A method for optimizing process parameters and die-casting equipment for die casting

Abstract

This invention provides a method for optimizing process parameters and die-casting equipment for die casting, relating to the field of die casting processing technology. It obtains the mechanical performance constraints of the component by performing load-stress analysis based on component design information and operating conditions, and then matches and obtains the die-casting raw materials. Based on the die-casting quality constraints, component design information, and die-casting raw materials, it performs network data matching and retrieval to obtain the starting point for die casting process optimization. Process optimization divergence is then performed on this starting point to obtain the die casting process parameters, which are then used to run the die casting equipment to produce the die-cast parts. This invention solves the technical problems of unstable quality control and poor repeatability in existing die casting technologies. It achieves precise setting of die casting process parameters, ensuring high-standard quality of die-cast parts while improving the repeatability and production efficiency of the die casting process, reducing trial-and-error costs in die casting process decisions, and accelerating product development cycles.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of die casting processing, in particular to a process parameter optimization method for die casting and a die casting equipment. BACKGROUND

[0002] In traditional die casting processes, technicians often need to rely on personal experience and intuition to set die casting parameters such as temperature, pressure, and cooling rate. This highly manual decision-making approach, while able to solve problems in some cases, also brings a series of defects.

[0003] For example, when technicians change or are not on duty, new employees may not be able to accurately reproduce previous process settings due to lack of sufficient experience, thereby affecting the consistency of product quality. In addition, the process of manually setting parameters lacks standardization, and each production may cause size deviation or internal defects such as bubbles and cracks due to slight differences, which may gradually expose in subsequent machining or product use.

[0004] Therefore, the problem of high manual involvement in the prior art not only limits production efficiency and process repeatability, but also brings instability to quality control, increasing production risk and cost. SUMMARY

[0005] The present application provides a process parameter optimization method for die casting and a die casting equipment, which is used to solve the technical problem of high manual involvement in the decision-making process of die casting process parameters in the prior art, leading to unstable die casting production quality control and poor die casting process repeatability.

[0006] In view of the above problems, the present application provides a process parameter optimization method for die casting and a die casting equipment.

[0007] In a first aspect, the present invention provides a method for optimizing process parameters in die casting. The method includes: interactively obtaining component design information and component operating conditions of a die casting to be processed; performing load stress analysis on the die casting to be processed based on the component design information and component operating conditions to obtain component mechanical performance constraints; extracting the component operating environment based on the component operating conditions, and matching and selecting production raw materials according to the component operating environment and component mechanical performance constraints to obtain a raw material formula; performing raw material scheduling and mixing processing according to the raw material formula to obtain a target die casting raw material; interactively obtaining component die casting quality constraints of the die casting to be processed, and performing network data matching and calling according to the component die casting quality constraints, component design information, and target die casting raw material to obtain a starting point for die casting process optimization; presetting process optimization rules, and performing process optimization divergence on the starting point for die casting process optimization according to the process optimization rules to obtain target die casting process parameters; and after feeding the target die casting raw material into the die casting equipment, running the die casting equipment using the target die casting process parameters to perform batch production of the die casting to be processed.

[0008] A second aspect of the present invention provides a die-casting apparatus, comprising: a component information interaction unit for interactively obtaining component design information and component operating conditions of a die-cast part to be processed; a stress analysis execution unit for performing load stress analysis on the die-cast part to be processed based on the component design information and component operating conditions to obtain component mechanical performance constraints; a raw material matching unit for extracting the component operating environment based on the component operating conditions, and matching and selecting raw materials according to the component operating environment and component mechanical performance constraints to obtain a raw material formula; and a die-casting raw material processing unit for scheduling and processing raw materials according to the raw material formula. The process involves a mixed processing unit to obtain the target die-casting raw material; an optimized starting point positioning unit to interactively obtain the component die-casting quality constraints of the die-casting part to be processed, and to perform network data matching and calling based on the component die-casting quality constraints, component design information, and target die-casting raw material to obtain the die-casting process optimization starting point; a process parameter optimization unit to preset process optimization rules, and to perform process optimization divergence on the die-casting process optimization starting point based on the process optimization rules to obtain the target die-casting process parameters; and a batch production execution unit to run the die-casting equipment using the target die-casting process parameters after the target die-casting raw material is fed into the die-casting equipment to execute the batch production of the die-casting part to be processed.

[0009] One or more technical solutions provided in this invention have at least the following technical effects or advantages:

[0010] The method provided in this invention obtains component design information and component operating conditions of the die-casting part to be processed through interaction; performs load stress analysis on the die-casting part to be processed based on the component design information and component operating conditions to obtain component mechanical performance constraints; extracts the component operating environment based on the component operating conditions, and selects production raw materials according to the component operating environment and component mechanical performance constraints to obtain raw material formula; performs raw material scheduling and mixing processing according to the raw material formula to obtain target die-casting raw material; obtains component die-casting quality constraints of the die-casting part to be processed through interaction, and performs network data matching and calling according to the component die-casting quality constraints, component design information and target die-casting raw material to obtain the starting point for die-casting process optimization; presets process optimization rules, and performs process optimization divergence on the starting point for die-casting process optimization according to the process optimization rules to obtain target die-casting process parameters; after feeding the target die-casting raw material into the die-casting equipment, runs the die-casting equipment using the target die-casting process parameters to perform batch production of the die-casting part to be processed. It enables precise setting of die-casting process parameters, ensuring high-standard quality of die-cast parts while improving the repeatability and production efficiency of the die-casting process, reducing trial-and-error costs in die-casting process decisions, and accelerating product development cycles. Attached Figure Description

[0011] Figure 1 A schematic diagram of a process parameter optimization method for die casting provided by the present invention;

[0012] Figure 2 A schematic diagram of the process for obtaining raw material formulation in a method for optimizing process parameters in die casting provided by the present invention;

[0013] Figure 3 This is a schematic diagram of the structure of a die-casting equipment provided by the present invention.

[0014] Explanation of reference numerals in the attached drawings: Component information interaction unit 11, force analysis execution unit 12, production raw material matching unit 13, die casting raw material processing unit 14, optimization starting point positioning unit 15, process parameter optimization unit 16, batch production execution unit 17. Detailed Implementation

[0015] This invention provides a method and equipment for optimizing process parameters in die casting, addressing the technical problems in existing die casting technologies where excessive human intervention in the process parameter decision-making leads to unstable quality control and poor repeatability. It achieves precise setting of die casting process parameters, ensuring high-standard quality of die castings while improving repeatability and production efficiency, reducing trial-and-error costs in process decision-making, and accelerating product development cycles.

[0016] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0017] Example 1, as Figure 1 As shown, the present invention provides a method for optimizing process parameters in die casting, the method comprising:

[0018] A100: Interactively obtain component design information and component operating conditions of the die-cast part to be processed.

[0019] Specifically, in this embodiment, the die-cast part to be processed is a component design completed by the designer based on functional requirements and production conditions. The die-cast part to be processed has not yet been put into production. This embodiment is used to analyze and select the processing materials and die-casting process parameters for the die-cast part to be processed.

[0020] The component design information refers to the detailed design parameters such as the geometry, dimensions, and tolerances of the die casting to be produced. The component design information details the shape characteristics and dimensional requirements of the die casting.

[0021] The component operating conditions refer to the specific conditions that the die-cast part to be produced will face in actual use, including the component operating environment and component operating conditions. The component operating environment includes information on ambient temperature, humidity, and chemical composition. The component operating conditions refer to the motion characteristics of a mechanical device during normal operation after the die-cast part to be produced is actually installed in it.

[0022] A200: Based on the component design information and component operating conditions, perform load stress analysis on the die-cast part to be processed to obtain the component mechanical performance constraints.

[0023] In one embodiment, the load-stress analysis of the die-cast part to be processed is performed based on the component design information and component operating conditions to obtain the component mechanical performance constraints. Step A200 of the method provided by this invention further includes:

[0024] A210: Interact to obtain multiple associated structural design information of the die casting part to be processed, and model the part to be processed and multiple associated structural models based on the part design information and multiple associated structural design information.

[0025] A220: Assemble the model of the component to be processed and multiple related structural models in a finite element analysis network to obtain the assembly model.

[0026] A230: Extract the component's operating conditions based on the component's operating conditions.

[0027] A240: Using the operating conditions of the component, the assembly model is simulated in the finite element analysis network to obtain multiple sets of load stresses.

[0028] A250: Aggregate the stress types of the multiple sets of load stresses to obtain M sets of stress parameters for M stress types.

[0029] A260: Extract extreme values ​​from the M sets of force parameters to obtain M types of force limits, which constitute the mechanical performance constraints of the component.

[0030] Specifically, it should be understood that the die-cast part to be produced is a component of a larger mechanical device. Based on this, this embodiment interactively obtains multiple related structural design information of other related components constituting the mechanical device. Each related structural design information includes detailed design parameters such as the geometry, size, and tolerance of the related component.

[0031] Based on the component design information, a 3D model of the die-cast part to be processed is created in CAD software to obtain the component model. Similarly, based on the multiple associated structure design information, multiple associated structure models of multiple associated components are created in CAD software.

[0032] The created die-cast part model and related component models are imported into the finite element analysis network (FEA software). In the finite element analysis network, the actual installation is simulated to assemble these models into a complete assembly model, which is used to simulate the operating state of the actual mechanical equipment.

[0033] The component's operating conditions are extracted based on the component's operating conditions. These operating conditions can be the motion characteristics faced by mechanical equipment during normal operation, such as rotation, vibration, and reciprocating motion. The component's operating conditions are used as input conditions for the finite element analysis network to simulate the performance of the assembly model under actual working conditions, thereby obtaining multiple sets of load stresses. These multiple sets of load stresses represent multiple force distributions of the die-cast part to be produced under different operating conditions.

[0034] The obtained multiple sets of load stresses are classified and aggregated according to the force type (such as tension, compression, bending, torsion, etc.) to obtain M sets of force parameters for M force types; the maximum value of the M sets of force parameters is extracted to obtain M force limits. The force limits represent the force conditions that the die casting may encounter under the most extreme conditions. The M force limits constitute the mechanical performance constraints of the component, and the mechanical performance constraints of the component are used to guide the selection of materials used in die casting.

[0035] This embodiment achieves the technical effect of providing guidance for the selection of materials used in subsequent die casting by analyzing the stress of the operating environment of the die casting parts to be produced.

[0036] A300: Based on the component's operating conditions, extract the component's operating environment, and select production raw materials according to the component's operating environment and mechanical performance constraints to obtain the raw material formula.

[0037] In one embodiment, such as Figure 2 As shown, the component operating environment is extracted based on the component operating conditions, and the raw materials for production are selected according to the component operating environment and the component mechanical performance constraints to obtain the raw material formula. Step A300 of the method provided by this invention further includes:

[0038] A310: Using the component's operating environment and mechanical performance constraints, a number of alternative raw materials are obtained through a comprehensive comparison. The component's operating environment includes temperature, humidity, and chemical environment. The multiple alternative operating environments of the multiple alternative raw materials are all inferior to the component's operating environment, and the multiple sets of alternative mechanical properties of the multiple alternative raw materials are all superior to the component's mechanical performance constraints.

[0039] A320: Interact to obtain multiple alternative raw material formulas for the multiple alternative raw materials.

[0040] A330: Perform raw material type aggregation on the multiple alternative raw material formulations to obtain multiple transportation costs - material costs for various raw materials.

[0041] A340: Calculate multiple procurement costs for the multiple alternative raw material formulations based on multiple transportation costs – material costs of the multiple raw materials.

[0042] A350: Serialize the multiple procurement costs and locate the target die-casting raw material among the multiple alternative raw materials according to the serial minimum value, and obtain the raw material formula by calling the multiple alternative raw material formulas according to the target die-casting raw material.

[0043] Specifically, in this embodiment, multiple historical raw materials of multiple historical die castings of the same type as the die casting to be produced are obtained interactively, and then multiple historical operating environments of multiple historical mechanical components installed on the multiple historical die castings are collected, and multiple historical mechanical performance constraints of the multiple historical die castings under the extreme stress conditions of the multiple historical mechanical components are collected.

[0044] It should be understood that in this embodiment, the operating environment and mechanical properties of each alternative raw material are assumed to be better than the conditions that the part to be produced will encounter during operation, in order to provide sufficient safety margin.

[0045] Using the component's operating environment and mechanical performance constraints, multiple candidate raw materials are obtained through a comprehensive comparison. The component's operating environment includes temperature, humidity, and chemical environment. The multiple candidate operating environments of the multiple candidate raw materials are all inferior to the component's operating environment, and the multiple sets of candidate mechanical properties of the multiple candidate raw materials are all superior to the component's mechanical performance constraints.

[0046] Interact with material suppliers to obtain multiple alternative raw material formulations, each including different proportions of alloying elements or other additives. Aggregate these multiple raw material formulations by raw material type to simplify the decision-making process, categorizing them into several main raw material types and obtaining multiple transportation costs – material costs – for each raw material.

[0047] Based on the transportation and material costs of each type of raw material, the procurement cost of each alternative raw material formulation is calculated, thereby achieving multiple procurement costs for the multiple alternative raw material formulations based on multiple transportation cost-material cost calculations for the multiple raw materials.

[0048] The calculated procurement costs are serialized, that is, sorted from low to high cost. Based on the serialized costs, the candidate raw materials with the lowest cost are selected as the target die-casting raw materials.

[0049] This embodiment achieves the technical effect of ensuring that the selected raw materials not only meet the performance requirements of the die castings, but are also cost-effective, thus optimizing production costs while guaranteeing the quality of the die castings.

[0050] A400: According to the raw material formula, raw material scheduling and mixing are carried out to obtain the target die casting raw material.

[0051] Specifically, in this embodiment, the target die-casting raw material is processed by conventionally controlling the temperature, atmosphere, and melting time during the smelting process. The raw materials obtained through raw material scheduling according to the raw material formula are mixed to obtain a target die-casting raw material with excellent metal purity and uniformity. The target die-casting raw material is in a molten state and is subsequently used for die-casting the parts to be produced in the injection chamber of the die-casting equipment.

[0052] A500: Interact with the die casting quality constraints of the die casting part to be processed, and perform network data matching and calling based on the die casting quality constraints, part design information and target die casting raw materials to obtain the starting point for die casting process optimization.

[0053] In one embodiment, the die-casting quality constraints of the die-cast part to be processed are obtained interactively, and network data matching and calling are performed based on the die-casting quality constraints, part design information and target die-casting raw materials to obtain the starting point for die-casting process optimization. The method step A500 provided by the present invention further includes:

[0054] A510: Using the component design information and target die-casting raw material as constraints, perform network data matching and retrieval to obtain reference component information, wherein the reference component information includes multiple reference die-casting process parameter sets and multiple reference die-casting quality sets.

[0055] A520: Introduce a die casting quality evaluation function, and locate the starting point of the die casting process optimization by traversing the reference part information based on the die casting quality constraints and the die casting quality evaluation function.

[0056] In one embodiment, the method step A510 of the present invention further includes: using the component design information and the target die-casting material as constraints to perform network data matching and retrieval to obtain reference component information;

[0057] A511: Using the target die-casting raw material as a constraint, perform network data matching and call to obtain multiple sample die-casting models.

[0058] A512: Calculate multiple geometric similarities between the model of the part to be processed and multiple sample die-casting models, and traverse the multiple geometric similarities to extract multiple reference die-casting models that meet the preset similarity threshold.

[0059] A513: The multiple reference die-casting models are networked to retrieve data, thereby obtaining multiple sets of reference die-casting process parameters and multiple sets of reference die-casting quality. The multiple sets of reference die-casting process parameters and multiple sets of reference die-casting quality constitute the reference component information.

[0060] In one embodiment, the component die-casting quality constraints include component density constraints, surface cleanliness constraints, surface flatness constraints, and structural dimensional accuracy constraints.

[0061] In one embodiment, method step A520 provided by the present invention further includes:

[0062] The die-casting quality evaluation function is as follows:

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] in, Due to component density constraints, For sample component density, To meet the requirements of cleanliness for tourism, For the cleanliness of the sample table, For apparent smoothness constraints, For the apparent smoothness of the sample, To constrain structural dimensional accuracy, To ensure the accuracy of sample structure dimensions, The interaction term affects the adjustment coefficient. Density component coefficient, For surface finish component coefficient, This is the flatness component coefficient. These are the dimensional component coefficients. This is the die-casting quality coefficient. These are the weighting coefficients for density, smoothness, flatness, and dimensional accuracy, respectively. .

[0069] Specifically, in this embodiment, network data matching and calling are performed with the target die casting material as a constraint to obtain multiple sample die casting models. These sample die casting models may come from different production cases, but they all use the same raw material (target die casting material). The die casting process parameters of these sample die casting models can provide a reference for the process optimization of the current die casting.

[0070] To enhance the reference role of the die-casting process parameters of the sample die-casting models in optimizing the process of the die-casting parts to be produced, this embodiment calculates the geometric similarity between the part model to be processed and these sample die-casting models. The technology for calculating the geometric similarity between models is now quite mature. This embodiment preferably uses the existing technology of evaluating model similarity based on the size, shape and features of the model, and evaluates and calculates multiple geometric similarities between the part model to be processed and multiple sample die-casting models.

[0071] By setting a preset similarity threshold, multiple reference die-casting models that are most similar in geometry to the model of the part to be processed are selected. The value of the similarity threshold is not limited in this embodiment, and can be specifically set according to the commonness of the die-casting part in the mechanical field and the production precision requirements of the manufacturer.

[0072] The multiple reference die-casting models are networked and data is retrieved to obtain multiple sets of reference die-casting process parameters and multiple sets of reference die-casting quality. The sets of reference die-casting process parameters include key die-casting process parameter values ​​such as temperature, pressure, and cooling rate. The sets of reference die-casting quality consist of the component density, surface cleanliness, surface flatness, and dimensional accuracy of the obtained reference die-casting model entities. The data composition of each set of reference die-casting quality specifically includes sample component density, sample surface cleanliness, sample surface flatness, and sample structural dimensional accuracy.

[0073] The multiple sets of reference die casting process parameters and multiple sets of reference die casting quality constitute the reference component information, which is used to guide and optimize the production process of the die casting parts to be produced in the die casting equipment.

[0074] It should be understood that the dimensional accuracy of a component is the degree of deviation of the solid dimensions of the reference die-casting model from the dimensions of the reference die-casting model. The component density is the result of dividing the density of the solid component of the reference die-casting model by the density of the target die-casting material. The closer the component density is to 0, the closer the bubbles and cracks inside the solid component are to none.

[0075] The die-casting quality constraints of the components include component density constraints, surface cleanliness constraints, surface flatness constraints, and structural dimensional accuracy constraints.

[0076] As mentioned above, component density is calculated by dividing the density of the die-cast component by the density of the target die-casting material. The closer the component density is to 0, the denser the die-cast part is internally, with few or no defects such as bubbles or cracks. Component density constraint refers to the minimum component density that a qualified component to be produced should meet. Surface cleanliness constraint refers to the maximum proportion of the surface area of ​​the qualified component to be produced, which can be occupied by burrs, bubbles, and inclusions. Surface flatness constraint refers to the maximum proportion of the surface area of ​​the qualified component to be produced, which can be occupied by the projected area of ​​abnormal, irregular protrusions caused by the die-casting process.

[0077] A pre-constructed die-casting quality evaluation function is provided, as follows:

[0078] ;

[0079] ;

[0080] ;

[0081] ;

[0082] ;

[0083] in, Due to component density constraints, For sample component density, To meet the requirements of cleanliness for tourism, For the cleanliness of the sample table, For apparent smoothness constraints, For the apparent smoothness of the sample, To constrain structural dimensional accuracy, To ensure the accuracy of sample structure dimensions, The interaction term affects the adjustment coefficient. Density component coefficient, For surface finish component coefficient, This is the flatness component coefficient. These are the dimensional component coefficients. This is the die-casting quality coefficient. These are the weighting coefficients for density, smoothness, flatness, and dimensional accuracy, respectively. .

[0084] Multiple die casting quality coefficients are calculated using a die casting quality evaluation function for the die casting quality constraints of the component and multiple reference die casting quality sets in the reference component information. It should be understood that the larger the value of the die casting quality coefficient, the closer the reference die casting quality set is to the die casting quality constraints of the component. Correspondingly, the reference die casting process parameter set corresponding to the reference die casting quality set is more suitable for the control of the die casting equipment of the die casting part to be produced.

[0085] Based on this, this embodiment serializes multiple die-casting quality coefficients and calls the reference die-casting process parameter set corresponding to the maximum value of the die-casting quality coefficient as the starting point for die-casting process optimization. The starting point for die-casting process optimization is a benchmark point provided for subsequent process adjustment and optimization. It is the initial process parameter of the die-casting equipment used for more targeted experiments and adjustments.

[0086] This embodiment reduces the subjectivity of manually setting die-casting process control parameters through data matching, geometric similarity evaluation, and quality similarity evaluation. It not only accelerates the selection of the starting point for optimizing die-casting process parameters for the parts to be manufactured, but also provides a benchmark for quickly determining the process parameters of the die-casting equipment that meet the required product quality.

[0087] A600: Preset process optimization rules, and perform process optimization divergence on the starting point of the die casting process according to the process optimization rules to obtain the target die casting process parameters.

[0088] In one embodiment, a process optimization rule is preset, and the process optimization starting point of the die-casting process is diverged according to the process optimization rule to obtain the target die-casting process parameters. Step A600 of the method provided by this invention further includes:

[0089] A610: After the target die casting raw material is fed into the die casting equipment, the die casting equipment is run at the starting point of the die casting process optimization to perform trial production of the die casting to be processed, and the first die casting entity is obtained.

[0090] A620: Measure and obtain the first die-casting mass set of the first die-casting part entity, and determine whether the first die-casting mass set satisfies the die-casting mass constraint of the part.

[0091] A630: If the first die-casting quality set satisfies the die-casting quality constraint of the component, then the starting point of the die-casting process optimization is taken as the target die-casting process parameter.

[0092] A640: If the first die-casting quality set does not meet the die-casting quality constraints of the component, then process optimization divergence is performed on the starting point of the die-casting process optimization according to the process optimization rules to obtain the target die-casting process parameters.

[0093] In one embodiment, if the first die-casting quality set does not meet the die-casting quality constraint of the component, then process optimization divergence is performed on the die-casting process optimization starting point according to the process optimization rules to obtain the target die-casting process parameters. Step A640 of the method provided by this invention further includes:

[0094] A641: Aggregate the multiple reference die-casting process parameter sets to obtain K sets of die-casting process parameters for K types of die-casting process adjustment indices.

[0095] A642: Extract the maximum and minimum values ​​of the K sets of die-casting process parameters to obtain K process adjustment ranges.

[0096] A643: Preset process parameter adjustment step size, wherein the process parameter adjustment step size and the K process adjustment intervals constitute the process optimization rule.

[0097] A644: Using the K process adjustment ranges as constraints, the process parameter adjustment step size is used to update the parameters of the die casting process optimization starting point to obtain the first die casting process parameters.

[0098] A645: Perform trial production of the die casting to be processed using the first die casting process parameters to obtain a second die casting entity.

[0099] A646: Measure the second die-casting mass set of the second die-casting entity and determine whether the first die-casting mass set satisfies the die-casting mass constraint of the component.

[0100] A647: If the second die-casting quality set does not meet the die-casting quality constraints of the component, then the process optimization divergence of the second die-casting quality set shall continue to be performed according to the process optimization rules until the target die-casting process parameters are obtained.

[0101] Specifically, in this embodiment, after the target die casting raw material is fed into the die casting equipment, the die casting equipment is run at the starting point of the die casting process optimization to perform trial production of the die casting to be processed, and the first die casting entity, i.e., the test sample, is obtained.

[0102] The first die-cast part is subjected to quality measurement, specifically measuring its density, surface finish, flatness, and dimensional accuracy to obtain the first die-cast quality set. This first die-cast quality set is then compared with preset component die-cast quality constraints to determine whether the trial-produced part meets the quality requirements.

[0103] If the first die-casting quality set satisfies the die-casting quality constraints of the component, it indicates that the current die-casting process optimization starting point is effective and can be determined as the target die-casting process parameter, that is, the process parameter ultimately used for mass production. Taking the die-casting process optimization starting point as the target die-casting process parameter avoids further process adjustments, saves time and costs, and ensures the product quality of the die-cast parts to be produced.

[0104] Conversely, if the first die-casting quality set does not meet the die-casting quality constraints of the component, then the process optimization starting point of the die-casting process is diverged according to the process optimization rules to obtain the target die-casting process parameters.

[0105] The specific method for optimizing and diverging processes to obtain the target die-casting process parameters is as follows:

[0106] By aggregating the multiple reference die-casting process parameter sets, K types of die-casting process adjustment indicators, including all adjustable process parameter indices, are obtained. Then, based on the K types of die-casting process adjustment indicators, the multiple reference die-casting process parameter sets are regrouped to obtain K groups of die-casting process parameters, including K types of die-casting boundary index values.

[0107] The maximum and minimum values ​​of the K sets of die-casting process parameters are extracted to obtain the adjustment limit of each process parameter. Then, based on the adjustment limit, the possible value range of each die-casting process adjustment index is defined to obtain K process adjustment intervals. The process adjustment intervals provide boundaries for the adjustment of process parameters to ensure that the adjustment of process parameters does not exceed the limitations of equipment or materials.

[0108] The preset process parameter adjustment step size is the amount of change in the die-casting process parameters each time an adjustment is made. The process parameter adjustment step size and the K process adjustment intervals constitute the process optimization rules. The process optimization rules are used to guide how to systematically adjust the die-casting process parameters in order to explore different combinations of process parameters in an orderly manner, so as to find the optimal process settings and thus improve the die-casting quality.

[0109] In this embodiment, the K process adjustment intervals determined in the early stage are used as constraints, and the step size of the preset process parameters is adjusted to update the K die casting process parameters of the K die casting process adjustment indexes in the starting point of die casting process optimization in an orderly manner within their respective adjustment intervals, so as to explore possible improvement directions and obtain a new set of process parameters, which is called the first die casting process parameters.

[0110] Using the same method as obtaining the first die-cast part entity, the trial production of the die-cast part to be processed is performed using the first die-casting process parameters to obtain the second die-cast part entity; the second die-casting quality set of the obtained second die-cast part entity is measured, and it is determined whether the first die-casting quality set meets the die-casting quality constraints of the component.

[0111] If the second die-casting quality set fails to meet the die-casting quality constraints of the part, it indicates that the current first die-casting process parameters need further optimization. According to the process optimization rules, the process parameters are further adjusted, which may include changing the values ​​of one or more process parameters to explore a wider range of process parameter spaces.

[0112] Repeat the trial production and quality assessment process until a set of process parameters that meets all quality constraints is found. This set of parameters that meets the conditions is the target die casting process parameters.

[0113] This embodiment achieves the technical effect of reducing subjective human judgment, improving the objectivity of die-casting process parameter decisions, reducing the number and time of trial and error, and quickly converging to the optimal die-casting process parameters.

[0114] A700: After the target die casting raw material is fed into the die casting equipment, the die casting equipment is run using the target die casting process parameters to perform batch production of the die casting parts to be processed.

[0115] Specifically, in this embodiment, after the target die-casting raw material is fed into the die-casting equipment, the die-casting equipment is run using the target die-casting process parameters to perform batch production of the die-casting parts to be processed.

[0116] This embodiment achieves precise setting of die casting process parameters, ensuring high-standard quality of die castings while improving the repeatability and production efficiency of the die casting process, reducing trial and error costs in die casting process decisions, and accelerating product development cycles.

[0117] Example 2, as Figure 3 As shown, the present invention provides a die-casting equipment, wherein a process parameter optimization method for die casting is applied to the die-casting equipment, the die-casting equipment comprising:

[0118] The component information interaction unit 11 is used to interactively obtain component design information and component operating conditions of the die-cast part to be processed.

[0119] The stress analysis execution unit 12 is used to perform load stress analysis on the die casting to be processed based on the component design information and component operating conditions, and obtain the component mechanical performance constraints.

[0120] The raw material matching unit 13 is used to extract the operating environment of the component based on the operating conditions of the component, and to select raw materials for production according to the operating environment and mechanical performance constraints of the component to obtain the raw material formula.

[0121] The die casting raw material processing unit 14 is used to schedule and mix raw materials according to the raw material formula to obtain the target die casting raw material.

[0122] The optimized starting point positioning unit 15 is used to interactively obtain the component die casting quality constraints of the die casting part to be processed, and to perform network data matching and calling based on the component die casting quality constraints, component design information and target die casting raw materials to obtain the starting point for die casting process optimization.

[0123] The process parameter optimization unit 16 is used to preset process optimization rules and perform process optimization divergence on the starting point of the die casting process according to the process optimization rules to obtain the target die casting process parameters.

[0124] The batch production execution unit 17 is used to feed the target die casting raw material into the die casting equipment and then run the die casting equipment using the target die casting process parameters to perform batch production of the die casting parts to be processed.

[0125] In one embodiment, the force analysis execution unit 12 is further configured to:

[0126] The process involves interactively obtaining multiple associated structural design information of the die-cast part to be processed, and modeling the part to be processed and multiple associated structural models based on the part design information and the multiple associated structural design information; assembling the part model and multiple associated structural models in a finite element analysis network to obtain an assembly model; extracting the part operating conditions based on the part operating conditions; simulating the assembly model in the finite element analysis network using the part operating conditions to obtain multiple sets of load stresses; aggregating the force types of the multiple sets of load stresses to obtain M sets of force parameters of M force types; extracting extreme values ​​from the M sets of force parameters to obtain M force limits, which constitute the mechanical performance constraints of the part.

[0127] In one embodiment, the raw material matching unit 13 is further configured to:

[0128] Using the component's operating environment and mechanical performance constraints, multiple candidate raw materials are obtained through a comprehensive comparison. The component's operating environment includes temperature, humidity, and chemical environment. Multiple candidate operating environments for these candidate raw materials are all inferior to the component's operating environment, and multiple sets of candidate mechanical properties for these candidate raw materials are superior to the component's mechanical performance constraints. Multiple candidate raw material formulations are obtained interactively from these candidate raw materials. Raw material type aggregation is performed on these multiple candidate raw material formulations to obtain multiple transportation costs – material costs for various raw materials. Multiple procurement costs for these multiple candidate raw material formulations are calculated based on these multiple transportation costs – material costs. These multiple procurement costs are serialized, and the target die-casting raw material is located among the multiple candidate raw materials based on the sequence minimum value. Finally, the raw material formulation is obtained by calling upon the multiple candidate raw material formulations based on the target die-casting raw material.

[0129] In one embodiment, the optimized starting point positioning unit 15 is further configured to:

[0130] Using the component design information and target die-casting raw materials as constraints, network data matching and retrieval are performed to obtain reference component information, which includes multiple reference die-casting process parameter sets and multiple reference die-casting quality sets. A die-casting quality evaluation function is introduced, and the starting point for die-casting process optimization is located by traversing the reference component information according to the component die-casting quality constraints and the die-casting quality evaluation function.

[0131] In one embodiment, the optimized starting point positioning unit 15 is further configured to:

[0132] Using the target die-casting raw material as a constraint, network data matching is performed to obtain multiple sample die-casting models; multiple geometric similarities between the model of the part to be processed and the multiple sample die-casting models are calculated, and multiple reference die-casting models that meet the preset similarity threshold are extracted by traversing the multiple geometric similarities; network data is performed on the multiple reference die-casting models to obtain multiple sets of reference die-casting process parameters and multiple sets of reference die-casting quality, and the multiple sets of reference die-casting process parameters and multiple sets of reference die-casting quality constitute the reference part information.

[0133] In one embodiment, the optimized starting point positioning unit 15 is further configured to:

[0134] The die-casting quality constraints of the components include component density constraints, surface cleanliness constraints, surface flatness constraints, and structural dimensional accuracy constraints.

[0135] In one embodiment, the optimized starting point positioning unit 15 is further configured to:

[0136] The die-casting quality evaluation function is as follows:

[0137] ;

[0138] ;

[0139] ;

[0140] ;

[0141] ;

[0142] in, Due to component density constraints, For sample component density, To meet the requirements of cleanliness for tourism, For the cleanliness of the sample table, For apparent smoothness constraints, For the apparent smoothness of the sample, To constrain structural dimensional accuracy, To ensure the accuracy of sample structure dimensions, The interaction term affects the adjustment coefficient. Density component coefficient, For surface finish component coefficient, This is the flatness component coefficient. These are the dimensional component coefficients. This is the die-casting quality coefficient. These are the weighting coefficients for density, smoothness, flatness, and dimensional accuracy, respectively. .

[0143] In one embodiment, the process parameter optimization unit 16 is further configured to:

[0144] After the target die-casting raw material is fed into the die-casting equipment, the die-casting equipment is run using the optimized starting point of the die-casting process to perform trial production of the die-casting part to be processed, and a first die-casting part entity is obtained. The first die-casting mass set of the first die-casting part entity is measured, and it is determined whether the first die-casting mass set meets the die-casting quality constraints of the part. If the first die-casting mass set meets the die-casting quality constraints of the part, the optimized starting point of the die-casting process is used as the target die-casting process parameter. If the first die-casting mass set does not meet the die-casting quality constraints of the part, the optimized starting point of the die-casting process is optimized and diverged according to the process optimization rules to obtain the target die-casting process parameter.

[0145] In one embodiment, the process parameter optimization unit 16 is further configured to:

[0146] By aggregating the multiple reference die-casting process parameter sets, K sets of die-casting process parameters with K types of die-casting process adjustment indices are obtained. Maximum and minimum values ​​are extracted from the K sets of die-casting process parameters to obtain K process adjustment intervals. A preset process parameter adjustment step size is established, and the process parameter adjustment step size and the K process adjustment intervals constitute the process optimization rules. Using the K process adjustment intervals as constraints, the starting point for die-casting process optimization is updated using the process parameter adjustment step size to obtain the first die-casting process parameters. Trial production of the die-casting part to be processed is performed using the first die-casting process parameters to obtain a second die-casting part entity. The second die-casting quality set of the obtained second die-casting part entity is measured, and it is determined whether the first die-casting quality set meets the component die-casting quality constraints. If the second die-casting quality set does not meet the component die-casting quality constraints, process optimization divergence is continued on the second die-casting quality set according to the process optimization rules until the target die-casting process parameters are obtained.

[0147] In summary, any of the methods or steps described above can be stored as computer instructions or programs in various types of computer memory, and the computer instructions or programs can be recognized by various types of computer processors to implement any of the above methods or steps.

[0148] Based on the above specific embodiments of the present invention, any improvements and modifications made to the present invention by those skilled in the art without departing from the principle of the present invention shall fall within the patent protection scope of the present invention.

Claims

1. A method for process parameter optimization for die casting, characterized in that, The method comprises: interactively obtaining component design information and component operating conditions of a to-be-processed die casting part; performing load stress analysis on the to-be-processed die casting part according to the component design information and the component operating conditions, and obtaining component mechanical performance constraints; extracting a component operating environment based on the component operating conditions, and selecting production raw materials according to the component operating environment and the component mechanical performance constraints to obtain a raw material formula; performing raw material scheduling and mixing processing according to the raw material formula to obtain a target die casting raw material; interactively obtaining component die casting quality constraints of the to-be-processed die casting part, and performing network data matching calling according to the component die casting quality constraints, the component design information and the target die casting raw material to obtain a die casting process optimization starting point; presetting a process optimization rule, and performing process optimization divergence on the die casting process optimization starting point according to the process optimization rule to obtain a target die casting process parameter; after the target die casting raw material is put into a die casting equipment, the die casting equipment is operated using the target die casting process parameter to perform batch production of the to-be-processed die casting part; interactively obtaining component die casting quality constraints of the to-be-processed die casting part, and performing network data matching calling according to the component die casting quality constraints, the component design information and the target die casting raw material to obtain a die casting process optimization starting point, the method further comprising: performing network data matching calling with the component design information and the target die casting raw material as constraints to obtain reference component information, wherein the reference component information comprises a plurality of reference die casting process parameter sets and a plurality of reference die casting quality sets; introducing a die casting quality evaluation function, and locating the die casting process optimization starting point in the reference component information according to the component die casting quality constraints and the die casting quality evaluation function; presetting a process optimization rule, and performing process optimization divergence on the die casting process optimization starting point according to the process optimization rule to obtain a target die casting process parameter, the method further comprising: after the target die casting raw material is put into a die casting equipment, the die casting equipment is operated using the target die casting process parameter to perform batch production of the to-be-processed die casting part; measuring a first die casting quality set of the first die casting entity, and determining whether the first die casting quality set meets the component die casting quality constraints; if the first die casting quality set meets the component die casting quality constraints, the die casting process optimization starting point is used as the target die casting process parameter; if the first die casting quality set does not meet the component die casting quality constraints, process optimization divergence is performed on the die casting process optimization starting point according to the process optimization rule to obtain the target die casting process parameter.

2. A method for process parameter optimization for die casting as claimed in claim 1 wherein, performing load stress analysis on the to-be-processed die casting part according to the component design information and the component operating conditions to obtain component mechanical performance constraints, the method further comprising: interactively obtaining a plurality of associated structure design information of the to-be-processed die casting part, and modeling to obtain a to-be-processed component model and a plurality of associated structure models according to the component design information and the plurality of associated structure design information; assembling the to-be-processed component model and the plurality of associated structure models in a finite element analysis network to obtain an assembly model; Based on the component operating condition extraction obtains component operating conditions; Adopt the component operating conditions in the finite element analysis network simulation running the assembly model, obtain a plurality of groups of load stress; The stress type of the plurality of groups of load stress is aggregated to obtain M groups of stress parameters of M stress types; The extreme value of the M groups of stress parameters is extracted to obtain M stress limits, and the M stress limits constitute the component mechanical property constraint.

3. A method for process parameter optimization for die casting as claimed in claim 2 wherein, Based on the component operating condition extraction obtains component operating environment, and according to the component operating environment and component mechanical property constraint matching selection production raw material, obtain the raw material formula, the method further comprises: Adopt the component operating environment and component mechanical property constraint, traversal comparison obtains a plurality of alternative raw materials, wherein the component operating environment includes temperature, humidity, chemical environment, the plurality of alternative operating environments of the plurality of alternative raw materials are all worse than the component operating environment, and the plurality of alternative mechanical properties of the plurality of alternative raw materials are all better than the component mechanical property constraint; Interactively obtain a plurality of alternative raw material formulas of the plurality of alternative raw materials; The raw material type of the plurality of alternative raw material formulas is aggregated to obtain a plurality of transportation costs-material costs of a plurality of raw materials; According to the plurality of transportation costs-material costs of the plurality of raw materials, a plurality of procurement costs of the plurality of alternative raw material formulas are calculated and obtained; The plurality of procurement costs are sequenced, and the target die casting raw material is positioned in the plurality of alternative raw materials according to the sequence minimum value, and the raw material formula is called according to the target die casting raw material in the plurality of alternative raw material formulas.

4. A method for process parameter optimization for die casting as claimed in claim 1 wherein, With the component design information and target die casting raw material as constraints, network data matching calling is carried out to obtain reference component information, and the method further comprises: With the target die casting raw material as a constraint, network data matching calling is carried out to obtain a plurality of sample die casting models; The plurality of geometric similarities of the to-be-processed component model and the plurality of sample die casting models are calculated, and a plurality of reference die casting models satisfying a preset similarity threshold are extracted by traversing the plurality of geometric similarities; The plurality of reference die casting models are called by network data to obtain a plurality of reference die casting process parameter sets and a plurality of reference die casting quality sets, and the plurality of reference die casting process parameter sets and the plurality of reference die casting quality sets constitute the reference component information.

5. A method for process parameter optimization for die casting as claimed in claim 1 wherein, The component die casting quality constraint includes component density constraint, apparent finish constraint, apparent flatness constraint and structure size precision constraint.

6. A method for process parameter optimization for die casting as claimed in claim 5 wherein, The die casting quality evaluation function is as follows: ； ； ； ； ； wherein, is a component density constraint, is a sample component density, is an apparent finish constraint, is a sample apparent finish, is an apparent flatness constraint, is a sample apparent flatness, is a structural dimension accuracy constraint, is a sample structural dimension accuracy, is an interaction term influence adjustment coefficient, is a density component coefficient, is a finish component coefficient, is a flatness component coefficient, is a dimension component coefficient, is a die casting quality coefficient, are weight coefficients for density, finish, flatness, and dimension accuracy, respectively, and .

7. A method for process parameter optimization for die casting as claimed in claim 1 wherein, If the first die casting quality set does not satisfy the component die casting quality constraint, the die casting process optimization starting point is diverged according to the process optimization rule, and the target die casting process parameter is obtained, and the method further comprises: The plurality of reference die casting process parameter sets are aggregated to obtain K groups of die casting process parameters of K die casting process adjustment indexes; The maximum value and the minimum value of the K groups of die casting process parameters are extracted to obtain K process adjustment intervals; A preset process parameter adjustment step is set, and the process parameter adjustment step and the K process adjustment intervals constitute the process optimization rule. The K process adjustment intervals are taken as constraints, and the process parameter adjustment step is used to update the die casting process optimization starting point, to obtain a first die casting process parameter; The first die casting process parameter is used to perform trial production of the to-be-processed die casting part, to obtain a second die casting part entity; The second die casting quality set obtained by the second die casting part entity is measured, and it is judged whether the first die casting quality set meets the part die casting quality constraint; If the second die casting quality set does not meet the part die casting quality constraint, the process optimization divergence is continued according to the process optimization rule to the second die casting quality set until the target die casting process parameter is obtained.

8. A die casting apparatus characterized by comprising: The process parameter optimization method for die casting according to any one of claims 1 to 7 is applied to the die casting equipment, and the die casting equipment comprises: A part information interaction unit is configured to interactively obtain part design information and part operating conditions of a to-be-processed die casting part. A stress analysis execution unit is configured to perform load stress analysis of the to-be-processed die casting part according to the part design information and the part operating conditions, to obtain part mechanical performance constraints. A production raw material matching unit is configured to extract a part operating environment based on the part operating conditions, and to match and select production raw materials according to the part operating environment and the part mechanical performance constraints, to obtain a raw material formula. A die casting raw material processing unit is configured to perform raw material scheduling and mixing processing according to the raw material formula, to obtain a target die casting raw material. An optimization starting point positioning unit is configured to interactively obtain part die casting quality constraints of the to-be-processed die casting part, and to perform network data matching calling according to the part die casting quality constraints, the part design information, and the target die casting raw material, to obtain a die casting process optimization starting point. The part die casting quality constraints of the to-be-processed die casting part are interactively obtained, and network data matching calling is performed according to the part die casting quality constraints, the part design information, and the target die casting raw material, to obtain a die casting process optimization starting point. The method further comprises: The part design information and the target die casting raw material are taken as constraints for network data matching calling, to obtain reference part information, wherein the reference part information comprises a plurality of reference die casting process parameter sets and a plurality of reference die casting quality sets. A die casting quality evaluation function is introduced, and the die casting process optimization starting point is located in the reference part information according to the part die casting quality constraints and the die casting quality evaluation function. A process parameter optimization unit is configured to preset a process optimization rule, and to perform process optimization divergence of the die casting process optimization starting point according to the process optimization rule, to obtain a target die casting process parameter. The process optimization rule is preset, and the process optimization divergence of the die casting process optimization starting point is performed according to the process optimization rule, to obtain a target die casting process parameter. The method further comprises: After the target die casting raw material is put into the die casting equipment, the die casting equipment is run using the die casting process optimization starting point to perform trial production of the to-be-processed die casting part, to obtain a first die casting part entity; The first die casting quality set obtained by the first die casting part entity is measured, and it is judged whether the first die casting quality set meets the part die casting quality constraint; if the first pressure casting quality set satisfies the part pressure casting quality constraint, taking the pressure casting process optimization starting point as the target pressure casting process parameter; if the first pressure casting quality set does not satisfy the part pressure casting quality constraint, carrying out process optimization divergence on the pressure casting process optimization starting point according to the process optimization rule to obtain the target pressure casting process parameter; a batch production execution unit, which is configured to, after the target pressure casting raw material is put into a pressure casting device, run the pressure casting device using the target pressure casting process parameter to execute batch production of the to-be-processed pressure casting part.

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