Injection mold production method, device, electronic equipment and storage medium

Abstract

The application discloses a kind of injection mold production method, device, electronic equipment and storage medium, it is related to industrial internet production technology, the method comprises: obtaining the demand value corresponding to the basic process parameter contained in the injection mold to be molded;The demand value corresponding to the basic process parameter is input to intelligent recommendation model, and the recommended value corresponding to the production process parameter is output based on the optimal principle of energy consumption of intelligent recommendation model;Control injection molding machine based on the demand value corresponding to the basic process parameter and the recommended value corresponding to the production process parameter to produce the injection mold to be molded.The application can analyze the basic process parameter based on the optimal principle of energy consumption by intelligent recommendation model, to realize the energy saving of injection molding machine production.In the production of the injection mold to be molded using the demand value corresponding to the basic process parameter and the recommended value corresponding to the production process parameter provided in the embodiment, the beneficial effect of producing high-quality injection products with the lowest production cost can be achieved.

Description

Technical Field

[0001] This invention relates to the field of industrial internet production technology, and in particular to a method, apparatus, electronic device and storage medium for producing injection molds. Background Technology

[0002] Injection molding machines are major consumers of electricity in industry. Statistics show that the annual electricity consumption of injection molding machines nationwide is equivalent to the power generation of several Three Gorges Dams. Therefore, how to further achieve energy conservation, cost reduction, and efficiency improvement in the plastics processing industry, ushering in a new peak of industry development, is a key research issue. Generally, the same type of injection molding machine can produce plastic products corresponding to various injection molds; correspondingly, plastic products corresponding to the same injection mold may also be produced by various types of injection molding machines. However, how to produce high-quality injection molded products with the lowest production cost is a crucial problem that urgently needs to be solved. Summary of the Invention

[0003] This invention provides a method, apparatus, electronic device, and storage medium for producing injection molds, which can improve existing methods for producing injection molds.

[0004] In a first aspect, the present invention provides a method for producing injection molds, comprising:

[0005] Obtain the required values ​​corresponding to the basic process parameters contained in the injection mold;

[0006] The demand values ​​corresponding to the basic process parameters are input into the intelligent recommendation model, and the intelligent recommendation model outputs the recommended values ​​corresponding to the production process parameters based on the principle of optimal energy consumption.

[0007] The injection molding machine is controlled to produce the mold to be injected based on the required values ​​corresponding to the basic process parameters and the recommended values ​​corresponding to the production process parameters.

[0008] Optionally, the intelligent recommendation model includes an objective function and constraints;

[0009] The step of inputting the demand values ​​corresponding to the basic process parameters into the intelligent recommendation model, and the intelligent recommendation model outputting recommended values ​​corresponding to the production process parameters based on the principle of optimal energy consumption, includes:

[0010] After inputting the required values ​​corresponding to the basic process parameters into the objective function, the constraints are controlled to optimize the objective function based on the principle of optimal energy consumption, so as to obtain the recommended values ​​corresponding to the production process parameters.

[0011] Optionally, the constraints include a periodicity impact factor and a quality impact factor; the objective function includes an energy consumption impact factor.

[0012] The control constraints are optimized based on the principle of optimal energy consumption to solve the objective function and obtain recommended values ​​for the production process parameters, including:

[0013] When the energy consumption impact factor obtained by solving the constraint condition is minimized, the recommended values ​​corresponding to the production process parameters are obtained based on the periodic impact factor and the quality impact factor corresponding to the constraint condition.

[0014] Optionally, the intelligent recommendation model is obtained in the following way:

[0015] For any injection molding machine, obtain the sample dataset corresponding to each mold sample in the injection molding machine, and each sample dataset includes at least two sample parameters;

[0016] Determine the common relationship between sample parameters and energy consumption parameters in each mold sample, and mark the sample parameters with common relationships in each mold sample to obtain marked sample parameters;

[0017] The intelligent recommendation model is obtained by training the labeled sample parameters and unlabeled sample parameters in each of the sample datasets.

[0018] Optionally, each of the sample datasets includes basic sample parameters and production sample parameters;

[0019] Determining the common relationship between sample parameters and energy consumption parameters among each mold sample includes:

[0020] For each mold sample, a nonlinear relationship is established based on the basic sample parameters and the production sample parameters;

[0021] The common relationship between the basic sample parameters, the production sample parameters, and the energy consumption parameters is determined based on the nonlinear relationship of each mold sample.

[0022] Optionally, after determining the common relationship between sample parameters and energy consumption parameters in each of the mold samples, the method further includes:

[0023] Feature extraction is performed on the basic sample parameters and the production sample parameters to obtain the correlation between each basic sample parameter and the production sample parameter, wherein the correlation includes positive correlation and negative correlation;

[0024] Accordingly, the step of marking sample parameters with common relationships in each of the mold samples to obtain marked sample parameters includes:

[0025] The sample parameters that have common relationships and positive or negative correlations in each of the mold samples are labeled to obtain the labeled sample parameters.

[0026] Optionally, the production process parameters include at least one; the method further includes:

[0027] Receive adjustment operations for the recommended values ​​corresponding to any of the aforementioned production process parameters to obtain the target production process parameters;

[0028] The target production process parameters are input into the intelligent recommendation model to obtain the adjustment values ​​corresponding to the basic process parameters.

[0029] Secondly, the present invention provides an injection mold production apparatus, the apparatus comprising:

[0030] The acquisition module is used to acquire the required values ​​corresponding to the basic process parameters contained in the injection mold;

[0031] The input module is used to input the demand values ​​corresponding to the basic process parameters into the intelligent recommendation model, and the intelligent recommendation model outputs the recommended values ​​corresponding to the production process parameters based on the principle of optimal energy consumption.

[0032] The control module is used to control the injection molding machine to produce the mold to be injected based on the required values ​​corresponding to the basic process parameters and the recommended values ​​corresponding to the production process parameters.

[0033] Thirdly, the present invention also provides an electronic device, the electronic device comprising:

[0034] At least one processor; and

[0035] A memory communicatively connected to the at least one processor; wherein,

[0036] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the injection mold production method according to any embodiment of the present invention.

[0037] Fourthly, the present invention also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the injection mold production method according to any embodiment of the present invention.

[0038] The injection mold production solution provided in this embodiment of the invention first obtains the required values ​​corresponding to the basic process parameters of the mold to be injected; then, the required values ​​of the basic process parameters are input into an intelligent recommendation model, so that the intelligent recommendation model outputs recommended values ​​corresponding to the production process parameters based on the principle of optimal energy consumption; finally, the injection molding machine is controlled to produce the mold to be injected based on the required values ​​of the basic process parameters and the recommended values ​​of the production process parameters. The solution provided in this embodiment allows the intelligent recommendation model to analyze the basic process parameters based on the principle of optimal energy consumption, thereby achieving energy saving and consumption reduction during injection molding production. When using the required values ​​of the basic process parameters and the recommended values ​​of the production process parameters provided in this embodiment to produce the mold to be injected, it can achieve the beneficial effect of producing injection molded products of higher quality while minimizing production costs.

[0039] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0040] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a schematic flowchart of the injection mold production method provided by the present invention;

[0042] Figure 2 This is a schematic diagram of the injection mold production device provided by the present invention;

[0043] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0044] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0045] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0046] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0047] Figure 1 This is a schematic flowchart of an injection mold production method provided by the present invention. This embodiment is applicable to the production of injection molds. The method can be executed by an injection mold production device, which can be implemented in hardware and / or software and can be configured in a computer device such as a server. (Reference) Figure 1 The method may specifically include the following steps:

[0048] S110. Obtain the required values ​​corresponding to the basic process parameters contained in the injection mold.

[0049] The injection mold refers to the mold corresponding to the product to be produced. For the same injection mold, if the performance of the injection molding machine is different, or the production process of the injection molding product is different, the cost and quality of the produced injection molded product will vary. In view of this, the solution provided in this embodiment aims to enable the injection molding machine to produce the product corresponding to the injection mold based on optimal process parameters, thereby achieving the technical effects of energy saving, cost reduction, and efficiency improvement.

[0050] Furthermore, the aforementioned basic process parameters indicate the known parameters of the injection mold to be input when simulating and estimating the required energy consumption before using the injection mold to produce the product. For example, the basic process parameters may include the raw material type of the injection mold, product weight, mold size, mold temperature, cooling efficiency, hot runner temperature, material type, raw material requirement, etc. The specific content of the basic process parameters is not limited here and shall be subject to actual needs.

[0051] The required values ​​corresponding to the above basic process parameters represent the relevant values ​​input based on the actual production needs of injection molded products. For example, the required values ​​corresponding to the basic process parameters can indicate that the product weight is 100g and the mold temperature is 200°. The specific required values ​​corresponding to each basic process parameter are not listed here, and the actual production needs shall prevail.

[0052] One way to obtain the required values ​​corresponding to the basic process parameters contained in the injection mold is to integrate an application for energy consumption simulation before injection molding production on the client side. The application's visual interface includes fields for each basic process parameter to be filled in, and the user enters the corresponding required values ​​in each field.

[0053] S120. Input the demand values ​​corresponding to the basic process parameters into the intelligent recommendation model. The intelligent recommendation model outputs the recommended values ​​corresponding to the production process parameters based on the principle of optimal energy consumption.

[0054] The intelligent recommendation model outputs recommended values ​​for production process parameters based on the required values ​​corresponding to the basic process parameters. Here, production process parameters refer to the actual process parameters used by the injection molding machine when producing products according to the basic process parameters. For example, production process parameters may include barrel temperature, injection speed, holding pressure, holding time, switching holding positions, and cooling time, etc. The specific content of the production process parameters is not limited here and depends on actual needs.

[0055] Generally, an injection molding machine contains multiple mold samples, each containing corresponding sample parameters. In this embodiment, the intelligent recommendation model is trained based on optimizing the sample parameters of multiple mold samples corresponding to the same injection molding machine. Therefore, when the intelligent recommendation model outputs recommended values ​​for production process parameters based on the demand values ​​corresponding to the basic process parameters, it calculates based on the principle of energy consumption optimization. The output recommended values ​​for production process parameters represent the values ​​that minimize energy consumption and optimize the quality of the produced injection molded products.

[0056] Accordingly, in this embodiment, one injection molding machine can correspond to one intelligent recommendation model; alternatively, the sample parameters of multiple injection molding machines corresponding to multiple mold samples can be uniformly trained, and an intelligent recommendation model can be obtained for all injection molding machines. During the usage phase, the identifier of the target injection molding machine is input, and the output parameters are determined based on the identifier of the target injection molding machine in the intelligent recommendation model. Specifically, the number of models in this embodiment is not limited.

[0057] Specifically, the intelligent recommendation model provided in this embodiment is obtained through the following steps:

[0058] a) For any injection molding machine, obtain the sample dataset corresponding to each mold sample in the injection molding machine. Each sample dataset includes at least two sample parameters.

[0059] In this step, we first use orthogonal design to design experiments and obtain a sample dataset corresponding to all mold samples that can be used by an injection molding machine. The sample dataset contains sample parameters such as mold size, mold temperature, cooling efficiency, hot runner temperature, material type, product weight, and raw material requirements for each mold sample. The specific sample parameters included in each sample dataset are not limited here. For different mold samples, the sample dataset corresponding to each mold sample has certain differences. For multiple mold samples that can be produced by the same injection molding machine, some sample parameters among the multiple mold samples have certain commonalities.

[0060] b) Determine the common relationship between sample parameters and energy consumption parameters in each mold sample, and mark the sample parameters with common relationships in each mold sample to obtain marked sample parameters.

[0061] The purpose of determining the common relationship between sample parameters and energy consumption parameters in each mold sample is to identify the sample parameters that affect the energy consumption of product production. This allows for feature analysis of the sample parameters affecting product production energy consumption during the model training phase, resulting in a better performance of the intelligent recommendation model.

[0062] Specifically, each sample dataset includes basic sample parameters and production sample parameters. The current step b) can be achieved as follows: for each mold sample, establish a nonlinear relationship based on the basic sample parameters and production sample parameters; determine the common relationships between the basic sample parameters, production sample parameters, and energy consumption parameters based on the nonlinear relationship of each mold sample.

[0063] The aforementioned basic sample parameters may include the type of raw material for the injection mold, product weight, mold dimensions, mold temperature, cooling efficiency, hot runner temperature, material type, and raw material requirement, etc., which are the same as the indications of the basic process parameters in step S110, and are used to distinguish them as basic sample parameters during the sample training phase. The aforementioned production sample parameters may include production process parameters such as barrel temperature, injection speed, holding pressure, holding time, switching holding positions, and cooling time, which are the same as the indications of the production process parameters in step S110, and are used to distinguish them as production sample parameters during the sample training phase.

[0064] In the current stage, since each mold sample is a previously produced sample, it can also include relevant parameters such as energy consumption and product quality required to produce the product corresponding to the current mold sample. Furthermore, when establishing a nonlinear relationship based on the basic sample parameters and production sample parameters, the optimized Xgboot algorithm can be used for fitting to obtain the parameters that have the most significant impact on product quality and energy consumption, thus establishing a complex nonlinear relationship between the energy consumption of a single mold sample and the sample parameters.

[0065] Furthermore, in the process of fitting using the optimized XGBoost algorithm, the basic sample parameters can be used as input, and indicators such as energy consumption, production cycle, and quality can be used as target values ​​to be fitted. Multiple small models can be trained to output production sample parameters, etc.

[0066] When determining the commonalities between basic sample parameters, production sample parameters, and energy consumption parameters based on the nonlinear relationship of each mold sample, it may be possible to analyze the commonalities between different molds, such as the commonalities between mold 1 and mold 2 in product weight, and the commonalities between mold 1 and mold 3 in temperature, injection pressure, etc.

[0067] Furthermore, after determining the common relationship between sample parameters and energy consumption parameters in each mold sample, the process also includes: extracting features from the basic sample parameters and production sample parameters to obtain the correlation between each basic sample parameter and the production sample parameter, including positive and negative correlations.

[0068] The purpose of obtaining the correlation between each basic sample parameter and the production sample parameter in this step is to discover, during correlation analysis, a positive correlation between cooling time and production cycle, and a relationship between injection speed and multiple quality indicators. These relationships have a significant impact and can be used as a guide for parameter adjustment during experiments. In the product manufacturing process, all quality indicators have a defined range that must be met; therefore, an accurate value is needed. When considering the influencing factors of this value, all possible factors must be taken into account to make the trained intelligent recommendation model more accurate.

[0069] Accordingly, the sample parameters with common relationships in each mold sample are labeled to obtain labeled sample parameters. This includes labeling sample parameters with common relationships and those with positive or negative correlations in each mold sample. By labeling sample parameters with common relationships and those with positive or negative correlations in this step, it is helpful to better train the model performance based on the labeled samples during the model training phase.

[0070] c) Train the intelligent recommendation model by combining the labeled and unlabeled sample parameters in each sample dataset.

[0071] That is, the sample datasets of Mold 1, Mold 2, Mold 3, ... Mold N are split into training and test sets respectively. The training set is used for algorithm development, and the test set is used for testing.

[0072] The established model is then used as the fitness function of the genetic algorithm. Employing a hierarchical optimization approach, the quality of products with higher importance is optimized first to obtain the optimal parameters that meet the quality requirements. Based on these parameters, energy consumption is then optimized to obtain the optimal parameters that ensure both product quality and energy efficiency. Simulation software is used to perform flow simulation analysis on the recommended process parameters to determine their impact on key product quality parameters and the final product quality. If the key parameters meet the quality requirements, the product validation team will conduct on-site testing. If the resulting product meets both quality and energy consumption requirements, mass production can proceed. If not, the sample dataset must be updated, a new prediction model must be established, and the genetic algorithm will be used again for optimization until the optimal parameter combination that meets both product quality and energy consumption requirements is obtained.

[0073] In a preferred embodiment, the intelligent recommendation model includes an objective function and constraints. Step S120 can be implemented as follows: after inputting the demand values ​​corresponding to the basic process parameters into the objective function, the control constraints optimize the objective function based on the principle of optimal energy consumption to obtain recommended values ​​for the production process parameters. Further, the constraints include a cycle influence factor and a quality influence factor; the objective function includes an energy consumption influence factor. The optimization of the objective function based on the principle of optimal energy consumption to obtain recommended values ​​for the production process parameters includes: when the energy consumption influence factor obtained from the constraint solution is minimized, obtaining recommended values ​​for the production process parameters based on the cycle influence factor and quality influence factor corresponding to the constraint conditions. That is, the recommended values ​​for the production process parameters output by the intelligent recommendation model represent the production process parameters with the lowest energy consumption, shortest production cycle, and best production quality for the current injection molding machine.

[0074] S130, Control the injection molding machine to produce the injection mold based on the required values ​​corresponding to the basic process parameters and the recommended values ​​corresponding to the production process parameters.

[0075] That is, if the recommended values ​​of the production process parameters are the optimal parameters for the user's needs, then the injection molding machine can be further controlled to produce the injection mold based on the required values ​​of the basic process parameters and the recommended values ​​of the production process parameters, thereby achieving energy saving, cost reduction and efficiency improvement in the field of injection molding production.

[0076] Furthermore, in this embodiment, the producible process parameters include at least one, and any producible process parameter can be adjusted to analyze relevant information of the basic process parameters. Specifically, the method further includes: receiving an adjustment operation on the recommended value corresponding to any of the said production process parameters to obtain the target production process parameter; inputting the target production process parameter into the intelligent recommendation model to obtain the adjusted value corresponding to the basic process parameter. In this step, the user can arbitrarily modify the recommended value corresponding to the production process parameter, so that the intelligent recommendation model can back-calculate based on the adjusted target production process parameter to obtain the adjusted value corresponding to the basic process parameter, i.e., obtain the adjusted input parameters, etc.

[0077] Optionally, for the same injection mold, if multiple injection molding machines can produce it, the required values ​​corresponding to the basic process parameters can be input into the intelligent recommendation model corresponding to each injection molding machine, thereby obtaining the recommended values ​​corresponding to the production process parameters of each injection molding machine. Based on the recommended values ​​corresponding to the production process parameters of each injection molding machine, it can be determined which injection molding machine requires the least energy to produce the current injection mold.

[0078] The injection mold production method provided in this embodiment first obtains the required values ​​corresponding to the basic process parameters of the mold to be injection molded; then, the required values ​​of the basic process parameters are input into an intelligent recommendation model, so that the intelligent recommendation model outputs recommended values ​​corresponding to the production process parameters based on the principle of optimal energy consumption; finally, the injection molding machine is controlled to produce the mold to be injection molded based on the required values ​​of the basic process parameters and the recommended values ​​of the production process parameters. The solution provided in this embodiment allows the intelligent recommendation model to analyze the basic process parameters based on the principle of optimal energy consumption, thereby achieving energy saving and consumption reduction during injection molding machine production. When using the required values ​​of the basic process parameters and the recommended values ​​of the production process parameters provided in this embodiment to produce the molded product with higher quality at the lowest production cost, the beneficial effect of producing injection molded products can be achieved.

[0079] Figure 2 This is a schematic diagram of an injection mold production apparatus provided by the present invention. This apparatus is suitable for performing the injection mold production method provided in this embodiment. Figure 2 As shown, the device may specifically include: an acquisition module 210, an input module 220, and a control module 230, wherein:

[0080] The acquisition module 210 is used to acquire the required values ​​corresponding to the basic process parameters contained in the injection mold;

[0081] Input module 220 is used to input the demand values ​​corresponding to the basic process parameters into the intelligent recommendation model, and the intelligent recommendation model outputs the recommended values ​​corresponding to the production process parameters based on the principle of optimal energy consumption.

[0082] The control module 230 is used to control the injection molding machine to produce the mold to be injected based on the required values ​​corresponding to the basic process parameters and the recommended values ​​corresponding to the production process parameters.

[0083] The injection mold production apparatus provided in this embodiment of the invention first obtains the required values ​​corresponding to the basic process parameters of the mold to be injected; then, it inputs these required values ​​into an intelligent recommendation model, enabling the model to output recommended values ​​for the production process parameters based on the principle of optimal energy consumption; finally, it controls the injection molding machine to produce the mold based on the required values ​​of the basic process parameters and the recommended values ​​of the production process parameters. The solution provided in this embodiment allows the intelligent recommendation model to analyze the basic process parameters based on the principle of optimal energy consumption, thereby achieving energy saving and consumption reduction during injection molding. When using the required values ​​of the basic process parameters and the recommended values ​​of the production process parameters provided in this embodiment to produce the molded product with the highest quality while minimizing production costs, the beneficial effect of producing high-quality injection molded products can be achieved.

[0084] In one embodiment, the intelligent recommendation model includes an objective function and constraints;

[0085] The input module 220 is specifically used to input the required values ​​corresponding to the basic process parameters into the objective function, and then control the constraints to optimize the objective function based on the principle of optimal energy consumption, so as to obtain the recommended values ​​corresponding to the production process parameters.

[0086] In one embodiment, the constraints include a periodicity impact factor and a quality impact factor; the objective function includes an energy consumption impact factor.

[0087] The input module 220 is further configured to obtain recommended values ​​for the production process parameters based on the periodicity influence factor and the quality influence factor corresponding to the constraint conditions when the energy consumption influence factor obtained by solving the constraint conditions is minimized.

[0088] In one embodiment, the apparatus further includes: a labeling module and a training module, wherein:

[0089] The acquisition module 210 is also configured to acquire, for any injection molding machine, a sample dataset corresponding to each mold sample in the injection molding machine, wherein each sample dataset includes at least two sample parameters;

[0090] A labeling module is used to determine the common relationship between sample parameters and energy consumption parameters in each mold sample, and to label the sample parameters with common relationships in each mold sample to obtain labeled sample parameters;

[0091] The training module is used to train the intelligent recommendation model by training the labeled sample parameters and unlabeled sample parameters in each of the sample datasets.

[0092] In one embodiment, each of the sample datasets includes basic sample parameters and production sample parameters;

[0093] The marking module is specifically used to establish a nonlinear relationship for each mold sample based on the basic sample parameters and the production sample parameters; and to determine the common relationship between the basic sample parameters, the production sample parameters and the energy consumption parameters based on the nonlinear relationship of each mold sample.

[0094] In one embodiment, the apparatus further includes: an extraction module, wherein:

[0095] The extraction module is used to extract features from the basic sample parameters and the production sample parameters to obtain the correlation between each basic sample parameter and the production sample parameter, wherein the correlation includes positive correlation and negative correlation.

[0096] The labeling module is specifically used to label the sample parameters in each of the mold samples that have common relationships and those that have positive or negative correlations, so as to obtain the labeled sample parameters.

[0097] In one embodiment, the production process parameters include at least one; the device further includes: an adjustment module, wherein:

[0098] The adjustment module is used to receive adjustment operations for the recommended values ​​corresponding to any of the production process parameters, and to obtain the target production process parameters;

[0099] The input module 220 is also used to input the target production process parameters into the intelligent recommendation model to obtain the adjustment value corresponding to the basic process parameters.

[0100] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0101] The present invention also provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the injection mold production method according to any embodiment of the present invention.

[0102] The present invention also provides a computer-readable medium storing computer instructions that, when executed by a processor, implement the injection mold production method according to any embodiment of the present invention.

[0103] The following is for reference. Figure 3 It shows a schematic diagram of the structure of a computer system 500 suitable for implementing the electronic device of the present invention. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of this embodiment.

[0104] like Figure 3 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 502 or programs loaded from storage section 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the system 500. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0105] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 510 as needed so that computer programs read from it can be installed into storage section 508 as needed.

[0106] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs the functions defined above in the system of this invention.

[0107] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0108] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0109] The modules and / or units described in this embodiment can be implemented in software or hardware. The described modules and / or units can also be housed in a processor; for example, a processor can be described as including an acquisition module, an input module, and a control module. The names of these modules do not necessarily limit the functionality of the module itself.

[0110] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to include: acquiring demand values ​​corresponding to basic process parameters of the mold to be injected; inputting the demand values ​​corresponding to the basic process parameters into an intelligent recommendation model, wherein the intelligent recommendation model outputs recommended values ​​corresponding to production process parameters based on the principle of optimal energy consumption; and controlling an injection molding machine to produce the mold to be injected based on the demand values ​​corresponding to the basic process parameters and the recommended values ​​corresponding to the production process parameters.

[0111] According to the technical solution of this embodiment, the intelligent recommendation model can analyze basic process parameters based on the principle of optimal energy consumption to achieve energy saving and consumption reduction during injection molding machine production. When using the required values ​​corresponding to the basic process parameters and the recommended values ​​corresponding to the production process parameters provided in this embodiment to produce injection molded products, it can achieve the beneficial effect of producing high-quality injection molded products with the lowest production cost.

[0112] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for producing injection molds, characterized in that, include: Obtain the required values ​​corresponding to the basic process parameters contained in the injection mold; The demand values ​​corresponding to the basic process parameters are input into the intelligent recommendation model, and the intelligent recommendation model outputs the recommended values ​​corresponding to the production process parameters based on the principle of optimal energy consumption. The injection molding machine is controlled to produce the mold to be injected based on the required values ​​corresponding to the basic process parameters and the recommended values ​​corresponding to the production process parameters; The intelligent recommendation model is obtained through the following method: For any injection molding machine, obtain the sample dataset corresponding to each mold sample in the injection molding machine, each sample dataset including at least two sample parameters; each sample dataset includes basic sample parameters and production sample parameters; For each mold sample, an optimized XGBoost algorithm is used to establish a nonlinear relationship based on the basic sample parameters and the production sample parameters to obtain the parameters that have the most significant impact on product quality and energy consumption. Based on the nonlinear relationship of each mold sample, the common relationship between the basic sample parameters, the production sample parameters, and the energy consumption parameters is determined, and the sample parameters with common relationships in each mold sample are marked to obtain the marked sample parameters. The intelligent recommendation model is obtained by training the labeled sample parameters and unlabeled sample parameters in each of the sample datasets.

2. The injection mold production method according to claim 1, characterized in that, The intelligent recommendation model includes an objective function and constraints; The step of inputting the demand values ​​corresponding to the basic process parameters into the intelligent recommendation model, and the intelligent recommendation model outputting recommended values ​​corresponding to the production process parameters based on the principle of optimal energy consumption, includes: After inputting the required values ​​corresponding to the basic process parameters into the objective function, the constraints are controlled to optimize the objective function based on the principle of optimal energy consumption, so as to obtain the recommended values ​​corresponding to the production process parameters.

3. The injection mold production method according to claim 2, characterized in that, The constraints include a periodicity impact factor and a quality impact factor; the objective function includes an energy consumption impact factor. The control constraints are optimized based on the principle of optimal energy consumption to solve the objective function and obtain recommended values ​​for the production process parameters, including: When the energy consumption impact factor obtained by solving the constraint condition is minimized, the recommended values ​​corresponding to the production process parameters are obtained based on the periodic impact factor and the quality impact factor corresponding to the constraint condition.

4. The injection mold production method according to claim 1, characterized in that, After determining the common relationship between sample parameters and energy consumption parameters in each of the mold samples, the method further includes: Feature extraction is performed on the basic sample parameters and the production sample parameters to obtain the correlation between each basic sample parameter and the production sample parameter, wherein the correlation includes positive correlation and negative correlation; Accordingly, the sample parameters with common relationships in each of the mold samples are labeled to obtain labeled sample parameters, including: The sample parameters that have common relationships and positive or negative correlations in each of the mold samples are labeled to obtain the labeled sample parameters.

5. The injection mold production method according to claim 1, characterized in that, The production process parameters include at least one; the method further includes: Receive adjustment operations for the recommended values ​​corresponding to any of the aforementioned production process parameters to obtain the target production process parameters; The target production process parameters are input into the intelligent recommendation model to obtain the adjustment values ​​corresponding to the basic process parameters.

6. An injection mold production apparatus, characterized in that, include: The acquisition module is used to acquire the required values ​​corresponding to the basic process parameters contained in the injection mold; The input module is used to input the demand values ​​corresponding to the basic process parameters into the intelligent recommendation model, and the intelligent recommendation model outputs the recommended values ​​corresponding to the production process parameters based on the principle of optimal energy consumption. The control module is used to control the injection molding machine to produce the mold to be injected based on the required values ​​corresponding to the basic process parameters and the recommended values ​​corresponding to the production process parameters; The acquisition module is also used to acquire, for any injection molding machine, a sample dataset corresponding to each mold sample in the injection molding machine, each sample dataset including at least two sample parameters, and each sample dataset including basic sample parameters and production sample parameters; The device further includes a labeling module and a training module, wherein: The labeling module is used to establish a nonlinear relationship between the basic sample parameters and the production sample parameters for each mold sample using an optimized XGBoost algorithm, so as to obtain the parameters that have the most important impact on product quality and energy consumption; determine the common relationship between the basic sample parameters, the production sample parameters and the energy consumption parameters according to the nonlinear relationship of each mold sample, and label the sample parameters with common relationships in each mold sample to obtain labeled sample parameters; The training module is used to train the intelligent recommendation model by training the labeled sample parameters and unlabeled sample parameters in each of the sample datasets.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the injection mold production method according to any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the injection mold production method as described in any one of claims 1-5.

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