A method for preparing a high-strength, high-thermal-conductivity Al-Si-Fe-Sr alloy by rheocasting

Abstract

This invention relates to the field of semi-solid metal material processing technology, and particularly to a method for preparing a high-strength, high-thermal-conductivity Al-Si-Fe-Sr alloy by rheostat die casting. The method includes preparing raw materials according to the alloy composition; melting the raw materials under a protective atmosphere to obtain an alloy melt; holding the alloy melt at a high temperature; determining the transfer time point of the alloy melt based on the similarity between the cooling rate curve of the alloy melt and a preset cooling rate curve; pouring the alloy melt into a gas-induced semi-solid slurry preparation device at the transfer time point; determining the dynamic flow rate adjustment curve of the inert gas based on the temperature fluctuation values ​​and the area ratio of irregular bubbles generated at several monitoring points during the pouring process; pouring the obtained semi-solid slurry into the die casting chamber of a die casting equipment for die casting; and heat-treating the die casting. This invention improves the preparation efficiency and quality by enhancing the accuracy of the analysis of the Al-Si-Fe-Sr alloy preparation process.

Description

Technical Field

[0001] This invention relates to the field of semi-solid metal material processing technology, and in particular to a method for preparing a high-strength, high-thermal-conductivity Al-Si-Fe-Sr alloy by rheo-die casting. Background Technology

[0002] Al-Si alloys are widely used in the automotive and 5G communication industries due to their low density, light weight, and good corrosion resistance. High-pressure die casting technology, with its advantages of high production efficiency and low cost, has become the main processing technology for manufacturing Al-Si alloy parts. However, Al-Si alloy parts prepared by high-pressure die casting often exhibit dendritic structures and casting defects, which reduce the alloy's mechanical and thermal conductivity. Rheological die casting technology provides an effective way to solve this problem.

[0003] With the rapid development of the automotive and 5G communication industries, the demand for lightweighting is increasing, and the urgent need for thin-walled parts has become a key development area, while the requirements for high comprehensive performance are also increasing. Currently, Al-Si alloys commonly used in rheocasting, including A356, ADC12, and A380, cannot simultaneously achieve both mechanical and thermal conductivity, which has become a major bottleneck limiting their application in the automotive and 5G communication fields. Therefore, it is necessary to synergistically improve the mechanical and thermal conductivity properties of Al-Si alloys. However, there is a trade-off between the mechanical and thermal conductivity properties of Al-Si alloys; improving the mechanical properties of Al-Si alloys can lead to a decrease in their thermal conductivity.

[0004] Chinese Patent Application Publication No. CN109652685A discloses a high thermal conductivity and high corrosion resistance cast aluminum alloy and its preparation method. The raw material components, by weight percentage, are: 7%–9% Si; 0.6%–1.0% Fe; 0.2%–0.6% Zn; 0.1%–0.5% Co; 0.05%–0.15% B; 0.2%–0.5% RE; 0.05%–0.2% Sr, with the balance being Al. During preparation, an aluminum alloy containing Si, Fe, and Co elements is first melted at high temperature and then allowed to cool. Pure Zn, Al-RE, Al-B, and Al-Sr master alloys are added to the melt for further alloying. The melt is then refined to remove slag and cast. The alloy preparation process of this invention has significant process effects, fully utilizing the multi-element composite synergistic effect of Co, B, Sr, and RE, resulting in alloy castings with excellent thermal conductivity, high mechanical properties, and good corrosion resistance.

[0005] However, existing technologies still have the following problems:

[0006] The lack of a dynamic control mechanism based on the real-time state of the melt leads to poor stability in the preparation process of semi-solid slurry, uncontrollable microstructure evolution, and difficulty in effectively coordinating the contradiction between mechanical properties and thermal conductivity. Summary of the Invention

[0007] Therefore, this invention provides a method for preparing high-strength and high-thermal-conductivity Al-Si-Fe-Sr alloy by rheo-die casting, in order to overcome the problem that the existing technology lacks a dynamic control mechanism based on the real-time state of the melt, resulting in poor stability of the preparation process of semi-solid slurry, uncontrollable microstructure evolution, and difficulty in effectively coordinating the contradiction between mechanical properties and thermal conductivity.

[0008] To achieve the above objectives, this invention provides a method for preparing a high-strength, high-thermal-conductivity Al-Si-Fe-Sr alloy by rheocasting. The method includes:

[0009] Step S1: Prepare raw materials according to the alloy composition, wherein the alloy composition contains 6.8% to 7.3% silicon, 0.4% to 0.5% iron, 0.02% to 0.08% strontium, and the remainder is aluminum by mass percentage;

[0010] Step S2: Under a protective atmosphere, the raw materials are melted to obtain an alloy melt;

[0011] Step S3: The alloy melt is kept at a constant temperature. After heating is stopped, the alloy melt is allowed to cool naturally. The transfer time of the alloy melt is determined based on the similarity between the cooling rate curve of the alloy melt and the preset cooling rate curve.

[0012] Step S4: At the material transfer time point, the alloy melt is poured into the gas-induced semi-solid slurry preparation device, and the alloy melt type is determined based on the temperature fluctuation value and the area ratio of irregular bubbles generated at several monitoring points during the alloy melt pouring process. Inert gas is introduced, and the dynamic flow rate adjustment curve of the inert gas is determined based on the alloy melt type.

[0013] Step S5: Real-time monitoring of the visual images of bubbles in the gas-induced semi-solid pulping device during the inert gas introduction process, and calculation of the effective mixing value based on the proportion of irregular bubbles and the uniformity of bubble distribution in the bubble visual images, and determination of the microstructure evolution stage of the pulp based on the effective mixing value.

[0014] Step S6: Determine the inert gas flow correction method based on the microstructure evolution stage of the slurry;

[0015] Step S7: Pour the obtained semi-solid slurry into the die casting chamber of the die casting equipment for die casting.

[0016] Step S8: Heat treat the die-cast part.

[0017] Further, in step S3, the transfer time point of the alloy melt is determined based on the similarity between the cooling rate curve of the alloy melt and a preset cooling rate curve, including:

[0018] The point at which the similarity between the cooling rate curve of the alloy melt calculated in real time and the preset cooling rate curve first changes from less than or equal to the preset similarity to greater than the preset similarity is defined as the primary material transfer time point.

[0019] After determining the initial material transfer time point, the real-time temperature of the alloy melt is monitored;

[0020] If the real-time temperature of the alloy melt is within the preset ideal transfer temperature range, the transfer action is triggered immediately, and the initial transfer time point at this time is the final transfer time point.

[0021] If the real-time temperature of the alloy melt is greater than the maximum temperature of the preset ideal transfer temperature range, the transfer will not be carried out for the time being. The temperature will be continuously monitored until the real-time temperature drops to the preset ideal transfer temperature range, and then the transfer action will be triggered. The time point when this temperature is reached is the final transfer time point.

[0022] If the real-time temperature of the alloy melt is less than the minimum temperature of the preset ideal transfer temperature range, the cooling process is determined to be abnormal, and the transfer is abandoned.

[0023] Furthermore, the real-time cooling rate curve of the alloy melt is dynamically compared with one or more preset cooling rate curves. The similarity is calculated using a dynamic time warping algorithm to calculate the real-time similarity distance between the two curves.

[0024] Further, in step S4, the type of alloy melt is determined based on the temperature fluctuation values ​​and the ratio of irregular bubble formation area at several monitoring points during the alloy melt casting process, including:

[0025] If the temperature fluctuation values ​​at several monitoring points during the alloy melt casting process are less than the preset temperature fluctuation value and the area ratio of irregular bubbles generated is less than the preset area ratio, then the alloy melt type is determined to be a stable type.

[0026] If the temperature fluctuation values ​​at several monitoring points during the alloy melt casting process are greater than or equal to the preset temperature fluctuation value and the area ratio of irregular bubbles generated is less than the preset area ratio, then the alloy melt type is determined to be the type with poor thermal uniformity.

[0027] If the temperature fluctuation values ​​at several monitoring points during the alloy melt casting process are less than the preset temperature fluctuation value and the ratio of irregular bubble generation area is greater than or equal to the preset generation area ratio, then the alloy melt type is determined to be the gas entrapment type.

[0028] If the temperature fluctuation values ​​at several monitoring points during the alloy melt casting process are greater than the preset temperature fluctuation value and the ratio of irregular bubble generation area is greater than or equal to the preset generation area ratio, then the alloy melt type is determined to be unstable.

[0029] Furthermore, in step S4, the step of calculating the temperature fluctuation values ​​at several monitoring points during the alloy melt pouring process includes,

[0030] Step S411: The melt temperature is synchronously and continuously measured at the same sampling frequency by at least three temperature sensors that are pre-set in different spatial positions in the pulping device, and the temperature-time series data corresponding to each sensor is obtained.

[0031] Step S412: Divide the entire pouring process into several consecutive calculation time windows according to time.

[0032] Step S413: For each calculation time window, extract the temperature measurement values ​​of all monitoring points within the window at the same time and calculate their standard deviation; calculate the arithmetic mean of the standard deviations of all times within the window and use it as the window temperature fluctuation value of the time window.

[0033] Step S414: Define the maximum value of the window temperature fluctuation value among all calculated time windows during the entire casting process as the alloy melt temperature fluctuation value for this casting process.

[0034] Furthermore, in step S4, the step of calculating the area ratio of irregular bubbles generated at several monitoring points during the alloy melt casting process includes,

[0035] Step S421: A high-speed camera positioned above the observation window or casting stream of the gas-induced semi-solid slurry preparation device continuously captures a sequence of dynamic images of the melt surface during the casting process at a fixed frame rate.

[0036] Step S422: Perform grayscale conversion, contrast enhancement and noise reduction filtering on each frame of the acquired image in sequence; use a threshold-based image segmentation algorithm or edge detection algorithm to separate the bubble region in the image from the melt background and generate a binary image, in which the white region represents the bubble.

[0037] Step S423: Perform morphological analysis on each independent connected region in the binarized image, calculate its roundness, and determine bubbles with roundness lower than the preset roundness as irregular bubbles.

[0038] Step S424: For each frame of image, calculate the sum of the areas of all regions identified as irregular bubbles, and then calculate the ratio of the sum to the total area of ​​the bubble regions identified in that frame of image to obtain the instantaneous irregular bubble area ratio of that frame of image.

[0039] Step S425: Calculate the instantaneous irregular bubble area ratios of all frame images during the entire pouring process, and define the maximum value as the irregular bubble generation area ratio of this pouring process.

[0040] Further, in step S4, determining the dynamic flow rate adjustment curve of the inert gas based on the alloy melt type includes,

[0041] If the alloy melt type is stable, then the dynamic flow rate adjustment curve of the inert gas is determined to be the standard optimized curve;

[0042] If the alloy melt type is of poor thermal uniformity, then the dynamic flow rate adjustment curve of the inert gas is determined to be a strong homogenization curve.

[0043] If the alloy melt type is a gas entrapment type, then the dynamic flow rate adjustment curve of the inert gas is determined to be a mild purification curve;

[0044] If the alloy melt type is unstable, then the dynamic flow rate adjustment curve of the inert gas is determined to be a staged processing curve.

[0045] Further, in step S5, the effective value of the mixture is calculated based on the weighted sum of the proportion of irregular bubbles and the uniformity of bubble distribution in the bubble visual image.

[0046] Further, in step S5, the step of calculating the bubble distribution uniformity value includes,

[0047] Step S51: For each frame of bubble visual image after preprocessing and binarization, divide it into a regular grid of M rows × N columns, thereby dividing the entire image area into K sub-regions of equal size.

[0048] Step S52: Identify and calculate the total area of ​​white pixels in each sub-region, and then calculate the bubble area ratio of that sub-region;

[0049] Step S53: Calculate the standard deviation of the bubble area ratio of all K sub-regions, and define this standard deviation as the bubble distribution uniformity of the frame image.

[0050] Further, in step S5, the microstructure evolution stage of the slurry is determined based on the effective mixing value, including:

[0051] If the effective mixing value is greater than the third preset effective mixing value, the slurry is determined to be in the initial melting period;

[0052] If the effective mixing value is greater than the second preset effective mixing value and less than or equal to the third preset effective mixing value, the slurry is determined to be in the grain refinement period.

[0053] If the effective mixing value is greater than the first preset effective mixing value and less than or equal to the second preset effective mixing value, the slurry is determined to be in the ideal spheroidizing period.

[0054] If the effective mixing value is less than or equal to the first preset effective mixing value, the slurry is determined to be in the transition cooling period.

[0055] Compared with existing technologies, the advantages of this invention are as follows: This invention uses a dynamic time warping algorithm to compare real-time and preset cooling rate curves, which can ignore short-term temperature fluctuations and focus on the core characteristic of cooling rate being fast at first and then slowing down. This avoids misjudging the material transfer timing due to local disturbances. After determining the initial material transfer time point based on similarity, it additionally detects whether the real-time temperature is within the ideal range of 10-30°C below the liquidus. This prevents both excessively high and excessively low temperatures, ensuring that the melt state during material transfer is fully adapted to subsequent processes. When the real-time temperature is lower than the minimum value of the ideal range, it directly determines that the cooling is abnormal and abandons the material transfer. This can screen out unqualified melts that may have already developed coarse dendrites in advance, preventing them from entering subsequent processes and causing defects such as shrinkage cavities and cracks in die castings, thus reducing the scrap rate in mass production. If the melt state is stable during material transfer, there is no need to frequently adjust the inert gas flow rate in step S4; the standard optimized curve can be used directly, simplifying the subsequent control logic. This avoids the need for significant parameter adjustments during slurry preparation due to improper material transfer timing, reducing energy consumption and process complexity.

[0056] Furthermore, this invention, through a combination of multi-sensor synchronous acquisition, time window segmentation, and standard deviation statistics, can accurately capture temperature differences in different regions of the melt during casting. It quantifies overall thermal uniformity using the maximum value of window fluctuations. Compared to single-point temperature measurement, this method avoids the problem of insufficient local temperature representativeness, ensuring the accuracy of determining the type of poor thermal uniformity. By combining high-speed imaging with image segmentation and roundness calculation, the degree of air entrapment is converted into a quantifiable irregular bubble area ratio, focusing on bubbles with roundness below a preset value. These bubbles are key hidden dangers leading to uneven mixing of subsequent slurry and porosity in die castings. Through a binary judgment logic of temperature fluctuation value and irregular bubble area ratio, the melt is clearly divided into stable, poor thermal uniformity, air entrapment, and unstable types, each with a clear quantification boundary. If the melt has poor thermal uniformity, Fe easily forms coarse needle-like phases, cutting the matrix and reducing strength. A strong homogenization strategy for the poor thermal uniformity type can avoid local low-temperature zones. The refining effect of Sr on the Si phase depends on a uniform temperature field; excessive temperature fluctuations can lead to uneven Sr distribution and local Si phase coarsening. By precisely controlling temperature fluctuations, the roundness of the Si phase can be improved, balancing strength and thermal conductivity; irregular bubbles remaining in the slurry will become thermally conductive blind spots; through a gentle purification strategy of air entrapment, the porosity of die castings can be reduced, ensuring that thermal conductivity meets the standards.

[0057] Furthermore, both core indicators of the mixed effective value of this invention are directly linked to the microstructure of the slurry. The uniformity of bubble distribution reflects the uniformity of melt flow driven by bubbles. The more uniform the distribution, the more dispersed the solid particles are stirred, avoiding local agglomeration. Irregularly shaped bubbles are easily stuck between particles. The higher their proportion, the more harmful bubbles there are in the slurry, which are prone to forming pore defects later. By weighted calculation, the two are integrated into a mixed effective value of 0 to 1, so that the complex states such as whether the solid fraction meets the standard, whether the particles are round, and whether the bubbles are harmful are transformed into a single value that can be monitored in real time. The core of semi-solid pulping is to complete pulping during the ideal spheroidization period. Adjusting too early or too late will lead to quality problems. This design divides the stages by mixed effective value, with clear stage boundaries to avoid misjudgment of the timing of adjustment. Based on three preset values ​​determined by batch experiments, four stages are clearly distinguished. Attached Figure Description

[0058] Figure 1 This is a flowchart illustrating the preparation method of the high-strength, high-thermal-conductivity Al-Si-Fe-Sr alloy by rheo-die casting according to the present invention.

[0059] Figure 2 This is a flowchart illustrating the process of calculating temperature fluctuations at several monitoring points during the casting of the alloy melt in the preparation method of the high-strength, high-thermal-conductivity Al-Si-Fe-Sr alloy by rheo-die casting of the present invention.

[0060] Figure 3 This is a flowchart illustrating the process of calculating the uniformity of bubble distribution in the preparation method of the high-strength, high-thermal-conductivity Al-Si-Fe-Sr alloy by rheo-die casting according to the present invention. Detailed Implementation

[0061] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0062] It should be noted that the data in this embodiment are all derived from a comprehensive analysis and evaluation of historical data from the six months prior to this determination and the corresponding historical determination results by the system described in this invention. Those skilled in the art will understand that the system described in this invention can determine the above-mentioned parameters for a single item by selecting the value with the highest proportion based on the data distribution as the preset standard parameter, using weighted summation to obtain the value as the preset standard parameter, substituting each historical data point into a specific formula and using the value obtained by that formula as the preset standard parameter, or other selection methods, as long as the system described in this invention can clearly define different specific situations in the single-item determination process through the obtained values.

[0063] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0064] Please see Figures 1-3 As shown, Figure 1 This is a flowchart illustrating the preparation method of the high-strength, high-thermal-conductivity Al-Si-Fe-Sr alloy by rheo-die casting according to the present invention. Figure 2 This is a flowchart illustrating the process of calculating temperature fluctuations at several monitoring points during the casting of the alloy melt in the preparation method of the high-strength, high-thermal-conductivity Al-Si-Fe-Sr alloy by rheo-die casting of the present invention. Figure 3 This is a flowchart illustrating the process of calculating the uniformity of bubble distribution in the preparation method of the high-strength, high-thermal-conductivity Al-Si-Fe-Sr alloy by rheo-die casting according to the present invention.

[0065] The present invention discloses a method for preparing a high-strength, high-thermal-conductivity Al-Si-Fe-Sr alloy by rheo-die casting, comprising:

[0066] Step S1: Prepare raw materials according to the alloy composition, wherein the alloy composition contains 6.8% to 7.3% silicon, 0.4% to 0.5% iron, 0.02% to 0.08% strontium, and the remainder is aluminum by mass percentage;

[0067] Step S2: Under a protective atmosphere, the raw materials are melted to obtain an alloy melt;

[0068] Step S3: The alloy melt is kept at a constant temperature. After heating is stopped, the alloy melt is allowed to cool naturally. The transfer time of the alloy melt is determined based on the similarity between the cooling rate curve of the alloy melt and the preset cooling rate curve.

[0069] Step S4: At the material transfer time point, the alloy melt is poured into the gas-induced semi-solid slurry preparation device, and the alloy melt type is determined based on the temperature fluctuation value and the area ratio of irregular bubbles generated at several monitoring points during the alloy melt pouring process. Inert gas is introduced, and the dynamic flow rate adjustment curve of the inert gas is determined based on the alloy melt type.

[0070] Step S5: Real-time monitoring of the visual images of bubbles in the gas-induced semi-solid pulping device during the inert gas introduction process, and calculation of the effective mixing value based on the proportion of irregular bubbles and the uniformity of bubble distribution in the bubble visual images, and determination of the microstructure evolution stage of the pulp based on the effective mixing value.

[0071] Step S6: Determine the inert gas flow correction method based on the microstructure evolution stage of the slurry;

[0072] Step S7: Pour the obtained semi-solid slurry into the die casting chamber of the die casting equipment for die casting.

[0073] Step S8: Heat treat the die-cast part.

[0074] In this embodiment of the invention, argon is used as the protective atmosphere, the melting temperature is 700℃~720℃, the holding time is 30min, the gas-induced semi-solid slurry preparation device is equipped with a temperature sensor to detect the melt temperature in real time, nitrogen is used as the inert gas, the gas introduction time is 0~3min, and the gas pressure is 0~2MPa; the die casting machine uses a die casting pressure of 0~40MPa, an injection speed of 0~7m / s, and a mold preheating temperature of 0~250℃; the finished die casting is subjected to heat treatment, the heat treatment regime is solution treatment at 530~540℃ for 1~12h, followed by water quenching, aging treatment at 160~180℃ for 2~24h.

[0075] Specifically, in step S3, the transfer time point of the alloy melt is determined based on the similarity between the cooling rate curve of the alloy melt and a preset cooling rate curve, including:

[0076] The point at which the similarity between the cooling rate curve of the alloy melt calculated in real time and the preset cooling rate curve first changes from less than or equal to the preset similarity to greater than the preset similarity is defined as the primary material transfer time point.

[0077] After determining the initial material transfer time point, the real-time temperature of the alloy melt is monitored;

[0078] If the real-time temperature of the alloy melt is within the preset ideal transfer temperature range, the transfer action is triggered immediately, and the initial transfer time point at this time is the final transfer time point.

[0079] If the real-time temperature of the alloy melt is greater than the maximum temperature of the preset ideal transfer temperature range, the transfer will not be carried out for the time being. The temperature will be continuously monitored until the real-time temperature drops to the preset ideal transfer temperature range, and then the transfer action will be triggered. The time point when this temperature is reached is the final transfer time point.

[0080] If the real-time temperature of the alloy melt is less than the minimum temperature of the preset ideal transfer temperature range, the cooling process is determined to be abnormal, and the transfer is abandoned.

[0081] Specifically, the real-time cooling rate curve of the alloy melt is dynamically compared with one or more preset cooling rate curves. The similarity is calculated using a dynamic time warping algorithm to calculate the real-time similarity distance between the two curves.

[0082] In this embodiment of the invention, calculating the real-time similarity distance between two curves includes calculating the distance (local distance) between individual point pairs: first, calculating the local distance between each pair of sampling points in the two curves to measure the difference between the two curves at a single moment, using the following formula: in, It is the cooling rate value at the i-th sampling point of the real-time curve. This is the cooling rate value at the j-th sampling point of the preset curve, where |·| represents the absolute value (or the difference of squares can be used depending on the accuracy requirements). (Here, since the cooling rate is a continuous variable, the absolute value is more likely to avoid interference from extreme values); Constructing the cumulative distance matrix (global matching): The cumulative distance matrix D is constructed through dynamic programming to achieve non-linear alignment of the two curves. The recursive formula for the cumulative distance is: ,in, This represents the cumulative similarity distance between the first i points of the real-time curve and the first j points of the preset curve. This indicates selecting the path with the minimum cumulative distance from the previous step; the initial conditions of the matrix are: (Align only along the first point of the preset curve) (Alignment only along the first point of the real-time curve); Calculate the final similarity distance: the real-time similarity distance between the two curves, which is the total cumulative distance of the globally optimal alignment path. The formula is: DTW_distance=D(m,n) where D(m,n) is the last element of the cumulative distance matrix, representing the total similarity distance between all sampling points of the real-time curve and all sampling points of the preset curve after non-linear alignment. The smaller this value is, the higher the similarity between the two curves.

[0083] In this embodiment of the invention, the similarity between the cooling rate curve of the alloy melt and the preset cooling rate curve can be calculated by the dynamic time warping method (the smaller the value, the higher the similarity). The preset cooling rate curve can be determined by the following method: for the alloy composition (silicon 6.8%~7.3%, iron 0.4%~0.5%, strontium 0.02%~0.08%, balance aluminum), multiple sets of comparative experiments are designed: with fixed melting temperature (700℃~720℃) and holding time (30min), the alloy melt is naturally cooled under the same protective atmosphere conditions; Temperature data during the cooling process is collected in real time (e.g., temperature is recorded every 1-5 seconds), and the cooling rate (temperature change per unit time, °C / s) at different times is calculated to generate multiple initial cooling rate curves. The criteria for selecting ideal curves are based on the requirements of semi-solid slurry preparation for the state of the alloy melt. The selection criteria are as follows: the curve must reflect the typical characteristics of the alloy melt from a high-temperature liquid state to the initial solidification stage (e.g., the cooling rate is fast at first and then slows down, corresponding to the transition from liquid phase to liquid-solid mixed phase); the temperature at the corresponding final transfer time point must fall within the preset ideal transfer temperature range. Based on the solidification characteristics of the alloy, this range is usually determined based on the liquidus temperature and solidus temperature of the alloy. For example, the liquidus temperature of aluminum-silicon alloy is about 610-620℃, and the ideal transfer temperature can be set to 10-30℃ below the liquidus temperature. The curve needs to have good repeatability (small deviation in multiple experiments) to ensure stability. Curve fitting and optimization involves fitting data (such as using polynomial fitting or piecewise linear fitting) to multiple initial cooling rate curves that meet the screening criteria to eliminate experimental errors and obtain a smooth and representative average curve, which is the preset cooling rate curve. The preset similarity can be determined by the following method: select multiple initial cooling rate curves that meet the screening criteria (i.e., the ideal curve when determining the preset curve in step S3), simulate the real-time cooling process, and calculate the similarity between them and the preset curve at different stages; locate the time preceding the final transfer time point in the ideal curve (i.e., the key node where the cooling rate begins to enter the liquid-solid mixed phase transformation), and statistically analyze the similarity value range at this time; take the upper limit of this range as the preset similarity, but the above value is not limited to this, and those skilled in the art can adjust it according to the actual situation.

[0084] This invention employs a dynamic time warping algorithm to compare real-time and preset cooling rate curves. It ignores short-term temperature fluctuations and focuses on the core characteristic of a cooling rate that is initially fast and then slows down. This avoids misjudging the material transfer timing due to local disturbances. After determining the initial material transfer time point based on similarity, it additionally checks whether the real-time temperature is within the ideal range of 10–30°C below the liquidus. This prevents both excessively high and low temperatures, ensuring that the melt state during material transfer is perfectly suited to subsequent processes. When the real-time temperature is below the minimum value of the ideal range, it directly determines an abnormal cooling and abandons the material transfer. This allows for early screening of unqualified melts that may have developed coarse dendrites, preventing them from entering subsequent processes and causing defects such as shrinkage cavities and cracks in die castings, thus reducing the scrap rate in mass production. If the melt state is stable during material transfer, there is no need to frequently adjust the inert gas flow rate in step S4; the standard optimized curve can be used directly, simplifying subsequent control logic. This avoids the need for significant parameter adjustments during slurry preparation due to improper material transfer timing, reducing energy consumption and process complexity.

[0085] Specifically, in step S4, the type of alloy melt is determined based on the temperature fluctuation values ​​and the ratio of irregular bubble formation area at several monitoring points during the alloy melt casting process, including:

[0086] If the temperature fluctuation values ​​at several monitoring points during the alloy melt casting process are less than the preset temperature fluctuation value and the area ratio of irregular bubbles generated is less than the preset area ratio, then the alloy melt type is determined to be a stable type.

[0087] If the temperature fluctuation values ​​at several monitoring points during the alloy melt casting process are greater than or equal to the preset temperature fluctuation value and the area ratio of irregular bubbles generated is less than the preset area ratio, then the alloy melt type is determined to be the type with poor thermal uniformity.

[0088] If the temperature fluctuation values ​​at several monitoring points during the alloy melt casting process are less than the preset temperature fluctuation value and the ratio of irregular bubble generation area is greater than or equal to the preset generation area ratio, then the alloy melt type is determined to be the gas entrapment type.

[0089] If the temperature fluctuation values ​​at several monitoring points during the alloy melt casting process are greater than the preset temperature fluctuation value and the ratio of irregular bubble generation area is greater than or equal to the preset generation area ratio, then the alloy melt type is determined to be unstable.

[0090] Specifically, in step S4, the step of calculating the temperature fluctuation values ​​at several monitoring points during the alloy melt pouring process includes,

[0091] Step S411: The melt temperature is synchronously and continuously measured at the same sampling frequency by at least three temperature sensors that are pre-set in different spatial positions in the pulping device, and the temperature-time series data corresponding to each sensor is obtained.

[0092] Step S412: Divide the entire pouring process into several consecutive calculation time windows according to time.

[0093] Step S413: For each calculation time window, extract the temperature measurement values ​​of all monitoring points within the window at the same time and calculate their standard deviation; calculate the arithmetic mean of the standard deviations of all times within the window and use it as the window temperature fluctuation value of the time window.

[0094] Step S414: Define the maximum value of the window temperature fluctuation value among all calculated time windows during the entire casting process as the alloy melt temperature fluctuation value for this casting process.

[0095] Specifically, in step S4, the step of calculating the area ratio of irregular bubbles generated at several monitoring points during the alloy melt pouring process includes,

[0096] Step S421: A high-speed camera positioned above the observation window or casting stream of the gas-induced semi-solid slurry preparation device continuously captures a sequence of dynamic images of the melt surface during the casting process at a fixed frame rate.

[0097] Step S422: Perform grayscale conversion, contrast enhancement and noise reduction filtering on each frame of the acquired image in sequence; use a threshold-based image segmentation algorithm or edge detection algorithm to separate the bubble region in the image from the melt background and generate a binary image, in which the white region represents the bubble.

[0098] Step S423: Perform morphological analysis on each independent connected region in the binarized image, calculate its roundness, and determine bubbles with roundness lower than the preset roundness as irregular bubbles.

[0099] Step S424: For each frame of image, calculate the sum of the areas of all regions identified as irregular bubbles, and then calculate the ratio of the sum to the total area of ​​the bubble regions identified in that frame of image to obtain the instantaneous irregular bubble area ratio of that frame of image.

[0100] Step S425: Calculate the instantaneous irregular bubble area ratios of all frame images during the entire pouring process, and define the maximum value as the irregular bubble generation area ratio of this pouring process.

[0101] In this embodiment of the invention, the preset temperature fluctuation value can be determined by the following method. For the aluminum-silicon alloy (silicon 6.8%–7.3%, iron 0.4%–0.5%, strontium 0.02%–0.08%), the core requirement for temperature uniformity in the semi-solid slurry preparation stage is first clarified: the liquidus temperature of the alloy is approximately 610–620°C, and the ideal transfer temperature range is 10–30°C below the liquidus (i.e., 580–610°C). During this stage, the melt is in a liquid-solid coexistence state, and even a small temperature difference can lead to local solid fraction fluctuations (e.g., a 5°C temperature difference may correspond to a 3%–5% solid fraction difference). Combining the microstructure requirements of the semi-solid slurry (e.g., solid fraction uniformity needs to be controlled within ±5%), the theoretical tolerance range for temperature fluctuation is initially determined: the temperature difference between different monitoring points at the same time should not exceed 5–8°C (the basic reference for the corresponding window temperature fluctuation value). Gradient experiments are designed to simulate different temperature fluctuations. The slurry preparation effect under dynamic conditions was investigated to screen out the maximum fluctuation value that would not affect the stability of subsequent processes. Experimental design: Other process parameters were fixed (melting temperature 700-720℃, holding time 30 min, nitrogen introduction conditions, etc.). Different degrees of temperature fluctuation were artificially created by adjusting the pouring speed or the preheating temperature of the slurry preparation device (e.g., setting gradients of 3℃, 5℃, 7℃, 10℃, 12℃, etc.). The semi-solid slurry of each group of experiments was tested, with a focus on: microstructure uniformity (e.g., grain size distribution, solid fraction deviation); bubble mixing effect (combined with the effective mixing value in step S5, to determine whether temperature fluctuations caused abnormal bubble distribution); and the defect rate after subsequent die casting (e.g., shrinkage cavity, crack ratio). The maximum temperature fluctuation value corresponding to qualified slurry uniformity and a die casting defect rate of less than 1% was selected as the preset temperature fluctuation value. However, the above value is not limited to this, and those skilled in the art can adjust it according to the actual situation.

[0102] The preset area ratio in this embodiment of the invention can be determined by the following method: For the semi-solid slurry preparation process of the aluminum-silicon alloy, a gradient experiment is designed to simulate the production effect under different irregular bubble ratios. Parameters such as smelting, material transfer, and nitrogen introduction are fixed. By adjusting the pouring speed (e.g., alternating between fast and slow) and the airflow disturbance within the slurry preparation device (e.g., slightly changing the initial nitrogen pressure), different proportions of irregular bubbles (e.g., 5%, 10%, 15%, 20%, 25%) are artificially created. Key results of each experimental group are tested, with core indicators including: microstructure of the semi-solid slurry: observing whether coarse grains or abnormal solidity areas appear around the irregular bubbles; effective mixing value: calculating the effective mixing value according to step S5, ensuring it is not lower than the minimum requirement for "stable slurry preparation" (e.g., effective mixing value ≥ 0.8); die-casting defect rate: statistically analyzing the proportion of defects such as porosity and looseness in the die-cast product, which must be controlled to ≤ 2%; the maximum irregular bubble area ratio that meets all the above evaluation indicators is selected as the preset area ratio; the preset roundness can be determined by the following method... The method for determining the bubble roundness in this invention typically uses the industry-standard formula: Roundness = 4π × (bubble area) / (bubble circumference)²; the calculation result ranges from 0 to 1. The closer the value is to 1, the closer the bubble shape is to a standard circle; the closer the value is to 0, the more irregular the bubble shape (e.g., elongated, broken, flat). Regular bubbles that can effectively participate in the semi-solid slurry mixing and are easy to escape during subsequent die casting are selected, while irregular bubbles that easily lead to local gas accumulation and affect the uniformity of the slurry are excluded. Based on the process principle of gas-induced semi-solid slurry preparation, regular bubbles must meet two core functions: Mixing effectiveness: Round or near-round bubbles have low resistance to movement in the melt, can be evenly dispersed and drive the melt flow, achieving liquid-solid mixing; if the bubble roundness is too low (e.g., <0.6), it is easy to get stuck in the local melt or flow in a specific direction, resulting in uneven mixing; Ease of escape: Regular bubbles have uniform buoyancy distribution, making them easier to float and escape during the slurry preparation stage before die casting, reducing porosity defects in the die casting; Irregular bubbles (e.g., flat) are easy to adhere to the surface of solid particles and are difficult to escape.Based on this, the theoretical range of the preset roundness is initially set to 0.6–0.8, meaning that roundness greater than or equal to this value is considered a regular bubble, and roundness less than this value is considered an irregular bubble. Binary images of bubbles of different shapes (including standard circles, slightly deformed bubbles, and severely irregular bubbles) are collected, and regular and irregular bubbles are labeled. The roundness distribution range of the two types of bubbles is statistically analyzed. If the roundness of the labeled regular bubbles is ≥0.7 and the roundness of the irregular bubbles is ≤0.65, then the preset roundness can be initially set to 0.7. Process effect verification: different roundness thresholds (e.g., 0.6, 0.65, 0.7, 0.8) are used. Group experiments were conducted using 0.75 and 0.8 to detect key indicators: accuracy of irregular bubble judgment: the misjudgment rate under the statistical threshold (e.g., misjudging regular bubbles as irregular or vice versa), the misjudgment rate needs to be controlled ≤5%; slurry mixing quality: the uniformity of the solid phase ratio of the semi-solid slurry corresponding to different thresholds needs to be detected, the uniformity deviation needs to be guaranteed ≤±5%; porosity of die castings: the proportion of porosity defects in the die-cast product is statistically analyzed, the porosity needs to be controlled ≤1%; the threshold with the lowest misjudgment rate and both slurry quality and die-casting performance are selected as the preset roundness, but the above values ​​are not limited to this, and those skilled in the art can adjust them according to the actual situation.

[0103] Specifically, in step S4, determining the dynamic flow rate adjustment curve of the inert gas based on the alloy melt type includes,

[0104] If the alloy melt type is stable, then the dynamic flow rate adjustment curve of the inert gas is determined to be the standard optimized curve;

[0105] If the alloy melt type is of poor thermal uniformity, then the dynamic flow rate adjustment curve of the inert gas is determined to be a strong homogenization curve.

[0106] If the alloy melt type is a gas entrapment type, then the dynamic flow rate adjustment curve of the inert gas is determined to be a mild purification curve;

[0107] If the alloy melt type is unstable, then the dynamic flow rate adjustment curve of the inert gas is determined to be a staged processing curve.

[0108] In this embodiment of the invention, if the dynamic flow rate adjustment curve of the inert gas is a standard optimized curve, a three-stage flow rate sequence is adopted: Initial stage (0–30 s): a medium flow rate (based on device parameters, theoretical calculations, and experimental verification of the dynamic equilibrium value) is used to quickly form a dispersed bubble group, breaking the static stratification of the melt; Stabilization stage (30–120 s): the flow rate remains constant, and continuous bubble stirring promotes the rounding and uniform distribution of solid particles; Final stage (120–180 s): the flow rate decreases linearly to reduce the formation of new bubbles. Entrainment promotes the escape of microbubbles; for example, inert gas (nitrogen) pressure range: 0.8–1.2 MPa; flow rate curve: 0–30 s: flow rate linearly increases from 0 to 15 L / min (rapidly establishes the bubble dispersion system); 30–120 s: maintain 15 L / min (stable stirring, allowing the solid fraction to increase from 15% to 40%); 120–180 s: linearly decrease from 15 L / min to 5 L / min (reduces residual bubbles, and the final solid fraction stabilizes at 40% ± 3%); if the inert gas dynamics… If the flow rate regulation curve is a strong homogenization curve, a high-flow-rate impact combined with pulse stirring strategy is adopted, utilizing the kinetic energy of bubbles to drive the macroscopic flow of the melt: Impact stage (0-20s): High flow rate is injected instantaneously, forming strong convection and breaking temperature stratification; Pulse stage (20-150s): Flow rate fluctuates periodically (alternating between high and low), strengthening local disturbances and promoting heat exchange; Homogenization stage (150-180s): Flow rate drops to a medium level, stabilizing the mixing state; For example, nitrogen pressure range: 1.2-2.0 MPa ( Higher pressure ensures impact kinetic energy); Flow curve: 0~20s: Flow rate rises sharply to 25L / min (impacting the high temperature zone, reducing the local temperature difference from 10℃ to 5℃); 20~150s: 25L / min (lasting 5s) is converted into a periodic pulse of 10L / min (lasting 10s) (through temperature sensor feedback, the pulse frequency is reduced when the temperature difference at each monitoring point is <5℃); 150~180s: stabilizes at 12L / min (final temperature difference is controlled within 3℃, solid phase uniformity ±4%).

[0109] In this embodiment of the invention, if the dynamic flow rate adjustment curve of the inert gas is a mild purification curve, a low-disturbance combined with slow exhaust strategy is adopted to avoid severe airflow disturbances that exacerbate air entrapment: Low flow start-up stage (0-60s): extremely small flow rate is injected to "wrap" irregular bubbles with fine bubbles, promoting aggregation; steady flow purification stage (60-150s): the flow rate is kept at a constant low level, and the buoyancy difference is used to drive the bubbles to float and escape; negative pressure auxiliary stage (150-180s): the pressure inside the device is slightly reduced (e.g., -0.02MPa) to accelerate the discharge of residual bubbles. For example, nitrogen pressure range: 0.3~0.6MPa (low pressure reduces impact); flow rate curve: 0~60s: flow rate controlled at 3~5L / min (forming fine bubbles with a diameter <1mm, which aggregate with irregular bubbles); 60~150s: maintain 5L / min (monitoring the irregular bubble area ratio decreases from 20% to 8%); 150~180s: flow rate decreases to 2L / min, while the pressure inside the device decreases to -0.02MPa (final total bubble area ratio <5%, irregular bubble ratio <3%); if the dynamic flow rate adjustment curve of the inert gas is a staged processing curve, then a three-stage strategy of first stabilizing the gas, then isothermal equalization, and finally slurry equalization is adopted to gradually correct the defects: gas suppression stage (0~50s): extremely low flow rate suppresses new entrained gas, while monitoring the escape of irregular bubbles; isothermal equalization stage (5 0–130s: Switch to medium pulse flow rate to promote temperature uniformity while reducing air entrainment; Stabilization stage (130–180s): Flow rate stabilizes at a low level to ensure that the solid fraction and bubble state meet the standards; For example, nitrogen pressure range: 0.5–1.5 MPa (required for dynamic adaptation stage); Flow rate curve: 0–50s: Flow rate 2–3 L / min (suppresses new air entrainment, irregular bubble area ratio decreases from 25% to 18%, while maintaining a temperature difference of 10℃); 50–130s: Pulse from 10 L / min (5s) to 5 L / min (15s) (temperature difference decreases from 10℃ to 6℃, irregular bubble area ratio decreases to 10%); 130–180s: Stabilize at 8 L / min (final temperature difference < 5℃, irregular bubble area ratio < 8%, solid fraction 38% ± 5%).

[0110] This invention utilizes a combination of multi-sensor synchronous acquisition, time window segmentation, and standard deviation statistics to accurately capture temperature differences in different regions of the melt during casting. It quantifies overall thermal uniformity using the maximum window fluctuation value. Compared to single-point temperature measurement, this method avoids the problem of insufficient local temperature representativeness, ensuring the accuracy of determining the type of poor thermal uniformity. By combining high-speed imaging with image segmentation and roundness calculation, the degree of air entrapment is converted into a quantifiable irregular bubble area ratio, focusing on bubbles with roundness below a preset value. These bubbles are key hidden dangers leading to uneven slurry mixing and porosity in die castings. Through a binary judgment logic of temperature fluctuation value and irregular bubble area ratio, the melt is clearly divided into stable, poor thermal uniformity, air entrapment, and unstable types, each with a clear quantification boundary. If the melt has poor thermal uniformity, Fe easily forms coarse needle-like phases, cutting the matrix and reducing strength. A strong homogenization strategy for poor thermal uniformity can avoid local low-temperature zones. The refining effect of Sr on the Si phase depends on a uniform temperature field; excessive temperature fluctuations lead to uneven Sr distribution and local Si phase coarsening. By precisely controlling temperature fluctuations, the roundness of the Si phase can be improved, balancing strength and thermal conductivity; irregular bubbles remaining in the slurry will become thermally conductive blind spots; through a gentle purification strategy of air entrapment, the porosity of die castings can be reduced, ensuring that thermal conductivity meets the standards.

[0111] Specifically, in step S5, the effective value of the mixture is calculated based on the weighted sum of the proportion of irregular bubbles and the uniformity of bubble distribution in the bubble visual image.

[0112] Specifically, in step S5, the step of calculating the bubble distribution uniformity value includes,

[0113] Step S51: For each frame of bubble visual image after preprocessing and binarization, divide it into a regular grid of M rows × N columns, thereby dividing the entire image area into K sub-regions of equal size.

[0114] Step S52: Identify and calculate the total area of ​​white pixels in each sub-region, and then calculate the bubble area ratio of that sub-region;

[0115] Step S53: Calculate the standard deviation of the bubble area ratio of all K sub-regions, and define this standard deviation as the bubble distribution uniformity of the frame image.

[0116] The mathematical tools used in calculating the mixed effective value in this embodiment of the invention include image processing algorithms (including image segmentation techniques, morphological analysis tools, and spatial distribution calculation tools) and multi-index weighted mathematical models (including linear weighted models, nonlinear weighted models, and fuzzy comprehensive evaluation models). The proportion of irregular bubbles in the bubble visual image is determined by the ratio of the area of ​​bubbles with a circularity less than a preset circularity to the total area of ​​all bubbles in the image. Calculating the mixed effective value involves first normalizing the bubble distribution uniformity value, and then assigning weighting coefficients to the two indicators according to the process priority of semi-solid pulping. The sum of the two weighting coefficients must be 1. The coefficient values ​​are determined through experimental verification (prioritizing the influence of key indicators). Generally, the bubble distribution uniformity has a more significant impact on the microstructure of the pulp (uneven distribution directly leads to differences in local solid fraction), so it is assigned a higher weight. For example, the weight of the proportion of irregular bubbles is 0.4, and the weight of the normalized distribution uniformity value is 0.6. Finally, the weighted sum is calculated to obtain the mixed effective value, but the above values ​​are not limited to these and can be adjusted by those skilled in the art according to the actual situation.

[0117] Specifically, in step S5, the microstructure evolution stage of the slurry is determined based on the effective mixing value, including:

[0118] If the effective mixing value is greater than the third preset effective mixing value, the slurry is determined to be in the initial melting period;

[0119] If the effective mixing value is greater than the second preset effective mixing value and less than or equal to the third preset effective mixing value, the slurry is determined to be in the grain refinement period.

[0120] If the effective mixing value is greater than the first preset effective mixing value and less than or equal to the second preset effective mixing value, the slurry is determined to be in the ideal spheroidizing period.

[0121] If the effective mixing value is less than or equal to the first preset effective mixing value, the slurry is determined to be in the transition cooling period.

[0122] In the embodiments of this invention, the initial melting period is characterized by the melt being predominantly liquid (solid content < 10%), with sparsely dispersed bubbles that are mostly regular in shape and no obvious grain formation. The grain refinement period is characterized by the precipitation of fine solid particles (solid content 10%–30%) in the liquid phase, with bubbles driving the particles to disperse and gradually becoming round. The ideal spheroidization period is characterized by a solid content of 30%–50%, with particles that are round and spherical and evenly distributed, and little residual material after thorough mixing of bubbles and slurry. The transitional cooling period is characterized by a solid content > 50%, with particles beginning to agglomerate or grow, bubbles easily becoming trapped between particles, resulting in uneven distribution and an increased proportion of irregular shapes.

[0123] In this embodiment of the invention, the first, second, and third preset effective mixing values ​​can be determined by the following method: A batch control experiment is designed, and the effective mixing value and the corresponding microstructure of the slurry are monitored synchronously to establish a correlation database. A target aluminum-silicon alloy (silicon 6.8%–7.3%) is used. Nitrogen gas is introduced to prepare the slurry according to step S4. A visual image of the bubbles is collected every 10 seconds to calculate the effective mixing value. Simultaneously, samples are taken to analyze the microstructure of the slurry (e.g., observing the solid fraction and grain morphology using a metallographic microscope). The experiment is repeated at least 30 times to ensure that the data covers four evolution stages (from initial melting to transition cooling). For each experiment, the effective mixing value and the corresponding microstructure of the slurry are recorded. The results of the structural determination are observed; the experimental data are statistically clustered, and the effective mixed value of the critical boundary point of each stage is selected as the preset value to ensure that the boundary point can clearly distinguish different stages; the lower limit of the effective mixed value of the initial melting period (e.g., the minimum effective mixed value of the initial melting period is 0.85 in multiple experiments) is statistically determined and set as the third preset value; the upper limit of the effective mixed value of the ideal spheroidizing period (e.g., the maximum effective mixed value of the ideal spheroidizing period is 0.75) is statistically determined and set as the second preset value; the lower limit of the effective mixed value of the ideal spheroidizing period (e.g., the minimum effective mixed value of the ideal spheroidizing period is 0.55) is statistically determined and set as the first preset value. However, the above values ​​are not limited to these, and those skilled in the art can adjust them according to the actual situation.

[0124] The two core indicators of the mixed effective value of this invention are directly linked to the microstructure of the slurry. The uniformity of bubble distribution reflects the uniformity of the melt flow driven by bubbles. The more uniform the distribution, the more dispersed the solid particles are stirred, avoiding local agglomeration. Irregular bubbles are easy to get stuck between particles. The higher their proportion, the more harmful bubbles there are in the slurry, which are prone to forming pore defects later. By weighted calculation, the two are integrated into a mixed effective value of 0 to 1, so that the complex state of whether the solid fraction meets the standard, whether the particles are round, and whether the bubbles are harmful are transformed into a single value that can be monitored in real time. The core of semi-solid pulping is to complete the pulping during the ideal spheroidization period. Adjusting too early or too late will lead to quality problems. This design divides the stages by mixed effective value. The stage boundaries are clear and avoid misjudgment of the timing of adjustment. Based on three preset values ​​determined by batch experiments, four stages are clearly distinguished.

[0125] Specifically, in step S6, an inert gas flow rate correction method is determined based on the microstructure evolution stage of the slurry. This includes adopting a high flow rate promotion strategy if the slurry is in the initial melting stage (the effective mixing value is greater than the third preset effective mixing value): increasing the inert gas flow rate to a higher level (e.g., linearly increasing the flow rate from the current value to 20-25 L / min, and maintaining the pressure at 1.0-1.5 MPa) to enhance melt convection, break temperature stratification, and promote the uniform dispersion of the initial solid particles. This stage aims to quickly establish a bubble dispersion system, avoid static stratification of the melt, and prepare for grain refinement. If the slurry is in the grain refinement period (the effective mixing value is greater than the second preset effective mixing value and less than or equal to the third preset effective mixing value), a stable pulse strategy is adopted: maintain the inert gas flow rate at a medium level (e.g., the flow rate is stable at 10-15 L / min), and introduce a periodic pulse mode (e.g., the flow rate is 10-15 L / min for 5-10 seconds, and then reduced to 5-10 L / min for 10-15 seconds), and adjust the pressure to 1.0-1.8 MPa. Pulsed stirring can enhance local disturbance, promote grain refinement and heat exchange, and ensure a steady increase in solid fraction (target solid fraction 10%-30%), while avoiding uneven bubble distribution. If the slurry is in the ideal spheroidization period (the effective mixing value is greater than the first preset effective mixing value and less than or equal to the second preset effective mixing value), a gradual cooling strategy is adopted: the inert gas flow rate is gradually reduced to a lower level (e.g., the flow rate is linearly reduced from the current value to 5-8 L / min, and the pressure is reduced to 0.5-1.0 MPa) to reduce the entrainment of new bubbles and promote the escape of microbubbles. During this stage, the focus is on maintaining the slurry solid fraction in the ideal range of 30%-50% to ensure the spheroidization and uniform distribution of solid particles and avoid particle agglomeration caused by excessive stirring. If the slurry is in the transition cooling period (the effective mixing value is less than or equal to the first preset effective mixing value), a flow cutoff or minimization strategy is adopted: the inert gas supply is significantly reduced or stopped (e.g., the flow rate is reduced to 0-3 L / min, and the pressure is reduced to 0-0.3 MPa) to inhibit further cooling and particle growth. At the same time, it can assist in short-term negative pressure suction (such as the pressure inside the device dropping to -0.02 to -0.05 MPa) to accelerate the discharge of residual bubbles and prevent the slurry viscosity from increasing and causing microstructure deterioration after the solid phase ratio exceeds 50%.

[0126] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for preparing a rheo-die cast high-strength high-thermal-conductivity Al-Si-Fe-Sr alloy, characterized in that, The application relates to a method for preparing a semi-solid slurry of an aluminum alloy, comprising the following steps: Step S1, preparing raw materials according to alloy components, wherein the alloy components contain, in percentage by mass, 6.8%-7.3% of silicon, 0.4%-0.5% of iron, 0.02%-0.08% of strontium, and the rest of aluminum; Step S2, melting the raw materials under a protective atmosphere to obtain an alloy melt; Step S3, performing a heat preservation treatment on the alloy melt, naturally cooling the alloy melt after stopping heating, and determining a material conversion time point of the alloy melt based on the similarity between a cooling rate curve of the alloy melt and a preset cooling rate curve; Step S4, pouring the alloy melt into a gas-induced semi-solid slurry device at the material conversion time point, determining a type of the alloy melt based on the temperature fluctuation value and the irregular bubble generation area ratio of a plurality of monitoring points in the alloy melt pouring process, introducing inert gas, and determining a dynamic flow adjustment curve of the inert gas based on the type of the alloy melt; Step S5, monitoring a visual image of bubbles in the gas-induced semi-solid slurry device in real time during the inert gas introduction process, calculating a mixing effective value based on the irregular shape proportion of the bubbles and the bubble distribution uniformity value in the visual image of the bubbles, and determining a microstructure evolution stage of the slurry based on the mixing effective value; Step S6, determining an inert gas flow correction method based on the microstructure evolution stage of the slurry; Step S7, pouring the obtained semi-solid slurry into a die casting chamber of a die casting equipment to perform die casting forming; Step S8, performing heat treatment on the die casting; In step S3, the material conversion time point of the alloy melt is determined based on the similarity between the cooling rate curve of the alloy melt and the preset cooling rate curve, comprising the following steps: Defining a time point when the similarity between the real-time calculated cooling rate curve of the alloy melt and the preset cooling rate curve changes from less than or equal to a preset similarity to greater than the preset similarity as a primary material conversion time point; After determining the primary material conversion time point, detecting the real-time temperature of the alloy melt; If the real-time temperature of the alloy melt is in a preset ideal material conversion temperature interval, a material conversion action is triggered immediately, and the primary material conversion time point is the final material conversion time point; If the real-time temperature of the alloy melt is greater than the maximum temperature of the preset ideal material conversion temperature interval, the material conversion is not performed temporarily, and the real-time temperature is continuously monitored until the real-time temperature drops to the preset ideal material conversion temperature interval, and then the material conversion action is triggered, and the temperature reaching time point is the final material conversion time point; If the real-time temperature of the alloy melt is less than the minimum temperature of the preset ideal material conversion temperature interval, it is determined that the cooling process is abnormal, and the material conversion is abandoned; In step S4, the type of the alloy melt is determined based on the temperature fluctuation value and the irregular bubble generation area ratio of the plurality of monitoring points in the alloy melt pouring process, comprising the following steps: If the temperature fluctuation value of the plurality of monitoring points in the alloy melt pouring process is less than a preset temperature fluctuation value and the irregular bubble generation area ratio is less than a preset generation area ratio, it is determined that the type of the alloy melt is a stable type. If the temperature fluctuation value of the alloy melt at the monitoring points during pouring is greater than or equal to the preset temperature fluctuation value and the irregular bubble generation area ratio is less than the preset generation area ratio, the alloy melt type is determined as a poor thermal homogeneity type; If the temperature fluctuation value of the alloy melt at the monitoring points during pouring is less than the preset temperature fluctuation value and the irregular bubble generation area ratio is greater than or equal to the preset generation area ratio, the alloy melt type is determined as a gas entrainment type; If the temperature fluctuation value of the alloy melt at the monitoring points during pouring is greater than the preset temperature fluctuation value and the irregular bubble generation area ratio is greater than or equal to the preset generation area ratio, the alloy melt type is determined as an unstable type; In step S4, a dynamic flow adjustment curve of the inert gas is determined based on the alloy melt type, including, If the alloy melt type is a stable type, the dynamic flow adjustment curve of the inert gas is determined as a standard optimization curve, the standard optimization curve including a nitrogen gas pressure range of 0.8-1.2 MPa and a flow curve of 0-30 s: linearly increasing from 0 to 15 L / min, 30-120 s: maintaining 15 L / min, and 120-180 s: linearly decreasing from 15 L / min to 5 L / min; If the alloy melt type is a poor thermal homogeneity type, the dynamic flow adjustment curve of the inert gas is determined as a strong homogenization curve, the strong homogenization curve including a nitrogen gas pressure range of 1.2-2.0 MPa and a flow curve of 0-20 s: suddenly increasing to 25 L / min, 20-150 s: a periodic pulse of 10 L / min converted from 25 L / min, and 150-180 s: stabilizing at 12 L / min; If the alloy melt type is a gas entrainment type, the dynamic flow adjustment curve of the inert gas is determined as a mild purification curve, the mild purification curve including a nitrogen gas pressure range of 0.3-0.6 MPa and a flow curve of 0-60 s: controlling the flow at 3-5 L / min, 60-150 s: maintaining 5 L / min, and 150-180 s: decreasing the flow to 2 L / min while the pressure in the device is decreased to -0.02 MPa; If the alloy melt type is an unstable type, the dynamic flow adjustment curve of the inert gas is determined as a staged treatment curve, the staged treatment curve including a nitrogen gas pressure range of 0.5-1.5 MPa and a flow curve of 0-50 s: a flow of 2-3 L / min, 50-130 s: a pulse of 5 L / min converted from 10 L / min, and 130-180 s: stabilizing at 8 L / min; In step S5, a microstructure evolution stage of the slurry is determined based on the mixing effective value, including, If the mixing effective value is greater than a third preset mixing effective value, it is determined that the slurry is in an initial melting period; If the mixing effective value is greater than a second preset mixing effective value and less than or equal to the third preset mixing effective value, it is determined that the slurry is in a grain refinement period; If the mixing effective value is greater than a first preset mixing effective value and less than or equal to the second preset mixing effective value, it is determined that the slurry is in an ideal spheroidization period; If the mixed effective value is less than or equal to the first preset mixed effective value, it is determined that the slurry is in a transition cooling period; Wherein, the first preset mixed effective value, the second preset mixed effective value and the third preset mixed effective value are determined by the following method: design batch control experiment, synchronously monitor mixed effective value and microstructure of slurry at corresponding time, establish correlation database of the two, adopt target aluminum-silicon alloy, according to step S4, pass nitrogen gas to make slurry, collect bubble visual image every 10 seconds to calculate mixed effective value, and at the same time, sample and analyze microstructure of slurry; repeat experiment at least 30 times to ensure that data covers four evolution stages; for each experiment, record mixed effective value and microstructure determination result of corresponding stage; statistically cluster experimental data, select mixed effective value of critical demarcation point of each stage as preset value to ensure that demarcation point can clearly distinguish different stages; statistically count lower limit of mixed effective value of initial melting period, and set it as third preset value; statistically count upper limit of mixed effective value of ideal spheroidization period, and set it as second preset value; statistically count lower limit of mixed effective value of ideal spheroidization period, and set it as first preset value; In step S6, the inert gas flow correction method is determined based on the microstructure evolution stage of the slurry, including if the slurry is in the initial melting period, a high-flow promotion strategy is adopted: the inert gas flow is linearly increased from the current value to 20-25 L / min, and the pressure is maintained at 1.0-1.5 MPa, to enhance the melt convection, break the temperature stratification, and promote the uniform dispersion of the initial solid phase particles; if the slurry is in the grain refinement period, a stable pulse strategy is adopted: the inert gas flow is stabilized at 10-15 L / min, and a periodic pulse mode is introduced, the inert gas flow is maintained at 10-15 L / min for 5-10 seconds, and then reduced to 5-10 L / min for 10-15 seconds, and the pressure is adjusted to 1.0-1.8 MPa; if the slurry is in the ideal spheroidization period, a gradual cooling strategy is adopted: the inert gas flow is linearly decreased from the current value to 5-8 L / min, and the pressure is reduced to 0.5-1.0 MPa, to reduce the entrainment of new bubbles and promote the escape of small bubbles; if the slurry is in the transition cooling period, a flow cutoff or minimization strategy is adopted: the inert gas flow is reduced to 0-3 L / min, and the pressure is reduced to 0-0.3 MPa, to inhibit further cooling and particle growth, while auxiliary short-time negative pressure suction is used to reduce the pressure in the device to -0.05 to -0.02 MPa, to accelerate the discharge of residual bubbles and prevent the viscosity of the slurry from rising after the solid phase rate exceeds 50%, causing the microstructure to deteriorate.

2. The method of producing a rheo-diecast high-strength high-thermal-conductivity Al-Si-Fe-Sr alloy according to claim 1, characterized by, The real-time cooling rate curve of the alloy melt is dynamically compared with one or more preset cooling rate curves, the similarity is calculated using dynamic time warping algorithm, and the real-time similarity distance between the two curves is calculated.

3. The method of producing a rheo-diecast high-strength high-thermal-conductivity Al-Si-Fe-Sr alloy according to claim 2, characterized by, In step S4, the step of calculating the temperature fluctuation value of the several monitoring points in the alloy melt pouring process includes, Step S411, at least three temperature sensors are pre-set at different spatial positions in the slurry making device, the melt temperature is synchronously and continuously measured at the same acquisition frequency, and temperature-time sequence data corresponding to each sensor is obtained; Step S411, at least three temperature sensors are pre-set at different spatial positions in the slurry making device, the melt temperature is synchronously and continuously measured at the same acquisition frequency, and temperature-time sequence data corresponding to each sensor is obtained; Step S412, divide the whole pouring process into several continuous calculation time windows according to time; Step S413, for each calculation time window, extract the temperature measurement value of all monitoring points at the same time in the window, and calculate the standard deviation thereof; Calculate the arithmetic mean of the standard deviations of all time points in the window as the window temperature fluctuation value of the time window; Step S414, define the maximum value of the window temperature fluctuation values of all calculation time windows in the whole pouring process as the alloy melt temperature fluctuation value of this pouring process.

4. The method of producing a rheo-diecast high-strength high-thermal-conductivity Al-Si-Fe-Sr alloy according to claim 3, characterized by, In step S4, the step of calculating the irregular bubble area ratio of several monitoring points in the alloy melt pouring process comprises, Step S421, a high-speed camera is arranged above the observation window or pouring stream of the gas-induced semi-solid slurry preparation device to continuously collect a dynamic image sequence of the melt surface in the pouring process at a fixed frame rate; Step S422, each collected image is sequentially preprocessed by grayscale, contrast enhancement and noise reduction filtering; An image segmentation algorithm or edge detection algorithm based on threshold is used to separate the bubble area from the melt background in the image to generate a binary image, wherein the white area represents the bubble; Step S423, the morphology of each independent connected region in the binary image is analyzed, and the circularity thereof is calculated, and the bubble with a circularity lower than a preset circularity is determined as an irregular bubble; Step S424, for each frame image, the sum of the areas of all bubbles determined as irregular bubbles is calculated, and then the ratio of the sum to the total area of the identified bubbles in the frame image is calculated to obtain the instantaneous irregular bubble area ratio of the frame image; Step S425, the instantaneous irregular bubble area ratios calculated for all frame images in the whole pouring process are counted, and the maximum value thereof is defined as the irregular bubble area ratio of the pouring process.

5. The method of producing a rheo-diecast high-strength high-thermal-conductivity Al-Si-Fe-Sr alloy according to claim 4, characterized by, In step S5, the irregular bubble area ratio of the bubble in the bubble visual image is determined by the ratio of the area of the bubble with a circularity less than a preset circularity to the total area of all bubbles in the image.

6. The method of producing a rheo-diecast high-strength high-thermal-conductivity Al-Si-Fe-Sr alloy according to claim 5, characterized by, In step S5, the step of calculating the bubble distribution uniformity value comprises, Step S51, after preprocessing and binarization, each frame of bubble visual image is divided into M rows×N columns of regular grids, so that the whole image area is divided into K sub-areas with equal size; Step S52, identify and calculate the total area of white pixels in each sub-area, and then calculate the bubble area ratio of the sub-area; Step S53, calculate the standard deviation of the bubble area ratios of all K sub-areas, and define the standard deviation as the bubble distribution uniformity value of the frame image.

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