A method and system for automatically adjusting injection temperature based on injection mold

Abstract

The present invention discloses an automatic adjustment method and system for injection temperature based on an injection mold, the method comprising obtaining injection parameters of a plastic melt, the injection parameters comprising the initial temperature flowing out from an injection molding machine nozzle, the property parameters of the plastic melt, and the length of the pipe covered by the flow path before flowing into each molding chamber; inputting the injection parameters into a pre-trained temperature prediction model to predict the temperature change value of the plastic melt before entering the molding chamber through pipes of different lengths; determining the initial cooling temperature of each molding chamber, compensating the initial cooling temperature of different molding chambers according to each temperature change value, and obtaining the target cooling temperature of each molding chamber. The present invention can quickly calculate the temperature change value according to the material properties and the fluid movement path, thereby compensating the initial cooling temperature of each chamber and achieving temperature balance of each chamber. The quality of the finished mold is improved by accurately controlling the injection temperature of different chambers.

Description

Technical Field

[0001] The present invention relates to the technical field of injection molding control, and in particular to an automatic injection molding temperature adjustment method and system based on an injection mold. Background Art

[0002] A multi-cavity injection mold refers to an injection mold with multiple chambers, and the shape of each chamber can be the same or different. During injection molding, multiple molds can be produced at the same time by injecting into the chambers that are not connected at one time, which greatly improves production efficiency. However, at present, for the injection temperature control of multiple chambers, a unified control method is usually used to uniformly set the cooling temperature of different chambers, or a temperature sensor is used to monitor the temperature of the chamber in real time for temperature control, so as to achieve temperature balance of multiple chambers and ensure the quality of the finished product. However, this method has the following shortcomings: First, the corresponding temperature sensor is set for each chamber, which greatly increases the injection molding cost. Second, when controlling the temperature, some important factors that affect the temperature are not considered. Even if the temperature is monitored, the precise adjustment of the injection molding temperature cannot be guaranteed, which leads to poor quality of the injection molded product. Summary of the invention

[0003] In order to solve at least one of the above-mentioned technical problems, the present invention provides a method and system for automatically adjusting the injection temperature based on an injection mold.

[0004] In a first aspect, the present invention provides a method for automatically adjusting injection temperature based on an injection mold, the method comprising:

[0005] Acquire injection molding parameters of the plastic melt, wherein the injection molding parameters include the initial temperature of the plastic melt flowing out of the injection molding machine nozzle, the property parameters of the plastic melt, and the pipe length covered by the flow path before flowing into each molding cavity;

[0006] The injection molding parameters are input into a pre-trained temperature prediction model to predict the temperature change value of the plastic melt before it passes through pipes of different lengths and enters the molding chamber;

[0007] The initial cooling temperature of each molding chamber is determined, and the initial cooling temperatures of different molding chambers are compensated according to each temperature change value, so that each molding chamber reaches the target cooling temperature.

[0008] Preferably, determining the initial cooling temperature of each molding cavity includes:

[0009] Based on the structure of each molding chamber, the zone initial cooling temperature is set in different molding areas in each molding chamber;

[0010] The temperature drop value of the plastic melt when passing through different molding areas is determined, and the initial cooling temperature of each partition is compensated according to the temperature drop value.

[0011] Preferably, the method further comprises:

[0012] Detect the quality of the finished injection mold after the injection molding process is completed, including the degree of surface defects, deformation and internal stress of the finished injection mold;

[0013] It is determined whether the quality of the finished product of the injection mold meets the preset conditions. If not, the initial cooling temperature of each molding cavity is optimized.

[0014] Preferably, optimizing the initial cooling temperature of each molding chamber includes:

[0015] Construct the objective function:

[0016] F(T in ,T mo )=w1D(T in ,T mo )+w2(T in ,T mo )+w1S(T in ,T mo )

[0017] Identify the constraints:

[0018] T min ≤T in ,T mo ≤T max

[0019] D(T in ,T mo )≤D max

[0020] C(T in ,T mo )≤C max

[0021] S(T in ,T mo )≤S max

[0022] Where, T in 、T mo They represent the injection cooling temperature and mold temperature respectively. D, C, and S represent the surface defect degree, deformation degree, and internal stress of the finished injection mold respectively. max , C max , S max They represent the maximum acceptable level of surface defects, the maximum acceptable level of deformation, and the maximum acceptable level of internal stress, respectively. w1, w2, and w3 represent weights, respectively.

[0023] The genetic algorithm is used to solve the objective function and output the optimal initial cooling temperature of each molding cavity.

[0024] Preferably, solving the objective function by using a genetic algorithm comprises:

[0025] Randomly generate a set of initial temperature combinations (T in ,T mo ), as individuals of the first generation population;

[0026] Calculate the fitness of each individual according to the objective function, and move individuals with fitness higher than the preset value into the next generation;

[0027] Randomly select two individuals, exchange their temperature values, and generate new offspring;

[0028] Perform small-probability random mutation on a preset number of individuals to generate mutant individuals;

[0029] The number of iterations is set, and after the iterations are completed, the individual with the highest fitness is taken as the optimal solution, and the optimal initial cooling temperature is determined according to the optimal solution.

[0030] Preferably, the property parameters of the plastic melt include the specific heat capacity, thermal conductivity and rate of change of viscosity of the plastic material with temperature.

[0031] Preferably, the pre-trained temperature prediction model is obtained by TCN network training.

[0032] In a second aspect, the present invention further provides an automatic injection temperature adjustment system based on an injection mold, the system comprising:

[0033] A parameter acquisition module, used to acquire the injection molding parameters of the plastic melt, wherein the injection molding parameters include the initial temperature of the plastic melt flowing out of the injection molding machine nozzle, the property parameters of the plastic melt, and the pipe length covered by the flow path before flowing into each molding cavity;

[0034] A temperature prediction module is used to input the injection molding parameters into a pre-trained temperature prediction model to predict the temperature change value of the plastic melt before entering the molding chamber through pipes of different lengths;

[0035] The temperature compensation module is used to determine the initial cooling temperature of each molding chamber, and compensate the initial cooling temperature of different molding chambers according to each temperature change value, so that each molding chamber reaches the target cooling temperature.

[0036] In a third aspect, the present invention further provides an electronic device, comprising: a processor and a memory, wherein the memory is used to store computer program code, and the computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes the method for automatic adjustment of injection temperature based on an injection mold as described in the first aspect above and any possible implementation method thereof.

[0037] In a fourth aspect, the present invention further provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the method for automatic adjustment of injection temperature based on an injection mold as described in the first aspect and any possible implementation thereof.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention discloses an automatic adjustment method and system for injection molding temperature based on an injection mold, the method comprising obtaining injection molding parameters of a plastic melt, the injection molding parameters comprising the initial temperature flowing out from the nozzle of an injection molding machine, the property parameters of the plastic melt, and the length of the pipe covered by the flow path before flowing into each molding chamber; inputting the injection molding parameters into a pre-trained temperature prediction model to predict the temperature change value of the plastic melt before entering the molding chamber through pipes of different lengths; determining the initial cooling temperature of each molding chamber, compensating the initial cooling temperature of different molding chambers according to each temperature change value, and obtaining the target cooling temperature of each molding chamber. The present invention takes into account the influence of the property parameters of the plastic melt and the flow path on the temperature change, and can quickly predict the temperature change value of the fluid before entering the chamber in combination with the temperature prediction model, thereby compensating the initial cooling temperature of each chamber and achieving temperature balance of each chamber. Therefore, the present invention improves the quality of the finished mold by accurately controlling the injection molding temperature of different chambers.

[0040] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0042] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0043] Figure 1A schematic flow chart of a method for automatically adjusting injection temperature based on an injection mold provided by an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of the principle of a multi-cavity injection molding machine provided by an embodiment of the present invention;

[0045] Figure 3 A schematic diagram of the structure of an automatic injection temperature adjustment system based on an injection mold provided by an embodiment of the present invention;

[0046] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] 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 are within the scope of protection of the present invention.

[0048] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. 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 limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0049] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0050] At present, the injection temperature control of multiple chambers still has the problems of high cost and poor control accuracy, which in turn affects the quality of the finished product. To this end, the present invention provides an automatic injection temperature adjustment method based on an injection mold, which can consider the influence of the fluid movement path and the plastic's own properties, calculate the temperature change before the fluid enters the chamber, thereby compensating the temperature of each chamber, achieving balanced control of the injection temperature, and improving the quality of the mold product while ensuring precise control.

[0051] See also Figure 1 , Figure 1 A schematic diagram of a flow chart of an automatic injection temperature adjustment method based on an injection mold provided by an embodiment of the present invention. Figure 1 It can be seen that a method for automatically adjusting the injection temperature based on an injection mold comprises the following steps:

[0052] S10, obtaining injection molding parameters of the plastic melt, wherein the injection molding parameters include the initial temperature of the plastic melt flowing out of the injection molding machine nozzle, the property parameters of the plastic melt, and the pipe length covered by the flow path before flowing into each molding cavity;

[0053] S20, inputting the injection molding parameters into a pre-trained temperature prediction model to predict the temperature change value of the plastic melt before entering the molding chamber through pipes of different lengths;

[0054] S30, determining the initial cooling temperature of each molding chamber, and compensating the initial cooling temperatures of different molding chambers according to each temperature change value, so that each molding chamber reaches the target cooling temperature.

[0055] See also Figure 2 , Figure 2 Provides a top view of the flow principle of plastic melt during injection molding. Figure 2 It can be seen that the multi-chamber injection molding machine contains multiple molding chambers. After the plastic melt is ejected from the injection nozzle, it enters each cavity through different pipes for cooling and molding, and each pipe and chamber together constitute a cooling system.

[0056] It is understandable that for the same batch of injection molds, the molding cooling temperature should be the same, and the temperature control will directly affect the quality of the finished product. For example, too high or too low temperature will reduce the quality of the finished product, including:

[0057] 1) The impact of too high injection temperature:

[0058] Mold sticking phenomenon: If the mold temperature is too high, the cooling and solidification speed of the plastic will slow down, causing the plastic part to still adhere tightly to the mold after cooling, making it difficult to demold and may even damage the surface of the plastic part.

[0059] Surface defects: Excessively high temperatures may cause plastic to accumulate on the mold surface, forming bright spots or burnt marks, affecting the appearance quality of the product.

[0060] Poor dimensional stability: High temperature makes the plastic too fluid, which may cause overfilling, causing the size of the plastic part to exceed the tolerance range and affect dimensional stability.

[0061] Material degradation: Long-term high temperature may cause the plastic molecular chain to break, resulting in a decrease in the physical properties of the material, such as reduced strength and toughness.

[0062] Extended cooling time: It takes longer to cool the plastic parts, affecting production efficiency.

[0063] 2) The impact of too low injection temperature:

[0064] Reduced fluidity: At low temperatures, the melt viscosity of the plastic increases and the fluidity deteriorates, which may lead to under-injection (insufficient filling), especially in complex or thin-walled areas.

[0065] Poor surface finish: For some plastics such as ABS, too low mold temperature will directly affect the crystallinity of the plastic, resulting in a rough and matte surface.

[0066] Increased internal stress: Due to the fast cooling speed, the cooling rates of the inner and outer layers of the plastic part are inconsistent, which can easily generate large internal stress and cause deformation or cracking of the plastic part.

[0067] Difficult demoulding: Although in theory low mold temperature should reduce mold sticking, in fact, if the mold is cooled too quickly, the plastic part will shrink and hold the mold tightly, which will also cause demoulding difficulties, especially in areas with complex structures or textures.

[0068] Uneven filling: Low temperature affects the fluidity and filling capacity of plastics, which may cause voids or uneven filling inside the product, affecting structural strength and performance.

[0069] Therefore, in order to ensure the quality of the finished product, the injection temperature must be accurately controlled. However, the current common practice is to set up a corresponding temperature monitoring module in each chamber. Figure 2 For example, corresponding sensors will be set in chambers A, B, and C to monitor the temperature of the chambers. If the ideal injection molding temperature is determined in advance, the temperature of each chamber will change dynamically even during the injection molding process. It can be adjusted accordingly through real-time monitoring. For example, when it is found that the temperature of chambers B and C is lower than that of chamber A, the temperature of chambers B and C will be adjusted in time to ensure that the temperatures of the three chambers are unified.

[0070] However, the above approach usually has the following problems:

[0071] First, the injection molding cost is increased. Figure 2 The three-chamber injection molding machine is only provided as an example. In actual application, more chambers may be involved, and even dozens of molds may be prepared at one time. In this case, corresponding temperature sensors must be configured, which obviously greatly increases the cost.

[0072] Second, since the accuracy of temperature control is critical to the quality of the finished product, general temperature sensors with large errors and low accuracy are difficult to apply to the injection molding scenario. Therefore, relatively high-precision temperature monitoring equipment must be configured, which undoubtedly increases the temperature control cost.

[0073] Third, the injection molding process is actually a cooling molding process, and its temperature changes relatively quickly. For temperature sensors, working in a high temperature environment for a long time, coupled with frequent temperature changes and pressure shocks during the injection molding process, may accelerate the aging process of the sensor, and mechanical vibration and shock can easily cause physical damage to the sensor. The use environment around the injection molding machine is relatively harsh, and the sensor is prone to accumulate dust and plastic residues, which in turn affects the progress. It is also easy for the sensor's wires and connecting parts to gradually deteriorate due to thermal cycles and mechanical stress, resulting in disconnection or poor contact. In this way, with the increase in the number of injection molding processes, the accuracy of temperature monitoring through sensors will be greatly reduced, and damage to the equipment and maintenance and cleaning will also increase the operation and maintenance costs.

[0074] Therefore, in order to solve the problems existing in the existing methods, the method of the embodiment of the present invention obtains the injection molding parameters of the plastic melt and inputs them into the pre-trained temperature prediction model, and then the temperature change of the fluid before entering the chamber at different positions can be quickly obtained. The injection molding parameters include the initial temperature of the plastic melt flowing out of the injection molding machine nozzle, the property parameters of the plastic melt, and the injection molding pipeline structure parameters.

[0075] In a preferred embodiment, the property parameters of the plastic melt include the specific heat capacity, thermal conductivity and viscosity change rate of the plastic material with temperature. These property parameters are considered because they affect the temperature sensitivity of the plastic material: the specific heat capacity is the internal energy absorbed or released when the unit mass of the object changes the unit temperature. Due to the difference in specific heat capacity, the heat conduction and convection heat exchange formed between the plastic melt and the pipe wall will be affected, so that the temperature change value of the plastic melt will be different over time or when the flow path becomes longer. The thermal conductivity usually increases with the increase of temperature, which means that at high temperatures, the heat transfer efficiency of the material is higher, which may cause the unevenness of the temperature distribution, thereby increasing the temperature sensitivity. With the increase of temperature, the viscosity of most fluids will decrease, which means that the fluidity of the fluid at high temperatures is enhanced, which may lead to the acceleration of heat transfer and further increase the temperature sensitivity. Therefore, these three material properties will affect the temperature value before entering the molding chamber within the same injection time.

[0076] As for the pipeline structure, in the same injection molding machine, the diameter, pipeline wall thickness, and pipeline material of each pipeline from the nozzle to the molding chamber are usually the same, and the only difference is the length of the pipeline, and the difference in length also determines the distance of injection molding of each chamber. Therefore, the flow path factor is mainly considered in this embodiment, that is, the pipeline length covered by the flow path before the plastic melt flows into each molding chamber.

[0077] Since there are many variables involved, in order to quickly predict the temperature change, this embodiment trains a temperature prediction model based on the neural network model according to the historical injection molding data. When applied, it is only necessary to input the initial temperature of the injection molding machine nozzle, the property parameters of the plastic melt and the pipe length covered by the flow path before flowing into each molding chamber into the temperature prediction model. The temperature change value of the plastic melt before entering the molding chamber through pipes of different lengths can be quickly obtained. For example, the longer the pipe through which the same material passes, the more likely the temperature will drop. At this time, it is necessary to adjust the temperature adaptability of each molding chamber to adapt to the temperature value before the plastic melt enters the chamber to ensure the temperature balance of each chamber.

[0078] Furthermore, when the temperature change value is obtained, it is necessary to measure the initial cooling temperature of each molding chamber. Since the temperatures of different chambers are usually set uniformly at the beginning, and the fluid reaches the chamber at different times due to different positions as the fluid moves, it is necessary to obtain the initial cooling temperature of the chamber before entering the chamber. Figure 2 For example, the fluid first moves to chamber A, then chamber B, and chamber C. Therefore, when the fluid reaches these three chambers, the time taken becomes longer successively, and the initial cooling temperature should also be reduced in the order of chambers A, B, and C. When the fluid reaches chambers A, B, and C, its own temperature should also be reduced in sequence, that is, the temperature change value is the smallest when it flows to chamber A, and the temperature change value is the largest when it flows to the farthest chamber C. At this time, in order to ensure the use of a unified molding injection temperature, different initial cooling temperatures of different molding chambers are compensated for by different temperature change values, so that the molding temperatures of each chamber during the final molding of the material are consistent, thereby ensuring the quality of the finished product.

[0079] In summary, this embodiment takes into account the influence of the property parameters of the plastic melt and the flow path on the temperature change, and can quickly predict the temperature change value of the fluid before entering the cavity in combination with the temperature prediction model, so as to compensate for the initial cooling temperature of each cavity and achieve temperature balance in each cavity. By accurately controlling the injection temperature of different cavities, the quality of the finished mold is improved.

[0080] In some embodiments, the pre-trained temperature prediction model is obtained by TCN network training.

[0081] It should be noted that the TCN network, i.e., the temporal convolutional network, includes two residual blocks, each of which includes four sub-residual modules; each sub-residual module includes a causal hole convolution layer, a gated dilated convolution layer, a normalization layer, a PReLu activation layer, and a Dropout layer connected in sequence; wherein, the Dropout layer of each sub-residual module is connected to the causal hole convolution layer of another sub-residual module. For the entire residual block, if the number of input channels is different from the number of output channels of the dilated causal convolution (the number of filters of the second dilated convolution), there is an optional 1x1 convolution when connecting the head and tail.

[0082] Specifically, the convolution kernel size is 1*3, the stride is (1,1), and the number of convolution kernels is 32; after convolution, a normalization layer is connected to accelerate training, improve the generalization ability of the model and have a certain regularization effect; then a PReLu activation layer is connected, which has stronger generalization ability, better sparsity and parameter sharing ability than the ReLu layer. Then, the output of the first sub-residual module is used as the input of the second sub-residual module, the output of the second sub-residual module is used as the input of the third sub-residual module, and the output of the third sub-residual module is used as the input of the fourth sub-residual module. Each sub-residual module adopts a basically the same structure. The number of convolution kernels in the convolution layer is 64, the stride is (1,2), and the kernel size is 2*5, which is used to encode and extract more information.

[0083] This embodiment uses the TCN network to process time series data in parallel without a loop structure, thereby reducing training time. Compared with the traditional recurrent neural network RNN, the TCN network does not have the problem of gradient disappearance / explosion and difficulty in capturing long-term dependencies. In addition, by introducing gated dilated convolution in the TCN network, it can be used to adaptively select historical information that needs to be retained or forgotten at the current moment. At the same time, the use of residual structure can further improve the training efficiency and accuracy of the model. That is, by using a large amount of historical injection molding data as training samples, combined with the TCN network, a temperature prediction model can be trained that can quickly and accurately predict the temperature change value of the plastic melt as it flows.

[0084] In some embodiments, determining the initial cooling temperature of each molding cavity includes:

[0085] Based on the structure of each molding chamber, the zone initial cooling temperature is set in different molding areas in each molding chamber;

[0086] The temperature drop value of the plastic melt when passing through different molding areas is determined, and the initial cooling temperature of each partition is compensated according to the temperature drop value.

[0087] Simple molds usually use a uniform temperature chamber during molding. However, for complex molds, if the molding temperature in a chamber is the same, the quality of the finished product may be poor. For example, the thicker part in the middle of the mold usually cools slower than other thinner parts. In order to ensure the molding quality, ensure that the material can be fully filled and avoid insufficient filling or internal voids caused by premature cooling, the mold molding temperature should be controlled by zone, such as Figure 2 In the figure, each chamber is divided into three parts: 1, 2, and 3. Assuming that the thickness of part 2 is the smallest, the temperature required for part 2 should be higher than that of parts 1 and 3. That is, the temperature of chamber 2 should be appropriately increased in the thick-walled area to promote material flow and provide enough time for uniform cooling and crystallization.

[0088] Therefore, in this embodiment, the initial cooling temperatures of different partitions can be determined first. These temperature values ​​are usually different and will be adjusted according to the mold molding requirements and the structure itself. Then, the temperature drop value of the plastic melt when passing through different molding areas is determined. The initial cooling temperature of the partition is compensated based on this value, and the final molding temperature of different partitions of the chamber should be determined to meet the finished product quality requirements of complex molds.

[0089] In some embodiments, the method further comprises:

[0090] Detect the quality of the finished injection mold after the injection molding process is completed, including the degree of surface defects, deformation and internal stress of the finished injection mold;

[0091] It is determined whether the quality of the finished product of the injection mold meets the preset conditions. If not, the initial cooling temperature of each molding cavity is optimized.

[0092] The setting of the initial cooling temperature of the chamber is usually based on historical injection molding experience and the properties of the material itself. In this embodiment, in order to accurately control this temperature value, the quality of the finished mold product obtained at the initial temperature will be tested. If the quality of the finished product meets the preset conditions, such as the degree of surface defects is less than 5%, etc., then it is considered that the use of this initial cooling temperature can ensure the production quality requirements. If not, it is necessary to optimize the initial cooling temperature of each molding chamber to find the optimal initial cooling temperature of the chamber.

[0093] In a preferred embodiment, optimizing the initial cooling temperature of each molding cavity comprises:

[0094] Construct the objective function:

[0095] F(T in ,T mo )=w1D(T in ,T mo)+w2(T in ,T mo )+w1S(T in ,T mo )

[0096] Identify the constraints:

[0097] T min ≤T in ,T mo ≤T max

[0098] D(T in ,T mo )≤D max

[0099] C(T in ,T mi )≤C max

[0100] S(T in ,T mo )≤S max

[0101] Where, T in 、T mo They represent the injection cooling temperature and mold temperature respectively. D, C, and S represent the surface defect degree, deformation degree, and internal stress of the finished injection mold respectively. max , C max , S max They represent the maximum acceptable level of surface defects, the maximum acceptable level of deformation, and the maximum acceptable level of internal stress, respectively. w1, w2, and w3 represent weights, respectively.

[0102] The genetic algorithm is used to solve the objective function and output the optimal initial cooling temperature of each molding cavity.

[0103] Furthermore, the use of a genetic algorithm to solve the objective function includes:

[0104] Randomly generate a set of initial temperature combinations (T in ,T mo ), as individuals of the first generation population;

[0105] Calculate the fitness of each individual according to the objective function, and move individuals with fitness higher than the preset value into the next generation;

[0106] Randomly select two individuals, exchange their temperature values, and generate new offspring;

[0107] Perform small-probability random mutation on a preset number of individuals to generate mutant individuals;

[0108] The number of iterations is set, and after the iterations are completed, the individual with the highest fitness is taken as the optimal solution, and the optimal initial cooling temperature is determined according to the optimal solution.

[0109] Therefore, this embodiment establishes an optimization model, takes the acceptable degree of surface defects, deformation and minimum internal stress as the objective function, and optimizes parameters through genetic algorithm, and finally determines the optimal initial cooling temperature, thereby further improving the quality of the finished product.

[0110] See also Figure 3 In some embodiments, the present invention further provides an automatic injection temperature adjustment system based on an injection mold, the system comprising:

[0111] The parameter acquisition module 100 is used to acquire the injection molding parameters of the plastic melt, wherein the injection molding parameters include the initial temperature of the plastic melt flowing out of the injection molding machine nozzle, the property parameters of the plastic melt, and the injection molding pipeline structure parameters; the injection molding pipeline structure parameters include the pipeline length, pipeline thickness, and pipeline diameter covered by the flow path before flowing into each molding chamber;

[0112] The temperature prediction module 200 is used to input the injection molding parameters into a pre-trained temperature prediction model to predict the temperature change value of the plastic melt before entering the molding chamber through pipes of different lengths;

[0113] The temperature compensation module 300 is used to determine the initial cooling temperature of each molding chamber, and compensate the initial cooling temperature of different molding chambers according to each temperature change value, so that each molding chamber reaches the target cooling temperature.

[0114] It can be understood that the functions or modules included in the system provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0115] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the method for automatic adjustment of injection temperature based on an injection mold as described in any of the above embodiments.

[0116] See also Figure 4 In some embodiments, the present invention also provides a hardware structure diagram of an electronic device. Figure 4 As shown, the electronic device 2 includes:

[0117] Processor 21, memory 22, input device 23, output device 24. The processor 21, memory 22, input device 23 and output device 24 are coupled via a connector, and the connector includes various interfaces, transmission lines or buses, etc., which are not limited in this embodiment. It should be understood that coupling refers to mutual connection in a specific manner, including direct connection or indirect connection through other devices, for example, through various interfaces, transmission lines, buses, etc.

[0118] The processor 21 may be one or more graphics processing units (GPUs). When the processor 21 is a GPU, the GPU may be a single-core GPU or a multi-core GPU. Optionally, the processor 21 may be a processor group consisting of multiple GPUs, and the multiple processors are coupled to each other via one or more buses. Optionally, the processor may also be other types of processors, etc., which are not limited in the embodiments of the present invention.

[0119] The memory 22 can be used to store computer program instructions and various computer program codes including program codes for executing the scheme of the present invention. Optionally, the memory includes but is not limited to random access memory (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM), or portable read only memory (CD-ROM), which is used for related instructions and data.

[0120] The input device 23 is used to input data and / or signals, and the output device 24 is used to output data and / or signals. The input device 23 and the output device 24 can be independent devices or an integrated device.

[0121] The memory 22 can be used not only to store relevant instructions, but this embodiment does not limit the specific data stored in the memory.

[0122] Understandably, Figure 4 Only a simplified design of an electronic device is shown. In practical applications, the electronic device may also include other necessary components, including but not limited to any number of input / output devices, processors, memories, etc., and all video analysis devices that can implement the embodiments of the present invention are within the protection scope of the present invention.

[0123] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0124] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

[0125] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0126] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0127] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0128] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0129] A person skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by a computer program to instruct the relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. The aforementioned storage medium includes: a read-only memory (ROM) or a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

Claims

1. A method for automatically adjusting injection temperature based on an injection mold, characterized in that: The method comprises: Obtaining injection molding parameters of the plastic melt, wherein the injection molding parameters include the initial temperature of the plastic melt flowing out of the injection molding machine nozzle, the property parameters of the plastic melt, and the length of the pipe covered by the flow path before flowing into each molding cavity; the property parameters of the plastic melt include the specific heat capacity, thermal conductivity and viscosity change rate of the plastic material with temperature; The injection molding parameters are input into a pre-trained temperature prediction model to predict the temperature change value of the plastic melt before entering the molding chamber through pipes of different lengths; Determine the initial cooling temperature of each molding chamber, and compensate the initial cooling temperature of different molding chambers according to each temperature change value, so that each molding chamber reaches the target cooling temperature; Determining the initial cooling temperature of each molding cavity includes: Based on the structure of each molding chamber, the zone initial cooling temperature is set in different molding areas in each molding chamber; The temperature drop value of the plastic melt when passing through different molding areas is determined, and the initial cooling temperature of each partition is compensated according to the temperature drop value.

2. The method for automatically adjusting the injection temperature based on the injection mold according to claim 1, characterized in that: The method further comprises: Detect the quality of the finished injection mold after the injection molding process is completed, including the degree of surface defects, deformation and internal stress of the finished injection mold; It is determined whether the quality of the finished product of the injection mold meets the preset conditions. If not, the initial cooling temperature of each molding cavity is optimized.

3. The method for automatically adjusting the injection temperature based on the injection mold according to claim 2, characterized in that: Optimizing the initial cooling temperature of each molding cavity includes: Construct the objective function: ； Identify the constraints: ； ； ； ； In the formula, , Respectively represent the injection cooling temperature and mold temperature, They respectively indicate the degree of surface defects, deformation and internal stress of the finished injection mold. , , They represent the maximum acceptable level of surface defects, deformation and internal stress respectively. Respectively represent weights; The genetic algorithm is used to solve the objective function and output the optimal initial cooling temperature of each molding cavity.

4. The method for automatically adjusting the injection temperature based on the injection mold according to claim 3, characterized in that: The method of solving the objective function by using a genetic algorithm comprises: Randomly generate a set of initial temperature combinations , as individuals of the first generation population; Calculate the fitness of each individual according to the objective function, and move individuals with fitness higher than the preset value into the next generation; Randomly select two individuals, exchange their temperature values, and generate new offspring; Perform small-probability random mutation on a preset number of individuals to generate mutant individuals; The number of iterations is set, and after the iterations are completed, the individual with the highest fitness is taken as the optimal solution, and the optimal initial cooling temperature is determined according to the optimal solution.

5. The method for automatically adjusting the injection temperature based on the injection mold according to claim 1, characterized in that: The pre-trained temperature prediction model is obtained by TCN network training.

6. An automatic injection temperature adjustment system based on an injection mold, characterized in that: The system comprises: A parameter acquisition module, used to acquire the injection molding parameters of the plastic melt, wherein the injection molding parameters include the initial temperature of the plastic melt flowing out of the injection molding machine nozzle, the property parameters of the plastic melt, and the pipe length covered by the flow path before flowing into each molding chamber; the property parameters of the plastic melt include the specific heat capacity, thermal conductivity and viscosity change rate of the plastic material with temperature; A temperature prediction module is used to input the injection molding parameters into a pre-trained temperature prediction model to predict the temperature change value of the plastic melt before entering the molding chamber through pipes of different lengths; A temperature compensation module is used to determine the initial cooling temperature of each molding chamber, and to compensate the initial cooling temperature of different molding chambers according to each temperature change value, so that each molding chamber reaches the target cooling temperature; Determining the initial cooling temperature of each molding cavity includes: Based on the structure of each molding chamber, the zone initial cooling temperature is set in different molding areas in each molding chamber; The temperature drop value of the plastic melt when passing through different molding areas is determined, and the initial cooling temperature of each partition is compensated according to the temperature drop value.

7. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and when the processor executes the computer instructions, the electronic device executes the method for automatically adjusting the injection temperature based on the injection mold as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the method for automatically adjusting the injection temperature based on an injection mold according to any one of claims 1 to 5.

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