage media on which computer-executable instructions are stored. Computer-readable data carriers or storage media may specifically be or may include storage media such as random access memory (RAM) and / or read-only memory (ROM).

[0060] This document further discloses and proposes a computer program product comprising instructions that, when executed by a computer or computer system, cause the computer or computer system to perform the computer-implemented methods as described above or as further described below. Therefore, for the possible definitions of most terms used herein, reference can again be made to the description of the methods disclosed in the first aspect of the invention.

[0061] In particular, a computer program product may include program code means stored on a computer-readable data carrier to perform methods according to one or more embodiments disclosed herein when the program is executed on a computer or computer network. As used herein, a computer program product refers to a program that is a tradable product. The product may generally exist in any format (e.g., paper format) or on a computer-readable data carrier. Specifically, the computer program product may be distributed via a data network.

[0062] This document further discloses and proposes a computer-readable storage medium comprising instructions that, when executed by a computer or computer system, cause the computer or computer system to perform a computer-implemented method as described above or as further described in detail below. Therefore, for the possible definitions of most terms used herein, reference can again be made to the description of the methods disclosed in the first aspect of the invention.

[0063] In another aspect, an automatic control system for an injection molding process in at least one injection molding machine is disclosed. The injection molding process is based on multiple process parameters.

[0064] The control system includes at least one external processing unit configured to simulate the injection molding process by applying an optimization algorithm on the simulation model based on an input parameter set including at least one simulation model, material-specific parameters, and injection molding machine parameters.

[0065] The control system includes at least one interface configured to provide predicted process parameters to an 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 generate at least one workpiece. The control system is configured to determine at least one attribute of the generated workpiece, compare the attribute with an optimization objective, and adapt at least one process parameter of the injection molding machine based on the comparison. The control system is configured to repeat the injection molding process, determine the attribute, compare the attribute with the optimization objective, and adapt the process parameters until the attribute of the generated workpiece is consistent with the optimization objective at least within a predetermined tolerance.

[0066] The control system is configured to determine at least one actual process parameter for the injection molding process. The control system is also configured to compare the actual process parameter with a predicted process parameter and to adapt the simulation model based on this comparison.

[0067] An automatic control system can be configured to perform the method according to the invention. Therefore, for most possible definitions of terms used herein, reference can again be made to the description of the method disclosed in the first aspect of the invention.

[0068] The methods, systems, and procedures of this invention offer numerous advantages over known methods, systems, and procedures in the art. In particular, the methods, systems, and procedures disclosed herein can improve the performance of injection molding processes compared to known devices, methods, and systems in the art. Simulations can be run on cloud solutions. This invention proposes running simulation models in the cloud to determine optimal parameters (to be processed), and this information can be correlated with actual parameters (such as the actual parameters being processed), enabling the running of fast and efficient evaluation loops. Through digital identity, the simulation model can also take into account material-specific properties to further improve the simulation.

[0069] In summary, without excluding other possible implementation schemes, the following implementation scheme can be envisioned:

[0070] Implementation Scheme 1 A computer-implemented method for controlling and / or monitoring at least one injection molding process in at least one injection molding machine, wherein the injection molding process is based on a plurality of process parameters, wherein the method includes the following steps:

[0071] a) Provides an input parameter set via at least one external processing unit, wherein the input parameter set includes at least one simulation model, material-specific parameters, and injection molding machine parameters;

[0072] b) An external processing unit that simulates the injection molding process based on the input parameter set and determines at least one predicted process parameter of the simulated injection molding process by applying an optimization algorithm on at least one optimization objective to the simulation model, wherein the predicted process parameter is provided to the injection molding machine via at least one interface.

[0073] c) Based on predicted process parameters, perform at least one injection molding process using an injection molding machine to generate at least one workpiece, determine at least one attribute of the generated workpiece, and compare the attribute with an optimization target, wherein if the attribute of the generated workpiece deviates from the optimization target, adapt at least one process parameter of the injection molding machine according to the comparison, repeat the injection molding process with the adapted process parameters, determine the attribute of the generated workpiece, and compare the attribute with the optimization target until the attribute of the generated workpiece is at least within a predetermined tolerance and consistent with the optimization target;

[0074] d) Determine at least one actual process parameter for the injection molding process, compare the actual process parameter with the predicted process parameter, and adapt the simulation model based on the comparison.

[0075] Implementation Scheme 2: The method described in the aforementioned implementation scheme, wherein steps a) to d) are repeated, wherein an adapted simulation model is used in step a).

[0076] Implementation Scheme 3 The method according to any of the foregoing implementation schemes, 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, holding pressure, holding time, cooling or curing time, and cooling or curing parameters.

[0077] Implementation Scheme 4: The method according to any of the foregoing implementation schemes, wherein the measurement parameters of the injection molding machine are registered and transmitted to an external processing unit, wherein the injection molding machine includes at least one element selected from the group consisting of: a temperature sensor; a pressure sensor; and a clock.

[0078] Implementation Scheme 5: The method according to any of the foregoing implementation schemes, wherein the simulation model includes a fill simulation.

[0079] Implementation Scheme 6: The method according to any of the foregoing implementation schemes, wherein the simulation model is configured to simulate filling a mold cavity with at least one molten material block.

[0080] Implementation Scheme 7: The method according to any of the foregoing implementation schemes, wherein the simulation model is configured to simulate the geometry and / or shape of the workpiece.

[0081] Implementation Scheme 8: The method according to any of the foregoing implementation schemes, wherein the simulation model includes strength analysis.

[0082] Implementation Scheme 9 The method according to any of the foregoing implementation schemes, wherein the material-specific parameter includes at least one parameter selected from the group consisting of: compressibility, flow characteristics, and temperature characteristics.

[0083] Implementation Scheme 10: The method according to any of the foregoing implementation schemes, wherein the simulation model is configured to take into account material-specific properties.

[0084] Implementation Scheme 11 The method according to any of the foregoing implementation schemes, wherein the simulation model is configured to take into account the batch attributes of the raw material batches.

[0085] Implementation Scheme 12 The method according to any of the foregoing implementation schemes, wherein the property of the workpiece is at least one element selected from the group consisting of: the weight of the workpiece, the size of the workpiece, and the warpage.

[0086] Implementation Scheme 13 The method according to any of the foregoing implementation schemes, wherein the optimization objective is at least one property of the workpiece.

[0087] Implementation Scheme 14 The method according to any of the foregoing implementation schemes, wherein the method further includes outputting the predicted process parameters and / or the comparison results of the actual process parameters and the predicted process parameters via at least one output interface or port.

[0088] Implementation Scheme 15: The method according to any of the foregoing implementation schemes, wherein the parameters of the simulation model are generated by using at least one artificial neural network.

[0089] Implementation Scheme 16 The method according to any of the foregoing implementation schemes, wherein the external processing unit is and / or includes a cloud computing system.

[0090] Implementation Scheme 17 A computer program comprising instructions that, when executed by a computer or computer system, cause the computer or computer system to perform the method according to any of the foregoing embodiments.

[0091] Implementation Scheme 18 A computer-readable storage medium comprising instructions that, when executed by a computer or computer system, cause to perform the method mentioned in any of the preceding embodiments.

[0092] Implementation Scheme 19: An automatic control system for an injection molding process in at least one injection molding machine, wherein the injection molding process is based on multiple process parameters, wherein the control system includes at least one external processing unit configured to simulate the injection molding process based on a set of input parameters, the set of input parameters including at least one simulation model, material-specific parameters, and injection molding machine parameters, and configured to apply optimization parameters in terms of at least one optimization objective to the simulation model, wherein the control system includes at least one interface configured to provide predicted process parameters to the injection molding machine, wherein the control system is configured to execute the injection molding process using the injection molding machine based on the predicted process parameters. Perform at least one injection molding process to generate at least one workpiece, wherein a control system is configured to determine at least one attribute of the generated workpiece, compare the attribute with an optimization objective, and adapt at least one process parameter of the injection molding machine based on the comparison, wherein the control system is configured to repeat the injection molding process, determine the attribute, compare the attribute with the optimization objective, and adapt the process parameter until the attribute of the generated workpiece is consistent with the optimization objective at least within a predetermined tolerance, wherein the control system is configured to determine at least one actual process parameter of the injection molding process, wherein the control system is configured to compare the actual process parameter with a predicted process parameter and adapt a simulation model based on the comparison.

[0093] Implementation Scheme 20: An automatic control system according to the foregoing implementation scheme, wherein the automatic control system is configured to perform the method mentioned in any of the foregoing implementation schemes. Attached Figure Description

[0094] Further optional features and embodiments will be disclosed in more detail in the following description of embodiments (preferably in conjunction with the dependent claims). The corresponding optional features may be implemented individually or in any feasible combination, as those skilled in the art will recognize. The scope of the invention is not limited to the preferred embodiments. Embodiments are schematically depicted in the accompanying drawings. In these drawings, the same reference numerals refer to the same or functionally similar elements.

[0095] In the attached diagram:

[0096] Figure 1 An exemplary embodiment of a computer-implemented method and an automatic control system for controlling and / or monitoring at least one injection molding process in at least one injection molding machine is shown; and

[0097] Figures 2A to 2D The experimental results are shown. Detailed Implementation

[0098] Figure 1 An exemplary embodiment of a computer-implemented method for controlling and / or monitoring at least one injection molding process in at least one injection molding machine 110 and an automatic control system 112 is shown.

[0099] 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 shaping 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. The mold may be a mold or a form, for example, a form that provides a casting or frame. In particular, as used herein, a mold may refer to any mold and / or form that includes at least one cavity, such as at least one form that provides a structure and / or cutouts. Specifically, a mold may be used in an injection molding process where at least one block of molten material may be injected into at least one cavity of the mold. As an example, a mold having at least one cavity may be used in a molding process for forming material. In particular, the block of molten material injected into the mold cavity may be given a negative form and / or geometry of the cavity. Specifically, a mold may be used to manufacture at least one workpiece 114, wherein the manufactured workpiece may have a negative form and / or shape of the mold cavity.

[0100] A molding process can be configured to manufacture at least one workpiece 114. Workpiece 114 can be any part or component. In particular, workpiece 114 can be or may include any component of a machine or apparatus. For example, workpiece 114 may at least partially have a negative shape of a mold or mold cavity in the molding process used to manufacture the part. Therefore, injection molding process can be or may refer to a shape-assigning procedure used to manufacture workpiece 114.

[0101] Injection molding is based on multiple process parameters. These process parameters can be settable and / or selectable and / or adjustable and / or configurable parameters that affect the injection molding process. Process parameters can relate to the operating conditions of the injection molding machine 110. In particular, process parameters can be injection molding machine parameters. For example, process parameters may include one or more of the following: polymer melt temperature, barrel temperature, injection unit temperature, screw speed, injection speed, holding pressure, holding time, cooling or curing time, and at least one cooling or curing parameter (such as the yield of the cooling or curing medium, or the temperature of the cooling or curing medium).

[0102] The method includes the following steps:

[0103] a) (represented by reference numeral 116) is provided with an input parameter set by at least one external processing unit 118, wherein the input parameter set includes at least one simulation model, material-specific parameters and injection molding machine parameters;

[0104] b) (represented by reference numeral 120) The external processing unit 118 simulates the injection molding process based on the input parameter set and determines at least one predicted process parameter 122 of the simulated injection molding process by applying an optimization algorithm on at least one optimization objective to the simulation model, wherein the predicted process parameter is provided to the injection molding machine 110 via at least one interface 126 (represented by reference numeral 124).

[0105] c) Based on predicted process parameters, at least one injection molding process is executed (indicated by reference numeral 130) using injection molding machine 110 to generate at least one workpiece 114, at least one attribute of the generated workpiece 114 is determined, and the attribute is compared with an optimization target (indicated by reference numeral 132), wherein if the attribute of the generated workpiece 114 deviates from the optimization target, at least one process parameter of injection molding machine 110 is adapted according to the comparison, the injection molding process is repeated with the adapted process parameters, the attribute of the generated workpiece 114 is determined, and the attribute is compared with the optimization target until the attribute of the generated workpiece 114 is at least within a predetermined tolerance and consistent with the optimization target;

[0106] d) (denoted by reference numeral 134) Determine at least one actual process parameter of the injection molding process, compare the actual process parameter with the predicted process parameter, and adapt (denoted by reference numeral 136) the simulation model based on the comparison.

[0107] 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, specifically configured for controlling and monitoring machine parameters. The external processing unit 118 may be configured to transmit data to and / or receive data from the internal processing unit via at least one communication interface. The internal processing unit may be configured to transmit data to and / or receive data from 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 include a cloud computing system.

[0108] External processing unit 118 may include at least one database. The database may be any collection of information. The database may be stored in at least one data storage device. External processing unit 118 may include at least one data storage device in which information is stored. In particular, the database may contain any collection of information. The data storage device may be or may include at least one element selected from the group consisting of: at least one server, at least one server system comprising multiple servers, at least one cloud server, or cloud computing infrastructure.

[0109] Providing a set of 116 input parameters may include retrieving and / or selecting the input parameter set. Retrieval may include processing by which a system (particularly a computer system) generates and / or obtains data from any data source (such as from a data storage device, a network, or a further computer or computer system). In particular, retrieval may be performed via at least one computer interface (such as via a port such as a serial or parallel port). Retrieval may include several sub-steps, such as obtaining one or more primary information items and generating secondary information by utilizing the primary information (such as by applying one or more algorithms to the primary information, for example, by using a processor).

[0110] The input parameter set may include information about the simulation model, material-specific parameters, and injection molding machine parameters. Injection molding machine parameters may be parameters that affect the operating conditions of the injection molding machine. Injection molding machine parameters may include settings for the machine components of the injection molding machine 110. Injection molding machine parameters may include specific values ​​and / or parameter curves. 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, holding pressure, holding time, cooling or curing time, and cooling or curing parameters (such as the yield of the cooling or curing medium and the temperature of the cooling or curing medium). Injection molding machine parameters may further include machine dimensions such as clamping force, tie rod clearance, injection unit, and machine equipment dimensions such as barrel diameter or maximum barrel temperature.

[0111] Material-specific parameters can be information about one or more materials used in the injection molding process. These parameters can be provided by the material supplier and / or downloaded from a website or other database. The material supplier may have product-specific data, such as rheological data, viscosity, and batch-specific data for each material produced. Material-specific parameters include at least one parameter selected from the group consisting of: compressibility, flow properties, and temperature properties. The material (particularly the material used in the molding process, for example, for manufacturing the workpiece) can be or may include a plastic material. Specifically, the plastic material can be or may include a thermoplastic material. Additionally or alternatively, the plastic material can be or may include a thermosetting material. Additionally or alternatively, the plastic material may include an elastic material. During the manufacture of workpiece 114, the material may be in a molten state.

[0112] The simulation model can be generated by software on external processing unit 118, or the simulation model can be a dataset in the software. The simulation model can include at least one trained and trainable model. External processing unit 118 can be configured to perform and / or execute at least one machine learning algorithm. The simulation model can be based on the results of at least one machine learning algorithm. Machine learning algorithms can include decision trees, Naive Bayes classification, nearest neighbor, neural networks, convolutional neural networks, generative adversarial networks, support vector machines, linear regression, logistic regression, random forests, and / or gradient boosting algorithms. Preferably, the machine learning algorithm is organized to process inputs with high dimensionality into outputs with much lower dimensionality. The algorithm can be trained using records of training data. The simulation model can include at least one algorithm and model parameters. The simulation model parameters can be generated using at least one artificial neural network. The simulation model (especially the model parameters) can be adapted in step d) so that it can be further trained.

[0113] The simulation model may include a digital twin of the injection molding process. The simulation model is configured to simulate the injection molding process. The simulation model may include filling simulation. Specifically, the simulation model may be configured to simulate filling a mold cavity with at least one molten material. The simulation model may be configured to simulate the manufacturing of a workpiece. The simulation model may be configured to simulate the geometry and / or shape of the workpiece. The simulation model may include strength analysis.

[0114] The simulation model can be configured to consider material-specific properties. The simulation model can include digital twins of the material. The simulation model can be configured to consider batch-specific properties of the raw material, such as the viscosity of the material batch.

[0115] It is possible to use simulation data, process data, and product-related data in the process optimization of cloud-based injection molding processes. As mentioned above, material suppliers may possess a wealth of product-specific data, such as rheological data, viscosity, and batch-specific data for each material produced. This invention proposes establishing a closed loop between simulation and the injection molding process, allowing simulation parameters to be directly applied to the injection molding process. Furthermore, conversely, process data can be used to optimize the modeling process using machine learning models. By using cloud-based digital twins of materials and injection molding processes, batch-specific information of the materials can be further linked to the simulation of the manufacturing process, thereby further improving the efficiency of the injection molding process.

[0116] The predicted process parameters 122 for simulated injection molding can be expected values ​​of process parameters, especially for achieving optimal manufacturing results and / or optimal use of resources.

[0117] Step b) may include at least one optimization step. Optimization may be a process of selecting the optimal set of parameters related to the optimization objective from a parameter space of possible parameters. The optimization objective may be at least one criterion under which optimization is performed. The optimization objective may include at least one optimization objective and accuracy and / or precision. The optimization objective may be at least one attribute of workpiece 114. The attribute of workpiece 114 may be at least one element selected from the group consisting of: weight of workpiece 114, dimensions of workpiece 114, and warpage. The optimization objective may be pre-specified, such as by at least one customer and / or at least one user of injection molding machine 110. The optimization objective may be a specification of at least one user. The user may select the optimization objective and the desired accuracy and / or precision. Predicted process parameters are provided to injection molding machine 110 via at least one interface (particularly via a communication interface).

[0118] In step c), the manufactured workpiece 114 can be measured, for example, using optical or tactile measurement techniques (such as scanning). Scanning may include determining the shape and dimensions of the workpiece 114. Scanning can be performed automatically. Scanning can be performed autonomously by a computer or computer network.

[0119] The determined properties of workpiece 114 can be compared with optimization targets. This comparison may include determining deviations from the target shape and / or target dimensions. If the difference between the determined properties and the optimization targets exceeds the tolerance limits, the resulting workpiece 114 is considered to deviate from the target shape and / or target dimensions. These tolerance limits may depend on the accuracy of factors such as the determination of properties and / or customer requirements.

[0120] If the properties of the generated workpiece 114 deviate from the optimization target, at least one process parameter of the injection molding machine 110 is adapted based on comparison. The injection molding process, using the adapted process parameters, repeatedly determines the properties of the generated workpiece 114 and compares these properties with the optimization target until the properties of the generated workpiece 114 are at least within a predetermined tolerance and consistent with the optimization target.

[0121] Step d) 134 includes determining at least one actual process parameter of the injection molding process. The injection molding machine 110 can be configured to measure and / or monitor at least one process parameter during the injection molding process. The injection molding machine 110 can be configured to measure process parameters in real time and adapt to process parameters during operation. The injection molding machine 110 may include at least one sensor. The measurement parameters of the injection molding machine 110 can be registered and transmitted 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.

[0122] Step d) 134 further includes comparing actual process parameters with predicted process parameters and adapting the simulation model based on the comparison. The comparison may include determining the deviation between the predicted and actual process parameters, or vice versa. If the difference exceeds a tolerance limit, the actual process parameters are considered to deviate from the predicted process parameters. This tolerance limit may depend on the measurement accuracy. The comparison can be performed by the internal processing unit of the injection molding machine. Information regarding the deviation and / or actual process parameters can be transmitted to an external processing unit. The external processing unit can be configured to adapt the simulation model, particularly the model parameters, based on information regarding the deviation and / or actual process parameters.

[0123] The method may also include outputting predicted process parameters and / or comparison results of actual process parameters with predicted process parameters via at least one output interface or port. The output may include a process of providing information to another system, data storage, person, or entity. As one embodiment, the output may be delivered via one or more interfaces (such as a computer interface or a human-machine interface). As one embodiment, the output may be delivered in one or more of a computer-readable format, a visible format, or an audible format.

[0124] Steps a) through d) can be repeated, wherein the adapted simulation model can be used in step a).

[0125] Furthermore, in Figure 1 An automatic control system 112 is shown. The injection molding process is based on multiple 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 a set of input parameters including at least one simulation model, material-specific parameters, and injection molding machine parameters, by applying an optimization algorithm on the simulation model in terms of at least one optimization objective. The control system 112 includes at least one interface, indicated 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 attribute of the generated workpiece 114, compare the attribute with the optimization objective, and adapt at least one process parameter of the injection molding machine 110 according to the comparison. The control system 112 is configured to repeat the injection molding process, determine attributes, compare the attributes with optimization objectives, and adapt process parameters until the attributes of the generated workpiece are consistent with the optimization objectives at least within predetermined tolerances. The control system 112 is configured to determine at least one actual process parameter for the injection molding process. The control system 112 is configured to compare actual process parameters with predicted process parameters and adapt the simulation model based on the comparison.

[0126] The automatic control system 112 can be configured to perform the method according to the invention. Therefore, for possible embodiments, reference can be made to the description of the method.

[0127] For example, comparing the determined properties of workpiece 114 with the optimization objective may reveal deviations from the desired shape, particularly warping such as twisting, warping, wavy surfaces, and angular deviations. This could be caused by different shrinkage tendencies (shrinkage potential) in different regions of workpiece 114. These shrinkage differences could be due to varying degrees of packing in different regions of workpiece 114, as well as differences in the orientation of the fiber and polymer chains. Further causes could include unfavorable mold temperatures, workpiece 114 with varying wall thicknesses, excessively high pressure gradients along the flow path, excessively short cooling times leading to workpiece 114 being removed from the mold at excessively high temperatures and deforming after removal, use of unfavorable materials, or the glass fiber reinforced thermoplastic glass fibers being primarily oriented in the flow direction. In the latter case, deviations occur if the glass fiber orientation varies at different locations. For example, this could be caused by flow offsets, or directional effects at the ends of the flow path, weld lines, and gates. Based on the comparison, at least one of the following process parameters of the injection molding machine 110 can be adapted: changing the temperature of the mold half and the sliding core, increasing the cooling time, adapting the process to prevent the molding from being seam-bound or held by negative draft, changing the holding pressure, and changing the holding time. Furthermore, considering the comparison, the materials used can be changed. In particular, materials with low warpage, such as mixtures with an amorphous phase, can be used. Additionally, the workpiece design can be changed. The process parameters of the injection molding machine can be adapted relative to a predetermined hierarchical structure. For example, the mold temperature can be adapted first, and then the cooling time can be adapted. Subsequently, further process parameters can be adapted. Figures 2A to 2C The effect of mold temperature on the warpage of clamping mandrels made of Ultraform® is shown. For Figures 2A to 2C The mold temperature of the cavity is 80℃. Figure 2A The mold temperature for the core is 80℃. Figure 2B It is 30℃. Figure 2C The temperature is 50℃. The gaps between the components holding the mandrel differ in each diagram. Figure 2A In the middle, the gap is 1.0 mm. Figure 2B It is 5.0 mm, while Figure 2C It is 2.4 mm. Figure 2D Another example of an insulation board made of glass fiber reinforced Ultradur® is shown. Figure 2D The upper part shows the geometry of the molded part optimized through simulation, while the lower part shows the original state.

[0128] Reference Marker

[0129] 110 Injection Molding Machine

[0130] 112 Automatic Control System

[0131] 114 workpieces

[0132] 116 provides a set of input parameters.

[0133] 118 External Processing Unit

[0134] 120 Simulation

[0135] 122 Predicted process parameters for simulated injection molding process

[0136] 124 provides predicted process parameters

[0137] 126 interface

[0138] 130 Execution

[0139] 132 Comparison

[0140] 134. Determine at least one actual process parameter.

[0141] 136 Adaptation

[0142] 138 interface

Claims

1. A computer-implemented method for controlling and / or monitoring at least one injection molding process in at least one injection molding machine (110), wherein the injection molding process is based on a plurality of process parameters, wherein the method comprises the following steps: a) providing an input parameter set by at least one external processing unit (118), wherein the input parameter set comprises at least one simulation model, material specific parameters and injection molding machine parameters; b) simulating the injection molding process based on the input parameter set by the external processing unit (118) and determining at least one predicted process parameter of the simulated injection molding process by applying at least one optimization algorithm in terms of an optimization goal on the simulation model, wherein the predicted process parameter is provided to the injection molding machine via at least one interface; c) performing the at least one injection molding process using the injection molding machine (110) based on the predicted process parameter to generate at least one workpiece (114), determining at least one property of the generated workpiece (114) and comparing the property with the optimization goal, wherein in case the property of the generated workpiece (114) deviates from the optimization goal, adapting at least one process parameter of the injection molding machine (110) according to the comparison, repeating the injection molding process with the adapted process parameter, determining the property of the generated workpiece (114) and comparing the property with the optimization goal until the property of the generated workpiece (114) at least within a predetermined tolerance coincides with the optimization goal, wherein the property is at least one element selected from the group comprising: weight of the workpiece, dimensions of the workpiece, warpage; 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.

2. The method according to the preceding claim, wherein the method steps a) to d) are repeated, wherein the adapted simulation model is used in step a).

3. The method of claim 1 or 2, 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, holding pressure, holding time, cooling or solidification time, cooling or solidification parameters.

4. The method according to claim 1 or 2, wherein the measured parameters of the injection molding machine (110) are registered and transmitted to an external processing unit (118), wherein the injection molding machine (110) comprises at least one element selected from the group comprising: Temperature sensor; pressure sensor; clock.

5. The method according to claim 1 or 2, wherein the simulation model comprises a filling simulation.

6. The method according to claim 1 or 2, wherein the simulation model is configured to simulate the filling of a mold cavity with at least one material melt block.

7. The method according to claim 1 or 2, wherein the simulation model is configured to simulate the geometry and / or shape of a workpiece.

8. The method according to claim 1 or 2, wherein the simulation model comprises a strength analysis.

9. The method of claim 1 or 2, wherein the material-specific parameters include at least one parameter selected from the group consisting of: Compressibility, flow characteristics, temperature characteristics.

10. The method according to claim 1 or 2, wherein the simulation model is configured to consider material specific properties.

11. The method according to claim 1 or 2, wherein the simulation model is configured to consider batch properties of a raw material batch.

12. The method according to claim 1 or 2, wherein the method further comprises outputting, via at least one output interface or port, a predicted process parameter and / or a comparison result of an actual process parameter and a predicted process parameter.

13. The method according to claim 1 or 2, wherein the external processing unit (118) is and / or comprises a cloud computing system.

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

15. An automatic control system (112) for an injection molding process in at least one injection molding machine (110), wherein the injection molding process is based on a plurality of process parameters, wherein the control system (112) comprises at least one external processing unit (118), wherein the external processing unit (118) is configured to simulate the injection molding process based on a set of input parameters, the set of input parameters comprising at least one simulation model, material specific parameters and injection molding machine parameters, and to apply optimization parameters in terms of at least one optimization objective on the simulation model, wherein the control system (112) comprises at least one interface configured to provide predicted process parameters to the injection molding machine (110), wherein 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), wherein the control system (112) is configured to determine at least one property of the generated workpiece (114), to compare the property to the optimization objective, and to adapt at least one process parameter of the injection molding machine (110) according to the comparison, wherein the control system is configured to repeat the injection molding process, to determine the property, to compare the property to the optimization objective, and to adapt the process parameter until the property of the generated workpiece (114) at least within a predetermined tolerance coincides with the optimization objective, wherein the control system (112) is configured to determine at least one actual process parameter of the injection molding process, wherein the control system (112) is configured to compare the actual process parameter and the predicted process parameter, and to adapt the simulation model based on the comparison, wherein the property is at least one element selected from the group comprising: Weight of the workpiece, dimensions of the workpiece, warpage.

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