An intelligent recommendation method and system for injection mold production process parameters

Abstract

The present invention discloses an intelligent recommendation method and system for injection mold production process parameters, the method comprising obtaining a three-dimensional model of a product to be injected, a group of influencing factors corresponding to the injection product, and an initial production process parameter group obtained based on the three-dimensional model of the product to be injected and the group of influencing factors; generating a first compensation ratio group according to a first reference production process parameter group and a first actual production process parameter group; generating a second compensation ratio group for optimizing the first compensation ratio group according to a second reference production process parameter group and a second actual production process parameter group; performing corresponding optimization on the initial production process parameter group based on the optimized first compensation ratio group to obtain an optimal production process parameter group, and recommending the optimal production process parameter group to a user. Thus, the optimized optimal production process parameters can be quickly obtained, production efficiency and quality can be improved, production cycle can be shortened, and production cost can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent recommendation method and system for injection mold production process parameters. Background Art

[0002] The injection mold production process refers to the process of making semi-finished products of a certain shape by pressurizing, injecting, cooling, and separating molten raw materials. It mainly includes six stages: mold closing, filling, pressure holding, cooling, mold opening, and demolding. Granular or powdered plastics are fed from the hopper of the injection molding machine into the heated barrel, heated and plasticized into a molten state, pushed by a screw or plunger to apply a large pressure, and quickly injected into the low-temperature closed mold cavity through the nozzle at the front end of the barrel. After cooling and solidification, the shape given by the mold cavity is obtained, and the mold is opened to obtain the desired injection molded product.

[0003] Due to the nonlinear and multivariable characteristics of injection molding, the quality of injection molded products is affected by many factors, such as product functional structure, mold cooling system, injection material fluidity and thermodynamic properties, injection molding machine model parameters and performance, etc., which will result in different generation process parameters, such as melt temperature, mold temperature, release agent use, injection pressure, injection time, etc. Therefore, in the injection molding production process, quickly optimizing process parameters is the key to ensuring product quality and efficiency.

[0004] At present, injection molding enterprises still mainly rely on the design experience of technicians to adjust the product quality through repeated mold trials and changes in injection molding process parameters. Since this method is only applicable to technicians with rich experience and the technicians need to be very familiar with the parameters and performance of the injection molding machine, this method often leads to problems such as extended injection molding production cycle and increased production costs, and the flexibility is low, which cannot meet the actual production needs. Summary of the invention

[0005] In order to solve at least one of the technical problems mentioned above, the present invention provides an intelligent recommendation method and system for injection mold production process parameters.

[0006] In a first aspect, the present invention provides an intelligent recommendation method for injection mold production process parameters, comprising:

[0007] Obtaining a three-dimensional model of a product to be injection molded, a group of influencing factors corresponding to the product to be injection molded, and an initial production process parameter group obtained based on the three-dimensional model of the product to be injection molded and the group of influencing factors; wherein the group of influencing factors includes product category, injection molding material, injection molding machine type, and injection mold structure;

[0008] generating a first compensation ratio group according to a first reference production process parameter group and a first actual production process parameter group associated with a reference influencing factor group similar to the influencing factor group;

[0009] generating a second compensation ratio group for optimizing the first compensation ratio group according to a second reference production process parameter group similar to the initial production process parameter group and a second actual production process parameter group corresponding to the second reference production process parameter group;

[0010] Based on each compensation ratio in the optimized first compensation ratio group, each initial production process parameter group in the initial production process parameter group is correspondingly adjusted to obtain an optimal production process parameter group, and the optimal production process parameter group is recommended to the user as the actual production process parameter corresponding to the product to be injected and the influencing factor group.

[0011] Preferably, obtaining a three-dimensional model of a product to be injection molded, a group of influencing factors corresponding to the product to be injection molded, and an initial production process parameter group obtained based on the three-dimensional model of the product to be injection molded and the group of influencing factors includes:

[0012] Acquire a three-dimensional model of the product to be injected input by a user;

[0013] According to the structural parameters of the three-dimensional model and the material data in the production order, the product category, injection molding material, injection molding machine type and injection mold structure corresponding to the product to be injected are determined to obtain the influencing factor group; wherein the product category includes one or more of complex products, large thin-walled products, high-performance engineering plastic products, microstructure products and multi-material combinations;

[0014] The initial production process parameter group is obtained by performing simulation and optimization based on the influencing factor group.

[0015] Preferably, simulation and optimization are performed based on the influencing factor group to obtain the initial production process parameter group, including:

[0016] Inputting key parameters corresponding to the injection molding material, the injection molding machine model and the injection mold structure into a simulation system respectively to obtain a simulation production process parameter group;

[0017] Based on the product category, the first significant parameter in the simulated production process parameter group is optimized, and the optimized first significant parameter is replaced into the simulated production process parameter group;

[0018] Use the prediction model to predict the replaced simulated production process parameter group to obtain a prediction result;

[0019] Determining whether the prediction result is within a preset allowable error range corresponding to the product to be injected;

[0020] If yes, the simulated production process parameter group is used as the initial production process parameter group;

[0021] If not, re-execute the optimization and the steps after the optimization according to the difference between the prediction result and the preset allowable error range until the prediction result is within the preset allowable error range corresponding to the product to be injected.

[0022] Preferably, generating a first compensation ratio group according to a first reference production process parameter group and a first actual production process parameter group associated with a reference influencing factor group similar to the influencing factor group includes:

[0023] Based on the influencing factor group, determining the most similar influencing factor group from the influencing factor groups stored in the first database as the reference influencing factor group;

[0024] Acquire a first reference production process parameter group and a first actual production process parameter group associated with the reference influencing factor group;

[0025] Based on each actual production process parameter in the first actual production process parameter group, the compensation ratio of each reference production process parameter in the first reference production process parameter group is calculated respectively to obtain the first compensation ratio group.

[0026] Preferably, based on the influencing factor group, determining the most similar influencing factor group from the influencing factor groups stored in the first database as the reference influencing factor group includes:

[0027] Obtaining a historical influencing factor group corresponding to a historical injection molding product and a preset influencing factor group corresponding to a preset injection molding product, and storing them in the first database; wherein the historical influencing factor group and the preset influencing factor group are both correspondingly associated with a reference production process parameter group and an actual production process parameter group;

[0028] respectively evaluating the similarity between each historical influencing factor group in the first database and the influencing factor group to obtain a first similarity group;

[0029] Identify a first similarity with the highest similarity from the first similarity groups, and mark the historical influencing factor group corresponding to the first similarity with the highest similarity as a pending historical influencing factor group;

[0030] Determining whether the first similarity of the pending historical influencing factor group is greater than a first preset value;

[0031] If yes, the undetermined historical influencing factor group is used as the reference influencing factor group that is most similar to the influencing factor group;

[0032] The reference production process parameter group and the actual production process parameter group associated with the reference influencing factor group are used as the first reference production process parameter group and the first actual production process parameter group.

[0033] Preferably, after the step of judging whether the first similarity of the to-be-determined historical influencing factor group is greater than a first preset value, the method further includes:

[0034] If not, respectively evaluate the similarity between each preset influencing factor group in the first database and the influencing factor group to obtain a second similarity group;

[0035] Identify the second similarity with the highest similarity from the second similarity group, and mark the preset influencing factor group corresponding to the second similarity with the highest similarity as a pending preset influencing factor group;

[0036] Determine whether the difference between the second similarity of the pending preset influencing factor group and the first similarity of the pending historical influencing factor group is greater than a second preset value;

[0037] If so, taking the undetermined preset influencing factor group as the reference influencing factor group that is most similar to the influencing factor group;

[0038] If not, the undetermined historical influencing factor group is used as the reference influencing factor group that is most similar to the influencing factor group.

[0039] Preferably, according to a second reference production process parameter group similar to the initial production process parameter group, and a second actual production process parameter group corresponding to the second reference production process parameter group, a second compensation ratio group for optimizing the first compensation ratio group is obtained, comprising:

[0040] According to the reference influencing factor group, the first actual production process parameter group, and the injection molding product quality parameter obtained based on the first actual production process parameter group, respectively obtain a first significant factor from the reference influencing factor group and obtain a second significant parameter from the first actual production process parameter group;

[0041] According to the first significant factor and the second significant parameter, respectively obtain a second significant factor from the influencing factor group and obtain a corresponding third significant parameter from the initial production process parameter group to obtain a second significant factor group and a third significant parameter group;

[0042] Determine from the first database a plurality of influencing factor groups matching the second significant factor group, to obtain a plurality of pending influencing factor groups;

[0043] Respectively obtaining reference production process parameter groups associated with the plurality of pending influencing factor groups to obtain a plurality of reference production process parameter groups to be matched;

[0044] Respectively evaluating the similarities between the plurality of reference production process parameter groups to be matched and the third significant parameter group to obtain a third similarity group;

[0045] Identify the third similarity with the highest similarity from the third similarity groups, and mark the reference production process parameter group to be matched corresponding to the third similarity as the second reference production process parameter group;

[0046] Based on each actual production process parameter in the second actual production process parameter group corresponding to the second reference production process parameter group, the compensation ratio of each reference production process parameter in the second reference production process parameter group is calculated respectively to obtain the second compensation ratio group.

[0047] Preferably, it also includes:

[0048] The compensation ratios in the first compensation ratio group are respectively set correspondingly to the compensation ratio groups in the second compensation ratio group to obtain a plurality of compensation ratio groups to be optimized; wherein the number of the plurality of groups is equal to the number of compensation ratios in the first compensation ratio group;

[0049] A corresponding weight value is configured for each parameter in each compensation ratio group to be optimized, and a weighted sum is performed on each parameter in each compensation ratio group to be optimized based on the weight value to calculate the optimized compensation ratio; wherein the weight value of the parameter of the first compensation ratio group in each compensation ratio group to be optimized is greater than the weight value of the parameter of the second compensation ratio group;

[0050] The optimized compensation ratios of each compensation ratio group to be optimized are obtained after calculation, and the first compensation ratio after optimization is obtained.

[0051] Preferably, obtaining the optimized compensation ratios of each compensation ratio group to be optimized after calculation to obtain the first compensation ratio after adjustment includes:

[0052] The optimized compensation ratio is tuned based on environmental parameters.

[0053] In a second aspect, the present invention further provides an intelligent recommendation system for injection mold production process parameters, comprising:

[0054] An acquisition module, used to acquire a three-dimensional model of a product to be injection molded, a group of influencing factors corresponding to the product to be injection molded, and an initial production process parameter group formed by the three-dimensional model of the product to be injection molded and the group of influencing factors; wherein the group of influencing factors includes product category, injection molding material, injection molding machine type and injection mold structure;

[0055] A first generating module, configured to generate a first compensation ratio group according to a first reference production process parameter group and a first actual production process parameter group associated with a reference influencing factor group similar to the influencing factor group;

[0056] A second generating module, configured to generate a second compensation ratio group for optimizing the first compensation ratio group according to a second reference production process parameter group similar to the initial production process parameter group and a second actual production process parameter group corresponding to the second reference production process parameter group;

[0057] A recommendation module is used to optimize each initial production process parameter group in the initial production process parameter group based on each compensation ratio in the optimized first compensation ratio group to obtain an optimal production process parameter group, and recommend the optimal production process parameter group to the user as the actual production process parameter corresponding to the product to be injected and the influencing factor group.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1) The intelligent recommendation method for injection mold production process parameters provided by the present invention first determines the corresponding initial production process parameter group based on the three-dimensional model of the product to be injection molded and the influencing factor group, can comprehensively consider multiple factors for parameters, and quickly output multiple production process parameters after comprehensive consideration of multiple factors, thereby reducing the trial and error time and cost of technicians.

[0060] 2) According to the influencing factor group corresponding to the injection molded product, a similar reference influencing factor group is matched, and a first compensation ratio group is generated according to the first reference production process parameter group and the first actual production process parameter group associated with the reference influencing factor group; since the initial production process parameter group is only obtained based on simulation under ideal conditions, and since the actual conditions of the corresponding influencing factor group are different in the actual injection molding production process, the resulting parameter changes are also different. Therefore, the present invention takes into account the actual conditions of the influencing factor group in the actual injection molding production process, and takes into account the relationship between the initial production process parameter group and the actual production process parameter group, thereby obtaining a first compensation ratio group that is convenient for subsequent optimization of the initial production process parameter group, so that the optimized parameters are more in line with the actual injection molding production situation, reducing the trial and error cost and avoiding excessive scrap rate.

[0061] 3) According to the initial production process parameter group, a similar second reference production process parameter group is matched, and according to the second reference production process parameter group and the associated second actual production process parameter group, a second compensation ratio group is generated for optimizing the first compensation ratio group; since the causes affecting the production process parameters in the injection molding process are very complex, only considering the parameters corresponding to the similar influencing factor group will still have shortcomings. The present invention takes into account the actual situation of similar second reference production process parameters in the injection molding production process, and according to the relationship between the second reference production process parameter group and the second actual production process parameter group, a second compensation ratio group is obtained to facilitate the subsequent optimization of the first compensation ratio group, so that the optimized first compensation ratio group is more in line with the optimization of the initial production process reference, thereby ensuring that the best production process parameter group based on multi-dimensional comprehensive analysis can be obtained, so as to be recommended to users for application in actual production, thereby reducing trial and error costs and avoiding excessive scrap rates.

[0062] 4) Based on each compensation ratio in the optimized first compensation ratio group, each initial production process parameter group in the initial production process parameter group is correspondingly optimized. After the optimal production process parameter group is obtained, the optimal production process parameter group is recommended to the user as the actual production process parameters of the corresponding injection molded product and the influencing factor group. The present invention uses the optimized first compensation ratio group to accurately optimize the initial production parameters, thereby achieving comprehensive consideration based on multiple aspects and dimensions. There is no need to rely on the design experience of technicians every time the mold is tried and produced, and there is no need to repeatedly try the mold and change the injection molding process parameters, and the optimized optimal production process parameters can be quickly obtained. In addition, the present invention can ensure that the adaptive parameter optimization is carried out according to the actual situation, flexible production is realized, and the needs of actual production are met, and the optimal production process parameters are recommended to the user as the actual production process parameters, so that the user can quickly and accurately use the production process parameters to perform injection molding trials and production of injection molded products, improve production efficiency and quality, shorten production cycles and reduce production costs, and ensure production stability and controllability.

[0063] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0065] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0066] Figure 1 A schematic flow chart of an intelligent recommendation method for injection mold production process parameters provided by an embodiment of the present invention;

[0067] Figure 2 A schematic flow chart of step S20 of an intelligent recommendation method for injection mold production process parameters provided by an embodiment of the present invention;

[0068] Figure 3 A schematic diagram of the structure of an intelligent recommendation system for injection mold production process parameters provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0071] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0072] In addition, in order to better illustrate the present invention, numerous specific details are provided in the following specific embodiments. It should be understood by those skilled in the art that the present invention can be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present invention.

[0073] See also Figure 1 , Figure 1The present invention provides a flow chart of an intelligent recommendation method for injection mold production process parameters according to an embodiment of the present invention. Figure 1 As shown, an intelligent recommendation method for injection mold production process parameters includes the following steps:

[0074] S10, obtaining a three-dimensional model of the product to be injection molded, a group of influencing factors corresponding to the product to be injection molded, and an initial production process parameter group obtained based on the three-dimensional model of the product to be injection molded and the group of influencing factors; wherein the group of influencing factors includes multiple types of product category, injection molding material, injection molding machine type and injection mold structure;

[0075] In this embodiment, since there are many factors that affect the injection molding production process parameters of the three-dimensional model of the product to be injection molded, and there is a correlation and influence relationship between the various production process parameters to a certain extent, the present invention determines the corresponding initial production process parameter group according to the three-dimensional model of the product to be injection molded and the influencing factor group, and can comprehensively consider multiple factors for parameters as well as the influence and correlation between the parameters, determine multiple production process parameters after comprehensive consideration of multiple factors, improve parameter accuracy, and reduce the trial and error time and cost of technicians.

[0076] The initial production process parameters refer to the parameters that can affect the injection molding process and the quality of the injection molded product. The initial production process parameter group may include one or more of time, position, pressure, speed and temperature. Specifically,

[0077] The temperature includes one or more of barrel temperature, material temperature, sol temperature, mold temperature, drying temperature, oil temperature, and ambient temperature;

[0078] The pressure includes one or more of injection pressure, holding pressure, back pressure, demoulding pressure, mold opening pressure, and clamping pressure;

[0079] The time includes one or more of injection time, holding time, cooling time, drying time, and metering delay time;

[0080] The speed includes one or more of the ejection speed, screw speed, injection speed, return speed, mold opening and closing speed, and demoulding speed;

[0081] The position includes one or more of a metering position, an injection position, an ejection position, and a mold opening position.

[0082] It should be noted that since there are multiple and interrelated parameters that affect the injection molding process and the quality of injection molded products, overall optimization can be performed by obtaining the initial production process parameter group, the first compensation parameter group and the second compensation parameter group. This ensures that overall optimization is performed on the basis of their correlation, and avoids the situation where the correlation and mutual influence between the parameters disappear after the individual and isolated optimization of each parameter, resulting in poor optimization results.

[0083] Specifically, the following steps are included:

[0084] S11, obtaining a three-dimensional model of the product to be injected input by a user;

[0085] S12. Determine the product category, injection molding material, injection molding machine type and injection mold structure corresponding to the product to be injection molded according to the structural parameters of the three-dimensional model and the material data in the production order, and obtain the influencing factor group; wherein the type includes one or more of complex products, large thin-walled products, high-performance engineering plastics, microstructured products and multi-material combinations.

[0086] It should be noted that the specific production process parameters corresponding to different injection molding materials, injection molding machine models and injection mold structures are also different, so they also need to be considered.

[0087] Similarly, due to the different structures and sizes of the products to be injected, the injection molding process technology often has different types of key process parameters and specific parameter fluctuations in actual applications. Therefore, the present invention classifies the products to be injected according to the three-dimensional model of the products to be injected, so as to better ensure that the recommended production process parameters can meet the actual production needs. The present invention classifies the product categories according to the structure of the products to be injected, and the product categories include one or more of complex products, large thin-walled products, high-performance engineering plastic products, micro-structure products and multi-material combinations; specifically including:

[0088] Complex products: refers to products with complex structures, thin walls or special shapes, such as mobile phone cases, auto parts, etc., which are difficult to be molded by injection molding. The molding of such products needs to overcome problems such as incomplete filling, thermal deformation, shrinkage deformation, etc., requiring precise mold design and strict control of molding process parameters.

[0089] The key process parameters for complex product molding are filling speed, holding time, mold temperature control and cooling time. For example, for complex products such as automobile dashboards, the filling speed and holding time need to be precisely controlled to ensure the molding integrity and surface quality of the product.

[0090] Large thin-walled products: refers to the molding of large thin-walled products, such as home appliance housings, engineering plastic components, etc., which often face problems such as slow filling speed, large shrinkage deformation, and concentrated thermal stress. This needs to be solved through mold design optimization, material selection, injection molding process control and other means.

[0091] The key process parameters for forming large thin-walled products are filling speed, holding pressure, cooling time and mold temperature. For example, for large thin-walled products such as automobile headlight housings, the control of filling speed and cooling time is particularly important to avoid thermal stress and shrinkage deformation.

[0092] High-performance engineering plastics: such as polyamide and polyetheretherketone, have high melt viscosity and thermal stability, and have high requirements for injection molding technology. How to ensure complete material filling and stable molding quality under high temperature and high pressure conditions is a technical difficulty.

[0093] Key process parameters for high-performance engineering plastics molding: melt temperature, injection pressure, mold temperature, cooling time. For example, for the molding of polyamide high-performance engineering plastics, the control of melt temperature and mold temperature is crucial to ensure molding quality and reduce thermal stress.

[0094] Microstructure products: refers to products with high requirements for microstructure and precision size, such as micro gears, micro pipes, etc. Injection molding requires solving problems such as mold processing accuracy, filling uniformity, and molding accuracy.

[0095] The key process parameters for the molding of microstructured products are: filling speed, mold temperature, injection pressure, and cooling time. For example, for microstructured products such as micro gears, the filling speed and mold temperature need to be precisely controlled to ensure the molding accuracy and surface quality of the product.

[0096] Multi-material combination: refers to products that require a combination of multiple materials, such as two-color injection molding, multi-layer injection molding, etc., which need to overcome problems such as adhesion between different materials, melt temperature control, and transition point control.

[0097] The key process parameters of multi-material combination molding: injection ratio, switching point setting, mold temperature and pressure control. For example, for two-color injection molding, the setting of injection ratio and switching point is the key process parameter, which directly affects the transition effect and quality of product color.

[0098] For different product categories, the selection and optimization of key process parameters will directly affect the product molding quality, production efficiency and cost savings. Therefore, these key process parameters need to be accurately controlled and optimized to ensure stable and high-quality production of this type of product.

[0099] Similarly, due to different mold structures, injection molding process technology often has different key process parameter types and specific parameter fluctuations in actual applications. Therefore, the present invention classifies and sets the molds according to the mold structure to better ensure that the recommended production process parameters can meet the actual production needs. The present invention classifies the mold categories according to the mold structure. For example, the mold structure includes one or more of two mold plates, three mold plates, hot runner molds, dual-cavity molds, single-type structures, composite structures, and mixed structures, or can be set accordingly according to actual conditions to meet the actual production needs.

[0100] S13. Simulate and optimize based on the influencing factor group to obtain the initial production process parameter group.

[0101] It should be noted that due to the different influencing factor groups, the obtained production process parameters are also different. Therefore, by simulating and tuning according to the actual influencing factor group, the initial production process parameter group corresponding to the actual influencing factor group can be obtained efficiently and accurately, thereby improving the parameter accuracy.

[0102] In one embodiment, simulation and optimization are performed based on the influencing factor group to obtain the initial production process parameter group, including:

[0103] S131, inputting key parameters corresponding to the injection molding material, the injection molding machine type and the injection mold structure into a simulation system respectively to obtain a simulation production process parameter group;

[0104] It should be noted that due to the on-site real-time mold trial and production under the condition of unstable and uncertain parameters, it is often necessary to repeatedly adjust the parameters, thereby lengthening the production cycle and increasing production costs. Therefore, in the present invention, pre-production simulation through a simulation system can effectively solve the above problems.

[0105] The simulation steps include:

[0106] Prepare data and software: Collect the 3D image of the product to be molded, the physical parameters of the injection material, the specifications of the injection molding machine, and the structural design of the injection mold. Obtain professional injection molding simulation software, such as Moldflow, SolidWorksPlastics, etc.

[0107] Model building: Use simulation software to import 3D images, set injection molding materials, injection molding machine type and mold structure. Also set key parameters such as mold opening and closing method, nozzle form, cooling system, etc.

[0108] Meshing: Meshing the model to ensure the accuracy and efficiency of simulation calculations.

[0109] Set process parameters according to actual needs: set injection speed, injection pressure, mold temperature, cooling time and other process parameters.

[0110] Perform simulation calculations: Run simulation software to simulate the injection molding process and obtain relevant data such as filling simulation, temperature distribution, stress and strain.

[0111] Analyze results: Analyze simulation results, evaluate possible defects (such as short flow, bubbles, shrinkage, etc.), and adjust process parameters and mold structure as needed.

[0112] Optimize process: Optimize process parameters according to simulation results to obtain a set of simulated production process parameters to achieve the best injection molding quality and production efficiency.

[0113] Through the above steps, the product simulation of the injection molded product can be realized accurately and efficiently, thereby reducing the trial and error cost and time, as shown in the following example:

[0114] Example products to be injection molded: car rearview mirror housing;

[0115] Example injection molding materials: polypropylene (PP);

[0116] Injection molding machine model: 260T injection molding machine;

[0117] Injection mold structure: double cavity mold

[0118] Model building: Use SolidWorks Plastics to import the 3D image of the car rearview mirror housing, set the material parameters of polypropylene, and select a simulation model suitable for the 260T injection molding machine.

[0119] Meshing: Perform detailed meshing of the model to ensure that subsequent simulation calculations are accurate and reliable.

[0120] Set process parameters: Set process parameters such as injection speed, injection pressure, mold temperature, etc., and consider the filling balance of the double-cavity mold.

[0121] Perform simulation calculations: Run SolidWorks Plastics to simulate the injection molding process and obtain data such as filling simulation and temperature distribution.

[0122] Analysis results: After analyzing the simulation results, it was found that one cavity was filled unevenly and the injection speed and pressure distribution needed to be adjusted.

[0123] Optimize process: Optimize process parameters according to simulation results, re-run simulation until the ideal molding effect is achieved, and obtain a simulated production process parameter group.

[0124] S132. Based on the product category, optimize the first significant parameter in the simulated production process parameter group, and replace the optimized first significant parameter into the simulated production process parameter group;

[0125] It should be noted that since the simulated production process parameter group obtained after the above simulation is under ideal conditions, it is necessary to refer to the key process parameters corresponding to the actual product category for optimization to further improve the accuracy of the parameters.

[0126] S133, using the prediction model to predict the replaced simulated production process parameter group to obtain a prediction result;

[0127] In this embodiment, the prediction model is used to predict the replaced simulated production process parameter group, so as to further improve the accuracy of the data. The prediction model is a pre-trained model, which is constructed according to the correlation between the result to be predicted and the production process parameter group. For example, by predicting the weight of the product with a strong correlation with the production process parameter group, the simulated production process parameter group is evaluated and optimized. Specifically:

[0128] Collect the simulated production process parameter group data set obtained by simulation and divide it into training set, validation set and test set. Including: melt temperature, mold temperature, injection speed, holding pressure, holding time, product weight; the obtained result data set can be divided into 70% training set, 20% validation set and 10% test set;

[0129] Construct the input layer, output layer and hidden layer respectively to obtain the BP neural network;

[0130] The input layer nodes are: melt temperature, mold temperature, injection speed, holding pressure, and holding time;

[0131] The output layer nodes are: product weight;

[0132] The hidden layer is a single layer, and the number of neurons in the hidden layer can be set to 8;

[0133] The input end of the hidden node layer is connected to the output end of the input layer, and the output end of the hidden node layer is connected to the input end of the output layer, so that a BP neural network can be obtained;

[0134] Train and verify the neural network, import the training set data into the BP neural network for training, and set the BP neural network parameters according to the model prediction; in order to obtain the pre-trained prediction model to predict the pre-trained model.

[0135] S134, determining whether the prediction result is within a preset allowable error range corresponding to the product to be injected;

[0136] S135: If yes, use the simulated production process parameter group as the initial production process parameter group;

[0137] S136: If not, re-execute the optimization and the steps after the optimization according to the difference between the prediction result and the preset allowable error range, until the prediction result is within the preset allowable error range corresponding to the product to be injected.

[0138] In this embodiment, a judgment is made based on the prediction results to determine whether the simulated production process parameter group meets the actual production requirements, thereby further improving the accuracy of the data.

[0139] See also Figure 2 , Figure 2 The following is a flow chart of step S20 of an intelligent recommendation method for injection mold production process parameters provided by an embodiment of the present invention. Figure 2 As shown, S20, generating a first compensation ratio group according to a first reference production process parameter group and a first actual production process parameter group associated with a reference influencing factor group similar to the influencing factor group; wherein the first reference production process parameter group is an initial production process parameter group of the reference influencing factor group;

[0140] In this embodiment, according to the influencing factor group corresponding to the injection molding product, a similar reference influencing factor group is matched, and according to the first reference production process parameter group and the first actual production process parameter group associated with the reference influencing factor group, a first compensation ratio group is generated; since the initial production process parameter group is only obtained by simulation based on an ideal state, and since the actual situation of the corresponding influencing factor group is different in the actual injection molding production process, the parameter changes generated are also different, so the present invention takes into account the actual situation of the influencing factor group in the actual injection molding production process, and considers the relationship between the initial production process parameter group and the actual production process parameter group, so as to obtain a first compensation ratio group that is convenient for subsequent optimization of the initial production process parameter group, so that the optimized parameters are more in line with the actual injection molding production situation, reduce the trial and error cost and avoid excessive scrap rate. The first reference production process parameter represents the initial production process parameter group of the reference influencing factor group, which is a simulated predicted value, and the first actual production process parameter represents the actual production process parameter of the reference influencing factor group in the actual injection molding process, which is an actual value, and the first actual production process parameter group is obtained by compensating the first reference production process parameters in the first reference production process parameter group according to the first compensation ratios in the first compensation ratio group. Specifically, the following steps are included:

[0141] S21, based on the influencing factor group, determining the most similar influencing factor group from the influencing factor groups stored in the first database as the reference influencing factor group;

[0142] In this embodiment, the most similar or closest influencing factor group is matched from the first database as a reference influencing factor group according to the actual influencing factor group, so as to optimize the initial production process parameter group according to the actual production, and further optimize the parameters accurately in combination with the influence of the influencing factor group in the actual production, so as to improve the accuracy of the parameters. Specifically, the following steps are included:

[0143] S211, obtaining a historical influencing factor group corresponding to a historical injection molding product and a preset influencing factor group corresponding to a preset injection molding product, and storing them in the first database; wherein the historical influencing factor group and the preset influencing factor group are both correspondingly associated with a reference production process parameter group and an actual production process parameter group;

[0144] Among them, by storing the historical influencing factor group corresponding to the historical injection molding products and the preset influencing factor group corresponding to the preset injection molding products in the first database, it is convenient for subsequent matching and parameter optimization; it should be noted that the historical influencing factor group corresponding to the historical injection molding products represents the influencing factor group that has actually been used for injection molding production, for example, the injection molding products of this product category have been injection molded by using the injection molding machine, injection mold, and injection molding material in the company or factory, which is the existing actual data; the preset influencing factor group corresponding to the preset injection molding products represents the injection molding products of this product category that have not been injection molded by using the injection molding machine, injection mold, and injection molding material in the company or factory, and is pre-constructed data by technicians based on the existing actual data in the company or factory combined with past experience or past actual injection molding parameters, so as to increase the base number of matching quantities and facilitate subsequent optimization use.

[0145] S212, respectively evaluating the similarity between each historical influencing factor group in the first database and the influencing factor group to obtain a first similarity group;

[0146] In this example, since the historical influencing factor groups are actual production data, the similarities between the historical influencing factor groups and the influencing factor groups in the first database are first analyzed to better determine the correlation with the influencing factor groups of the products to be injected, so as to facilitate optimization based on the reference with the highest correlation.

[0147] S213: Identify a first similarity with the highest similarity from the first similarity groups, and mark the historical influencing factor group corresponding to the first similarity with the highest similarity as a pending historical influencing factor group;

[0148] S214, determining whether the first similarity of the pending historical influencing factor group is greater than a first preset value;

[0149] S215: If yes, taking the pending historical influencing factor group as the reference influencing factor group that is most similar to the influencing factor group;

[0150] Among them, a first preset value is set to determine whether the first similarity of the pending historical influencing factor group meets the preset standard. If it is greater than the first preset value, it means that the first similarity of the pending historical influencing factor group meets the preset standard, so that the pending historical influencing factor group is used as the reference influencing factor group that is most similar to the said influencing factor group.

[0151] S216: If not, respectively evaluate the similarity between each preset influencing factor group in the first database and the influencing factor group to obtain a second similarity group;

[0152] Specifically, since the first similarity of the pending historical influencing factor group is not greater than the first preset value, it means that the first similarity of the pending historical influencing factor group does not meet the preset standard. Therefore, it is necessary to perform similarity matching on the preset influencing factor group to further determine the specific reference influencing factor group to ensure the accuracy of the tuning parameters.

[0153] S217: Identify the second similarity with the highest similarity from the second similarity group, and mark the preset influencing factor group corresponding to the second similarity with the highest similarity as a pending preset influencing factor group;

[0154] S218, determining whether the difference between the second similarity of the pending preset influencing factor group and the first similarity of the pending historical influencing factor group is greater than a second preset value;

[0155] Among them, by setting a second preset value, it is possible to determine whether the second similarity of the pending preset influencing factor group meets the preset standard. If the difference between the second similarity of the pending preset influencing factor group and the first similarity of the pending historical influencing factor group is greater than the second preset value, it means that the second similarity of the pending preset influencing factor group meets the preset standard, and the pending preset influencing factor group is used as the reference influencing factor group most similar to the influencing factor group. If it is not greater than, it means that the second similarity of the pending preset influencing factor group does not meet the preset standard, and the pending historical influencing factor group is used as the reference influencing factor group most similar to the influencing factor group to ensure the accuracy of the tuning parameters.

[0156] S218-1, if yes, taking the pending preset influencing factor group as the reference influencing factor group that is most similar to the influencing factor group;

[0157] S218-2. If not, use the pending historical influencing factor group as the reference influencing factor group that is most similar to the influencing factor group;

[0158] S219: Use the reference production process parameter group and the actual production process parameter group associated with the reference influencing factor group as the first reference production process parameter group and the first actual production process parameter group.

[0159] It should be noted that since the reference influencing factor group is closest to the influencing factor group of the product to be injection molded, the reference production process parameter group and the actual production process parameter group associated with the reference influencing factor group are used as the first reference production process parameter group and the first actual production process parameter group to tune the initial production process parameter group, thereby ensuring the accuracy of the tuning parameters and ensuring that the tuned parameters can be actually applied to injection molding production, so as to shorten the production cycle, reduce trial and error costs and improve production quality.

[0160] S22, obtaining a first reference production process parameter group and a first actual production process parameter group associated with the reference influencing factor group;

[0161] S23. Based on each actual production process parameter in the first actual production process parameter group, respectively calculate the compensation ratio of each reference production process parameter in the first reference production process parameter group to obtain the first compensation ratio group.

[0162] In this embodiment, the actual error ratio of the reference influencing factor group is determined based on the ratio of the actual production process parameters to the corresponding reference production process parameters, and the actual error ratio of the influencing factor group similar to the reference influencing factor group is calculated based on the actual error ratio of the reference influencing factor group, thereby obtaining a first compensation ratio group to improve the actual accuracy of parameter recommendation, for example: the first compensation ratio group is obtained by calculating the actual error ratios according to preset weights, or the actual error ratios are directly used as the first compensation ratio group.

[0163] S30. Generate a second compensation ratio group for optimizing the first compensation ratio group based on a second reference production process parameter group similar to the initial production process parameter group and a second actual production process parameter group corresponding to the second reference production process parameter group; wherein the second reference production process parameter group is the initial production process parameter group corresponding to the influencing factor group, the similarity between the corresponding influencing factor group and the influencing factor group is less than the similarity between the reference influencing factor group and the influencing factor group, and the similarity between the second reference production process parameter and the third significant parameter group of the initial production process parameter group is greater than the similarity between the first reference production process parameter group and the third significant parameter group of the initial production process parameter group.

[0164] For example, the reference influencing factor group is a first reference influencing factor group, the similarity between the first reference influencing factor group and the influencing factor group is a first similarity, and the similarity between the first reference production process parameter group and the third significant parameter group of the initial production process parameter group is a second similarity;

[0165] The corresponding influencing factor group is a second reference influencing factor group, the similarity between the second reference influencing factor group and the influencing factor group is a third similarity, and the first similarity is greater than the third similarity;

[0166] The similarity between the second reference production process parameters and the third significant parameter group of the initial production process parameter group is a fourth similarity, and the fourth similarity is greater than the second similarity.

[0167] In this embodiment, according to the initial production process parameter group, a similar second reference production process parameter group is matched, and according to the second reference production process parameter group and the associated second actual production process parameter group, a second compensation ratio group for optimizing the first compensation ratio group is generated; since the causes affecting the production process parameters in the injection molding process are very complex, only considering the parameters corresponding to the similar influencing factor group will still have shortcomings. The present invention takes into account the actual situation of similar second reference production process parameters in the injection molding production process, and according to the relationship between the second reference production process parameter group and the second actual production process parameter group, a second compensation ratio group is obtained that is convenient for subsequent optimization of the first compensation ratio group, so that the optimized first compensation ratio group is more in line with the optimization of the initial production process reference, thereby ensuring that the best production process parameter group based on multi-dimensional comprehensive analysis can be obtained, so as to be recommended to users for application in actual production, thereby reducing trial and error costs and avoiding excessive scrap rates. The second reference production process parameters represent the initial production process parameter group of the corresponding influencing factor group, which is a simulated predicted value. The second actual production process parameters represent the actual production process parameters of the corresponding influencing factor group during the actual injection molding process, which is an actual value. The second actual production process parameter group is obtained by compensating for each second reference production process parameter in the second reference production process parameter group according to each second compensation ratio in the second compensation ratio group.

[0168] In one embodiment, according to a second reference production process parameter group similar to the initial production process parameter group, and a second actual production process parameter group corresponding to the second reference production process parameter group, a second compensation ratio group for optimizing the first compensation ratio group is obtained, including:

[0169] S31, according to the reference influencing factor group, the first actual production process parameter group, and the injection molding product quality parameter obtained based on the first actual production process parameter group, respectively obtain a first significant factor from the reference influencing factor group and obtain a second significant parameter from the first actual production process parameter group;

[0170] In this embodiment, based on the injection molding product quality parameters as the reference standard for evaluating the injection molding production effect of the first actual production process parameter group, the main influencing factors affecting the injection molding product quality parameters are selected as the first significant factors, and the main production process parameters affecting the injection molding product quality parameters are selected as the second significant parameters. This can quickly eliminate the influencing factors that have less influence on the injection molding product quality parameters corresponding to the reference influencing factor group and the first actual production process parameter group, reduce the complexity of data analysis, and improve the efficiency of calculating the optimal production process parameters.

[0171] S32, according to the first significant factor and the second significant parameter, respectively obtain a second significant factor from the influencing factor group and obtain a corresponding third significant parameter from the initial production process parameter group, to obtain a second significant factor group and a third significant parameter group;

[0172] In this embodiment, by using the first significant factor and the second significant parameter of the reference influencing factor group as reference standards for evaluating the injection molding production effect of the influencing factor group and the initial production process parameter group of the injection molded product, the main influencing factors affecting the quality parameters of the injection molded product are selected as the second significant factor, and the main production process parameters affecting the quality parameters of the injection molded product are selected as the third significant parameter. The influencing factors and the initial production process parameter group that have less influence on the quality parameters of the injection molded product corresponding to the influencing factor group and the initial production process parameter group can be quickly excluded, the complexity of data analysis can be reduced, and the efficiency of calculating the optimal production process parameters can be improved.

[0173] S33, determining a plurality of influencing factor groups matching the second significant factor group from the first database, to obtain a plurality of pending influencing factor groups;

[0174] Specifically, matching multiple similar influencing factor groups from the first database according to the second significant factor group can improve the accuracy of data matching and optimization; for example, the second significant factor is product category and injection molding material, then based on the product category and injection molding material, the same product category and injection molding material are matched in the first database as the pending influencing factor group, thereby ensuring that historical data corresponding to the actual production situation is matched, so as to optimize the initial production process parameter group according to the actual production situation and improve the accuracy of the parameters.

[0175] S34, respectively obtaining a plurality of reference production process parameter groups associated with the to-be-determined influencing factor groups to obtain a plurality of reference production process parameter groups to be matched;

[0176] S35, respectively evaluating the similarities between the plurality of reference production process parameter groups to be matched and the third significant parameter group to obtain a third similarity group;

[0177] S36, identifying the third similarity with the highest similarity from the third similarity groups, and marking the reference production process parameter group to be matched corresponding to the third similarity as the second reference production process parameter group;

[0178] In this embodiment, the similarity of multiple reference production process parameter groups to be matched is evaluated according to the third significant parameter group, and the reference production process parameter group to be matched corresponding to the third similarity with the highest similarity is used as the second reference production process parameter group, which can further improve the accuracy of data matching and optimization; for example, the third significant parameter is the melt temperature and the injection speed, then based on the specific parameters of the melt temperature and the injection speed, the reference production process parameter group with the same or closest parameters is matched among the multiple reference production process parameter groups to be matched, and used as the second reference production process parameter group, thereby ensuring the matching of historical data corresponding to the actual production situation, so as to optimize the initial production process parameter group according to the actual production situation and improve the accuracy of the parameters.

[0179] S37. Based on each actual production process parameter in the second actual production process parameter group corresponding to the second reference production process parameter group, respectively calculate the compensation ratio of each reference production process parameter in the second reference production process parameter group to obtain the second compensation ratio group.

[0180] In this embodiment, the actual error ratio of the second reference production process parameter group is determined based on the ratio of the actual production process parameters to the corresponding reference production process parameters, and the second compensation ratio for optimizing the first compensation ratio group is obtained based on the actual error ratio of the second reference production process parameter group and the preset weight, thereby further improving the actual accuracy of the parameter recommendation.

[0181] S40. Based on each compensation ratio in the optimized first compensation ratio group, each initial production process parameter group in the initial production process parameter group is correspondingly optimized to obtain an optimal production process parameter group, and the optimal production process parameter group is recommended to the user as the actual production process parameter corresponding to the product to be injected and the influencing factor group.

[0182] In one embodiment, each initial production process parameter group in the initial production process parameter group is correspondingly optimized based on each compensation ratio in the optimized first compensation ratio group, and further includes:

[0183] S41, respectively setting each compensation ratio in the first compensation ratio group and each compensation ratio group in the second compensation ratio group to correspond to each other, to obtain a plurality of compensation ratio groups to be optimized; wherein the number of the plurality of groups is equal to the number of compensation ratios in the first compensation ratio group;

[0184] For example, the first compensation ratio group includes compensation ratios of melt temperature and injection speed, and the second compensation ratio group also includes compensation ratios of melt temperature and injection speed. The melt temperature in the first compensation ratio group is set correspondingly to the melt temperature in the second compensation ratio group, and the injection speed in the first compensation ratio group is set correspondingly to the injection speed in the second compensation ratio group, so as to obtain two groups of compensation ratio groups to be optimized.

[0185] S42, respectively configuring corresponding weight values ​​for each parameter in each compensation ratio group to be optimized, and performing weighted summation on each parameter in each compensation ratio group to be optimized based on the weight values ​​to calculate an optimized compensation ratio; wherein the weight value of the parameter of the first compensation ratio group in each compensation ratio group to be optimized is greater than the weight value of the parameter of the second compensation ratio group;

[0186] S43, obtaining the optimized compensation ratios obtained after calculation of each compensation ratio group to be optimized, and obtaining the first compensation ratio after adjustment. Specifically, it also includes: adjusting the optimized compensation ratio based on environmental parameters.

[0187] In this embodiment, since changes in environmental parameters will also cause changes in the specific values ​​of the production process parameters, by further adjusting the optimization compensation ratio according to the environmental parameters, the initial production process parameter group can be effectively optimized according to actual conditions to ensure the output of the optimal production process parameters that meet actual conditions.

[0188] It should be noted that the present invention accurately adjusts the initial production parameters through the optimized first compensation ratio group, thereby achieving comprehensive consideration based on multiple aspects and dimensions. It is not necessary to rely on the design experience of technical personnel every time the mold is tried and produced, and there is no need to conduct repeated mold trials and change the injection molding process parameters, and the optimized best production process parameters can be quickly obtained. In addition, the present invention can ensure that the adaptive parameter tuning is refined according to the actual situation, flexible production is achieved, and the needs of actual production are met. The optimal production process parameters are recommended to users as actual production process parameters, so that users can quickly and accurately use the production process parameters to perform injection molding trials and production of injection molded products or further optimize the production process parameters, improve production efficiency and quality, shorten production cycles and reduce production costs, and ensure production stability and controllability.

[0189] Second, see Figure 3 , Figure 3 The structure diagram of an intelligent recommendation system for injection mold production process parameters provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the present invention also provides an intelligent recommendation system for injection mold production process parameters, including:

[0190] An acquisition module, used to acquire a three-dimensional model of a product to be injection molded, a group of influencing factors corresponding to the product to be injection molded, and an initial production process parameter group formed by the three-dimensional model of the product to be injection molded and the group of influencing factors; wherein the group of influencing factors includes product category, injection molding material, injection molding machine type and injection mold structure;

[0191] A first generating module, configured to generate a first compensation ratio group according to a first reference production process parameter group and a first actual production process parameter group associated with a reference influencing factor group similar to the influencing factor group;

[0192] A second generating module, configured to generate a second compensation ratio group for optimizing the first compensation ratio group according to a second reference production process parameter group similar to the initial production process parameter group and a second actual production process parameter group corresponding to the second reference production process parameter group;

[0193] A recommendation module is used to optimize each initial production process parameter group in the initial production process parameter group based on each compensation ratio in the optimized first compensation ratio group to obtain an optimal production process parameter group, and recommend the optimal production process parameter group to the user as the actual production process parameter corresponding to the product to be injected and the influencing factor group.

[0194] It can be understood that the functions or modules included in the system provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0195] The present invention also provides an electronic device, including a processor and a memory, wherein the memory is used to store computer program code, and the computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any possible implementation manner.

[0196] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any possible implementation manner.

[0197] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0198] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

[0199] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0200] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0201] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0202] A person skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by a computer program to instruct the relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. The aforementioned storage medium includes: a read-only memory (ROM) or a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

Claims

1. An intelligent recommendation method for injection mold production process parameters, characterized in that: include: Obtain a three-dimensional model of a product to be injection molded, a group of influencing factors corresponding to the product to be injection molded, and an initial production process parameter group obtained based on the three-dimensional model of the product to be injection molded and the group of influencing factors; wherein the group of influencing factors includes product category, injection molding material, injection molding machine type and injection mold structure; the initial production process parameter group obtained based on the three-dimensional model of the product to be injection molded and the group of influencing factors includes: respectively inputting key parameters corresponding to the injection molding material, the injection molding machine type and the injection mold structure into a simulation system to obtain a simulated production process parameter group; based on the product category, the simulated production process parameters The first significant parameter in the group is tuned, and the tuned first significant parameter is replaced into the simulated production process parameter group; the replaced simulated production process parameter group is predicted using the prediction model to obtain a prediction result; it is determined whether the prediction result is within the preset allowable error range corresponding to the product to be injection molded; if so, the simulated production process parameter group is used as the initial production process parameter group; if not, according to the difference between the prediction result and the preset allowable error range, the tuning and the steps after the tuning are re-executed until the prediction result is within the preset allowable error range corresponding to the product to be injection molded; Generate a first compensation ratio group based on a first reference production process parameter group and a first actual production process parameter group associated with a reference influencing factor group similar to the influencing factor group; wherein the first reference production process parameter group is the initial production process parameter group of the reference influencing factor group; generating a first compensation ratio group based on a first reference production process parameter group and a first actual production process parameter group associated with a reference influencing factor group similar to the influencing factor group includes: based on the influencing factor group, determining the most similar influencing factor group from the influencing factor groups stored in the first database as the reference influencing factor group; obtaining the first reference production process parameter group and the first actual production process parameter group associated with the reference influencing factor group; based on each actual production process parameter in the first actual production process parameter group, respectively calculating the compensation ratio of each reference production process parameter in the first reference production process parameter group to obtain the first compensation ratio group; According to a second reference production process parameter group similar to the initial production process parameter group, and a second actual production process parameter group corresponding to the second reference production process parameter group, a second compensation ratio group for optimizing the first compensation ratio group is generated; wherein the second reference production process parameter group is the initial production process parameter group corresponding to the influencing factor group; according to a second reference production process parameter group similar to the initial production process parameter group, and a second actual production process parameter group corresponding to the second reference production process parameter group, the second compensation ratio group for optimizing the first compensation ratio group comprises: according to the reference influencing factor group, the first actual production process parameter group, and the injection molding product quality parameter obtained based on the first actual production process parameter group, respectively obtaining a first significant factor from the reference influencing factor group and a second significant parameter from the first actual production process parameter group; according to the first significant factor and the second significant parameter, respectively obtaining a first significant factor from the influencing factor group; The invention relates to a method for preparing a production process parameter group comprising: determining a first significant factor and a second significant factor and obtaining a corresponding third significant parameter from the initial production process parameter group to obtain a second significant factor group and a third significant parameter group; determining a plurality of influencing factor groups matching the second significant factor group from the first database to obtain a plurality of pending influencing factor groups; respectively obtaining a plurality of reference production process parameter groups associated with the pending influencing factor groups to obtain a plurality of reference production process parameter groups to be matched; respectively evaluating the similarities between the plurality of reference production process parameter groups to be matched and the third significant parameter group to obtain a third similarity group; identifying the third similarity with the highest similarity from the third similarity group, and marking the reference production process parameter group to be matched corresponding to the third similarity as the second reference production process parameter group; and calculating the compensation ratios of the reference production process parameters in the second reference production process parameter group based on the respective actual production process parameters in the second actual production process parameter group corresponding to the second reference production process parameter group to obtain the second compensation ratio group; Based on each compensation ratio in the optimized first compensation ratio group, each initial production process parameter group in the initial production process parameter group is correspondingly adjusted to obtain an optimal production process parameter group, and the optimal production process parameter group is recommended to the user as the actual production process parameter corresponding to the product to be injected and the influencing factor group.

2. The intelligent recommendation method for injection mold production process parameters according to claim 1, characterized in that: Acquiring a three-dimensional model of a product to be injected, a group of influencing factors corresponding to the product to be injected, and an initial production process parameter group obtained based on the three-dimensional model of the product to be injected and the group of influencing factors, including: Acquire a three-dimensional model of the product to be injected input by a user; According to the structural parameters of the three-dimensional model and the material data in the production order, the product category, injection molding material, injection molding machine type and injection mold structure corresponding to the product to be injected are determined to obtain the influencing factor group; wherein the product category includes one or more of complex products, large thin-walled products, high-performance engineering plastic products, microstructure products and multi-material combinations; The initial production process parameter group is obtained by performing simulation and optimization based on the influencing factor group.

3. The intelligent recommendation method for injection mold production process parameters according to claim 1, characterized in that: Based on the influencing factor group, determining the most similar influencing factor group from the influencing factor groups stored in the first database as the reference influencing factor group includes: Obtaining a historical influencing factor group corresponding to a historical injection molding product and a preset influencing factor group corresponding to a preset injection molding product, and storing them in the first database; wherein the historical influencing factor group and the preset influencing factor group are both correspondingly associated with a reference production process parameter group and an actual production process parameter group; respectively evaluating the similarity between each historical influencing factor group in the first database and the influencing factor group to obtain a first similarity group; Identify a first similarity with the highest similarity from the first similarity groups, and mark the historical influencing factor group corresponding to the first similarity with the highest similarity as a pending historical influencing factor group; Determining whether the first similarity of the pending historical influencing factor group is greater than a first preset value; If yes, the undetermined historical influencing factor group is used as the reference influencing factor group that is most similar to the influencing factor group; The reference production process parameter group and the actual production process parameter group associated with the reference influencing factor group are used as the first reference production process parameter group and the first actual production process parameter group.

4. The intelligent recommendation method for injection mold production process parameters according to claim 3, characterized in that: After the step of judging whether the first similarity of the to-be-determined historical influencing factor group is greater than a first preset value, the method further includes: If not, respectively evaluate the similarity between each preset influencing factor group in the first database and the influencing factor group to obtain a second similarity group; Identify the second similarity with the highest similarity from the second similarity group, and mark the preset influencing factor group corresponding to the second similarity with the highest similarity as a pending preset influencing factor group; Determine whether the difference between the second similarity of the pending preset influencing factor group and the first similarity of the pending historical influencing factor group is greater than a second preset value; If so, taking the undetermined preset influencing factor group as the reference influencing factor group that is most similar to the influencing factor group; If not, the undetermined historical influencing factor group is used as the reference influencing factor group that is most similar to the influencing factor group.

5. The intelligent recommendation method for injection mold production process parameters according to claim 1, characterized in that: Also includes: The compensation ratios in the first compensation ratio group are respectively set correspondingly to the compensation ratio groups in the second compensation ratio group to obtain a plurality of compensation ratio groups to be optimized; wherein the number of the plurality of groups is equal to the number of compensation ratios in the first compensation ratio group; A corresponding weight value is configured for each parameter in each compensation ratio group to be optimized, and a weighted sum is performed on each parameter in each compensation ratio group to be optimized based on the weight value to calculate the optimized compensation ratio; wherein the weight value of the parameter of the first compensation ratio group in each compensation ratio group to be optimized is greater than the weight value of the parameter of the second compensation ratio group; The optimized compensation ratios of each compensation ratio group to be optimized are obtained after calculation, and the first compensation ratio after optimization is obtained.

6. The intelligent recommendation method for injection mold production process parameters according to claim 5, characterized in that: Obtaining the optimized compensation ratios of each compensation ratio group to be optimized after calculation, and obtaining the first compensation ratio after adjustment, including: The optimized compensation ratio is tuned based on environmental parameters.

7. An intelligent recommendation system for injection mold production process parameters, characterized in that: include: The acquisition module is used to acquire the three-dimensional model of the product to be injection molded, the influencing factor group corresponding to the product to be injection molded, and the initial production process parameter group obtained based on the three-dimensional model of the product to be injection molded and the influencing factor group; wherein the influencing factor group includes product category, injection molding material, injection molding machine type and injection mold structure; the initial production process parameter group obtained based on the three-dimensional model of the product to be injection molded and the influencing factor group includes: respectively inputting the key parameters corresponding to the injection molding material, the injection molding machine type and the injection mold structure into the simulation system to obtain a simulated production process parameter group; based on the product category, the simulated production The first significant parameter in the process parameter group is tuned, and the tuned first significant parameter is replaced into the simulated production process parameter group; the replaced simulated production process parameter group is predicted using a prediction model to obtain a prediction result; it is determined whether the prediction result is within a preset allowable error range corresponding to the product to be injection molded; if so, the simulated production process parameter group is used as the initial production process parameter group; if not, according to the difference between the prediction result and the preset allowable error range, the tuning and the steps after the tuning are re-executed until the prediction result is within the preset allowable error range corresponding to the product to be injection molded; A first generating module is used to generate a first compensation ratio group according to a first reference production process parameter group and a first actual production process parameter group associated with a reference influencing factor group similar to the influencing factor group; wherein the first reference production process parameter group is the initial production process parameter group of the reference influencing factor group; generating the first compensation ratio group according to the first reference production process parameter group and the first actual production process parameter group associated with a reference influencing factor group similar to the influencing factor group includes: based on the influencing factor group, determining the most similar influencing factor group from the influencing factor groups stored in the first database as the reference influencing factor group; obtaining the first reference production process parameter group and the first actual production process parameter group associated with the reference influencing factor group; based on each actual production process parameter in the first actual production process parameter group, respectively calculating the compensation ratio of each reference production process parameter in the first reference production process parameter group to obtain the first compensation ratio group; A second generating module is used to generate a second compensation ratio group for optimizing the first compensation ratio group according to a second reference production process parameter group similar to the initial production process parameter group and a second actual production process parameter group corresponding to the second reference production process parameter group; wherein the second reference production process parameter group is the initial production process parameter group corresponding to the influencing factor group; generating the second compensation ratio group for optimizing the first compensation ratio group according to the second reference production process parameter group similar to the initial production process parameter group and the second actual production process parameter group corresponding to the second reference production process parameter group includes: obtaining a first significant factor from the reference influencing factor group and a second significant parameter from the first actual production process parameter group according to the reference influencing factor group, the first actual production process parameter group, and the injection molding product quality parameter obtained based on the first actual production process parameter group; obtaining a first significant factor from the influencing factor group according to the first significant factor and the second significant parameter, respectively; obtaining a second significant parameter from the influencing factor group according to the first significant factor and the second significant parameter; obtaining a second significant parameter from the influencing factor group according to the first significant factor and the second significant parameter; obtaining a second significant parameter from the influencing factor group according to the first significant factor and the second significant parameter; obtaining a second significant parameter from the influencing factor group according to the first significant factor and the second significant parameter; obtaining a second significant parameter from the influencing factor group according to the second ... The second significant factor is obtained from the first database and the corresponding third significant parameter is obtained from the initial production process parameter group to obtain the second significant factor group and the third significant parameter group; multiple influencing factor groups matching the second significant factor group are determined from the first database to obtain multiple pending influencing factor groups; reference production process parameter groups associated with multiple pending influencing factor groups are obtained respectively to obtain multiple reference production process parameter groups to be matched; the similarities between multiple reference production process parameter groups to be matched and the third significant parameter group are evaluated respectively to obtain a third similarity group; the third similarity with the highest similarity is identified from the third similarity group, and the reference production process parameter group to be matched corresponding to the third similarity is marked as the second reference production process parameter group; based on each actual production process parameter in the second actual production process parameter group corresponding to the second reference production process parameter group, the compensation ratio of each reference production process parameter in the second reference production process parameter group is calculated respectively to obtain the second compensation ratio group; A recommendation module is used to optimize each initial production process parameter group in the initial production process parameter group based on each compensation ratio in the optimized first compensation ratio group to obtain an optimal production process parameter group, and recommend the optimal production process parameter group to the user as the actual production process parameter corresponding to the product to be injected and the influencing factor group.

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