Die-casting process parameter adjusting method and device, electronic equipment and storage medium

Abstract

The invention discloses a die-casting process parameter adjusting method and device, electronic equipment and a storage medium. The method comprises the steps that a current die in the die casting process is obtained, whether the current die is replaced or not is detected, and a die replacement detection result is obtained; according to the die replacement detection result, whether die-casting process parameters are adjusted or not is judged; when the die casting interruption is monitored, acquiring interruption duration; according to the interruption duration, whether the die-casting process parameters are adjusted or not is judged; the real-time molten metal temperature is obtained; according to the real-time molten metal temperature, whether the die-casting process parameters are adjusted or not is judged; the real-time ladle casting frequency of the current casting is obtained; according to the real-time ladle casting times, whether the die-casting process parameters are adjusted or not is judged; and die-casting at least one casting by adopting the die-casting process parameters. According to the embodiment of the invention, the stability and production efficiency of die-casting products can be improved.

Description

Technical Field

[0001] The present invention relates to the field of software detection, and in particular to a method, device, electronic equipment and storage medium for adjusting die-casting process parameters. Background Art

[0002] A die-casting machine is a device used for metal forming processing. It injects molten metal into a mold at low pressure, and then obtains parts of the desired shape after the metal cools and solidifies.

[0003] At present, manual adjustments are generally performed in the field of die-casting machine control. After discovering abnormal molten metal temperature, abnormal interruption duration and product appearance problems, the process parameters of the die-casting machine are manually adjusted one by one. The work intensity is high, the adjustment efficiency is low, and the product quality is unstable. Summary of the invention

[0004] The present invention provides a method, device, electronic equipment and storage medium for adjusting die-casting process parameters, which can improve the stability and production efficiency of die-casting products.

[0005] According to one aspect of the present invention, a method for adjusting die casting process parameters is provided, comprising:

[0006] Acquire a current mold of a die-casting process, detect whether the current mold has been replaced, and obtain a mold replacement detection result;

[0007] According to the mold replacement detection result, determine whether to adjust the die-casting process parameters; the die-casting process parameters include at least one of the following: the number of die-casting parts, the holding time, the cooling time and the cooling channel;

[0008] When a die-casting interruption is detected, obtain the interruption duration;

[0009] Determining whether to adjust the die-casting process parameters according to the interruption duration;

[0010] Get real-time molten metal temperature;

[0011] Determining whether to adjust the die-casting process parameters according to the real-time molten metal temperature;

[0012] Get the real-time pouring times of the current casting;

[0013] According to the real-time pouring times, determining whether to adjust the die-casting process parameters;

[0014] At least one casting is die-casted using the die-casting process parameters.

[0015] According to another aspect of the present invention, there is provided a die-casting process parameter adjustment device, characterized in that it comprises:

[0016] A mold type acquisition module is used to acquire the current mold of the die-casting process, detect whether the current mold has been replaced, and obtain a mold replacement detection result;

[0017] A mold replacement adjustment module, used to determine whether to adjust the die-casting process parameters according to the mold replacement detection result; the die-casting process parameters include at least one of the following: the number of die-casting parts, the holding time, the cooling time and the cooling channel;

[0018] The interruption duration acquisition module is used to obtain the interruption duration when the die casting interruption is detected;

[0019] An interruption adjustment module, used for determining whether to adjust the die-casting process parameters according to the interruption duration;

[0020] Metal temperature acquisition module, used to obtain real-time metal liquid temperature;

[0021] A temperature adjustment module, used to determine whether to adjust the die-casting process parameters according to the real-time molten metal temperature;

[0022] The ladle number acquisition module is used to obtain the real-time ladle number of the current casting;

[0023] A ladle adjustment module, used to determine whether to adjust the die-casting process parameters according to the real-time ladle pouring times;

[0024] The die-casting module is used to die-cast at least one casting using the die-casting process parameters.

[0025] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0026] at least one processor; and

[0027] a memory communicatively connected to the at least one processor; wherein,

[0028] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the die-casting process parameter adjustment method described in any embodiment of the present invention.

[0029] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for adjusting die-casting process parameters described in any embodiment of the present invention when executed.

[0030] The technical solution of the embodiment of the present invention, by real-time judgment of whether the mold has been replaced, the temperature of the molten metal, the interruption duration and the ladle information, responds and adjusts to the appropriate die-casting process parameters in turn, thereby automatically adjusting the process parameters of the die-casting machine based on different products and different working conditions to adapt to them, thereby improving the production efficiency of the product. At the same time, it can adapt to the product and working conditions, die-cast to form high-quality products, and improve the stability of product quality. At the same time, the adjusted die-casting process parameters can be sent to the die-casting machine in real time, and even multiple die-casting machines can be controlled at the same time to achieve real-time control, reduce the labor intensity of on-site operating users, improve production efficiency, and increase the yield rate.

[0031] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 is a flow chart of a method for adjusting die-casting process parameters provided according to an embodiment of the present invention;

[0034] Figure 2 is a structural schematic diagram of a die-casting process parameter adjustment device provided according to an embodiment of the present invention;

[0035] Figure 3 It is a structural schematic diagram of an electronic device for implementing the die-casting process parameter adjustment method according to an embodiment of the present invention;

[0036] Figure 4 It is a schematic diagram of a casting interruption automatic adjustment system for implementing a die-casting process parameter adjustment method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0038] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0039] Figure 1 A flowchart of a method for adjusting die-casting process parameters provided by an embodiment of the present invention. The embodiment of the present invention is applicable to the case of adjusting die-casting process parameters during the die-casting process of a product. The method can be performed by a die-casting process parameter adjustment device, which can be implemented in the form of hardware and / or software. The die-casting process parameter adjustment device can be configured in an electronic device with a certain data computing capability, which can be a client device or a server, etc.

[0040] See also Figure 1 The die casting process parameter adjustment method shown includes:

[0041] In the embodiment of the present invention, the process flow of the die-casting process mainly includes: mold closing stage, liquid raising stage, mold filling stage, crusting stage, pressurization stage, pressure holding stage, pressure relief stage and mold opening stage, and the casting product is obtained in the mold opening stage.

[0042] S101, obtaining a current mold of a die-casting process, and detecting whether the current mold has been replaced, to obtain a mold replacement detection result.

[0043] The current mold may refer to the mold for the casting product obtained in this round of die casting. Detecting whether the current mold has been replaced may refer to detecting whether the current mold is the same as the mold for the casting product generated by the previous round of die casting. Usually, the die-casting process parameters used in the previous round of die casting are adapted to the products and working conditions of the previous round. If at least one of the products and working conditions of this round of die casting changes, it is necessary to adjust the die-casting process parameters of this round to adapt to the new products and / or working conditions. Obtain the historical mold type (such as model) of the historical mold of the adjacent previous round, and the current mold type of the current mold of this round, compare whether the historical mold type and the current mold type are consistent, and obtain the mold replacement detection result. The mold replacement detection result may refer to the detection result of whether the type of the current mold of this round has been changed. When the historical mold type and the current mold type are consistent, it is determined that the mold replacement detection result is that the mold has not been replaced; when the historical mold type and the current mold type are inconsistent, it is determined that the mold replacement detection result is that the mold has been replaced.

[0044] S102. Determine whether to adjust die-casting process parameters according to the mold replacement detection result; the die-casting process parameters include at least one of the following: the number of die-casting parts, holding time, cooling time and cooling channel.

[0045] In fact, one casting product corresponds to one die-casting process parameter. Due to the different types of casting products, the corresponding mold designs are different, that is, the types of molds are different, and thus the cooling channels are different. Usually, the number of cooling channels is 17. The number of die-castings may refer to the number of die-castings that need to adjust the die-casting process parameters from the start of this round. And, after the number of die-castings, the die-casting process parameters remain stable and will not change until at least one of the mold type, interruption duration information, molten metal temperature information, and ladle information meets the die-casting process parameter adjustment conditions.

[0046] In an optional embodiment, judging whether to adjust the die-casting process parameters according to the mold replacement detection result includes: adjusting the die-casting process parameters when the mold replacement detection result is mold replacement.

[0047] When the mold replacement detection result is mold replacement, it indicates that the production state of the die-casting machine using the newly replaced mold has become a new production state. Since the new production state does not reach the expected production state, the generated die-casting products cannot meet the expected product quality requirements, so it is necessary to adjust the die-casting process parameters so that the production state of the die-casting machine can reach the expected production state as soon as possible. The expected production state can be understood as the optimal production state when the die-casting machine produces the current model of products. In addition, mold replacement indicates that the die-casting product is replaced, and the die-casting process parameters corresponding to different die-casting products are different. The die-casting process parameters need to be adjusted to the standard process parameters corresponding to the product to adapt to the replaced current mold. This process has been completed when the die-casting process parameter adjustment method is triggered. On the basis of the standard process parameters, the die-casting process parameters are further adjusted according to the result of the mold replacement so that the die-casting machine reaches the expected production state, and then the standard process parameters are restored to produce die-casting products. When the mold replacement detection result is that the mold has not been replaced, it indicates that the die-casting product has not been replaced, the die-casting process parameters are already standard process parameters adapted to the current mold, and the die-casting machine will not cause changes in the production state due to mold replacement, so there is no need to adjust the standard process parameters.

[0048] In some embodiments, the model of the mold is received and it is determined whether the model of the mold has been changed; if the mold has not been changed, the die-casting process parameters are not adjusted; if the mold has been changed, the die-casting process parameters are changed to die-casting process parameters corresponding to the model of the replaced mold.

[0049] In one example, when the die-casting machine replaces the mold, different die-casting process parameters are adjusted according to the model corresponding to the mold. Specifically, the die-casting process adjustment process: when the die-casting machine is monitored to replace the mold, the model of the replaced mold is obtained, such as mold model A, and the die-casting process parameters corresponding to the mold model are: the number of die-cast products is 3, among which, after the mold is replaced, the holding time is set to 30 seconds at the beginning of the die-casting of the first wheel hub, and all cooling channels are closed; the holding time is set to 90 seconds at the beginning of the die-casting of the second wheel hub, the cooling time remains unchanged, and only the B3 and B4 cooling channels are closed; at the beginning of the die-casting of the third wheel hub, the holding time, cooling time and cooling channels are restored to the die-casting process parameters before adjustment (that is, the standard process parameters before control). The subsequent n wheel hub die-castings all use the original die-casting process parameters.

[0050] It can be seen that by adjusting the die-casting process parameters when the mold is replaced, the die-casting process parameters can be adjusted when the die-casting product is replaced to adapt to the impact of the production status brought about by the replacement of the current mold, and ensure that the expected production status is quickly restored after the mold is replaced, so as to produce die-cast products in the expected production status, so as to ensure the production quality of the die-casting products after the mold is replaced, and improve the die-casting efficiency of the die-casting products after the mold is replaced.

[0051] S103, when the die casting interruption is detected, obtaining the interruption duration.

[0052] The interruption duration is the time from the end of die-casting of the previous wheel hub to the start of die-casting of the next wheel hub in the continuous production process of die-casting products based on the same single mold. Specifically, the interruption duration can be the time from the opening of the mold of the previous die-casting to the closing of the mold of the next die-casting. The timing starts at the end of the previous round of die-casting, and stops when the current round of die-casting process is triggered to start, and the interruption duration is obtained.

[0053] S104: Determine whether to adjust the die-casting process parameters according to the interruption duration.

[0054] Among them, different interruption durations lead to different production states of the die-casting machine, so in order to restore the die-casting process of the die-casting machine under different production states, it is necessary to adapt the die-casting process parameters to different production states. Generally speaking, the interruption duration is short, the production state of the die-casting machine changes little, and there is no need to adjust the die-casting process parameters. In some embodiments, when the interruption duration is less than the preset duration threshold, the die-casting process parameters are not adjusted; when the interruption duration is greater than or equal to the preset duration threshold, the die-casting process parameters are adjusted. The duration threshold can be 70 seconds. In addition, when the interruption duration is greater than or equal to the duration threshold, at least one interruption duration range can be experimentally determined in advance, and different interruption duration ranges correspond to different die-casting machine states, thereby configuring multiple interruption duration ranges, and setting different die-casting process parameters for different interruption duration ranges, and determining the adapted die-casting process parameters according to the interruption duration range to which the interruption duration belongs.

[0055] In an optional embodiment, judging whether to adjust the die-casting process parameters based on the interruption duration includes: obtaining a plurality of preset interruption duration ranges; in each of the interruption duration ranges, detecting the interruption duration range to which the interruption duration belongs; judging whether to adjust the die-casting process parameters based on the interruption duration range to which the interruption duration belongs.

[0056] The die-casting process parameters corresponding to different interruption time ranges can be set. When the interruption time information is received, different die-casting process parameters are determined according to the scenarios of different interruption time ranges.

[0057] In some embodiments, the interruption duration range includes four interruption duration ranges: a first interruption duration range of [70, 120) seconds, a second interruption duration range of [120, 180) seconds, a third interruption duration range of [180, 320) seconds, and a fourth interruption duration range of [320, 450) seconds.

[0058] For example, for mold model A, when the interruption duration is detected to be within any of the above interruption duration ranges, the die-casting process parameters are adjusted according to the interruption duration range. In one example, when the interruption duration is within the first interruption duration range, when the first wheel hub is die-cast after the interruption is resumed, the holding time is reduced by 10 seconds, the B4 cooling channel is closed, and the second and subsequent wheel hubs are restored to the die-casting process parameters before adjustment; when the interruption duration is within the second interruption duration range, when the first wheel hub is die-cast after the interruption is resumed, the holding time is set to 100 seconds, and the cooling time remains unchanged. B4 cooling channel is closed, and the second and subsequent wheel hubs are restored to the die-casting process parameters (standard process parameters) before adjustment; when the interruption duration falls within the third interruption duration range, after the interruption is restored, the holding time is set to 30 seconds and the cooling time is 60 seconds when the first wheel hub is die-casted, and the holding time is set to 100 seconds and the cooling time is set to 60 seconds when the second wheel hub is die-casted, and the third and subsequent wheel hubs are restored to the die-casting process parameters before adjustment; when the interruption duration falls within the fourth interruption duration range, after the interruption is restored, the holding time is set to 30 seconds and the cooling time is 60 seconds when the first wheel hub is die-casted, and the holding time is set to 100 seconds and the cooling time is set to 60 seconds when the second wheel hub is die-casted, and the third wheel hub is not adjusted, and the fourth and subsequent wheel hubs are restored to the die-casting process parameters before adjustment.

[0059] In addition, the interruption duration range that is not adjusted and the interruption duration range that is manually adjusted can be preset. For example, if the interruption duration does not exceed the duration threshold, such as 70 seconds, the interruption duration belongs to the fifth interruption duration range (0,70], and the die-casting process parameters are not adjusted. If the interruption duration exceeds the set maximum interruption duration, such as greater than 450 seconds, the interruption duration belongs to the fifth interruption duration range (450,+∞), the die-casting process parameters are not automatically adjusted, and the on-site operator is prompted to make manual adjustments.

[0060] It can be seen that by adjusting the die-casting process parameters according to the different interruption durations between different die-casting products of the same mold, the die-casting process parameters can be adjusted in time when switching to die-casting different products to adapt to the die-casting machine states corresponding to different interruption durations, thereby ensuring the production quality of the die-casting products produced after the interruption, and improving the die-casting efficiency of the die-casting products produced after the interruption.

[0061] S105, obtaining real-time molten metal temperature.

[0062] The molten metal may be molten aluminum.

[0063] S106: Determine whether to adjust the die-casting process parameters according to the real-time molten metal temperature.

[0064] Detect whether the real-time molten metal temperature belongs to the preset molten metal temperature range. If it does, do not adjust the die-casting process parameters; if it does not, adjust the die-casting process parameters. The real-time molten metal temperature that belongs to the molten metal temperature range can be less than or equal to the upper temperature limit of the molten metal temperature range, and greater than or equal to the lower temperature limit of the molten metal temperature range; the real-time molten metal temperature that does not belong to the molten metal temperature range can be greater than the upper temperature limit of the molten metal temperature range, or less than the lower temperature limit of the molten metal temperature range. Different real-time molten metal temperatures correspond to different die-casting process parameters. When the molten metal temperature exceeds the upper temperature limit or the lower temperature limit, the production status of the die-casting machine changes accordingly, and the die-casting process parameters are adjusted to adjust the production status of the die-casting machine to the expected production status. When the molten metal temperature returns to the normal range, it indicates that the die-casting machine has reached the expected production status. At this time, the die-casting machine can perform the die-casting process. At this time, the die-casting process parameters need to be adjusted to the standard process parameters before adjustment, so that the die-casting machine can produce die-casting products that meet the requirements based on the standard process parameters.

[0065] In an optional embodiment, judging whether to adjust the die-casting process parameters based on the real-time molten metal temperature includes: obtaining an upper temperature limit and a lower temperature limit corresponding to the type of the current mold; comparing the real-time molten metal temperature with the upper temperature limit and the lower temperature limit to obtain a temperature comparison result; judging whether to adjust the die-casting process parameters based on the temperature comparison result.

[0066] The temperature comparison result includes the comparison result between the real-time molten metal temperature and the upper temperature limit value and the comparison result between the real-time molten metal temperature and the lower temperature limit value. When the real-time molten metal temperature is less than or equal to the upper temperature limit value and greater than or equal to the lower temperature limit value, the die-casting process parameters are not adjusted; when the real-time molten metal temperature is greater than the upper temperature limit value, or the real-time molten metal temperature is less than the lower temperature limit value, the die-casting process parameters are adjusted.

[0067] In some embodiments, when the real-time molten metal temperature is greater than the upper temperature limit, a first target process parameter is generated to replace the die-casting process parameter; when the real-time molten metal temperature is less than the lower temperature limit, a second target process parameter is generated to replace the die-casting process parameter.

[0068] In one example, the molten metal is aluminum liquid, the upper temperature limit is 706 degrees Celsius, and the lower temperature limit is 695 degrees Celsius. When the aluminum liquid temperature is higher than the upper limit of 706 degrees Celsius, the die-casting process parameters are adjusted to generate the first target process parameters, specifically: the holding time is increased by 5 seconds, and when it returns to the normal range of 698 to 704 degrees Celsius, the holding time is restored to the die-casting process parameters before adjustment. When the aluminum liquid temperature is lower than the lower limit of 695 degrees Celsius, the die-casting process parameters are adjusted to generate the second target process parameters, specifically: the holding time is reduced by 5 seconds; when the aluminum liquid temperature is greater than or equal to the lower limit and less than or equal to the upper limit, the die-casting process parameters are not adjusted.

[0069] It can be seen that by adjusting the die-casting process parameters according to different real-time molten metal temperatures, the die-casting process parameters can be adjusted in time according to the real-time changing molten metal temperature to adapt to the changes in the production state of the die-casting machine caused by the casting process with different molten metal temperatures, ensuring that the die-casting machine maintains the expected production state in real time, thereby ensuring the production quality of the die-casting products produced, and improving the die-casting efficiency of the die-casting products produced.

[0070] S107, obtaining the real-time pouring number of the current casting.

[0071] Different pouring times require different die-casting process parameters to ensure the stability of the die-casting process and the quality of the casting products.

[0072] S108: Determine whether to adjust the die-casting process parameters according to the real-time pouring times.

[0073] When the real-time ladle pouring number is less than the preset ladle pouring number threshold, the die-casting process parameters are not adjusted; when the real-time ladle pouring number is greater than or equal to the preset ladle pouring number threshold, the die-casting process parameters are adjusted.

[0074] In an optional embodiment, judging whether to adjust the die-casting process parameters based on the real-time ladle number includes: comparing the real-time ladle number with a ladle number threshold to obtain a ladle comparison result; judging whether to adjust the die-casting process parameters based on the ladle comparison result.

[0075] Receive the ladle information, generate the corresponding process parameters according to the number of times the ladle is poured, and send them to the machine to adjust the die-casting process parameters. The ladle number threshold is used to determine whether to adjust the die-casting process parameters. For example, the ladle number threshold is 15 times.

[0076] In some embodiments, the real-time number of ladles is automatically obtained in real time, such as the model A of the mold, and the number of ladles is compared with 15. When the real-time number of ladles reaches 15, the holding time increases by 5 seconds. In addition, after the real-time number of ladles is greater than the threshold number of ladles, the die-casting process parameters are adjusted accordingly for each increase in the real-time number of ladles. For example, the holding time of the model A of the mold is adjusted according to the real-time number of ladles. When the real-time number of ladles is greater than 15, the holding time of each ladle will increase by 5 seconds.

[0077] It can be seen that by adjusting the die-casting process parameters according to different pouring times, the die-casting process parameters can be adjusted in time as the pouring times increase, so as to adapt to the production states of different die-casting machines caused by different pouring times, and ensure that different pouring times can reach the expected production state of the die-casting machine, thereby ensuring the production quality of the die-casting products produced by the die-casting machine, and improving the die-casting efficiency of the die-casting products with real-time adjustment of the pouring times.

[0078] S109, die-casting at least one casting using the die-casting process parameters.

[0079] When the die-casting process parameters need to be adjusted, the die-casting process parameters are adjusted and updated in real time, and the castings are die-casted based on the adjusted die-casting process parameters so that the die-casting machine reaches the expected production state. After the die-casting machine reaches the expected production state, it is necessary to restore the die-casting machine to the die-casting process parameters before adjustment, that is, the die-casting process parameters before adjustment are issued to the die-casting machine, and the die-casting machine continues to use the die-casting process parameters before adjustment (standard process parameters) to produce casting products, wherein the adjusted die-casting process parameters are used to enable the die-casting machine to quickly reach the expected production state. In this process, the die-casting machine does not reach the expected production state, and accordingly, most of the products produced by the die-casting machine are scrap parts. After the die-casting machine reaches the expected production state, the product generated by the die-casting process parameters before adjustment is the expected production product. Among them, the expected production state can be that the temperature of the die-casting machine's mold reaches the expected temperature threshold. Specifically, the die-casting process parameters before or after adjustment are issued to the die-casting machine that produces castings, and can take effect immediately.

[0080] The embodiment of the present invention receives the mold replacement detection result, interruption duration, real-time molten metal temperature and real-time ladle number in real time, and responds to the reception and determines whether the die-casting process parameters need to be adjusted in sequence according to the preset priority from high to low, that is, the mold replacement detection result, interruption duration, real-time molten metal temperature and real-time ladle number. When adjustment is required, new die-casting process parameters are generated and updated to the die-casting process parameters of the die-casting machine to adjust the die-casting process parameters.

[0081] Specifically, it is first determined whether to adjust based on the mold replacement detection result, and after the adjustment is completed or not, it is determined whether to adjust based on the interruption duration, and after the adjustment is completed or not, it is determined whether to adjust based on the real-time molten metal temperature, and after the adjustment is completed or not, it is finally determined whether to adjust based on the real-time number of pouring ladles, and after the adjustment is completed or not, the die-casting machine obtains the latest die-casting process parameters and executes the die-casting process to produce this round of die-casting products.

[0082] In the prior art, manual adjustment of the process parameters of the die-casting machine, high work intensity, low adjustment efficiency, and untimely adjustment lead to product quality defects and unstable product quality. In the embodiment of the present invention, the reasons for determining and analyzing the need for adjustment include: mold replacement, interruption time, molten metal temperature, and ladle information, etc., and the corresponding adjustment parameters and adjustment rules are formulated in various situations to achieve automatic adjustment of die-casting process parameters for different working conditions.

[0083] The technical solution of the embodiment of the present invention determines in real time whether the mold has been replaced, the metal liquid temperature, the interruption duration and the ladle information, and responds in turn to the die-casting process parameters that are adapted to the current production status, thereby automatically adjusting the process parameters of the die-casting machine to adapt to the current production status based on different products and different working conditions, and quickly adjusting the unexpected production status to the expected production status, so as to obtain the die-cast product in the expected production status, thereby improving the production efficiency of the product. At the same time, it can adapt to the product and the working conditions, die-cast to form high-quality products, and improve the stability of product quality. At the same time, the adjusted die-casting process parameters can be sent to the die-casting machine in real time, and even multiple die-casting machines can be controlled at the same time to achieve real-time control, reduce the labor intensity of on-site operating users, improve production efficiency, and improve the yield rate.

[0084] The embodiments of the present application provide a method, system and algorithm model for automatic adjustment of casting interruptions, which can automatically adjust different process parameters for different models of products and different working conditions according to the aluminum liquid temperature, interruption time, ladle information and mold model information collected in real time during the production process of the die-casting machine, and can simultaneously control multiple die-casting machines, replacing manual adjustment and improving the stability of product quality and production efficiency.

[0085] In an optional embodiment, the die-casting process parameter adjustment method further includes: when the die-casting machine is in die closing, obtaining preset die closing adjustment information; and judging whether to adjust the die-casting process parameters according to the die closing adjustment information.

[0086] The mold closing adjustment information may refer to information pre-set as to whether to adjust the die casting process parameters during the mold closing stage of the die casting process. When the mold closing adjustment information is to adjust the die casting process parameters, it is determined that the die casting process parameters are adjusted; when the mold closing adjustment information is not to adjust the die casting process parameters, it is determined that the die casting process parameters are not adjusted.

[0087] In some embodiments, when each casting product is molded, it is determined whether the current mold model has mold adjustment information for adjusting the die-casting process parameters after mold closing. If there is mold adjustment information for adjusting the die-casting process parameters after mold closing, the die-casting process parameters are adjusted; if there is no mold adjustment information for adjusting the die-casting process parameters after mold closing, the die-casting process parameters are not adjusted. If the mold adjustment information is to close the cooling channel B4 when the die-casting of the first wheel hub is completed, the die-casting process parameters are adjusted, and the adjusted die-casting process parameters are to close the cooling channel B4 when the die-casting of the first wheel hub is completed, and the die-casting of the second wheel hub is restored to the die-casting process parameters before adjustment.

[0088] In fact, since the mold closing signal of the casting product is outside the die-casting process of the casting, there is no conflict between it and the mold change detection result, interruption duration, real-time molten metal temperature and real-time pouring number, so it does not participate in the setting of priority order.

[0089] It can be seen that by determining whether to adjust the die-casting process parameters in the mold adjustment information to determine whether to adjust the die-casting process parameters, different mold adjustment requirements can be flexibly adapted to improve the production quality of die-casting products with different mold adjustment requirements, and improve the die-casting efficiency of die-casting products with different mold adjustment requirements.

[0090] In an optional embodiment, when the die-casting machine triggers a controlled operation, standard process parameters are acquired as die-casting process parameters.

[0091] Triggering controlled operation is used to trigger the die-casting machine to enter the mode of automatically adjusting die-casting process parameters. Standard process parameters may refer to die-casting process parameters adapted to the current round of die-casting products. Users can choose whether to be controlled on the die-casting machine central control HMI (Human Machine Interface). When controlled is selected, controlled operations are generated, and when uncontrolled is selected, controlled operations are not generated. If controlled is selected, the current die-casting process parameters are automatically set to standard process parameters. The standard process parameters are actually the initial die-casting process parameters to be adjusted, and the subsequent automatic adjustment is made on the basis of the standard process parameters. When uncontrolled, users can manually adjust the die-casting process parameters until they are adjusted to the standard process parameters required by the user. If controlled is selected at this time, the current die-casting process parameters are determined as standard process parameters.

[0092] Only when the die-casting machine triggers the controlled operation, will it determine whether to trigger the corresponding adjustment conditions such as mold change detection results, interruption duration, real-time molten metal temperature and real-time ladle number. In other words, the die-casting process parameters will be adjusted only when the die-casting machine triggers the controlled operation; the user can choose whether to automatically adjust the die-casting process parameters. When the corresponding adjustment conditions are met, the die-casting process parameters used for the casting equipment are adjusted; when the corresponding adjustment conditions are not met, the die-casting process parameters used for the casting equipment are not adjusted.

[0093] It can be seen that by triggering automatic adjustment of the die-casting process parameters only when the die-casting machine triggers a controlled operation, the flexibility of adjusting the die-casting process parameters can be improved.

[0094] In an optional embodiment, the adjustment of the die-casting process parameters includes: obtaining the type of the current mold; inputting the type of the current mold into a pre-trained process parameter generation model for processing, and outputting target process parameters; and adjusting the die-casting process parameters according to the target process parameters.

[0095] The target process parameters may refer to the die-casting process parameters that need to be adjusted and the corresponding adjustment amounts. Parameters other than the target process parameters do not need to be adjusted, and the original die-casting process parameters before the adjustment can continue to be used. In an example, the die-casting process parameters include parameter A, parameter B, and parameter C, and the target process parameters include parameter A. In the adjusted die-casting process parameters, the parameter value of parameter A is updated to the target process parameter, and the parameter values ​​of parameter B and parameter C are not adjusted. The input of the process parameter generation model is the type of the current mold, and the output is the target process parameter that needs to be adjusted.

[0096] It should be noted that the input of the process parameter generation model may also include the interruption duration, the real-time molten metal temperature and the real-time number of ladles, and the output is the target process parameter corresponding to the input. Alternatively, the corresponding models can be trained for different inputs respectively, and each model obtains the corresponding target process parameter for only one input. For example, the process parameter generation model corresponding to the interruption duration, the process parameter generation model corresponding to the real-time molten metal temperature and the process parameter generation model corresponding to the real-time number of ladles can also be pre-trained.

[0097] It can be seen that by adjusting the die-casting process parameters through the pre-trained process parameter generation model output of the target process parameters corresponding to at least one of the current mold type, interruption duration, real-time molten metal temperature and real-time ladle number, the target process parameters can be obtained quickly and accurately, thereby improving the adjustment efficiency of the die-casting process parameters.

[0098] First, let me introduce the construction process of the decision tree algorithm model, as follows:

[0099] Input: training data set D, parameter conditions for stopping calculation.

[0100] Output: decision tree T.

[0101] The training data set includes: the type of the current mold after replacement and the corresponding target process parameters, the interruption duration and the corresponding target process parameters, the real-time molten metal temperature and the corresponding target process parameters, and the real-time number of ladles and the corresponding target process parameters; or the training data set only includes the type of the current mold after replacement and the corresponding target process parameters; or the training data set only includes the interruption duration and the corresponding target process parameters; or the training data set only includes the real-time molten metal temperature and the corresponding target process parameters; or the training data set only includes the real-time number of ladles and the corresponding target process parameters.

[0102] According to the training data set, starting from the root node, recursively perform the following operations on each node to build a binary decision tree:

[0103] 1: If the number of samples is less than the threshold or there is no feature, the decision subtree is returned and the recursion of the current node stops.

[0104] 2: Calculate the Gini coefficient of sample set D. If the Gini coefficient is less than the threshold, return to the decision tree subtree and stop recursion at the current node.

[0105] 3: Identify each feature type, whether it is discrete value or continuous value, use the corresponding processing method for each type and calculate the Gini coefficient under each segmentation. The formula of the Gini coefficient is as follows:

[0106]

[0107]

[0108] Among them, K represents the total number of trees, and Ck represents the number of samples belonging to the kth category in the kth tree.

[0109] 4: Select the feature A and the corresponding feature value a with the smallest Gini coefficient from the calculated Gini coefficients of each feature value for the data set D. Based on the optimal feature and the optimal feature value, divide the data set into two parts D1 and D2, and establish the left and right nodes of the current node. The data set D of the node is D1, and the data set D of the right node is D2.

[0110] 5: Recursively call steps 1-4 for the left and right child nodes to generate a decision tree. The algorithm stops calculating when: as shown in steps 1 and 2, the number of samples in the node is less than the predetermined threshold, or the Gini coefficient of the sample set is less than the predetermined threshold (the samples basically belong to the same class), or there are no more features.

[0111] XGBoost (eXtreme Gradient Boosting) is based on the GBDT (GradientBoosting Decision Tree) gradient boosting algorithm, which uses the Taylor second-order expansion objective function to accelerate model training and convergence, and improve the model overfitting problem (L2 penalty term). In addition, there is a special treatment for missing values, so missing values ​​are not sensitive. During training, feature bucketing and parallel computing, as well as out-of-core block storage operations, greatly speed up the model training speed. This case uses the XGBoost algorithm model, and its model formula is as follows:

[0112] For a dataset with n rows and m dimensions, the XGBoost model can be expressed as:

[0113]

[0114] in,

[0115] F is a set of CART (Classification and Regression Tree) decision tree structures, q is the tree structure of samples mapped to leaf nodes, T is the number of leaf nodes, w is the real number score of leaf nodes, and R is the number of iterations in the model training process. When building an XGBoost model, it is necessary to find the optimal parameters based on the principle of minimizing the objective function to establish the optimal model. The objective function of the XGBoost model can be divided into the error function term L and the model complexity function term Ω. The objective function can be written as:

[0116] Obj=L+Ω

[0117] L=

[0118] Ω=

[0119] in, is the L1 regularization term, is the L2 regularization term. When using training data to optimize the model, it is necessary to keep the original model unchanged and add a new function f to the model to reduce the objective function as much as possible. The specific process is:

[0120]

[0121]

[0122]

[0123] …

[0124]

[0125] At this time, the objective function is expressed as:

[0126]

[0127] In the XGBoost algorithm, in order to quickly find the parameters that minimize the objective function, the objective function is expanded by a second-order Taylor expansion to obtain an approximate objective function:

[0128]

[0129] When the constant term is removed, it can be seen that the objective function is only related to the first and second order derivatives of the error function. At this time, the objective function is expressed as:

[0130]

[0131]

[0132] If the structure part q of the tree is known, the objective function can be used to find the optimal Wj and obtain the optimal objective function value. Its essence can be attributed to the problem of solving the minimum value of the quadratic function. The solution is:

[0133]

[0134]

[0135] Among them, Obj is a scoring function that can be used to evaluate the model. The smaller the Obj value, the better the model effect. Represents the weight value of each leaf node, and the weight value is determined by optimizing the objective function. By recursively calling the above tree building method, a large number of tree structures can be obtained, and Obj is used to search for the optimal tree structure, which is put into the existing model to establish the optimal XGBoost model.

[0136] The specific construction process is as follows:

[0137] Initialize the model

[0138] First, you need to initialize a base model, which is usually a simple model such as a linear regression model or a constant model. In XGBoost, this base model is usually set to the average of the predicted target values ​​(for regression problems) or the probability of a certain category (for classification problems).

[0139] Calculate residuals

[0140] The residual is the difference between the model’s predicted value and the actual value. In XGBoost, the residual is used as the basis for building the next weak model. Specifically, for each sample, the residual between its predicted value and the actual value is calculated, and these residuals are used as the input of the next weak model.

[0141] Building a weak model

[0142] Use decision trees as weak models. At each iteration, a new decision tree is built to fit the current residual. This decision tree tries to divide the sample into different subsets and assign a prediction value to each subset to minimize the residual.

[0143] Update the model

[0144] Add the newly constructed decision tree to the model and update the model's predictions. Specifically, for each sample, the new prediction will be the previous model's prediction plus the current decision tree's prediction.

[0145] Calculating the loss function

[0146] The loss function is used to measure the difference between the model's predicted value and the actual value. In XGBoost, the squared loss function (for regression problems) or the logarithmic loss function (for classification problems) is usually used. In addition, XGBoost also introduces a regularization term to control the complexity of the model to prevent overfitting.

[0147] Optimize the objective function (as shown in the above model formula)

[0148] The goal of XGBoost is to minimize a loss function with a regularization term. To achieve this goal, XGBoost uses a second-order Taylor expansion to approximate the loss function and optimizes the objective function through gradient descent. At each iteration, the first and second derivatives of the loss function are calculated and this information is used to update the model parameters.

[0149] Iteratively build the model

[0150] Repeat the above steps (calculating residuals, building weak models, updating models, calculating loss functions, and optimizing objective functions) until the specified number of iterations is reached or the loss function value reaches the specified threshold. In each iteration, a new decision tree is built to correct the errors of the previous model and gradually improve the prediction accuracy of the model.

[0151] Output the final model

[0152] When the iteration process is over, the final XGBoost model is output and determined as the process parameter generation model. The process parameter generation model is a strong model composed of multiple decision trees that can handle complex data sets and give accurate prediction results.

[0153] XGBoost also specially designed an algorithm for sparse data. If the i-th feature of a sample is missing, the feature cannot be used to divide the sample. The approach here is to assign the sample to the specified child node by default. As for which specific node it is assigned to, an algorithm is needed to calculate it. The main idea of ​​the algorithm is to assume that samples with missing features belong to the right subtree and the left subtree respectively, and only iterate on non-missing samples, and calculate the gains of the overall missing samples belonging to the right subtree and the left subtree respectively, and select the direction with the largest gain as the default direction of the missing data.

[0154] By using the process parameter generation model, it is possible to detect whether the die-casting process parameters of the die-casting machine need to be adjusted according to the type of the current mold after replacement, the interruption duration, the real-time molten metal temperature and the real-time number of ladle pouring times. If adjustment is required, the die-casting process parameters are adjusted according to the target process parameters output by the algorithm model. And when each casting is molded, it is determined whether the die-casting process parameters need to be adjusted according to the mold adjustment information. The adjusted die-casting process parameters are sent to each die-casting machine for immediate effect.

[0155] The training data of the process parameter generation model is preprocessed using a clustering algorithm to remove missing values ​​and outliers; the clustering algorithm is used to select features of the training data to select features related to the target variable; then, the selected features are used to train and predict the decision tree. This method combines the advantages of clustering algorithms and decision trees to improve the accuracy and efficiency of data processing.

[0156] The clustering algorithm is used to obtain the initial data, set the center point of the initial data, and divide the initial data into at least two initial clusters according to the center point; calculate the distance between the data in each initial cluster and the center point of the initial cluster and form a distance matrix, and obtain the weight of data similarity according to the distance matrix; adjust the parameters and update the data according to the weight of data similarity and generate new clusters. This method aims to automatically classify incomplete multi-data morphological information, so as to merge similar data that lacks some information, so as to obtain data containing all information, which is convenient for information extraction and application.

[0157] The decision tree algorithm is used for classification and regression problems. The core idea of ​​the decision tree model is to divide the training data and build a tree structure based on feature attributes so that each leaf node corresponds to a category or numerical output. When predicting, the new sample is judged based on the attributes starting from the root node until the leaf node corresponds to a category or value. It generally includes three parts: 1. Feature selection: Feature selection refers to selecting a feature from the numerous features in the training data as the splitting criterion for the current node. There are many different quantitative evaluation standards for how to select features, which derive different decision tree algorithms. 2. Decision tree generation: According to the selected feature evaluation criteria, child nodes are recursively generated from top to bottom until the data set is indivisible and the decision tree stops growing. For tree structures, recursive structures are the easiest way to understand. 3. Pruning: Decision trees are prone to overfitting, and generally require pruning to reduce the size of the tree structure and alleviate overfitting.

[0158] In some embodiments, a data processing method based on the integration of fuzzy clustering and decision tree aims to improve the accuracy and stability of data classification by combining the fuzzy clustering algorithm and the decision tree algorithm.

[0159] Figure 2 A schematic diagram of the structure of a die-casting process parameter adjustment device provided in an embodiment of the present invention. The embodiment of the present invention can be applied to the situation of adjusting die-casting process parameters during the die-casting process of a product. The device can execute a die-casting process parameter adjustment method. The device can be implemented in the form of hardware and / or software. The device can be configured in an electronic device that carries a function with certain data computing capabilities.

[0160] See also Figure 2 The die casting process parameter adjustment device shown comprises:

[0161] The mold type acquisition module 201 is used to acquire the current mold of the die-casting process, detect whether the current mold has been replaced, and obtain a mold replacement detection result;

[0162] The mold replacement adjustment module 202 is used to determine whether to adjust the die-casting process parameters according to the mold replacement detection result; the die-casting process parameters include at least one of the following: the number of die-casting parts, the holding time, the cooling time and the cooling channel;

[0163] The interruption duration acquisition module 203 is used to acquire the interruption duration when the die casting interruption is detected;

[0164] An interruption adjustment module 204 is used to determine whether to adjust the die-casting process parameters according to the interruption duration;

[0165] The metal temperature acquisition module 205 is used to acquire the real-time metal liquid temperature;

[0166] The temperature adjustment module 206 is used to determine whether to adjust the die-casting process parameters according to the real-time molten metal temperature;

[0167] The ladle number acquisition module 207 is used to obtain the real-time ladle number of the current casting;

[0168] The ladle adjustment module 208 is used to determine whether to adjust the die-casting process parameters according to the real-time pouring times;

[0169] The die-casting module 209 is used to die-cast at least one casting using die-casting process parameters.

[0170] The technical solution of the embodiment of the present invention, by real-time judgment of whether the mold has been replaced, the temperature of the molten metal, the interruption duration and the ladle information, responds and adjusts to the appropriate die-casting process parameters in turn, thereby automatically adjusting the process parameters of the die-casting machine based on different products and different working conditions to adapt to them, thereby improving the production efficiency of the product. At the same time, it can adapt to the product and working conditions, die-cast to form high-quality products, and improve the stability of product quality. At the same time, the adjusted die-casting process parameters can be sent to the die-casting machine in real time, and even multiple die-casting machines can be controlled at the same time to achieve real-time control, reduce the labor intensity of on-site operating users, improve production efficiency, and increase the yield rate.

[0171] In an optional embodiment of the present invention, the mold replacement adjustment module 202 is specifically used to:

[0172] When the mold change detection result is a mold change, the die-casting process parameters are adjusted.

[0173] In an optional embodiment of the present invention, the interrupt adjustment module 204 is specifically configured to:

[0174] Obtaining a plurality of preset interruption duration ranges; in each interruption duration range, detecting the interruption duration range to which the interruption duration belongs;

[0175] According to the interruption duration range to which the interruption duration belongs, it is determined whether to adjust the die-casting process parameters.

[0176] In an optional embodiment of the present invention, the temperature adjustment module 206 is specifically configured to:

[0177] Get the upper and lower temperature values ​​corresponding to the current mold type;

[0178] Compare the real-time molten metal temperature with the upper temperature limit and the lower temperature limit to obtain a temperature comparison result;

[0179] According to the temperature comparison results, determine whether to adjust the die-casting process parameters.

[0180] In an optional embodiment of the present invention, the ladle adjustment module 208 is specifically used to:

[0181] Compare the real-time ladle number with the ladle number threshold to obtain a ladle comparison result;

[0182] According to the ladle comparison results, determine whether to adjust the die-casting process parameters.

[0183] In an optional embodiment of the present invention, the die-casting process parameter adjustment device further includes:

[0184] A mold closing adjustment information acquisition module, used for acquiring preset mold closing adjustment information when the die-casting machine closes the mold;

[0185] The mold closing adjustment module is used to determine whether to adjust the die-casting process parameters according to the mold closing adjustment information.

[0186] In an optional embodiment of the present invention, the mold replacement adjustment module 202 is specifically used to:

[0187] Get the type of the current mold;

[0188] Input the current mold type into the pre-trained process parameter generation model for processing, and output the target process parameters;

[0189] According to the target process parameters, the die casting process parameters are adjusted.

[0190] The die-casting process parameter adjustment device provided in the embodiment of the present invention can execute the die-casting process parameter adjustment method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0191] In the technical solutions of the embodiments of the present invention, the acquisition, storage and application of the data involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0192] Figure 3 A schematic diagram of the structure of an electronic device 300 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0193] like Figure 3As shown, the electronic device 300 includes at least one processor 301, and a memory connected to the at least one processor 301 in communication, such as a read-only memory (ROM) 302, a random access memory (RAM) 303, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 301 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 302 or the computer program loaded from the storage unit 308 to the random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 can also be stored. The processor 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0194] A number of components in the electronic device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0195] The processor 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 301 executes the various methods and processes described above, such as a distribution network line planning method.

[0196] In some embodiments, the distribution network line planning method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the processor 301, one or more steps of the distribution network line planning method described above can be performed. Alternatively, in other embodiments, the processor 301 can be configured to execute the distribution network line planning method in any other appropriate manner (for example, by means of firmware).

[0197] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0198] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0199] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0200] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0201] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0202] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS (Virtual Private Server) services.

[0203] In an application scenario, such as Figure 4 As shown, an embodiment of the present invention provides a casting interruption automatic adjustment system, which includes a receiving module, a priority setting module, a response module and a process parameter adjustment module.

[0204] Among them, the receiving module may include a mold type acquisition module, an interruption duration acquisition module, a metal temperature acquisition module and a ladle number acquisition module, etc. Specifically, the mold type acquisition module is used to obtain the type of the current mold, the interruption duration acquisition module is used to obtain the interruption duration, the metal temperature acquisition module is used to obtain the real-time molten metal temperature, the ladle number acquisition module is used to obtain the real-time ladle number, and the mold closing adjustment information acquisition module is used to obtain the mold closing adjustment information, so that the receiving module is used to obtain the current mold type, interruption duration, real-time molten metal temperature, real-time ladle number and mold closing adjustment information.

[0205] The priority setting module is used to set the priority of the information obtained above. For example, the priority is set from high to low in the order of the current mold type, interruption duration, real-time molten metal temperature and real-time pouring times.

[0206] The response module is used to respond to the acquired information in order from high to low priority, and the process parameter generation model outputs the die-casting process parameters corresponding to the information. The response module may include: a mold model response module, an interruption time response module, an aluminum liquid temperature response module, a ladle information response module and a mold closing signal response module, wherein the mold model response module includes a mold replacement adjustment module, which is used to determine the adjustment of the die-casting process parameters when the mold is replaced, and output the target process parameters corresponding to the replaced current mold based on the process parameter generation model; the interruption time response module includes an interruption adjustment module, which is used to determine the adjustment of the die-casting process parameters when the interruption time is greater than or equal to a preset interruption time threshold, and output the target process parameters corresponding to the interruption time based on the process parameter generation model. The aluminum liquid temperature response module includes a temperature adjustment module, which is used to determine that the die-casting process parameters are adjusted when the real-time molten metal temperature is greater than the upper temperature limit or less than the lower temperature limit, and output the target process parameters corresponding to the real-time molten metal temperature based on the process parameter generation model; the ladle information response module includes a ladle adjustment module, which is used to determine that the die-casting process parameters are adjusted when the real-time number of ladles is greater than the ladle number threshold, and output the target process parameters corresponding to the real-time number of ladles based on the process parameter generation model; the mold closing signal response module includes a mold closing adjustment module, which is used to determine that the die-casting process parameters are adjusted when the mold closing adjustment information is to adjust the die-casting process parameters, and output the target process parameters corresponding to the mold closing adjustment information based on the process parameter generation model.

[0207] The process parameter adjustment module is used to adjust the die-casting process parameters of the die-casting machine according to the target process parameters output by the process parameter generation model. Specifically, the process parameter adjustment module includes: a mold replacement adjustment module, which is used to adjust the die-casting process parameters according to the target process parameters corresponding to the current mold replaced when the mold is replaced; an interruption adjustment module, which is used to adjust the die-casting process parameters according to the target process parameters corresponding to the interruption duration when it is greater than or equal to the preset interruption duration threshold; the aluminum liquid temperature response module includes a temperature adjustment module, which is used to adjust the die-casting process parameters according to the target process parameters corresponding to the real-time metal liquid temperature when the real-time metal liquid temperature is greater than the upper temperature limit or less than the lower temperature limit; the ladle information response module includes a ladle adjustment module, which is used to adjust the die-casting process parameters according to the target process parameters corresponding to the real-time ladle number when the real-time ladle number is greater than the ladle number threshold; the mold closing signal response module includes a mold closing adjustment module, which is used to adjust the die-casting process parameters according to the target process parameters corresponding to the mold closing adjustment information when the mold closing adjustment information is to adjust the die-casting process parameters. Thus, the process parameter adjustment module is used to adjust the die-casting process parameters according to at least one of the target process parameters corresponding to the current mold, the target process parameters corresponding to the interruption duration, the target process parameters corresponding to the real-time molten metal temperature, the target process parameters corresponding to the real-time pouring number, and the target process parameters corresponding to the mold adjustment information.

[0208] The adjusted die-casting process parameters are sent to a die-casting machine individually, or in batches to multiple die-casting machines.

[0209] In addition, when the casting interruption automatic adjustment system uses the process parameter generation model, it is necessary to prepare data, classify the training data based on the data processing method of the clustering algorithm, and use the induction algorithm to generate readable rules and decision trees, build the XGBoost decision tree model, and use the processed training data to train the constructed XGBoost decision tree model to obtain the process parameter generation model.

[0210] Among other things, the rules may include:

[0211] Rule 1: After mold change, the holding time is set to 30 seconds at the beginning of die casting of the first wheel hub, and all cooling channels are closed. The holding time is set to 90 seconds at the beginning of die casting of the second wheel hub, and the cooling time remains unchanged. Only the B3 and B4 cooling channels are closed. At the beginning of die casting of the third wheel hub, the holding time, cooling time, and cooling channels return to the original die casting process parameters.

[0212] Rule 2: When the interruption time is within the first interruption time range, when the first wheel hub is die-cast after the interruption time, the holding time is reduced by 10 seconds, the B4 cooling channel is closed, and the original process is resumed after the second wheel hub.

[0213] Rule 3: When the interruption time is within the second interruption time range, the holding time is set to 100 seconds when the first wheel hub is die-cast after the interruption time, the cooling time remains unchanged, the B4 cooling channel is closed, and the original process is restored for the second piece.

[0214] Rule 4: When the interruption time is within the third interruption time range, the holding time for die-casting of the first wheel hub after the interruption time is set to 30 seconds, and the cooling time is set to 60 seconds. The holding time for die-casting of the second wheel hub is set to 100 seconds, and the cooling time is set to 60 seconds. After the die-casting of the third wheel hub begins, the original process is restored.

[0215] Rule 5: When the interruption time is within the fourth interruption time range, the adjustment rules for the first two wheel hubs after the interruption time are the same as Rule 4, the third wheel hub is not adjusted, and the fourth wheel hub returns to the original process.

[0216] Rule 6: When the number of ladles is 15, the holding time increases by 5 seconds. The model adjusts the holding time of the model according to the number of ladles. When the number of ladles is greater than 15, the holding time of each ladle will increase by 5 seconds.

[0217] Rule 7: When the aluminum liquid temperature is higher than the upper limit of 706 degrees Celsius, the holding time is increased by 5 seconds. When it returns to the normal range of 698 to 704 degrees Celsius, the holding time returns to the original process.

[0218] Rule 8: When the aluminum liquid temperature is greater than or equal to the lower limit and less than or equal to the upper limit, the holding time returns to the original process.

[0219] Rule 9: When the aluminum liquid temperature is lower than the lower limit of 695 degrees Celsius, the holding time is reduced by 5 seconds.

[0220] Rule 10: Close cooling channel B4 at the end of die casting of the first wheel hub.

[0221] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0222] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for adjusting die casting process parameters, characterized in that: The method comprises: Acquire a current mold of a die-casting process, detect whether the current mold has been replaced, and obtain a mold replacement detection result; According to the mold replacement detection result, determine whether to adjust the die-casting process parameters; the die-casting process parameters include at least one of the following: the number of die-casting parts, the holding time, the cooling time and the cooling channel; When a die-casting interruption is detected, obtain the interruption duration; Determining whether to adjust the die-casting process parameters according to the interruption duration; Get real-time molten metal temperature; Determining whether to adjust the die-casting process parameters according to the real-time molten metal temperature; Get the real-time pouring times of the current casting; According to the real-time pouring times, determining whether to adjust the die-casting process parameters; At least one casting is die-casted using the die-casting process parameters.

2. The method according to claim 1, characterized in that The step of judging whether to adjust the die-casting process parameters according to the mold replacement detection result includes: When the mold replacement detection result is mold replacement, the die-casting process parameters are adjusted.

3. The method according to claim 1, characterized in that The step of determining whether to adjust the die-casting process parameters according to the interruption duration includes: Get multiple preset interruption duration ranges; In each of the interruption duration ranges, detecting the interruption duration range to which the interruption duration belongs; According to the interruption duration range to which the interruption duration belongs, it is determined whether to adjust the die-casting process parameters.

4. The method according to claim 1, characterized in that: The step of determining whether to adjust the die-casting process parameters according to the real-time molten metal temperature includes: Obtain the upper temperature limit and the lower temperature limit corresponding to the type of the current mold; Comparing the real-time molten metal temperature with the temperature upper limit value and the temperature lower limit value to obtain a temperature comparison result; According to the temperature comparison result, it is determined whether to adjust the die-casting process parameters.

5. The method according to claim 1, characterized in that The determining whether to adjust the die-casting process parameters according to the real-time pouring ladle number includes: Compare the real-time ladle number with the ladle number threshold to obtain a ladle comparison result; According to the ladle comparison result, it is determined whether to adjust the die-casting process parameters.

6. The method according to claim 1, characterized in that Also includes: When the die-casting machine is in mold closing mode, obtaining preset mold closing adjustment information; According to the mold clamping adjustment information, it is determined whether to adjust the die-casting process parameters.

7. The method according to claim 2, characterized in that: The step of adjusting the die-casting process parameters includes: Get the type of the current mold; Inputting the type of the current mold into a pre-trained process parameter generation model for processing, and outputting target process parameters; The die-casting process parameters are adjusted according to the target process parameters.

8. A die casting process parameter adjustment device, characterized in that: include: A mold type acquisition module is used to acquire the current mold of the die-casting process, detect whether the current mold has been replaced, and obtain a mold replacement detection result; A mold replacement adjustment module, used to determine whether to adjust the die-casting process parameters according to the mold replacement detection result; the die-casting process parameters include at least one of the following: the number of die-casting parts, the holding time, the cooling time and the cooling channel; The interruption duration acquisition module is used to obtain the interruption duration when the die casting interruption is detected; An interruption adjustment module, used for determining whether to adjust the die-casting process parameters according to the interruption duration; Metal temperature acquisition module, used to obtain real-time metal liquid temperature; A temperature adjustment module, used to determine whether to adjust the die-casting process parameters according to the real-time molten metal temperature; The ladle number acquisition module is used to obtain the real-time ladle number of the current casting; A ladle adjustment module, used to determine whether to adjust the die-casting process parameters according to the real-time ladle pouring times; The die-casting module is used to die-cast at least one casting using the die-casting process parameters.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the die-casting process parameter adjustment method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the die-casting process parameter adjustment method according to any one of claims 1 to 7 when executed.

Images