Mould application simulation method and system based on big data

Abstract

The invention is suitable for the technical field of mold application simulation, and provides a mold application simulation method and system based on big data. Based on a big data technology, an application record database is constructed; carrying out coincidence matching, and selecting a reference production product and a reference product mold; performing application simulation of cavity adjustment on the reference mold model to generate an adjusted mold model; a mold main runner is optimized for the adjusted mold model, and a first optimized mold model is generated; and performing mold sub-runner optimization on the first optimized mold model to generate a second optimized mold model. According to the invention, coincidence matching, reference mold model selection, application simulation of cavity adjustment, and optimization of the mold main runner and the mold sub-runner can be carried out based on the application record database, so that automatic auxiliary application development of the injection mold is realized, the application development period of the injection mold is effectively shortened, and the application development cost of the injection mold is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of mold application simulation, and in particular, relates to a mold application simulation method and system based on big data. Background Art

[0002] Injection molds are molds for producing plastic products. They heat and melt plastic and inject it into the mold cavity. After cooling and solidification, the desired plastic product is obtained. They are widely used in the production of plastic parts and products, and are particularly suitable for mass production of products with complex geometric shapes and high precision requirements, such as automotive parts, electronic housings, home appliances, toys, etc.

[0003] In the prior art, the application and development of injection molds usually requires different development and design according to different products, and mainly relies on the experience of the staff. It takes many trials and mold repairs to meet the corresponding requirements, which has the technical defects of long cycle and high cost. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a mold application simulation method and system based on big data, aiming to solve the technical problems existing in the prior art mentioned in the background technology.

[0005] The embodiment of the present invention is implemented as follows:

[0006] A mold application simulation method based on big data, the method specifically comprises the following steps:

[0007] Based on big data technology, record the application of product molds and build an application record database;

[0008] Determine the target production product, obtain the target product model, production equipment data and production process data, perform overlap matching based on the application record database, and select the reference production product and reference product mold;

[0009] Constructing an application simulation environment, importing the target product model and the reference mold model of the reference product mold, performing application simulation of cavity adjustment on the reference mold model, and generating an adjusted mold model;

[0010] Optimizing the main channel of the mold for the adjusted mold model according to the production equipment data to generate a first optimized mold model;

[0011] According to the production process data, the mold runners of the first optimized mold model are optimized to generate a second optimized mold model.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the application record of the product mold based on the big data technology and the construction of the application record database specifically include the following steps:

[0013] Based on big data technology, determine multiple application production products and corresponding application product molds;

[0014] Acquire application product models of a plurality of the application production products;

[0015] Acquire a plurality of application mold models of the application product molds;

[0016] Model data are recorded for the plurality of application product models and the corresponding application mold models to construct an application record database.

[0017] As a further limitation of the technical solution of the embodiment of the present invention, the determination of the target production product, obtaining the target product model, production equipment data and production process data, performing coincidence matching based on the application record database, and selecting the reference production product and the reference product mold specifically include the following steps:

[0018] Receive application simulation requests and determine target production products;

[0019] Acquire a target product model, production equipment data, and production process data of the target production product;

[0020] Based on the application record database, the target product model is overlapped and matched, and a reference production product is selected;

[0021] According to the reference product production, select the corresponding reference product mold.

[0022] As a further limitation of the technical solution of the embodiment of the present invention, the matching of the target product model based on the application record database and the selection of the reference production product specifically include the following steps:

[0023] Performing appearance analysis on the target product model and selecting and locating feature surfaces;

[0024] Based on the application record database, the positioning feature surface is matched, and a plurality of matching product models and corresponding matching production products are selected from a plurality of application product models;

[0025] Overlapping the target product model with the plurality of matching product models in terms of positioning feature surfaces, and performing overall overlap analysis to calculate overall overlap ratios corresponding to the plurality of matching product models;

[0026] Arrange and compare the multiple overall overlap ratios to select the optimal overlap ratio;

[0027] Selecting a reference product model corresponding to an optimal overlap ratio from the plurality of matching product models;

[0028] A reference production product corresponding to the reference product model is selected from the plurality of matching production products.

[0029] As a further limitation of the technical solution of the embodiment of the present invention, the construction of the application simulation environment, importing the target product model and the reference mold model of the reference product mold, performing application simulation of cavity adjustment on the reference mold model, and generating the adjusted mold model specifically include the following steps:

[0030] Build application simulation environment;

[0031] In the application simulation environment, importing the target product model and the reference mold model of the reference product mold;

[0032] According to the product dimensions of the target product model, an application simulation of cavity adjustment is performed on the reference mold model, and cavity adjustment data is recorded;

[0033] According to the cavity adjustment data, the reference mold model is subjected to cavity adjustment processing to generate an adjusted mold model.

[0034] As a further limitation of the technical solution of the embodiment of the present invention, the optimization of the main channel of the mold for the adjusted mold model according to the production equipment data to generate the first optimized mold model specifically includes the following steps:

[0035] Determining the optimization boundary parameters of the main channel of the mold according to the production equipment data;

[0036] receiving a parameter selection operation, and selecting first optimization parameter data from the optimization boundary parameters;

[0037] According to the first optimization parameter data, the main flow channel of the mold is optimized for the adjusted mold model to generate a first optimized mold model.

[0038] As a further limitation of the technical solution of the embodiment of the present invention, the expression of the optimized boundary parameter is:

[0039] ;

[0040] ;

[0041] in, is the spherical contact radius of the main channel, is the nozzle spherical radius, is the inlet diameter, is the nozzle diameter.

[0042] As a further limitation of the technical solution of the embodiment of the present invention, the optimization of the mold runner of the first optimized mold model according to the production process data to generate the second optimized mold model specifically includes the following steps:

[0043] Calculating second optimization parameter data of the mold runner according to the production process data;

[0044] According to the second optimization parameter data, the mold runner of the first optimized mold model is optimized to generate a second optimized mold model.

[0045] As a further limitation of the technical solution of the embodiment of the present invention, the calculation formula of the second optimization parameter data is:

[0046] ;

[0047] ;

[0048] in, is the flow channel radius, is the flow channel length, is the volume flow rate, is the shear rate, is the pressure drop, is the melt viscosity.

[0049] A mold application simulation system based on big data, the system includes a database construction processing module, a coincidence matching processing module, a cavity adjustment processing module, a mold main channel optimization module and a mold branch channel optimization module, wherein:

[0050] The database construction processing module is used to record the application of product molds and build an application record database based on big data technology;

[0051] The coincidence matching processing module is used to determine the target production product, obtain the target product model, production equipment data and production process data, perform coincidence matching based on the application record database, and select the reference production product and the reference product mold;

[0052] A cavity adjustment processing module is used to construct an application simulation environment, import the target product model and the reference mold model of the reference product mold, perform application simulation of cavity adjustment on the reference mold model, and generate an adjusted mold model;

[0053] A mold main channel optimization module, used for optimizing the mold main channel of the adjusted mold model according to the production equipment data, and generating a first optimized mold model;

[0054] The mold runner optimization module is used to optimize the mold runner of the first optimized mold model according to the production process data to generate a second optimized mold model.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The embodiment of the present invention constructs an application record database based on big data technology; performs coincidence matching, selects reference production products and reference product molds; performs application simulation of cavity adjustment on the reference mold model to generate an adjusted mold model; optimizes the mold main channel of the adjusted mold model to generate a first optimized mold model; optimizes the mold branch channel of the first optimized mold model to generate a second optimized mold model. It is possible to perform coincidence matching based on the application record database, select a reference mold model, perform application simulation of cavity adjustment, and then optimize the mold main channel and the mold branch channel to achieve automatic auxiliary application development of injection molds, thereby reducing the application development cycle of injection molds and effectively reducing the application development cost of injection molds. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flowchart of a mold application simulation method based on big data provided by an embodiment of the present invention is shown;

[0058] Figure 2 A flowchart of constructing an application record database in a method provided by an embodiment of the present invention is shown;

[0059] Figure 3 A flowchart of performing coincidence matching processing in the method provided by an embodiment of the present invention is shown;

[0060] Figure 4 A flowchart of selecting a reference production product in a method provided by an embodiment of the present invention is shown;

[0061] Figure 5 A flow chart showing the simulation of cavity adjustment application in the method provided by the embodiment of the present invention is shown;

[0062] Figure 6 A flow chart showing optimization of a mold main channel in a method provided by an embodiment of the present invention is shown;

[0063] Figure 7 A flow chart showing the optimization of the mold runner in the method provided by an embodiment of the present invention;

[0064] Figure 8 An application architecture diagram of a mold application simulation system based on big data provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0066] It is understandable that in the prior art, the application and development of injection molds usually requires different development and design according to different products, and mainly relies on the experience of the staff. It takes many trials and mold repairs to meet the corresponding requirements, which has the technical defects of long cycle and high cost.

[0067] To solve the above problems, the embodiment of the present invention discloses a mold application simulation method and system based on big data, which records the application of product molds based on big data technology and builds an application record database; determines the target production product, obtains the target product model, production equipment data and production process data, performs overlap matching based on the application record database, and selects the reference production product and the reference product mold; builds an application simulation environment, imports the target product model and the reference mold model of the reference product mold, performs application simulation of cavity adjustment on the reference mold model, and generates an adjusted mold model; optimizes the mold main channel of the adjusted mold model according to the production equipment data to generate a first optimized mold model; optimizes the mold runner of the first optimized mold model according to the production process data to generate a second optimized mold model. It can perform overlap matching based on the application record database, select the reference mold model, perform application simulation of cavity adjustment, and then optimize the mold main channel and the mold runner, so as to realize the automatic auxiliary application development of the injection mold, effectively reduce the application development cycle of the injection mold, and reduce the application development cost of the injection mold.

[0068] Specifically, Figure 1 A flow chart of a mold application simulation method based on big data provided by an embodiment of the present invention is shown.

[0069] In a preferred embodiment of the present invention, a mold application simulation method based on big data comprises the following steps:

[0070] Step S101: Based on big data technology, the application records of product molds are recorded and an application record database is constructed.

[0071] In an embodiment of the present invention, based on big data technology, multiple application production products that have been applied in production and corresponding application product molds are determined through the Internet and intranet, and then application product models of the multiple application production products are obtained, and application mold models of the multiple application product molds are obtained. Then, according to different application production products, the corresponding relationship between application production products and application product molds, the model data of the multiple application product models and the multiple application mold models are classified and recorded to build an application record database.

[0072] It can be understood that the Internet approach can obtain application product models of application production products and application mold models of application product molds that have been publicly available on the Internet; the intranet approach can obtain application product models of application production products and application mold models of application product molds within the enterprise.

[0073] Furthermore, Figure 2 A flow chart of constructing an application record database in the method provided by an embodiment of the present invention is shown.

[0074] Specifically, in another preferred embodiment of the present invention, the application record of the product mold based on the big data technology and the construction of the application record database specifically include the following steps:

[0075] Step S1011: Based on big data technology, determine multiple application production products and corresponding application product molds.

[0076] Step S1012: Acquire application product models of a plurality of the application production products.

[0077] Step S1013: Acquire application mold models of a plurality of application product molds.

[0078] Step S1014: record model data for the plurality of application product models and corresponding application mold models, and construct an application record database.

[0079] Furthermore, the mold application simulation method based on big data also includes the following steps:

[0080] Step S102: determine the target production product, obtain the target product model, production equipment data and production process data, perform overlap matching based on the application record database, and select a reference production product and a reference product mold.

[0081] In an embodiment of the present invention, an application simulation request input by a staff member is received, and a target production product is determined by performing object recognition on the application simulation request, and then a target product model, production equipment data, and production process data of the target production product are obtained, and a positioning feature surface on the target product model is selected by performing appearance analysis on the target product model, and then the positioning feature surface is matched based on an application record database, and multiple matching product models that also have positioning feature surfaces are selected from multiple application product models, and corresponding matching production products are determined, and the overall overlap ratios corresponding to the multiple matching product models are obtained by aligning the positioning feature surfaces of the target product model with the multiple matching product models, and then analyzing the percentage of overall overlap between the multiple matching product models and the target product model, and then arranging and comparing the multiple overall overlap ratios, marking the overall overlap ratio with the largest value as the optimal overlap ratio, and then selecting a reference product model corresponding to the optimal overlap ratio from the multiple matching product models, and selecting a reference production product corresponding to the reference product model from the multiple matching production products.

[0082] It can be understood that the positioning feature surface is a surface with a positioning and fitting relationship in the target product model of the target production product, corresponding to the product installation positioning, product processing positioning or product testing positioning. It can be a plane or a curved surface, for example, a surface with a pin hole.

[0083] Furthermore, Figure 3 A flow chart of coincidence matching processing in the method provided by an embodiment of the present invention is shown.

[0084] Specifically, in another preferred embodiment provided by the present invention, the determining of the target production product, obtaining the target product model, production equipment data and production process data, performing coincidence matching based on the application record database, and selecting the reference production product and the reference product mold specifically include the following steps:

[0085] Step S1021: Receive an application simulation request and determine a target production product.

[0086] Step S1022: Acquire the target product model, production equipment data and production process data of the target production product.

[0087] Step S1023: Based on the application record database, the target product model is overlapped and matched, and a reference production product is selected.

[0088] Furthermore, Figure 4 A flow chart of selecting a reference production product in the method provided by an embodiment of the present invention is shown.

[0089] Specifically, in another preferred embodiment of the present invention, the target product model is overlapped and matched based on the application record database, and the reference production product is selected, which specifically includes the following steps:

[0090] Step S10231: perform appearance analysis on the target product model and select and locate feature surfaces.

[0091] Step S10232: Based on the application record database, the positioning feature surface is matched, and a plurality of matching product models and corresponding matching production products are selected from the plurality of application product models.

[0092] Step S10233: Overlap the target product model with the plurality of matching product models in terms of positioning feature surfaces, and perform overall overlap analysis to calculate overall overlap ratios corresponding to the plurality of matching product models.

[0093] Step S10234: Arrange and compare the multiple overall overlap ratios, and select the optimal overlap ratio.

[0094] Step S10235: Select a reference product model corresponding to the optimal overlap ratio from the multiple matching product models.

[0095] Step S10236: Select a reference production product corresponding to the reference product model from the multiple matching production products.

[0096] Furthermore, the determination of the target production product, acquisition of the target product model, production equipment data and production process data, matching based on the application record database, and selection of the reference production product and the reference product mold also include the following steps:

[0097] Step S1024: produce the product according to the reference and select a corresponding reference product mold.

[0098] Furthermore, the mold application simulation method based on big data also includes the following steps:

[0099] Step S103: construct an application simulation environment, import the target product model and the reference mold model of the reference product mold, perform application simulation of cavity adjustment on the reference mold model, and generate an adjusted mold model.

[0100] In an embodiment of the present invention, an application simulation environment is constructed, and then the target product model and the reference mold model of the reference product mold are imported into the application simulation environment, and then the application simulation of cavity adjustment is performed on the reference mold model according to the product shape dimensions of the target product model. During the application simulation, the cavity adjustment data is recorded, and then the reference mold model is subjected to cavity adjustment processing based on the cavity adjustment data to generate an adjusted mold model, so that the adjusted cavity completely fits the shape of the target product model.

[0101] It can be understood that the application simulation environment is a software environment for three-dimensional models, which can be CATIA, CAE, UG, etc.

[0102] Furthermore, Figure 5 A flow chart of cavity adjustment application simulation in the method provided by an embodiment of the present invention is shown.

[0103] Specifically, in another preferred embodiment provided by the present invention, the construction of the application simulation environment, importing the target product model and the reference mold model of the reference product mold, performing application simulation of cavity adjustment on the reference mold model, and generating the adjusted mold model specifically include the following steps:

[0104] Step S1031: construct an application simulation environment.

[0105] Step S1032: importing the target product model and the reference mold model of the reference product mold into the application simulation environment.

[0106] Step S1033: perform application simulation of cavity adjustment on the reference mold model according to the product appearance dimensions of the target product model, and record cavity adjustment data.

[0107] Step S1034: perform cavity adjustment processing on the reference mold model according to the cavity adjustment data to generate an adjusted mold model.

[0108] Furthermore, the mold application simulation method based on big data also includes the following steps:

[0109] Step S104: Optimize the main flow channel of the mold for the adjusted mold model according to the production equipment data to generate a first optimized mold model.

[0110] In the embodiment of the present invention, the optimization boundary parameters of the main channel of the mold are determined by analyzing the production equipment data, and then the parameter selection operation of the staff is received, and then according to the parameter selection operation, the first optimization parameter data is selected from the optimization boundary parameters, and then according to the first optimization parameter data, the main channel of the mold is optimized for the adjusted mold model to generate the first optimized mold model. Specifically, the expression of the optimization boundary parameter is:

[0111] ;

[0112] ;

[0113] in, is the spherical contact radius of the main channel, is the nozzle spherical radius, is the inlet diameter, is the nozzle diameter.

[0114] Furthermore, Figure 6 A flow chart of optimization of a mold main channel in a method provided by an embodiment of the present invention is shown.

[0115] Specifically, in another preferred embodiment of the present invention, the step of optimizing the main channel of the mold for the adjusted mold model according to the production equipment data to generate the first optimized mold model specifically includes the following steps:

[0116] Step S1041: Determine the optimization boundary parameters of the main channel of the mold according to the production equipment data.

[0117] Step S1042: receiving a parameter selection operation, and selecting first optimization parameter data from the optimization boundary parameters.

[0118] Step S1043: Optimize the main channel of the mold for the adjusted mold model according to the first optimization parameter data to generate a first optimized mold model.

[0119] Furthermore, the mold application simulation method based on big data also includes the following steps:

[0120] Step S105 : optimizing the mold runners of the first optimized mold model according to the production process data to generate a second optimized mold model.

[0121] In the embodiment of the present invention, the second optimization parameter data of the mold runner is calculated by analyzing the production process data, and then the mold runner is optimized for the first optimized mold model according to the second optimization parameter data to generate the second optimized mold model. Specifically, the calculation formula of the second optimization parameter data is:

[0122] ;

[0123] ;

[0124] in, is the flow channel radius, is the flow channel length, is the volume flow rate, is the shear rate, is the pressure drop, is the melt viscosity.

[0125] Furthermore, Figure 7 A flow chart of optimization of a mold runner in a method provided by an embodiment of the present invention is shown.

[0126] Specifically, in another preferred embodiment of the present invention, the step of optimizing the mold runner of the first optimized mold model according to the production process data to generate the second optimized mold model specifically includes the following steps:

[0127] Step S1051, calculating second optimization parameter data of the mold runner according to the production process data.

[0128] Step S1052: Optimize the mold runner of the first optimized mold model according to the second optimization parameter data to generate a second optimized mold model.

[0129] Furthermore, Figure 8 An application architecture diagram of a mold application simulation system based on big data provided by an embodiment of the present invention is shown.

[0130] Specifically, in another preferred embodiment provided by the present invention, a mold application simulation system based on big data includes:

[0131] The database construction processing module 101 is used to record the application of product molds and build an application record database based on big data technology.

[0132] In the embodiment of the present invention, the database construction processing module 101 is based on big data technology, and determines multiple application production products that have been applied in production and corresponding application product molds through the Internet and the intranet, and then obtains the application product models of the multiple application production products, and obtains the application mold models of the multiple application product molds, and then classifies and records the model data of the multiple application product models and the multiple application mold models according to the corresponding relationship between different application production products and application production products and application product molds, and constructs an application record database.

[0133] The coincidence matching processing module 102 is used to determine the target production product, obtain the target product model, production equipment data and production process data, perform coincidence matching based on the application record database, and select the reference production product and the reference product mold.

[0134] In an embodiment of the present invention, the overlap matching processing module 102 receives an application simulation request input by a staff member, determines a target production product by performing object recognition on the application simulation request, obtains a target product model, production equipment data and production process data of the target production product, selects a positioning feature surface on the target product model by performing appearance analysis on the target product model, and then matches the positioning feature surface based on an application record database, selects a plurality of matching product models that also have positioning feature surfaces from a plurality of application product models, and determines the corresponding matching production products, overlaps the target product model with a plurality of matching product models by locating feature surfaces, and then analyzes the percentage of overall overlap between the plurality of matching product models and the target product model, to obtain overall overlap ratios corresponding to the plurality of matching product models, arranges and compares the plurality of overall overlap ratios, marks the overall overlap ratio with the largest value as the optimal overlap ratio, selects a reference product model corresponding to the optimal overlap ratio from the plurality of matching product models, and selects a reference production product corresponding to the reference product model from the plurality of matching production products.

[0135] The cavity adjustment processing module 103 is used to construct an application simulation environment, import the target product model and the reference mold model of the reference product mold, perform application simulation of cavity adjustment on the reference mold model, and generate an adjusted mold model.

[0136] In an embodiment of the present invention, the cavity adjustment processing module 103 constructs an application simulation environment, and then imports the target product model and the reference mold model of the reference product mold into the application simulation environment, and then performs application simulation of cavity adjustment on the reference mold model according to the product shape size of the target product model. During the application simulation, the cavity adjustment data is recorded, and then the reference mold model is subjected to cavity adjustment processing based on the cavity adjustment data to generate an adjusted mold model, so that the adjusted cavity completely fits the shape of the target product model.

[0137] The mold main channel optimization module 104 is used to optimize the mold main channel of the adjusted mold model according to the production equipment data to generate a first optimized mold model.

[0138] In the embodiment of the present invention, the mold main channel optimization module 104 determines the optimization boundary parameters of the mold main channel by analyzing the production equipment data, and then receives the parameter selection operation of the staff, and then selects the first optimization parameter data from the optimization boundary parameters according to the parameter selection operation, and then optimizes the mold main channel of the adjusted mold model according to the first optimization parameter data to generate a first optimized mold model. Specifically, the expression of the optimization boundary parameter is:

[0139] ;

[0140] ;

[0141] in, is the spherical contact radius of the main channel, is the nozzle spherical radius, is the inlet diameter, is the nozzle diameter.

[0142] The mold runner optimization module 105 is used to optimize the mold runner of the first optimized mold model according to the production process data to generate a second optimized mold model.

[0143] In the embodiment of the present invention, the mold runner optimization module 105 calculates the second optimization parameter data of the mold runner by analyzing the production process data, and then optimizes the mold runner of the first optimized mold model according to the second optimization parameter data to generate a second optimized mold model. Specifically, the calculation formula of the second optimization parameter data is:

[0144] ;

[0145] ;

[0146] in, is the flow channel radius, is the flow channel length, is the volume flow rate, is the shear rate, is the pressure drop, is the melt viscosity.

[0147] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0148] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0149] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A mold application simulation method based on big data, characterized in that: The method specifically comprises the following steps: Based on big data technology, record the application of product molds and build an application record database; Determine the target production product, obtain the target product model, production equipment data and production process data, perform overlap matching based on the application record database, and select the reference production product and reference product mold; Constructing an application simulation environment, importing the target product model and the reference mold model of the reference product mold, performing application simulation of cavity adjustment on the reference mold model, and generating an adjusted mold model; According to the production equipment data, optimizing the main channel of the mold for the adjusted mold model to generate a first optimized mold model; According to the production process data, the mold runners of the first optimized mold model are optimized to generate a second optimized mold model.

2. The mold application simulation method based on big data according to claim 1 is characterized in that: The application record of the product mold based on the big data technology and the construction of the application record database specifically include the following steps: Based on big data technology, determine multiple application production products and corresponding application product molds; Acquire application product models of a plurality of the application production products; Acquire a plurality of application mold models of the application product molds; Model data are recorded for the plurality of application product models and the corresponding application mold models to construct an application record database.

3. The mold application simulation method based on big data according to claim 2 is characterized in that: The determination of the target production product, acquisition of the target product model, production equipment data and production process data, matching based on the application record database, and selection of the reference production product and the reference product mold specifically include the following steps: Receive application simulation requests and determine target production products; Acquire a target product model, production equipment data, and production process data of the target production product; Based on the application record database, the target product model is overlapped and matched, and a reference production product is selected; According to the reference product production, select the corresponding reference product mold.

4. The mold application simulation method based on big data according to claim 3 is characterized in that: The method of performing overlap matching on the target product model based on the application record database and selecting a reference production product specifically comprises the following steps: Performing appearance analysis on the target product model and selecting and locating feature surfaces; Based on the application record database, the positioning feature surface is matched, and a plurality of matching product models and corresponding matching production products are selected from a plurality of application product models; Overlapping the target product model with the plurality of matching product models in terms of positioning feature surfaces, and performing overall overlap analysis to calculate overall overlap ratios corresponding to the plurality of matching product models; Arrange and compare the multiple overall overlap ratios to select the optimal overlap ratio; Selecting a reference product model corresponding to an optimal overlap ratio from the plurality of matching product models; A reference production product corresponding to the reference product model is selected from the plurality of matching production products.

5. The mold application simulation method based on big data according to claim 1 is characterized in that: The construction of the application simulation environment, importing the target product model and the reference mold model of the reference product mold, performing application simulation of cavity adjustment on the reference mold model, and generating the adjusted mold model specifically include the following steps: Build application simulation environment; In the application simulation environment, importing the target product model and the reference mold model of the reference product mold; According to the product dimensions of the target product model, an application simulation of cavity adjustment is performed on the reference mold model, and cavity adjustment data is recorded; According to the cavity adjustment data, the reference mold model is subjected to cavity adjustment processing to generate an adjusted mold model.

6. The mold application simulation method based on big data according to claim 1 is characterized in that: The step of optimizing the main channel of the mold for the adjusted mold model according to the production equipment data to generate a first optimized mold model specifically comprises the following steps: Determining the optimization boundary parameters of the main channel of the mold according to the production equipment data; receiving a parameter selection operation, and selecting first optimization parameter data from the optimization boundary parameters; According to the first optimization parameter data, the main flow channel of the mold is optimized for the adjusted mold model to generate a first optimized mold model.

7. The mold application simulation method based on big data according to claim 6 is characterized in that: The expression of the optimization boundary parameter is: ； ； in, is the spherical contact radius of the main channel, is the nozzle spherical radius, is the inlet diameter, is the nozzle diameter.

8. The mold application simulation method based on big data according to claim 1 is characterized in that: The step of optimizing the mold runner of the first optimized mold model according to the production process data to generate the second optimized mold model specifically comprises the following steps: Calculating second optimization parameter data of the mold runner according to the production process data; According to the second optimization parameter data, the mold runner of the first optimized mold model is optimized to generate a second optimized mold model.

9. The mold application simulation method based on big data according to claim 8 is characterized in that: The calculation formula of the second optimization parameter data is: ； ； in, is the flow channel radius, is the flow channel length, is the volume flow rate, is the shear rate, is the pressure drop, is the melt viscosity.

10. A mold application simulation system based on big data, characterized in that: The system includes a database construction processing module, a coincidence matching processing module, a cavity adjustment processing module, a mold main channel optimization module and a mold branch channel optimization module, wherein: The database construction processing module is used to record the application of product molds and build an application record database based on big data technology; The coincidence matching processing module is used to determine the target production product, obtain the target product model, production equipment data and production process data, perform coincidence matching based on the application record database, and select the reference production product and the reference product mold; A cavity adjustment processing module is used to construct an application simulation environment, import the target product model and the reference mold model of the reference product mold, perform application simulation of cavity adjustment on the reference mold model, and generate an adjusted mold model; A mold main channel optimization module, used for optimizing the mold main channel of the adjusted mold model according to the production equipment data, and generating a first optimized mold model; The mold runner optimization module is used to optimize the mold runner of the first optimized mold model according to the production process data to generate a second optimized mold model.

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