Intelligent die casting processing method and system for automobile parts

Abstract

The application discloses a wisdom die-casting processing method and system for automobile parts, and relates to the technical field of metal casting. The method comprises the following steps: obtaining automobile part die-casting raw material feature data flow of automobile parts to be die-cast; loading die feature data flow corresponding to a die-casting die; activating automobile part die-casting temperature control factors; performing die-casting temperature expectation mining to obtain automobile part die-casting temperature expectation distribution; constructing automobile part die-casting temperature control population; setting automobile part die-casting temperature evaluation factors; generating die-casting temperature control optimization seeds; and controlling the die-casting temperature of the automobile parts to be die-cast. The method solves the technical problem that the existing die-casting temperature control cannot adaptively adjust the die-casting temperature according to the raw material features and die features of the automobile parts to be die-cast in the face of complex and changeable die-casting environments and material characteristics, thereby resulting in insufficient die-casting temperature control accuracy and lack of adaptability. The method achieves the technical effects of improving the die-casting temperature control accuracy and adaptability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of metal casting, and particularly relates to a smart die casting processing method and system for automobile parts. BACKGROUND

[0002] With the rapid development of the automobile industry, the production efficiency and quality requirements of automobile parts are continuously improved. In the production process of automobile parts, die casting technology is widely used because it can efficiently and accurately produce complex-shaped parts. However, the control of temperature in the die casting process has a decisive influence on the quality of the final product. Traditional die casting temperature control methods often rely on the experience of workers and the setting of fixed parameters, and cannot adaptively adjust the die casting temperature according to the characteristics of the raw materials and the mold of the automobile parts to be die cast. Therefore, the traditional die casting temperature control methods lack adaptability and accuracy, and are difficult to cope with complex and variable die casting environments and material properties.

[0003] Therefore, in the current die casting temperature control related technology, there is a technical problem that it is difficult to adaptively adjust the die casting temperature according to the characteristics of the raw materials and the mold of the automobile parts to be die cast in the face of complex and variable die casting environments and material properties, which further leads to insufficient accuracy and lack of adaptability of die casting temperature control. SUMMARY

[0004] The present application provides a smart die casting processing method and system for automobile parts, which uses activation temperature control factors, expected mining, search optimization and other technical means to solve the technical problem that the existing die casting temperature control is difficult to adaptively adjust the die casting temperature according to the characteristics of the raw materials and the mold of the automobile parts to be die cast in the face of complex and variable die casting environments and material properties, which further leads to insufficient accuracy and lack of adaptability of die casting temperature control. The technical effect of improving the accuracy and adaptability of die casting temperature control is achieved.

[0005] The application provides a smart die casting processing method for automobile parts, which comprises the following steps: obtaining automobile part die casting raw material feature data flow of automobile parts to be die cast; loading die feature data flow corresponding to a die casting die of the automobile parts to be die cast; activating automobile part die casting temperature control factors, wherein the automobile part die casting temperature control factors include automobile part raw material pouring temperature, automobile part die casting die temperature, automobile part die casting cooling water temperature and automobile part die casting environment temperature; based on the automobile part die casting raw material feature data flow and the die feature data flow, die casting temperature expectation mining is performed according to the automobile part die casting temperature control factors to obtain automobile part die casting temperature expectation distribution; based on the automobile part die casting temperature expectation distribution and control seed constraint rules, an automobile part die casting temperature control population is constructed; setting automobile part die casting temperature evaluation factors, wherein the automobile part die casting temperature evaluation factors include die casting automobile part size precision, die casting automobile part surface quality and die casting automobile part performance; searching and optimizing the automobile part die casting temperature control population according to the automobile part die casting temperature evaluation factors to generate a die casting temperature control optimization seed; and controlling the die casting temperature of the automobile parts to be die cast according to the die casting temperature control optimization seed.

[0006] In a possible implementation, based on the automobile part die casting raw material feature data flow and the die feature data flow, die casting temperature expectation mining is performed according to the automobile part die casting temperature control factors to obtain automobile part die casting temperature expectation distribution, and the following processing is further performed: based on the automobile part die casting raw material feature data flow and the die feature data flow, the automobile part raw material pouring temperature is expected to be mined to obtain automobile part raw material pouring temperature expectation distribution; based on the automobile part die casting raw material feature data flow and the die feature data flow, the automobile part die casting die temperature is expected to be mined to obtain automobile part die casting die temperature expectation distribution; based on the automobile part die casting raw material feature data flow and the die feature data flow, the automobile part die casting cooling water temperature is expected to be mined to obtain automobile part die casting cooling water temperature expectation distribution; based on the automobile part die casting raw material feature data flow and the die feature data flow, the automobile part die casting environment temperature is expected to be mined to obtain automobile part die casting environment temperature expectation distribution; and the automobile part raw material pouring temperature expectation distribution, the automobile part die casting die temperature expectation distribution, the automobile part die casting cooling water temperature expectation distribution and the automobile part die casting environment temperature expectation distribution are integrated to generate the automobile part die casting temperature expectation distribution.

[0007] In a possible implementation, based on the automobile part die casting raw material feature data stream and the mold feature data stream, expected mining is performed on the automobile part raw material pouring temperature to obtain an automobile part raw material pouring temperature expected distribution, and the following processing is performed: obtaining part attribute feature information of the automobile part to be die cast; taking the part attribute feature information, the automobile part die casting raw material feature data stream, and the mold feature data stream as die casting temperature retrieval constraints; taking the automobile part raw material pouring temperature as a die casting temperature retrieval target; performing die casting record retrieval according to the die casting temperature retrieval constraints and the die casting temperature retrieval target to obtain an automobile part raw material pouring temperature retrieval set; constructing an automobile part raw material pouring temperature distribution map according to the automobile part raw material pouring temperature retrieval set; performing discrete point cleaning according to the automobile part raw material pouring temperature distribution map to generate an automobile part raw material pouring temperature centralized map; and performing centralized trend analysis according to the automobile part raw material pouring temperature centralized map to generate the automobile part raw material pouring temperature expected distribution.

[0008] In a possible implementation, according to the automobile part die casting temperature expected distribution and a control seed constraint rule, an automobile part die casting temperature control population is constructed, and the following processing is performed: the control seed constraint rule includes a control seed capacity constraint and a control seed mutation constraint; die casting temperature particle modulation is performed according to the automobile part die casting temperature expected distribution to obtain an automobile part raw material pouring temperature modulation particle group, an automobile part die casting mold temperature modulation particle group, an automobile part die casting cooling water temperature modulation particle group, and an automobile part die casting environment temperature modulation particle group; modulation particle random combination is performed according to the automobile part raw material pouring temperature modulation particle group, the automobile part die casting mold temperature modulation particle group, the automobile part die casting cooling water temperature modulation particle group, and the automobile part die casting environment temperature modulation particle group to obtain an initial automobile part die casting temperature control population that satisfies the control seed capacity constraint; and mutation verification optimization is performed on the initial automobile part die casting temperature control population according to the control seed mutation constraint to generate the automobile part die casting temperature control population.

[0009] In a possible implementation, mutation verification optimization is performed on the initial automobile part die casting temperature control population according to the control seed mutation constraint to generate the automobile part die casting temperature control population, and the following processing is further performed: pairwise difference comparison evaluation is performed on the initial automobile part die casting temperature control population to generate a plurality of control seed mutation indexes; it is judged whether the plurality of control seed mutation indexes satisfy the control seed mutation constraint; if the plurality of control seed mutation indexes all satisfy the control seed mutation constraint, the initial automobile part die casting temperature control population is added to the automobile part die casting temperature control population.

[0010] In a possible implementation, the judging whether the plurality of control seed variation indexes satisfy the control seed variation constraint further performs the following processing: if any one of the plurality of control seed variation indexes does not satisfy the control seed variation constraint, obtaining an identified die casting temperature control seed; optimizing the initial automobile part die casting temperature control population according to the automobile part die casting temperature expectation distribution and the identified die casting temperature control seed to generate an optimized automobile part die casting temperature control population; and performing mutation verification optimization on the optimized automobile part die casting temperature control population according to the control seed variation constraint to obtain the automobile part die casting temperature control population.

[0011] In a possible implementation, the searching and optimizing the automobile part die casting temperature control population according to the automobile part die casting temperature evaluation factor to generate a die casting temperature control optimization seed further performs the following processing: extracting a first die casting temperature control seed according to the automobile part die casting temperature control population; evaluating the first die casting temperature control seed according to the automobile part die casting temperature evaluation factor to obtain a first die casting temperature control evaluation coefficient; extracting a second die casting temperature control seed according to the automobile part die casting temperature control population; calculating a second die casting temperature control evaluation coefficient according to the second die casting temperature control seed based on the automobile part die casting temperature evaluation factor; performing current optimization according to the first die casting temperature control evaluation coefficient, the second die casting temperature control evaluation coefficient, the first die casting temperature control seed and the second die casting temperature control seed to generate a current optimization die casting temperature control evaluation coefficient and a current optimization die casting temperature control seed; and performing iterative optimization on the current optimization die casting temperature control seed according to the automobile part die casting temperature control population and the automobile part die casting temperature evaluation factor based on the current optimization die casting temperature control evaluation coefficient to obtain the die casting temperature control optimization seed satisfying an iterative optimization number threshold.

[0012] In a possible implementation, the first die casting temperature control seed is evaluated according to the automobile part die casting temperature evaluation factor, and a first die casting temperature control evaluation coefficient is obtained. Further, the following processing is performed: a die casting temperature control simulation is performed according to the first die casting temperature control seed, and a first die casting temperature control simulation result is obtained, wherein the first die casting temperature control simulation result includes die casting automobile part size feature simulation data, die casting automobile part surface feature simulation data, and die casting automobile part performance feature simulation data; an automobile part die casting temperature evaluation assembly is constructed according to the automobile part die casting temperature evaluation factor, wherein the automobile part die casting temperature evaluation assembly includes a die casting automobile part size precision evaluation channel, a die casting automobile part surface quality evaluation channel, and a die casting automobile part performance evaluation channel; the first die casting temperature control simulation result is input into the automobile part die casting temperature evaluation assembly, and a first automobile part die casting temperature evaluation result is obtained; and the first automobile part die casting temperature evaluation result is weighted and calculated according to a die casting temperature evaluation weight condition, and the first die casting temperature control evaluation coefficient is generated.

[0013] The application also provides a smart die casting processing system for automobile parts, comprising: a die casting raw material feature data stream obtaining module, which is used to obtain automobile part die casting raw material feature data stream of a to-be-die-cast automobile part; a die feature data stream loading module, which is used to load die feature data stream corresponding to a die casting die of the to-be-die-cast automobile part; a die casting temperature control factor activation module, which activates automobile part die casting temperature control factors, wherein the automobile part die casting temperature control factors include automobile part raw material pouring temperature, automobile part die casting die temperature, automobile part die casting cooling water temperature, and automobile part die casting environment temperature; a die casting temperature expectation distribution obtaining module, which is used to mine die casting temperature expectations based on the automobile part die casting raw material feature data stream and the die feature data stream, according to the automobile part die casting temperature control factors, to obtain automobile part die casting temperature expectation distribution; a die casting temperature control population construction module, which is used to construct an automobile part die casting temperature control population according to the automobile part die casting temperature expectation distribution and control seed constraint rules; a die casting temperature evaluation factor setting module, which is used to set automobile part die casting temperature evaluation factors, wherein the automobile part die casting temperature evaluation factors include die casting automobile part size precision, die casting automobile part surface quality, and die casting automobile part performance; a die casting temperature control optimization seed generation module, which is used to search and optimize the automobile part die casting temperature control population according to the automobile part die casting temperature evaluation factors, to generate a die casting temperature control optimization seed; and a die casting temperature control module, which is used to control the die casting temperature of the to-be-die-cast automobile part according to the die casting temperature control optimization seed.

[0014] The smart die casting processing method and system for automobile parts provided by the application obtain automobile part die casting raw material feature data stream of a to-be-die-cast automobile part, load die feature data stream corresponding to a die casting die, activate automobile part die casting temperature control factors, mine die casting temperature expectations, obtain automobile part die casting temperature expectation distribution, construct an automobile part die casting temperature control population, set automobile part die casting temperature evaluation factors, generate a die casting temperature control optimization seed, and control the die casting temperature of the to-be-die-cast automobile part. The technical problems of the prior art, i.e., the difficulty in adaptively adjusting the die casting temperature according to the raw material features and die features of the to-be-die-cast automobile part in the face of complex and changeable die casting environments and material characteristics, and the resulting insufficient die casting temperature control precision and lack of adaptability, are solved, and the technical effects of improving die casting temperature control precision and adaptability are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. The flowcharts are used to illustrate the operations performed by the system according to the embodiments of the present disclosure. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously according to needs. Meanwhile, other operations can be added to these processes, or one or more steps of operations can be removed from these processes.

[0016] Figure 1 The flowchart of the intelligent die casting processing method of the automobile part provided by the embodiments of the present application;

[0017] Figure 2 The structural schematic diagram of the intelligent die casting processing system of the automobile part provided by the embodiments of the present application.

[0018] Legend: die casting raw material feature data stream obtaining module 10, mold feature data stream loading module 20, die casting temperature control factor activation module 30, die casting temperature expectation distribution obtaining module 40, die casting temperature control population construction module 50, die casting temperature evaluation factor setting module 60, die casting temperature control optimization seed generation module 70, and die casting temperature control module 80. DETAILED DESCRIPTION

[0019] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described.

[0020] In order to make the purposes, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by those skilled in the art without making creative labor are within the scope of protection of the present application.

[0021] In the following description, "some embodiments" are referred to, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict, the term "first\second" referred to only distinguishes similar objects, and does not represent a specific order for the objects. The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0022] The embodiments of the present application provide a smart die casting processing method for automobile parts, as shown in Figure 1 The method comprises the following steps:

[0023] In step S100, the automobile part die casting raw material feature data stream of the automobile part to be die cast is obtained. Specifically, detailed data and information about the physical, chemical and mechanical properties of the raw material of the automobile part to be die cast are obtained through various technical means and data analysis methods. Specifically, the automobile part die casting raw material feature data stream can include material composition data, chemical element composition of the raw material, such as the content and proportion of magnesium, aluminum, zinc and cerium elements in Mg-9Al-1Zn-0.5Ce alloy, and impurity content affecting the melting point and die casting performance of the material; physical property data, such as the density, melting point and thermal expansion coefficient of the material, which directly affect the temperature control in the die casting process, the thermal conductivity and thermal stability of the material, which are crucial for controlling the heat distribution and preventing overheating or rapid cooling of the material in the die casting process; mechanical property data, such as the tensile strength, yield strength and elongation of the material, which affect the final strength and toughness of the product, and the fatigue and impact performance of the material, which can evaluate the durability and reliability of the product in actual use; die castable performance data, such as the flowability of the material, which is the ability of the material to fill the mold in the die casting process, directly affecting the forming effect and integrity of the product, and the solidification characteristics and shrinkage rate of the material, affecting defects such as pores and shrinkage holes in the product; in the die casting process, the temperature, pressure and other state data of the raw material are obtained in real time through sensors. By collecting and analyzing these raw material feature data streams, the temperature, pressure and other parameters in the die casting process can be more accurately controlled, thereby ensuring that the dimensional accuracy, surface quality and performance of the product meet the requirements.

[0024] Step S200, load the mold feature data stream corresponding to the die casting mold of the automobile part to be die cast. Obtain and load the data and information related to the die casting mold of the automobile part to be die cast, so as to provide accurate mold parameters and features for adaptive die casting temperature control, which can better understand the structure, performance and characteristics of the mold, so as to ensure that the temperature control in the die casting process is more accurate and effective. Specifically, the mold feature data stream may include mold design parameters (size and shape of the mold, number, layout and size of the cavity), mold material properties (thermal conductivity, thermal expansion coefficient and other physical properties of the mold material, hardness, strength and wear resistance of the mold material), mold usage state (real-time temperature distribution, wear degree and damage condition of the mold), mold heat exchange performance (cooling water channel design, number and layout of the mold, heating element layout and power of the mold), mold and die casting machine matching (mold and die casting machine interface size and fitting accuracy, mold and die casting machine control system compatibility) and historical die casting data (mold temperature control parameters, production efficiency and product quality data in the past die casting process). By loading these mold feature data streams, the structure, performance and characteristics of the mold can be comprehensively understood, and accurate data support can be provided for adaptive die casting temperature control.

[0025] Step S300, activating the automobile part die casting temperature control factor, wherein the automobile part die casting temperature control factor includes automobile part raw material pouring temperature, automobile part die casting mold temperature, automobile part die casting cooling water temperature and automobile part die casting environment temperature. Activating the automobile part die casting temperature control factor refers to setting and adjusting various temperature factors affecting the quality of the die casting in the die casting process to ensure that the die casting process is carried out under the best temperature conditions. The temperature control factor includes automobile part die casting temperature control factor including automobile part raw material pouring temperature, automobile part die casting mold temperature, automobile part die casting cooling water temperature and automobile part die casting environment temperature. Specifically, the automobile part raw material pouring temperature refers to the temperature of the metal liquid when it fills the cavity from the pressure chamber. For castings of different shapes and structures, the pouring temperature can be controlled within a certain range, for example, 630-730℃. Thin-walled complex parts should use higher temperature to improve the fluidity of the metal liquid and obtain good forming. Thick-walled structural parts can use lower temperature to reduce solidification shrinkage. Too high pouring temperature may increase the amount of air in the aluminum water, making it easy to produce pinholes, shrinkage holes and surface bubbles in thick-walled castings, and accelerating mold corrosion. Too low pouring temperature will result in poor flowability, which is easy to produce cold shut, flow lines and insufficient pouring. The automobile part die casting mold temperature refers to the mold surface temperature, which has a significant impact on the mechanical properties, dimensional accuracy of the die casting and the service life of the die casting mold. The standard state of the mold temperature should be about 1 / 3 of the alloy liquid pouring temperature, usually 100-300℃. The die casting mold needs to be preheated to a certain temperature before use and always maintained within a certain temperature range during production. Too low mold temperature may cause the liquid metal to lose flowability quickly due to rapid cooling in the mold, affecting the forming of the casting. Too high mold temperature may cause the die casting to deform due to incomplete solidification, and even the mold moving parts may be stuck. The automobile part die casting cooling water temperature is used to control the cooling speed of the die casting mold, which indirectly affects the quality of the die casting and the service life of the mold. It needs to be reasonably set according to the die casting material and mold design to ensure that the mold maintains an appropriate temperature during the die casting process. The automobile part die casting environment temperature refers to the temperature around the die casting workshop or die casting equipment. Although the environment temperature does not directly affect the die casting process like the above-mentioned temperature control factors, it also affects the mold preheating, heat preservation and the cooling speed of the die casting. Higher environment temperature may cause difficulty in heat dissipation of the mold, which needs to be adjusted appropriately. Lower environment temperature may increase the mold preheating time and energy consumption.

[0026] Step S400, based on the automobile parts die casting raw material characteristic data stream and the mold characteristic data stream, according to the automobile parts die casting temperature control factor, the die casting temperature expectation is excavated, and the automobile parts die casting temperature expectation distribution is obtained. By using data analysis technology (such as machine learning, data mining, etc.), combined with historical die casting data and real-time monitoring data, the temperature control factor is deeply analyzed, according to the raw material characteristic data stream and the mold characteristic data stream, the quality and performance of the die casting under different process parameters are predicted, through simulation and experimental verification, the best temperature control factor combination, that is, the die casting temperature expectation, is found out, according to the obtained die casting temperature expectation, the distribution of temperature in different regions and different time points is further analyzed, considering the differences of materials, molds and environment, as well as the dynamic changes in the die casting process, the reasonable range and variation trend of temperature distribution are determined, and finally the automobile parts die casting temperature expectation distribution is formed, which provides accurate guidance for temperature control in the die casting process.

[0027] In a possible implementation manner, step S400 further includes step S410, based on the automobile parts die casting raw material characteristic data stream and the mold characteristic data stream, the automobile parts raw material pouring temperature is expected to be excavated, and the automobile parts raw material pouring temperature expectation distribution is obtained. By using the automobile parts die casting raw material characteristic data stream (including the chemical composition, physical properties, historical use data, etc. of the raw material), combined with the die casting process requirements and material properties, through data analysis and model prediction technology, the best automobile parts raw material pouring temperature range is excavated, for example, for aluminum alloy material, the pouring temperature is generally selected between 600-700 degrees, but the specific range needs to be adjusted according to the actual material and process.

[0028] Step S400 further includes step S420, based on the automobile parts die casting raw material characteristic data stream and the mold characteristic data stream, the automobile parts die casting mold temperature is expected to be excavated, and the automobile parts die casting mold temperature expectation distribution is obtained. By analyzing the mold data stream, combined with the die casting process requirements and the heat resistance of the mold, the appropriate mold preheating temperature, holding temperature and cooling temperature range can be determined, and the control of the mold temperature is crucial for the forming quality of the casting and the service life of the mold. For example, for aluminum alloy die casting mold, the preheating temperature is generally selected at about 200 degrees, and the mold temperature is controlled between 220-280 degrees.

[0029] Step S400 further includes step S430, based on the automobile part die casting raw material feature data stream and the mold feature data stream, excavating the expected automobile part die casting cooling water temperature, and obtaining the automobile part die casting cooling water temperature expected distribution. By analyzing the cooling water temperature data stream in the die casting process, combined with the characteristics of the mold material and the casting material, the appropriate cooling water temperature range can be determined. The temperature of the cooling water has little effect on the actual production, but appropriate cooling water temperature can improve production efficiency. Generally, the cooling water temperature can be set between 30-50 degrees.

[0030] Step S400 further includes step S440, based on the automobile part die casting raw material feature data stream and the mold feature data stream, excavating the expected automobile part die casting environment temperature, and obtaining the automobile part die casting environment temperature expected distribution. By analyzing the environment temperature data stream of the die casting workshop, combined with the die casting process requirements, the environment temperature range required to be maintained in the die casting process can be determined. The die casting working environment temperature is usually high, especially in the summer high temperature weather, effective measures need to be taken to reduce the environment temperature to improve the working efficiency and safety.

[0031] Step S400 further includes step S450, integrating the automobile part raw material pouring temperature expected distribution, the automobile part die casting mold temperature expected distribution, the automobile part die casting cooling water temperature expected distribution and the automobile part die casting environment temperature expected distribution, and generating the automobile part die casting temperature expected distribution. The obtained automobile part raw material pouring temperature expected distribution, mold temperature expected distribution, cooling water temperature expected distribution and environment temperature expected distribution are integrated, the mutual influence and constraint relationship between each factor are comprehensively considered, and the complete automobile part die casting temperature expected distribution is generated through data analysis and optimization algorithm, which provides an important reference for the temperature control of the die casting process and helps to realize the high-quality die casting products.

[0032] In one possible implementation, step S410 further includes step S411 of obtaining part attribute feature information of the automobile part to be die cast. The part attribute feature information includes part name, category, size, quality requirement, etc. It also includes step S412 of taking the part attribute feature information, the automobile part die casting raw material feature data stream, and the mold feature data stream as die casting temperature retrieval constraints. The part attribute feature information, the automobile part die casting raw material feature data stream (such as chemical composition and physical properties of the raw material), and the mold feature data stream (such as mold material, size, and heat conduction performance) are taken as constraints of die casting temperature retrieval. It also includes step S413 of taking the automobile part raw material pouring temperature as a die casting temperature retrieval target. The retrieval target is the automobile part raw material pouring temperature, that is, the target temperature to be retrieved and analyzed. It also includes step S414 of performing die casting record retrieval according to the die casting temperature retrieval constraints and the die casting temperature retrieval target to obtain an automobile part raw material pouring temperature retrieval set. According to the defined retrieval constraints and target, retrieval is performed in the die casting record database to obtain an automobile part raw material pouring temperature retrieval set related to the current automobile part to be die cast, including information such as raw material pouring temperature used in historical die casting processes, corresponding part attributes, raw material features, and mold features.

[0033] Step S410 further includes step S415 of constructing an automobile part raw material pouring temperature distribution map according to the automobile part raw material pouring temperature retrieval set. The retrieved raw material pouring temperature data is sorted, including removing duplicate data and abnormal data, and an automobile part raw material pouring temperature distribution map is drawn according to the sorted data to visually display the distribution of the temperature. It also includes step S416 of performing discrete point cleaning according to the automobile part raw material pouring temperature distribution map to generate an automobile part raw material pouring temperature concentration map. Discrete point cleaning refers to identifying and removing discrete points in the distribution map that have less impact on the overall trend according to a set threshold, so that the data is more concentrated and accurate, and an automobile part raw material pouring temperature concentration map is generated. Compared with the original distribution map, the data points in the concentration map are more concentrated, and the main data distribution trend can be more clearly displayed. It also includes step S417 of performing concentration trend analysis according to the automobile part raw material pouring temperature concentration map to generate an expected distribution of the automobile part raw material pouring temperature. Statistical methods (such as mean, median, and mode) are used to analyze the data in the concentration map to evaluate the concentration degree and trend of the data, and based on the results of the concentration trend analysis, an expected distribution of the automobile part raw material pouring temperature is generated, that is, a predicted pouring temperature range interval.

[0034] Step S500, according to the automobile parts die casting temperature expectation distribution and control seed constraint rules, build automobile parts die casting temperature control population. Control seed constraint rules are a series of restrictions set to ensure the safety, efficiency and product quality of the die casting process, which may include the maximum and minimum temperature limits of the mold, the flow and pressure limits of the cooling water, the range of pouring temperature, etc. The automobile parts die casting temperature control population refers to a group of candidate die casting temperature control parameter sets, each set containing specific values of all temperature control factors. Specifically, a group of initial temperature control parameter sets is randomly generated according to the temperature expectation distribution and constraint rules. The effectiveness of these parameter sets in the actual die casting process is verified through simulation or experiment, and their advantages and disadvantages are evaluated. According to the evaluation results, select a part of the parameter sets with better performance as seeds, and perform crossover (combine the advantages of multiple seeds) and mutation (introduce new elements or changes) on the selected seeds to generate new parameter sets. Repeat the steps of evaluation, selection, crossover and mutation to gradually optimize the automobile parts die casting temperature control population.

[0035] In a possible implementation, step S500 further includes step S510, the control seed constraint rules include control seed capacity constraints and control seed mutation constraints. Control seed capacity constraints refer to the limitation of the number of initial automobile parts die casting temperature control population control seeds (or particles). Based on experimental conditions, etc., ensure that the population size is within a processable range; control seed mutation constraints limit the degree or range of particle mutation in the population. Mutation constraints ensure that mutation is within a reasonable range to avoid excessive or insufficient variation, in order to maintain the diversity and optimization efficiency of the population. It also includes step S520, according to the automobile parts die casting temperature expectation distribution, die casting temperature particle modulation is carried out to obtain automobile parts raw material pouring temperature modulation particle group, automobile parts die casting mold temperature modulation particle group, automobile parts die casting cooling water temperature modulation particle group and automobile parts die casting environment temperature modulation particle group. Die casting temperature particle modulation refers to the random setting of automobile parts raw material pouring temperature, automobile parts die casting mold temperature, automobile parts die casting cooling water temperature and automobile parts die casting environment temperature based on automobile parts die casting temperature expectation distribution. Specifically, the automobile parts raw material pouring temperature modulation particle group includes a plurality of randomly set pouring temperatures, the automobile parts die casting mold temperature modulation particle group includes a plurality of randomly set mold temperatures, the automobile parts die casting cooling water temperature modulation particle group includes a plurality of randomly set cooling water temperatures, and the automobile parts die casting environment temperature modulation particle group includes a plurality of randomly set die casting environment temperatures.

[0036] Step S500 further includes step S530 of modulating particles randomly combining the automobile part raw material pouring temperature modulation particle group, the automobile part die casting mold temperature modulation particle group, the automobile part die casting cooling water temperature modulation particle group and the automobile part die casting environment temperature modulation particle group to obtain an initial automobile part die casting temperature control population satisfying the control seed capacity constraint. The particles in the four modulation particle groups are randomly combined, and each combination represents a possible die casting temperature control scheme, i.e., the initial automobile part die casting temperature control population, and the combinations need to satisfy the control seed capacity constraint, i.e., the total number of combinations (or population size) is within a preset range. Step S540 further includes mutating and verifying optimization of the initial automobile part die casting temperature control population according to the control seed mutation constraint to generate the automobile part die casting temperature control population. According to the control seed mutation constraint, the particles in the initial population are mutated, which can be a small adjustment or a larger change, depending on the setting of the mutation constraint. After mutation and verification optimization, the final automobile part die casting temperature control population is obtained, which contains multiple possible temperature control schemes.

[0037] In a possible implementation, step S540 further includes step S541 of performing pairwise difference comparison and evaluation on the initial automobile part die casting temperature control population to generate a plurality of control seed mutation indexes. The pairwise difference comparison between each temperature control scheme (or "seed") in the initial automobile part die casting temperature control population is performed, and the Euclidean distance, Manhattan distance, etc. are used to calculate the difference index between the seeds to evaluate the similarity and difference between the temperature control schemes. Based on the above difference comparison, a mutation index is calculated for each seed, reflecting the difference between the seed and other seeds in the population. Step S542 further includes judging whether the plurality of control seed mutation indexes satisfy the control seed mutation constraint. Each control seed mutation index is compared with the control seed mutation constraint, wherein the control seed mutation constraint is a threshold range preset in advance to judge whether the mutation index meets the requirement. If all control seed mutation indexes meet the mutation constraint, the next operation is performed, otherwise, the mutation strategy is adjusted or the mutation operation is performed again. Step S543 further includes adding the initial automobile part die casting temperature control population to the automobile part die casting temperature control population if the plurality of control seed mutation indexes satisfy the control seed mutation constraint. When all control seed mutation indexes satisfy the mutation constraint, all seeds (i.e., temperature control schemes) in the initial automobile part die casting temperature control population are added to the automobile part die casting temperature control population, and finally a set of automobile part die casting temperature control populations satisfying the requirement is obtained.

[0038] In a possible implementation, step S542 further includes step S544: if any of the plurality of control seed mutation indexes does not satisfy the control seed mutation constraint, obtaining an identified die casting temperature control seed. If any of the control seed mutation indexes is found not to satisfy the constraint, the control seed that does not satisfy the constraint is identified, i.e., the identified die casting temperature control seed is obtained. Step S545 is further included: optimizing the initial automobile part die casting temperature control population according to the automobile part die casting temperature expectation distribution and the identified die casting temperature control seed, to generate an optimized automobile part die casting temperature control population. The die casting temperature control seed that does not satisfy the constraint is adjusted to satisfy the mutation constraint, and the expectation distribution is used to guide the optimization direction, such as fine-tuning to the center or a better region in the expectation distribution, to generate an optimized automobile part die casting temperature control population, i.e., the optimized automobile part die casting temperature control population.

[0039] Step S542 further includes step S546: performing mutation verification optimization on the optimized automobile part die casting temperature control population according to the control seed mutation constraint, to obtain the automobile part die casting temperature control population. The optimized automobile part die casting temperature control population is further optimized by using a mutation operation, and it is ensured that the population after mutation satisfies the preset control seed mutation constraint. Specifically, the mutation operation is used to generate new possible solutions on the basis of existing solutions, i.e., adjusting one or more parameters in the temperature control scheme (such as raw material pouring temperature, mold temperature, cooling water temperature, etc.). After the mutation operation, the newly generated solutions need to be verified to ensure that they satisfy the control seed mutation constraint. If the solutions after mutation satisfy the mutation constraint and are better (such as closer to the expectation distribution, have a higher quality prediction, etc.) than the original solutions in some evaluation standard, the solutions are added to the automobile part die casting temperature control population. Finally, the obtained automobile part die casting temperature control population contains a plurality of solutions that satisfy the mutation constraint and have better performance.

[0040] Step S600, set the automobile part die casting temperature evaluation factor, wherein the automobile part die casting temperature evaluation factor includes die casting automobile part size precision, die casting automobile part surface quality and die casting automobile part performance. The influence of temperature on the final quality of the automobile part during the die casting process is set by the automobile part die casting temperature evaluation factor, which includes die casting automobile part size precision, die casting automobile part surface quality and die casting automobile part performance. Specifically, the die casting automobile part size precision refers to the degree of conformity between the actual size of the die casting and the designed size. The mold temperature directly affects the shrinkage rate of the casting, thereby affecting the size precision. For example, excessively high mold temperature may cause the size of the casting to be too large, while excessively low mold temperature may cause the size of the casting to be too small. Excessively high pouring temperature may cause the metal liquid to have too good fluidity, resulting in the size of the casting being too large, while excessively low pouring temperature may cause the casting to be incompletely filled, resulting in the size of the casting being too small. The cooling water temperature has a direct impact on the cooling effect of the mold, thereby affecting the size precision of the casting. The die casting automobile part surface quality refers to the smoothness of the casting surface, defects (such as pores, inclusions, sand holes, cracks, etc.) and coating quality, etc. Non-uniform or excessively high mold temperature may cause defects such as sticking film and scratches on the surface of the casting. Excessively low pouring temperature may cause defects such as cold shut and flow lines on the surface of the casting. Excessively high or low ambient temperature may affect the surface quality of the casting, such as causing the surface of the casting to oxidize and peel off, etc. The die casting automobile part performance refers to the mechanical performance, physical performance and chemical performance of the casting under specific conditions, etc. The mold temperature has a direct impact on the crystalline structure, grain size and mechanical properties of the casting. The pouring temperature affects the chemical composition and organizational structure of the casting, thereby affecting its performance. The cooling water temperature affects the solidification speed and microstructure of the casting, thereby affecting its performance.

[0041] Step S700, search and optimize the die casting temperature control population according to the automobile part die casting temperature evaluation factor, and generate a die casting temperature control optimization seed. According to the evaluation factor set, an evaluation value is calculated for each temperature control parameter set, i.e. based on the die casting automobile part size precision, die casting automobile part surface quality and die casting automobile part performance, the comprehensive score of the temperature control parameters in the die casting temperature control population is calculated by weighting. Specifically, using the particle swarm optimization algorithm, the particles search for the optimal solution in the search space by simulating the foraging behavior of bird flocks. In each iteration, the fitness of each individual in the population is calculated according to the evaluation function, and then the population is updated using the search strategy to generate a new set of temperature control parameters. One or more convergence conditions are set, such as reaching the maximum number of iterations, the fitness of the population not improving significantly for consecutive generations, etc. When the convergence condition is met, the search is stopped, and the individual with the highest fitness is selected from the last generation of the population as the die casting temperature control optimization seed, which includes the optimized pouring temperature of the raw material, mold temperature, cooling water temperature, etc.

[0042] In a possible implementation, step S700 further includes step S710 of extracting a first die casting temperature control seed from the die casting temperature control population of the automobile part. A die casting temperature control scheme is randomly or strategically (e.g., roulette selection) extracted from the die casting temperature control population of the automobile part as the first die casting temperature control seed. Step S700 further includes step S720 of evaluating the first die casting temperature control seed according to the die casting temperature evaluation factors of the automobile part to obtain a first die casting temperature control evaluation coefficient. The first die casting temperature control seed is evaluated according to the preset die casting temperature evaluation factors (e.g., casting quality, production efficiency, energy consumption, etc.) of the automobile part to obtain a specific quantitative value as the first die casting temperature control evaluation coefficient for measuring the comprehensive performance of the control scheme. Step S700 further includes step S730 of extracting a second die casting temperature control seed from the die casting temperature control population of the automobile part. Similarly, another die casting temperature control scheme is randomly extracted from the die casting temperature control population of the automobile part as the second die casting temperature control seed. Step S700 further includes step S740 of calculating a second die casting temperature control evaluation coefficient based on the die casting temperature evaluation factors of the automobile part and the second die casting temperature control seed. The second die casting temperature control seed is also evaluated according to the die casting temperature evaluation factors of the automobile part to obtain the second die casting temperature control evaluation coefficient.

[0043] Step S700 further includes step S750 of performing current optimization according to the first die casting temperature control evaluation coefficient, the second die casting temperature control evaluation coefficient, the first die casting temperature control seed, and the second die casting temperature control seed to generate a current optimization die casting temperature control evaluation coefficient and a current optimization die casting temperature control seed. The first die casting temperature control evaluation coefficient and the second die casting temperature control evaluation coefficient are compared to find a control scheme with better performance, and the control scheme with better performance is taken as the current optimization die casting temperature control seed, and the corresponding evaluation coefficient is taken as the current optimization die casting temperature control evaluation coefficient.

[0044] Step S700 further includes step S760, based on the current optimization die casting temperature control evaluation coefficient, iteratively optimizing the current optimization die casting temperature control seed according to the die casting temperature control seed group of the automobile part and the die casting temperature evaluation factor of the automobile part, to obtain the die casting temperature control optimization seed satisfying the iteration optimization number threshold. Based on the current optimization die casting temperature control evaluation coefficient, continue to extract a new die casting temperature control seed from the die casting temperature control seed group of the automobile part, evaluate the newly extracted seed, and compare it with the current optimization die casting temperature control seed. If the evaluation coefficient of the new seed is better, update the current optimization die casting temperature control seed and the current optimization die casting temperature control evaluation coefficient, repeat the above iteration process, until the preset iteration optimization number threshold is reached, and output the final die casting temperature control optimization seed, that is, the die casting temperature control scheme with the best performance in the whole control group.

[0045] In a possible implementation, step S720 further includes step S721, performing die casting temperature control simulation according to the first die casting temperature control seed to obtain first die casting temperature control simulation results, wherein the first die casting temperature control simulation results include die casting automobile part size characteristic simulation data, die casting automobile part surface characteristic simulation data, and die casting automobile part performance characteristic simulation data. The die casting temperature control simulation is performed using the first die casting temperature control seed, that is, the influence of temperature control on the automobile part in the die casting process is simulated through simulation software or experimental equipment to predict and evaluate the size, surface quality, and performance of the automobile part under specific temperature control conditions. The first die casting temperature control simulation results include die casting automobile part size characteristic simulation data, die casting automobile part surface characteristic simulation data, and die casting automobile part performance characteristic simulation data. Specifically, the die casting automobile part size characteristic simulation data reflects the influence of different temperature control parameters (such as mold preheating temperature, pouring temperature, and cooling rate) on the size of the automobile part in the die casting process, which may include key size parameters such as length, width, height, and wall thickness, as well as the correlation and variation trend between these parameters and the temperature control parameters. The die casting automobile part surface characteristic simulation data focuses on the quality of the surface of the automobile part, which may include the number, distribution, and size of defects such as pores, cracks, and impurities, as well as the appearance characteristics such as roughness and glossiness of the surface. The die casting automobile part performance characteristic simulation data evaluates the performance of the automobile part under specific temperature control conditions, which may include mechanical properties (such as tensile strength, yield strength, and elongation), physical properties (such as thermal stability and electrical conductivity), or chemical properties (such as corrosion resistance and wear resistance), the relationship between these performance parameters and the temperature control parameters, and the performance variation trend under different temperature control conditions.

[0046] The step S722 further comprises constructing an automobile part die casting temperature evaluation component according to the automobile part die casting temperature evaluation factor. The automobile part die casting temperature evaluation component comprises a die casting automobile part size precision evaluation channel, a die casting automobile part surface quality evaluation channel and a die casting automobile part performance evaluation channel. According to the evaluation factor, the corresponding evaluation channel can be constructed, for example, the die casting automobile part size precision evaluation channel, the die casting automobile part surface quality evaluation channel and the die casting automobile part performance evaluation channel can be set. Each evaluation channel will focus on evaluating the quality performance of the die casting in the corresponding specific aspect. Specifically, the evaluation model is constructed based on the neural network or the decision tree. The data related to the temperature control in the historical die casting process, such as the melting temperature, the pouring temperature, the mold temperature and the like, is collected. The historical data is used for supervised training of the evaluation model. The die casting automobile part size precision evaluation channel, the die casting automobile part surface quality evaluation channel and the die casting automobile part performance evaluation channel are obtained. Through the integration of the three evaluation channels, the automobile part die casting temperature evaluation component is obtained. The performance of the die casting in the size precision, the surface quality and the performance can be comprehensively evaluated. Therefore, the automobile part die casting temperature evaluation component can provide strong support for optimizing the die casting temperature control scheme. The die casting automobile part size precision evaluation channel is used for evaluating the precision of the die casting in the size. According to the preset size tolerance or standard, the size data of the simulated or actual die casting is compared. Whether the die casting meets the requirements in the size is judged. The die casting automobile part surface quality evaluation channel focuses on the quality evaluation of the surface of the die casting. The surface finish, the flatness, the defects such as the pores, the cracks and the impurities and the like are considered. The die casting automobile part performance evaluation channel is used for evaluating the performance of the die casting under specific conditions.

[0047] The step S720 further comprises the step S723 of inputting the first die casting temperature control simulation result into the automobile part die casting temperature evaluation component to obtain a first automobile part die casting temperature evaluation result. The automobile part die casting temperature evaluation component compares the first die casting temperature control simulation result with the preset standard or expected value to evaluate whether the die casting meets the requirements in the size, the surface quality and the performance. Based on the comparison result, the evaluation component comprehensively evaluates the overall quality of the die casting. Finally, the first automobile part die casting temperature evaluation result is output. The performance of the die casting in each index and whether the quality requirements are met are indicated. The step S724 further comprises weighting calculation of the first automobile part die casting temperature evaluation result according to the die casting temperature evaluation weight condition to generate the first die casting temperature control evaluation coefficient. The weight values of different evaluation channels (size precision, surface quality and performance) are set. The evaluation results of each channel are weighted and summed according to the weight values. The weighted sum finally obtained is the first die casting temperature control evaluation coefficient. The effect of the die casting temperature control can be more accurately understood.

[0048] Step S800, according to the die casting temperature control optimization seed, the automobile part to be die cast is die cast for temperature control. In the actual die casting process, the optimal temperature control parameter set obtained by searching and optimization (i.e. the die casting temperature control optimization seed) is applied to accurately control the temperature in the die casting process, so as to ensure that the size precision, surface quality and performance of the automobile part reach the optimum.

[0049] In the foregoing, with reference to Figure 1 The intelligent die casting processing method of the automobile part according to the embodiment of the application is described in detail. Next, with reference to Figure 2 The intelligent die casting processing system of the automobile part according to the embodiment of the application is described.

[0050] The intelligent die casting processing system of the automobile part according to the embodiment of the application is used to solve the technical problem that the existing die casting temperature control is difficult to adaptively adjust the die casting temperature according to the material characteristics and mold characteristics of the automobile part to be die cast, and thus leads to insufficient accuracy and lack of adaptability of the die casting temperature control, so as to achieve the technical effects of improving the accuracy and adaptability of the die casting temperature control. The intelligent die casting processing system of the automobile part comprises a die casting material characteristic data stream obtaining module 10, a mold characteristic data stream loading module 20, a die casting temperature control factor activating module 30, a die casting temperature expectation distribution obtaining module 40, a die casting temperature control population constructing module 50, a die casting temperature evaluation factor setting module 60, a die casting temperature control optimization seed generating module 70, and a die casting temperature control module 80.

[0051] The die casting material characteristic data stream obtaining module 10 is used to obtain the automobile part die casting material characteristic data stream of the automobile part to be die cast.

[0052] The mold characteristic data stream loading module 20 is used to load the mold characteristic data stream corresponding to the die casting mold of the automobile part to be die cast.

[0053] The die casting temperature control factor activating module 30 activates the automobile part die casting temperature control factor, wherein the automobile part die casting temperature control factor comprises an automobile part material pouring temperature, an automobile part die casting mold temperature, an automobile part die casting cooling water temperature, and an automobile part die casting environment temperature.

[0054] The die casting temperature expectation distribution obtaining module 40 is used to mine the die casting temperature expectation based on the automobile part die casting material characteristic data stream and the mold characteristic data stream according to the automobile part die casting temperature control factor, and obtain the automobile part die casting temperature expectation distribution.

[0055] The die casting temperature control population construction module 50 is configured to construct a die casting temperature control population of the automobile part according to the die casting temperature expectation distribution of the automobile part and the control seed constraint rule.

[0056] The die casting temperature evaluation factor setting module 60 is configured to set a die casting temperature evaluation factor of the automobile part, wherein the die casting temperature evaluation factor of the automobile part includes a die casting automobile part size precision, a die casting automobile part surface quality and a die casting automobile part performance.

[0057] The die casting temperature control optimization seed generation module 70 is configured to search and optimize the die casting temperature control population of the automobile part according to the die casting temperature evaluation factor of the automobile part, and generate a die casting temperature control optimization seed.

[0058] The die casting temperature control module 80 is configured to control the die casting temperature of the automobile part to be die cast according to the die casting temperature control optimization seed.

[0059] In the following, a specific configuration of the die casting temperature expectation distribution obtaining module 40 will be described in detail. The die casting temperature expectation distribution obtaining module 40 can further include: obtaining a die casting raw material pouring temperature expectation distribution of the automobile part by desirably excavating the die casting raw material pouring temperature of the automobile part based on the die casting raw material characteristic data stream of the automobile part and the mold characteristic data stream; obtaining a die casting mold temperature expectation distribution of the automobile part by desirably excavating the die casting mold temperature of the automobile part based on the die casting raw material characteristic data stream of the automobile part and the mold characteristic data stream; obtaining a die casting cooling water temperature expectation distribution of the automobile part by desirably excavating the die casting cooling water temperature of the automobile part based on the die casting raw material characteristic data stream of the automobile part and the mold characteristic data stream; obtaining a die casting environment temperature expectation distribution of the automobile part by desirably excavating the die casting environment temperature of the automobile part based on the die casting raw material characteristic data stream of the automobile part and the mold characteristic data stream; and generating the die casting temperature expectation distribution of the automobile part by integrating the die casting raw material pouring temperature expectation distribution of the automobile part, the die casting mold temperature expectation distribution of the automobile part, the die casting cooling water temperature expectation distribution of the automobile part and the die casting environment temperature expectation distribution of the automobile part.

[0060] In the following, the specific configuration of the die casting temperature expectation distribution obtaining module 40 will be described in detail. The die casting temperature expectation distribution obtaining module 40 further comprises: obtaining part attribute characteristic information of the automobile part to be die cast; taking the part attribute characteristic information, the automobile part die casting raw material characteristic data stream and the mold characteristic data stream as die casting temperature retrieval constraints; taking the automobile part raw material pouring temperature as a die casting temperature retrieval target; performing die casting record retrieval according to the die casting temperature retrieval constraints and the die casting temperature retrieval target to obtain an automobile part raw material pouring temperature retrieval set; constructing an automobile part raw material pouring temperature distribution map according to the automobile part raw material pouring temperature retrieval set; performing discrete point cleaning according to the automobile part raw material pouring temperature distribution map to generate an automobile part raw material pouring temperature concentration map; and performing concentration trend analysis according to the automobile part raw material pouring temperature concentration map to generate the automobile part raw material pouring temperature expectation distribution.

[0061] In the following, the specific configuration of the die casting temperature control population construction module 50 will be described in detail. The die casting temperature control population construction module 50 can further comprise: the control seed constraint rule comprises a control seed capacity constraint and a control seed mutation constraint; performing die casting temperature particle modulation according to the automobile part die casting temperature expectation distribution to obtain an automobile part raw material pouring temperature modulation particle group, an automobile part die casting mold temperature modulation particle group, an automobile part die casting cooling water temperature modulation particle group and an automobile part die casting environment temperature modulation particle group; performing modulation particle random combination according to the automobile part raw material pouring temperature modulation particle group, the automobile part die casting mold temperature modulation particle group, the automobile part die casting cooling water temperature modulation particle group and the automobile part die casting environment temperature modulation particle group to obtain an initial automobile part die casting temperature control population satisfying the control seed capacity constraint; and performing mutation verification optimization on the initial automobile part die casting temperature control population according to the control seed mutation constraint to generate the automobile part die casting temperature control population.

[0062] In the following, the specific configuration of the die casting temperature control population construction module 50 will be described in detail. The die casting temperature control population construction module 50 can further comprise: performing pairwise difference comparison evaluation according to the initial automobile part die casting temperature control population to generate a plurality of control seed mutation indexes; judging whether the plurality of control seed mutation indexes satisfy the control seed mutation constraint; and if the plurality of control seed mutation indexes all satisfy the control seed mutation constraint, adding the initial automobile part die casting temperature control population to the automobile part die casting temperature control population.

[0063] In the following, the specific configuration of the die casting temperature control population construction module 50 will be described in detail. The die casting temperature control population construction module 50 can further comprise: if any one of the plurality of control seed mutation indexes does not satisfy the control seed mutation constraint, obtaining an identified die casting temperature control seed; optimizing the initial automobile part die casting temperature control population according to the automobile part die casting temperature expectation distribution and the identified die casting temperature control seed to generate an optimized automobile part die casting temperature control population; and verifying and optimizing the optimized automobile part die casting temperature control population according to the control seed mutation constraint to obtain the automobile part die casting temperature control population.

[0064] In the following, the specific configuration of the die casting temperature control optimization seed generation module 70 will be described in detail. The die casting temperature control optimization seed generation module 70 further comprises: extracting a first die casting temperature control seed according to the automobile part die casting temperature control population; evaluating the first die casting temperature control seed according to the automobile part die casting temperature evaluation factor to obtain a first die casting temperature control evaluation coefficient; extracting a second die casting temperature control seed according to the automobile part die casting temperature control population; calculating a second die casting temperature control evaluation coefficient based on the automobile part die casting temperature evaluation factor and the second die casting temperature control seed; performing current optimization according to the first die casting temperature control evaluation coefficient, the second die casting temperature control evaluation coefficient, the first die casting temperature control seed and the second die casting temperature control seed to generate a current optimization die casting temperature control evaluation coefficient and a current optimization die casting temperature control seed; and performing iterative optimization on the current optimization die casting temperature control seed based on the current optimization die casting temperature control evaluation coefficient, the automobile part die casting temperature control population and the automobile part die casting temperature evaluation factor to obtain the die casting temperature control optimization seed satisfying the iterative optimization number threshold.

[0065] Next, the specific configuration of the die casting temperature control optimization seed generation module 70 will be described in detail. The die casting temperature control optimization seed generation module 70 further comprises: performing die casting temperature control simulation according to the first die casting temperature control seed to obtain first die casting temperature control simulation results, wherein the first die casting temperature control simulation results include die casting automobile part size feature simulation data, die casting automobile part surface feature simulation data and die casting automobile part performance feature simulation data; constructing an automobile part die casting temperature evaluation assembly according to the automobile part die casting temperature evaluation factor, wherein the automobile part die casting temperature evaluation assembly includes a die casting automobile part size precision evaluation channel, a die casting automobile part surface quality evaluation channel and a die casting automobile part performance evaluation channel; inputting the first die casting temperature control simulation results into the automobile part die casting temperature evaluation assembly to obtain first automobile part die casting temperature evaluation results; and performing weighted calculation on the first automobile part die casting temperature evaluation results according to the die casting temperature evaluation weight condition to generate the first die casting temperature control evaluation coefficient.

[0066] The intelligent die casting processing system for automobile parts provided in the embodiments of the present application can execute the intelligent die casting processing method for automobile parts provided in any of the embodiments of the present application, and has the corresponding functional modules and beneficial effects of the execution method.

[0067] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server, and each unit and module included is only divided according to the functional logic, but is not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy mutual differentiation, and do not limit the protection scope of the present application.

[0068] The above specific embodiments do not constitute a limitation on the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A smart die casting processing method of an automobile part, characterized by, The method comprises: obtaining automobile part die casting raw material feature data stream of automobile part to be die cast; loading die feature data stream corresponding to die casting die of automobile part to be die cast; activating automobile part die casting temperature control factor, wherein the automobile part die casting temperature control factor comprises automobile part raw material pouring temperature, automobile part die casting die temperature, automobile part die casting cooling water temperature and automobile part die casting environment temperature; based on the automobile part die casting raw material feature data stream and the die feature data stream, die casting temperature expectation mining is carried out according to the automobile part die casting temperature control factor, and automobile part die casting temperature expectation distribution is obtained; based on the automobile part die casting temperature expectation distribution and control seed constraint rule, automobile part die casting temperature control population is constructed; setting automobile part die casting temperature evaluation factor, wherein the automobile part die casting temperature evaluation factor comprises die casting automobile part size precision, die casting automobile part surface quality and die casting automobile part performance; according to the automobile part die casting temperature evaluation factor, the automobile part die casting temperature control population is searched and optimized, and die casting temperature control optimization seed is generated; according to the die casting temperature control optimization seed, the automobile part to be die cast is controlled in die casting temperature.

2. The intelligent die casting process of an automobile part according to claim 1, wherein, Based on the automobile part die casting raw material feature data stream and the die feature data stream, die casting temperature expectation mining is carried out according to the automobile part die casting temperature control factor, and automobile part die casting temperature expectation distribution is obtained, comprising: based on the automobile part die casting raw material feature data stream and the die feature data stream, the automobile part raw material pouring temperature is expected to be mined, and the automobile part raw material pouring temperature expectation distribution is obtained; based on the automobile part die casting raw material feature data stream and the die feature data stream, the automobile part die casting die temperature is expected to be mined, and the automobile part die casting die temperature expectation distribution is obtained; based on the automobile part die casting raw material feature data stream and the die feature data stream, the automobile part die casting cooling water temperature is expected to be mined, and the automobile part die casting cooling water temperature expectation distribution is obtained; based on the automobile part die casting raw material feature data stream and the die feature data stream, the automobile part die casting environment temperature is expected to be mined, and the automobile part die casting environment temperature expectation distribution is obtained; integrating the automobile part raw material pouring temperature expectation distribution, the automobile part die casting die temperature expectation distribution, the automobile part die casting cooling water temperature expectation distribution and the automobile part die casting environment temperature expectation distribution, the automobile part die casting temperature expectation distribution is generated.

3. The intelligent die casting process of automobile parts as claimed in claim 2, wherein, Based on the automobile part die casting raw material feature data stream and the die feature data stream, the automobile part raw material pouring temperature is expected to be mined, and the automobile part raw material pouring temperature expectation distribution is obtained, comprising: obtaining part attribute feature information of the automobile part to be die cast; taking the part attribute feature information, the automobile part die casting raw material feature data stream and the die feature data stream as die casting temperature retrieval constraint; taking the automobile part raw material pouring temperature as die casting temperature retrieval target; According to the die casting temperature retrieval constraint and the die casting temperature retrieval target, die casting record retrieval is performed to obtain a die casting temperature retrieval set of an automobile part raw material pouring temperature; According to the die casting temperature retrieval set of the automobile part raw material pouring temperature, a die casting temperature distribution map of the automobile part raw material pouring temperature is constructed; According to the die casting temperature distribution map of the automobile part raw material pouring temperature, discrete point cleaning is performed to generate a die casting temperature concentration map of the automobile part raw material pouring temperature; According to the die casting temperature concentration map of the automobile part raw material pouring temperature, concentration trend analysis is performed to generate a die casting temperature expectation distribution of the automobile part raw material pouring temperature.

4. The intelligent die casting process of automobile parts as claimed in claim 1, wherein, According to the die casting temperature expectation distribution of the automobile part and the control seed constraint rule, a die casting temperature control population of the automobile part is constructed, including: The control seed constraint rule includes a control seed capacity constraint and a control seed mutation constraint; According to the die casting temperature expectation distribution of the automobile part, die casting temperature particle modulation is performed to obtain a die casting temperature modulation particle group of the automobile part raw material pouring temperature, a die casting mold temperature modulation particle group of the automobile part, a die casting cooling water temperature modulation particle group of the automobile part, and a die casting environment temperature modulation particle group of the automobile part; According to the die casting temperature modulation particle group of the automobile part raw material pouring temperature, the die casting mold temperature modulation particle group of the automobile part, the die casting cooling water temperature modulation particle group of the automobile part, and the die casting environment temperature modulation particle group of the automobile part, modulation particle random combination is performed to obtain an initial die casting temperature control population of the automobile part that satisfies the control seed capacity constraint; According to the control seed mutation constraint, mutation verification optimization is performed on the initial die casting temperature control population of the automobile part to generate the die casting temperature control population of the automobile part.

5. The intelligent die casting process of an automobile part according to claim 4, wherein, According to the control seed mutation constraint, mutation verification optimization is performed on the initial die casting temperature control population of the automobile part to generate the die casting temperature control population of the automobile part, including: According to the initial die casting temperature control population of the automobile part, pairwise difference comparison evaluation is performed to generate a plurality of control seed mutation indexes; It is judged whether the plurality of control seed mutation indexes satisfy the control seed mutation constraint; If the plurality of control seed mutation indexes all satisfy the control seed mutation constraint, the initial die casting temperature control population of the automobile part is added to the die casting temperature control population of the automobile part.

6. The intelligent die casting process of an automobile part according to claim 5, wherein, It is judged whether the plurality of control seed mutation indexes satisfy the control seed mutation constraint, including: If any one of the plurality of control seed mutation indexes does not satisfy the control seed mutation constraint, an identified die casting temperature control seed is obtained; According to the die casting temperature expectation distribution of the automobile part and the identified die casting temperature control seed, optimization is performed on the initial die casting temperature control population of the automobile part to generate an optimized die casting temperature control population of the automobile part; According to the control seed mutation constraint, mutation verification optimization is performed on the optimized die casting temperature control population of the automobile part to obtain the die casting temperature control population of the automobile part.

7. The smart die casting process of automobile parts as claimed in claim 1, wherein, According to the die casting temperature evaluation factor of the automobile part, search optimization is performed on the die casting temperature control population of the automobile part to generate a die casting temperature control optimization seed, including: According to the die casting temperature control population of the automobile part, a first die casting temperature control seed is extracted; According to the automobile part pressure casting temperature evaluation factor, the first pressure casting temperature control seed is evaluated, and a first pressure casting temperature control evaluation coefficient is obtained; According to the automobile part pressure casting temperature control population, a second pressure casting temperature control seed is extracted; Based on the automobile part pressure casting temperature evaluation factor, a second pressure casting temperature control evaluation coefficient is calculated according to the second pressure casting temperature control seed; According to the first pressure casting temperature control evaluation coefficient, the second pressure casting temperature control evaluation coefficient, the first pressure casting temperature control seed and the second pressure casting temperature control seed, current optimization is carried out, and a current optimization pressure casting temperature control evaluation coefficient and a current optimization pressure casting temperature control seed are generated; Based on the current optimization pressure casting temperature control evaluation coefficient, the current optimization pressure casting temperature control seed is iteratively optimized according to the automobile part pressure casting temperature control population and the automobile part pressure casting temperature evaluation factor, and the pressure casting temperature control optimization seed meeting the iteration optimization number threshold is obtained.

8. The intelligent die casting process of an automobile part according to claim 7, wherein, According to the automobile part pressure casting temperature evaluation factor, the first pressure casting temperature control seed is evaluated, and a first pressure casting temperature control evaluation coefficient is obtained, including: According to the first pressure casting temperature control seed, a pressure casting temperature control simulation is carried out, and a first pressure casting temperature control simulation result is obtained, wherein the first pressure casting temperature control simulation result includes pressure casting automobile part size feature simulation data, pressure casting automobile part surface feature simulation data and pressure casting automobile part performance feature simulation data; According to the automobile part pressure casting temperature evaluation factor, an automobile part pressure casting temperature evaluation assembly is constructed, wherein the automobile part pressure casting temperature evaluation assembly includes a pressure casting automobile part size precision evaluation channel, a pressure casting automobile part surface quality evaluation channel and a pressure casting automobile part performance evaluation channel; The first pressure casting temperature control simulation result is input into the automobile part pressure casting temperature evaluation assembly, and a first automobile part pressure casting temperature evaluation result is obtained; According to the pressure casting temperature evaluation weight condition, the first automobile part pressure casting temperature evaluation result is weighted and calculated, and the first pressure casting temperature control evaluation coefficient is generated.

9. A smart die casting processing system for automobile parts, characterized by, The system is used to implement the intelligent pressure casting processing method of the automobile part according to any one of claims 1-8, and the system comprises: A pressure casting raw material feature data stream obtaining module is used to obtain automobile part pressure casting raw material feature data stream of a to-be-pressure-cast automobile part; A mold feature data stream loading module is used to load mold feature data stream corresponding to a pressure casting mold of the to-be-pressure-cast automobile part; A pressure casting temperature control factor activation module is used to activate automobile part pressure casting temperature control factors, wherein the automobile part pressure casting temperature control factors include automobile part raw material pouring temperature, automobile part pressure casting mold temperature, automobile part pressure casting cooling water temperature and automobile part pressure casting environment temperature; The die casting temperature expectation distribution obtaining module is used for obtaining a die casting temperature expectation distribution of the automobile part according to the automobile part die casting temperature control factor based on the automobile part die casting raw material feature data stream and the mold feature data stream; The die casting temperature control population constructing module is used for constructing a die casting temperature control population of the automobile part according to the automobile part die casting temperature expectation distribution and a control seed constraint rule; The die casting temperature evaluation factor setting module is used for setting a die casting temperature evaluation factor of the automobile part, wherein the die casting temperature evaluation factor of the automobile part includes a die casting automobile part size precision, a die casting automobile part surface quality and a die casting automobile part performance; The die casting temperature control optimization seed generating module is used for searching and optimizing the die casting temperature control population of the automobile part according to the die casting temperature evaluation factor of the automobile part, and generating a die casting temperature control optimization seed; The die casting temperature control module is used for controlling a die casting temperature of the automobile part to be die cast according to the die casting temperature control optimization seed.

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