3D printing mold cooling method and device based on dynamic visual feedback

Abstract

The application provides a 3D printing mold cooling method and device based on dynamic visual feedback, which comprises the following steps: determining initial cooling conditions in a cooling process; preliminarily cooling a 3D printing mold in a printing process based on the initial cooling conditions; performing feature analysis on a first mold surface image collected by a visual camera to obtain a first image analysis result; if the surface temperature does not reach a preset target surface temperature, adjusting the initial cooling conditions based on the first image analysis result and the preset target surface temperature to obtain optimized cooling conditions, and performing re-cooling based on the optimized cooling conditions; collecting a second mold surface image of the 3D printing mold in the re-cooling process based on the visual camera, and adjusting the optimized cooling conditions based on a second image analysis result of the second mold surface image until the surface temperature is cooled to the preset target surface temperature. The application improves the production quality and production efficiency of the mold.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a 3D printing mold cooling method and device based on dynamic visual feedback. BACKGROUND

[0002] In the process of manufacturing 3D printing mold, the cooling step is crucial to ensure the quality and production efficiency of the mold. The existing 3D printing mold cooling method is usually to set fixed cooling pipes around the mold, and use circulating cooling liquid to take away the heat of the mold to achieve cooling.

[0003] However, the uniformity of cooling of the existing 3D printing mold cooling method is difficult to guarantee. Because the shape of the mold is often complex and diverse, the fixed position of the cooling pipe may be too far from the surface of the mold in some parts, resulting in low cooling efficiency, while in some parts, stress concentration may be caused by overcooling, affecting the overall performance and service life of the mold. This uneven cooling problem is particularly prominent when printing large and complex structures of the mold, not only affecting the production quality of the mold, but also greatly limiting the application of 3D printing mold in high-end manufacturing field, affecting the production efficiency of the mold. SUMMARY

[0004] The present application provides a 3D printing mold cooling method and device based on dynamic visual feedback to improve the production quality and production efficiency of the mold.

[0005] In the first aspect, the present application provides a 3D printing mold cooling method based on dynamic visual feedback, which uniformly arranges a plurality of visual camera devices around the mold printing position in the printing working area of the 3D printing equipment; the 3D printing mold cooling method based on dynamic visual feedback comprises:

[0006] Based on the parameter information obtained from the design drawing of the 3D printing mold and the thermal physical properties of the 3D printing material, the initial cooling condition of the 3D printing mold in the cooling process is determined;

[0007] In response to the printing start instruction of the 3D printing mold, the 3D printing mold in the printing process is preliminarily cooled based on the initial cooling condition;

[0008] Based on the visual camera device, the first mold surface image of the 3D printing mold in the preliminary cooling process is collected, and the first mold surface image is analyzed to obtain the first image analysis result;

[0009] if it is determined, based on the first image analysis result, that the surface temperature of the 3D printing mold does not reach the preset target surface temperature, adjusting the initial cooling condition based on the first image analysis result and the preset target surface temperature to obtain an optimized cooling condition, and re-cooling the 3D printing mold in the printing process based on the optimized cooling condition;

[0010] collecting, by the visual camera device, a second mold surface image of the 3D printing mold in the re-cooling process, and adjusting the optimized cooling condition according to a second image analysis result of the second mold surface image until the surface temperature of the 3D printing mold is cooled to the preset target surface temperature.

[0011] In a second aspect, the present application further provides a 3D printing mold cooling device based on dynamic visual feedback, which is applied to the 3D printing mold cooling method based on dynamic visual feedback as described in the first aspect. A plurality of visual camera devices are uniformly arranged around the mold printing position in the printing working area of the 3D printing device. The 3D printing mold cooling device based on dynamic visual feedback comprises:

[0012] An initialization module is configured to determine the initial cooling condition of the 3D printing mold in the cooling process based on the parameter information obtained from the design drawing of the 3D printing mold and the thermal physical properties of the 3D printing material.

[0013] A mold preliminary cooling module is configured to respond to the printing start instruction of the 3D printing mold, and perform preliminary cooling on the 3D printing mold in the printing process based on the initial cooling condition.

[0014] A dynamic visual feedback module is configured to collect, by the visual camera device, a first mold surface image of the 3D printing mold in the preliminary cooling process, and perform feature analysis on the first mold surface image to obtain a first image analysis result.

[0015] A mold re-cooling module is configured to, if it is determined, based on the first image analysis result, that the surface temperature of the 3D printing mold does not reach the preset target surface temperature, adjust the initial cooling condition based on the first image analysis result and the preset target surface temperature to obtain an optimized cooling condition, and re-cool the 3D printing mold in the printing process based on the optimized cooling condition.

[0016] A dynamic feedback cooling module is configured to collect, by the visual camera device, a second mold surface image of the 3D printing mold in the re-cooling process, and adjust the optimized cooling condition according to a second image analysis result of the second mold surface image until the surface temperature of the 3D printing mold is cooled to the preset target surface temperature.

[0017] In a third aspect, the present application also provides an electronic device, comprising: a memory for storing a computer software program; and a processor for reading and executing the computer software program, thereby implementing the 3D printing mold cooling method based on dynamic visual feedback according to any one of the above aspects.

[0018] In a fourth aspect, the present application also provides a non-transitory computer readable storage medium, wherein the storage medium stores a computer software program, and the computer software program, when executed by a processor, implements the 3D printing mold cooling method based on dynamic visual feedback according to any one of the above aspects.

[0019] In a fifth aspect, the present application provides a computer program product comprising a computer program, and the computer program, when executed by a processor, implements the 3D printing mold cooling method based on dynamic visual feedback according to any one of the above aspects.

[0020] The 3D printing mold cooling method based on dynamic visual feedback provided by the embodiments of the present application can realize real-time acquisition of the mold surface image of the 3D printing mold through the visual camera device, so that the temperature distribution of the mold surface of the 3D printing mold can be intuitively obtained regardless of the complexity of the structure of the 3D printing mold. The optimized cooling conditions of each part of the mold surface of the 3D printing mold can be dynamically generated through the image analysis result of the mold surface image in combination with the preset target surface temperature, so that each part of the mold surface of the 3D printing mold can be uniformly cooled, the disadvantage of uneven cooling is effectively solved, the overall performance and service life are improved, and at the same time, the process is continuously monitored and optimized to ensure that the temperature of each part of the mold surface is always in a suitable range until the cooling is to the preset target surface temperature, the problems of excessive or insufficient cooling are avoided, and the cooling efficiency is improved. Therefore, the embodiments of the present application improve the mold quality and production efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 is a flow chart of the 3D printing mold cooling method based on dynamic visual feedback provided by the embodiments of the present application;

[0022] Figure 2 is a structural diagram of the 3D printing mold cooling device based on dynamic visual feedback provided by the embodiments of the present application;

[0023] Figure 3 is an embodiment diagram of the electronic device provided by the embodiments of the present application;

[0024] Figure 4 is an embodiment diagram of the computer readable storage medium provided by the embodiments of the present application. DETAILED DESCRIPTION

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0027] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0028] Optional, see below Figure 1 As shown, Figure 1 This is a flowchart of a 3D printing mold cooling method based on dynamic visual feedback provided by the present invention. In this embodiment, the main body executing the 3D printing mold cooling method based on dynamic visual feedback is a mold cooling device. Optionally, in this embodiment, multiple visual cameras are evenly arranged around the mold printing position within the printing working area of ​​the 3D printing equipment. Therefore, the 3D printing mold cooling method based on dynamic visual feedback includes:

[0029] Step 10: Based on the parameter information obtained from the design drawings of the 3D printed mold and combined with the thermophysical properties of the 3D printed material, determine the initial cooling conditions of the 3D printed mold during the cooling process.

[0030] Optionally, the mold cooling device may pre-store the design drawings of the 3D printed mold and the thermophysical properties of various 3D printing materials, or, during 3D printing, the technician may output the design drawings of the 3D printed mold and the thermophysical properties of various 3D printing materials to the mold cooling device.

[0031] Therefore, the mold cooling device analyzes the design drawing of the 3D printing mold to obtain parameter information of the 3D printing mold, wherein the parameter information includes size, shape, etc. of the 3D printing mold. At the same time, the thermal physical properties of the 3D printing material used are obtained, including thermal conductivity, specific heat capacity, thermal diffusivity, etc.

[0032] Further, the mold cooling device determines the initial cooling condition of the 3D printing mold at the beginning of the cooling process according to the parameter information of the 3D printing mold combined with the thermal physical properties of the 3D printing material, specifically as described in steps 101 to 104. The initial cooling condition includes the initial cooling temperature, the initial cooling flow rate, the initial cooling flow rate, etc. of the cooling liquid in the cooling system.

[0033] Step 20, in response to the printing start instruction of the 3D printing mold, the 3D printing mold in the printing process is preliminarily cooled based on the initial cooling condition.

[0034] Further, the mold cooling device responds to the printing start instruction of the 3D printing mold, and starts the cooling system according to the initial cooling condition. The cooling medium in the cooling system starts to preliminarily cool the 3D printing mold in the printing process according to the initial cooling condition, and takes away the heat generated by the 3D printing mold in the printing process, preventing the 3D printing mold from deforming or affecting the printing quality due to overheating. During the cooling process, the mold surface temperature distribution prediction model is established as:

[0035]

[0036] Wherein, T represents the mold surface temperature, t represents time, x and y represent the coordinates of the mold surface, a mod represents the corrected thermal diffusivity, h represents the convective heat transfer coefficient, k mod represents the corrected material thermal conductivity, T coolant0 represents the initial cooling temperature.

[0037] Step 30, based on the visual camera device, a first mold surface image of the 3D printing mold in the preliminary cooling process is collected, and a feature analysis is performed on the first mold surface image to obtain a first image analysis result.

[0038] Further, the mold cooling device calls the visual camera equipment to collect images of the 3D printing mold in the preliminary cooling process, and obtains a first mold surface image of the 3D printing mold in the preliminary cooling process. After collecting the image, the mold cooling device analyzes the features of the first mold surface image by using the built-in image analysis algorithm, identifies the features in the first mold surface image, such as the color distribution features and texture features of the mold surface, and infers the temperature distribution of the mold surface of the 3D printing mold through the features in the first mold surface image, to obtain a first image analysis result, which is specifically described in steps 301 to 304.

[0039] In an embodiment, the visual camera equipment takes a picture of the mold surface of the 3D printing mold being preliminarily cooled every 1 minute. At the 5th minute after the printing starts, a first mold surface image is collected. The mold cooling device processes the image by using the image analysis algorithm based on the principle of thermal radiation. By analyzing the color brightness of different areas in the image, and according to the corresponding relationship between the color and the temperature, it is identified that the temperature of most areas of the mold surface is between 50-60 degrees Celsius, but the temperature of a thin-walled area reaches 70 degrees Celsius, to obtain a first image analysis result.

[0040] Step 40, if it is determined based on the first image analysis result that the surface temperature of the 3D printing mold does not reach the preset target surface temperature, the initial cooling condition is adjusted based on the first image analysis result and the preset target surface temperature, to obtain an optimized cooling condition, and the 3D printing mold in the printing process is cooled again based on the optimized cooling condition.

[0041] Further, the mold cooling device determines whether the surface temperature of the 3D printing mold reaches the preset target surface temperature according to the first image analysis result, wherein the preset target surface temperature is set by the technician in the mold cooling device in advance. If the surface temperature of the 3D printing mold does not reach the preset target surface temperature, the mold cooling device optimizes the initial cooling condition by adjusting the temperature, flow rate, flow volume and other parameters of the cooling medium in combination with the temperature distribution in the first image analysis result and the preset target surface temperature, to obtain an optimized cooling condition, which is specifically described in steps 401 to 404.

[0042] Further, the mold cooling device starts the cooling system again to cool the 3D printing mold in the printing process by using the optimized cooling condition.

[0043] In an embodiment, the preset target surface temperature is 40 degrees Celsius, and the mold surface temperature of the 3D printing mold is determined to be 48 degrees Celsius according to the first image analysis result. At this time, the mold surface temperature of the 3D printing mold is greater than the preset target surface temperature, and therefore, the mold cooling device adjusts the algorithm, such as reducing the temperature of the cooling medium water to 18 degrees Celsius, increasing the flow rate to 0.6 cubic meters / hour, and increasing the flow to 6 liters / minute, so as to obtain the optimized cooling condition. Then, the cooling system is started again to cool the mold according to the optimized cooling condition, and the mold temperature change is continuously monitored in the next 5 minutes.

[0044] In step 50, the second mold surface image of the 3D printing mold during the re-cooling process is collected based on the visual camera equipment, and the optimized cooling condition is adjusted according to the second image analysis result of the second mold surface image until the surface temperature of the 3D printing mold is cooled to the preset target surface temperature.

[0045] Further, during the re-cooling process, the mold cooling device calls the visual camera equipment to continue collecting the second mold surface image of the 3D printing mold, and analyzes the second mold surface image to obtain the second image analysis result. Further, the mold cooling device judges again whether the mold surface temperature reaches the preset target surface temperature according to the second image analysis result. If it still does not reach, the optimized cooling condition is adjusted, such as further changing the cooling medium parameters, and then the cooling is performed again, so as to cycle until the surface temperature of the 3D printing mold is cooled to the preset target surface temperature.

[0046] Continuing the above embodiment, after the 3D printing mold is cooled again for 3 minutes according to the optimized cooling condition, the visual camera equipment collects the second mold surface image. After analysis, the mold surface temperature is overall decreased to 43 degrees Celsius, but still does not reach the target temperature of 40 degrees Celsius. The mold cooling device further reduces the temperature of the cooling medium water to 16 degrees Celsius, and increases the flow rate to 0.7 cubic meters / hour, and then adjusts the cooling condition again to continue cooling. After multiple times of image collection, analysis and cooling condition adjustment, finally at the 20th minute after the printing starts, the mold surface temperature is successfully cooled to the preset target surface temperature of 40 degrees Celsius.

[0047] The embodiment of the present application can intuitively obtain the temperature distribution of the mold surface of the 3D printing mold through the visual camera equipment to collect the mold surface image of the 3D printing mold in real time, regardless of the complexity of the structure of the 3D printing mold. Through the image analysis result of the mold surface image combined with the preset target surface temperature, the optimized cooling conditions of each part of the mold surface of the 3D printing mold can be dynamically generated, so that each part of the mold surface of the 3D printing mold can be uniformly cooled. The disadvantage of uneven cooling is effectively solved, the overall performance and service life are improved, and at the same time, the process is continuously monitored and optimized to ensure that the temperature of each part of the mold surface is always in a suitable range until it is cooled to the preset target surface temperature, avoiding the problem of excessive or insufficient cooling, improving the cooling efficiency, and thus improving the mold quality and production efficiency.

[0048] In an embodiment, steps 101 to 104 are described as follows:

[0049] Step 101, based on the thermal physical properties of the 3D printing material, the original thermal diffusivity of the 3D printing material is corrected to obtain the corrected thermal diffusivity of the 3D printing material.

[0050] Optionally, the mold cooling device obtains the mold internal gap volume and the mold total volume of the 3D printing mold, and considers the influence of the complex internal structure of the 3D printing mold on heat conduction, and obtains the structure influence factor according to the complexity of the mold structure of the 3D printing mold combined with expert experience.

[0051] Further, the mold cooling device corrects the original thermal diffusivity of the 3D printing material according to the thermal physical properties of the 3D printing material combined with the mold internal gap volume, the mold total volume and the structure influence factor of the 3D printing mold, to obtain the corrected thermal diffusivity of the 3D printing material, and the specific formula is:

[0052]

[0053] Wherein, α mod represents the corrected thermal diffusivity, a0 represents the original thermal diffusivity, β represents the structure influence factor, V void represents the mold internal gap volume, V total represents the mold total volume.

[0054] Step 102, based on the mold size and mold shape of the 3D printing mold combined with the corrected thermal diffusivity, the heat flux estimation is carried out to obtain the initial heat flux of the mold surface of the 3D printing mold.

[0055] Optionally, the mold surface of the 3D printing mold in the embodiment of the present application is a uniform heat dissipation surface, therefore, the mold cooling device obtains the mold size and mold shape of the 3D printing mold, and the corrected material thermal conductivity related to the corrected thermal diffusion coefficient, and determines the mold surface area S of the 3D printing mold according to the mold shape surface . Further, the mold cooling device determines the equivalent heat conduction length L eff of the 3D printing mold according to the mold surface area and the total volume of the mold, and the specific formula is: eff L total = V surface / S pre .

[0056] Further, the mold cooling device obtains the current environment temperature and the preset target surface temperature of the 3D printing mold when the printing is completed, wherein the preset target surface temperature is set according to the actual pre-setting.

[0057] Further, the mold cooling device performs heat flux estimation according to the corrected material thermal conductivity, the current environment temperature, the equivalent heat conduction length and the preset target surface temperature, and obtains the initial heat flux of the mold surface of the 3D printing mold, and the specific formula is:

[0058] q mod = [k target *(T amb -T eff )] / L pre .

[0059] Wherein, q mod represents the initial heat flux, k target represents the corrected material thermal conductivity, T amb represents the preset target surface temperature, and T u represents the current environment temperature.

[0060] Step 103, based on the pipe structure of the cooling system and the flow characteristics of the cooling liquid in the cooling system, determine the convective heat transfer coefficient between the cooling liquid in the cooling system and the mold surface of the 3D printing mold.

[0061] Further, the mold cooling device obtains the pipe structure of the cooling system and the flow characteristics of the cooling liquid in the cooling system, wherein the pipe structure in the embodiment of the present application is the hydraulic diameter, and the flow characteristics is the thermal conductivity.

[0062] Further, the mold cooling device calculates the convective heat transfer coefficient between the cooling liquid in the cooling system and the mold surface of the 3D printing mold according to the hydraulic diameter of the cooling system and the thermal conductivity of the cooling liquid in the cooling system, and the specific formula is:

[0063] h = (N u *k fluid ) / Dhyd .

[0064] wherein h represents a convective heat transfer coefficient, N u represents a Nusselt number, k fluid represents a thermal conductivity of the cooling liquid, D hyd represents a hydraulic diameter, wherein the specific formula of the Nusselt number is:

[0065]

[0066] R e = (p fluid *v*D hyd ) / μ fluid .

[0067] wherein C1, C2, C3, C4 are constants fitted according to experimental data, R e represents a Reynolds number, P r represents a Prandtl number, ∈ represents a roughness of a pipe wall of the cooling system, p fluid represents a density of the cooling liquid, v represents a flow rate of the cooling liquid, μ fluid represents a dynamic viscosity of the cooling liquid.

[0068] Step 104, determining an initial cooling condition based on the initial heat flux and the convective heat transfer coefficient.

[0069] Further, the mold cooling device determines the initial cooling condition according to the initial heat flux and the convective heat transfer coefficient, which is specifically described in steps 1041 to 1043, wherein the initial cooling condition includes an initial cooling temperature and an initial cooling flow rate of the cooling liquid in the cooling system.

[0070] The embodiment of the present application can determine the most suitable initial cooling condition according to the specific characteristics of the 3D printing mold and the material, and the actual situation of the cooling system, thereby reducing the deformation and internal stress concentration of the mold caused by improper cooling, improving the yield of the 3D printing product, and effectively improving the production quality and production efficiency of the 3D printing mold cooling process.

[0071] In an embodiment, steps 1041 to 1043 are described as follows:

[0072] Step 1041, performing energy transfer prediction based on the initial heat flux, the convective heat transfer coefficient, the mold mass of the 3D printing mold, the specific heat capacity of the mold material, and the contact surface area of the cooling liquid in the cooling system and the mold surface of the 3D printing mold, to obtain a heat reduction amount.

[0073] Optionally, the mold cooling device obtains the mold mass of the 3D printing mold, the specific heat capacity of the mold material, and the contact surface area of the cooling liquid in the cooling system and the mold surface of the 3D printing mold.

[0074] Further, the mold cooling device predicts energy transfer based on the initial heat flux, the convective heat transfer coefficient, and the contact surface area of the cooling liquid in the cooling system and the mold surface of the 3D printing mold, in combination with the principle of energy conservation, to obtain the heat reduction amount, and the specific formula is as follows:

[0075] wherein W q represents the heat reduction amount, S cool represents the contact surface area, c mold represents the specific heat capacity of the mold material, m mold represents the mass of the mold.

[0076] Step 1042, based on the heat reduction amount and the preset target surface temperature of the 3D printing mold, the initial cooling temperature of the cooling liquid in the cooling system is determined.

[0077] Further, the mold cooling device determines the initial cooling temperature T coolant0 of the cooling liquid in the cooling system based on the heat reduction amount and the preset target surface temperature of the 3D printing mold, and the specific formula is as follows:

[0078] T coolant0 = T target +W q .

[0079] Step 1043, based on the initial heat flux, the convective heat transfer coefficient, the preset target surface temperature, the initial cooling temperature, in combination with the density, the cooling rate and the constant-pressure specific heat capacity of the cooling liquid in the cooling system, the flow rate is predicted to obtain the initial cooling flow rate of the cooling liquid in the cooling system.

[0080] Further, the mold cooling device obtains the density, the cooling rate and the constant-pressure specific heat capacity of the cooling liquid in the cooling system, wherein the cooling rate represents the volume flow rate of the cooling liquid in the cooling pipeline per unit time.

[0081] Further, the mold cooling device predicts the flow rate based on the initial heat flux, the convective heat transfer coefficient, the preset target surface temperature, the initial cooling temperature, in combination with the density, the cooling rate and the constant-pressure specific heat capacity of the cooling liquid in the cooling system, to obtain the initial cooling flow rate of the cooling liquid in the cooling system, and the specific formula is as follows:

[0082] v0=(q pre *S cool ) / [ρ fluid *c p，fluid *(T target -T coolant0 )*V flow ]。

[0083] wherein v0 represents the initial cooling flow rate, ρfluid denotes the density of the cooling liquid, c p，fluid denotes the specific heat capacity at constant pressure of the cooling liquid, V flow denotes the cooling rate of the cooling liquid.

[0084] The embodiment of the present application realizes accurate regulation of cooling conditions, can optimize the initial temperature and flow rate of the cooling liquid according to the actual situation of the 3D printing mold, thereby improving the cooling efficiency, ensuring that the temperature of the 3D printing mold uniformly decreases during the cooling process, reducing defects such as deformation and cracking of the mold caused by uneven cooling, and effectively improving the production quality and production efficiency of the 3D printing mold cooling process.

[0085] In an embodiment, steps 301 to 304 are described as follows:

[0086] Step 301, the mold surface of the 3D printing mold is divided into multiple thermal zones, and the temperature difference coefficient of each thermal zone with the preset target surface temperature is obtained by analyzing the temperature data in the first image analysis result and the thermal field distribution difference of each thermal zone.

[0087] Optionally, the mold cooling device processes the first mold surface image, and divides the mold surface of the 3D printing mold into multiple thermal zones according to factors such as shape and structure, wherein the division of the thermal zones aims to more carefully analyze the temperature distribution of different regions of the mold surface. Then, the temperature data corresponding to each thermal zone is obtained from the first image analysis result, and for each thermal zone, the average temperature is compared with the preset target surface temperature, and the temperature difference coefficient of the thermal zone with the preset target surface temperature is obtained by calculating the ratio of the difference value to the preset target surface temperature.

[0088] In an embodiment, the 3D printing mold is a complex mechanical part mold, and the mold cooling device divides its surface into 5 thermal zones. From the first image analysis result, it is known that the average temperature of thermal zone 1 is 60 degrees Celsius, and the preset target surface temperature is 50 degrees Celsius. Then the temperature difference coefficient of thermal zone 1 is |60-50| / 50=0.2. Similarly, the temperature difference coefficients of other thermal zones are calculated.

[0089] Step 302, according to the temperature difference coefficient of each thermal zone and the corresponding volume of the mold of each thermal zone, and combining the heat exchange principle of the mold and the cooling liquid, the heat capacity adjustment amount of the cooling liquid in each thermal zone is calculated.

[0090] Optionally, heat transfer is related to factors such as mold specific volume, temperature change, and material specific heat capacity, therefore, the mold cooling device obtains the mold material specific heat capacity, the mold specific volume corresponding to each thermal zone, and the cooling liquid specific heat capacity of the cooling liquid flowing through each thermal zone, and according to the mold material specific heat capacity, the temperature difference coefficient of each thermal zone, and the mold specific volume corresponding to each thermal zone, and the cooling liquid specific heat capacity of the cooling liquid flowing through each thermal zone, and combining the heat exchange principle of the mold and the cooling liquid, the heat capacity adjustment amount of the cooling liquid corresponding to each thermal zone is calculated. Therefore, for each thermal zone, the mold material specific heat capacity and its temperature difference coefficient and the mold specific volume are used to deduce by combining the heat exchange formula Q=vVcΔTα, to obtain the heat change amount that each thermal zone needs to absorb or release due to temperature difference, wherein Q represents heat, p represents mold material density, V represents mold specific volume, c represents mold material specific heat capacity, ΔT represents temperature change, and a represents temperature difference coefficient.

[0091] Further, the mold cooling device calculates the ratio of the heat change amount of each thermal zone and the cooling liquid specific heat capacity of the cooling liquid flowing through each thermal zone to obtain the heat capacity adjustment amount of the cooling liquid corresponding to each thermal zone, that is, the heat capacity adjustment amount=heat change amount / cooling liquid specific heat capacity.

[0092] Step 303, based on the cooling liquid mass of the cooling liquid flowing through each thermal zone and the cooling liquid specific heat capacity, and combining the heat capacity adjustment amount of the cooling liquid corresponding to each thermal zone, the initial temperature adjustment value of the cooling liquid of each thermal zone is determined.

[0093] Further, the mold cooling device obtains the cooling liquid mass of the cooling liquid flowing through each thermal zone, and calculates the product of the cooling liquid mass of the cooling liquid flowing through each thermal zone and the cooling liquid specific heat capacity to obtain the heat capacity of the cooling liquid flowing through each thermal zone. Further, the mold cooling device calculates the ratio of the heat capacity of the cooling liquid flowing through each thermal zone and the heat capacity adjustment amount of the cooling liquid corresponding to each thermal zone to obtain the initial temperature adjustment value of the cooling liquid of each thermal zone, that is, the initial temperature adjustment value=heat capacity adjustment amount / heat capacity.

[0094] Step 304, based on the temperature difference coefficient of each thermal zone and the initial temperature adjustment value, the initial cooling condition is adjusted to obtain the optimized cooling condition.

[0095] Further, the mold cooling device adjusts the initial cooling condition according to the temperature difference coefficient of each thermal zone and the initial temperature adjustment value to obtain the optimized cooling condition, which is specifically described in steps 3041 to 3044.

[0096] The embodiments of the present invention can achieve fine adjustment of cooling conditions according to the temperature distribution of different areas on the mold surface. Therefore, it can effectively improve the uniformity of mold cooling, avoid problems such as mold deformation and internal stress concentration caused by local overheating or overcooling, improve the quality and service life of 3D printed molds, and effectively improve the production quality and efficiency of the 3D printed mold cooling process.

[0097] In one embodiment, steps 3041 to 3044 are described as follows:

[0098] Step 3041: Adjust the initial temperature adjustment value of each hot zone based on the temperature difference coefficient of each hot zone to obtain the target temperature adjustment value of each hot zone.

[0099] Optionally, for each hot zone, the mold cooling device obtains the correction factor corresponding to the temperature difference coefficient from a preset mapping table based on the temperature difference coefficient. The preset mapping table is a pre-established association table based on the temperature difference coefficient and its corresponding correction factor. In one embodiment, in the preset mapping table, when α = 0.2, the correction factor is 1.5; when α = 0.1, the correction factor is 1.2.

[0100] Furthermore, the mold cooling device adjusts the initial temperature adjustment value of each hot zone according to the correction factor corresponding to the temperature difference coefficient of each hot zone, to obtain the target temperature adjustment value of each hot zone. The specific formula is: T tn =T ti *exp(1+α t ), where T tn Indicates the target temperature adjustment value, T ti Indicates the initial temperature adjustment value; α t This represents the correction factor corresponding to the temperature difference coefficient α.

[0101] Step 3042: Adjust the initial cooling flow rate of the coolant in each hot zone based on the target temperature adjustment value of each hot zone to obtain the optimized flow rate of the coolant in each hot zone.

[0102] Furthermore, the mold cooling device adjusts the initial cooling flow rate of the coolant based on the target temperature adjustment value for each hot zone. A higher target temperature adjustment value means that the coolant needs to remove more or less heat from that hot zone (depending on whether the temperature is higher or lower than the preset target surface temperature), thus requiring a corresponding increase or decrease in the coolant flow rate. According to the principles of heat transfer, the heat removed by the coolant is related to the flow rate. The optimized flow rate is calculated by establishing a functional relationship between the flow rate and the temperature adjustment value. Generally, a linear or nonlinear function model can be used, such as: Optimized flow rate = Initial cooling flow rate + Target temperature adjustment value * Flow rate adjustment coefficient, where the flow rate adjustment coefficient is determined based on factors such as coolant characteristics and mold structure.

[0103] In step 3043, based on the optimized flow rate of each hot zone and the cross-sectional area of the cooling channel corresponding to each hot zone, the flow adjustment amount of the cooling liquid of each hot zone is determined.

[0104] Further, the mold cooling device obtains the cross-sectional area information of the cooling channel corresponding to each hot zone. For each hot zone, according to the principle of fluid mechanics, the flow rate is equal to the flow rate multiplied by the cross-sectional area. First, the flow corresponding to the optimized flow rate of each hot zone is calculated, and then compared with the flow corresponding to the initial flow rate, so as to determine the flow adjustment amount. Flow adjustment amount = optimized flow rate * cross-sectional area - initial flow rate * cross-sectional area.

[0105] In step 3044, the optimized flow rate and flow adjustment amount of the cooling liquid of each hot zone are determined as the optimized cooling condition.

[0106] Further, the mold cooling device determines the optimized flow rate and flow adjustment amount of the cooling liquid of each hot zone as the optimized cooling condition.

[0107] The embodiment of the present application can realize precise and differentiated adjustment of the cooling condition, improve the uniformity and efficiency of mold cooling, reduce the problems of deformation, internal stress concentration and the like caused by local overheating or overcooling of the mold, and thus improve the quality and service life of the 3D printing mold, and effectively improve the production quality and production efficiency of the 3D printing mold cooling process.

[0108] In an embodiment, steps 401 to 404 are described as follows:

[0109] In step 401, the first mold surface image is converted from a color image to a gray scale image.

[0110] Optionally, the mold cooling device obtains a color image of the first mold surface, wherein the color image is usually composed of three color channels of red (R), green (G) and blue (B), and each pixel point contains color information of the three channels. Further, the mold cooling device converts the color image into a gray scale image, wherein each pixel point in the gray scale image has only one brightness value, representing the gray scale degree of the point, and the value range is generally 0 (black) to 255 (white). Common gray scale conversion methods include weighted average method, for example, calculating the gray scale value of each pixel point by the formula Gray = 0.299R + 0.587G + 0.114B, and converting each pixel point in the color image into the corresponding gray scale value according to the formula, thereby obtaining a complete gray scale image.

[0111] In step 402, the gray scale image is divided into a plurality of sub-regions, and edge detection is performed on each sub-region to obtain the edge features of each sub-region.

[0112] Furthermore, the mold cooling device divides the grayscale image into multiple sub-regions according to certain rules. The division method can be uniform, such as dividing the image into square or rectangular sub-regions of equal size, or adaptive division based on the structural characteristics of the mold.

[0113] Furthermore, after the segmentation is completed, the mold cooling device performs edge detection on each sub-region to obtain the edge features of each sub-region. Edge detection aims to identify regions in the image where grayscale values ​​change drastically; these regions typically correspond to the edges of objects or the boundaries of different temperature zones. Commonly used edge detection algorithms include the Canny algorithm and the Sobel algorithm. Taking the Canny algorithm as an example, it performs Gaussian filtering to remove noise from the image, calculates the gradient magnitude and direction of the image, then performs non-maximum suppression to refine the edges, and finally determines the final edges through double threshold detection and edge connection.

[0114] In one embodiment, the mold cooling device uniformly divides the grayscale image into 100 square sub-regions, each 10*10 pixels in size. For one of these sub-regions, edge detection is performed using the Canny algorithm. The sub-region is then filtered using a Gaussian filter, for example, a Gaussian kernel of size 3*3 with a standard deviation of 1.4. After filtering, the sub-region image becomes smoother, and noise is suppressed. Next, the gradient magnitude and direction of the sub-region image are calculated; for example, at a certain pixel, the calculated gradient magnitude is 20, and the direction is 45 degrees. Then, non-maximum suppression is performed to remove pixels that are not local gradient maxima, thus refining the edges. Finally, edge detection and concatenation are performed by setting dual thresholds (e.g., a low threshold of 50 and a high threshold of 150) to obtain the edge features of the sub-region, which are represented as a series of connected edge pixels.

[0115] Step 403: Construct the temperature correlation matrix for each sub-region based on the edge features of each sub-region.

[0116] Furthermore, for each sub-region, the mold cooling device constructs a temperature correlation matrix based on its edge features. Since edge features reflect the changes in grayscale values ​​between different locations within the sub-region, and grayscale values ​​are temperature-dependent (in thermal imaging, different temperatures correspond to different grayscale values), the matrix elements in the temperature correlation matrix represent the degree of temperature correlation between different locations within the sub-region based on edge features. The method for constructing the temperature correlation matrix can be based on factors such as spatial distance and edge strength. For example, for two pixels P within a sub-region... i and P j If pixel P i and P j The pixels P that are close to each other and connected i If the edge intensity on the path of Pj is weaker (indicating a more gradual temperature change), then pixel P...i and P j The corresponding element value in the temperature correlation matrix is large, indicating that the temperature correlation degree is high; on the contrary, if the distance is far and the edge strength on the path is strong (the temperature change is large), the corresponding element value is small. The size of the matrix is related to the number of pixel points in the sub-region, and is generally N*N, N being the number of pixel points in the sub-region.

[0117] In an embodiment, there are 100 pixel points in a certain sub-region. For pixel points P1 and P 20 , the pixel points P1 and P 20 are close in space in the sub-region, and the edge strength obtained by edge detection on the path connecting the pixel points P1 and P 20 is weak (few edge pixel points), so the value of the element T 1，20 (representing the temperature correlation degree between the pixel points P1 and P 20 ) in the temperature correlation matrix is set to 0.8 (the value range is 0-1, and the larger the value, the higher the correlation degree). For the pixel points P5 and P 90 which are far apart and have strong edge strength on the path, the value of the element T 5，90 is set to 0.2. In this way, a complete 100*100 temperature correlation matrix of the sub-region is constructed.

[0118] Step 404, performing fusion analysis based on the edge features and the temperature correlation matrix of each sub-region to obtain a first image analysis result.

[0119] Further, the mold cooling device performs fusion analysis based on the edge features and the temperature correlation matrix of each sub-region to obtain a first image analysis result, as described in steps 4041 to 4044.

[0120] The embodiment of the present application can perform in-depth analysis on the first mold surface image, obtain more accurate temperature distribution information, and more accurately judge the difference between the mold surface temperature and the preset target surface temperature, thereby providing a reliable basis for adjusting the initial cooling conditions based thereon, thereby improving the pertinence and effectiveness of the cooling process, better ensuring the quality of the 3D printing mold, reducing problems such as mold deformation and internal defects caused by improper temperature control, and effectively improving the production quality and production efficiency of the 3D printing mold cooling process.

[0121] In an embodiment, steps 4041 to 4044 are described as follows:

[0122] Step 4041, fusing the edge features and the temperature correlation matrix in each sub-region to obtain a temperature feature representation of each sub-region.

[0123] Optionally, for each sub-region, the mold cooling device fuses its edge feature and temperature correlation matrix, where the edge feature contains information of the temperature variation boundary in the sub-region, such as the position of the edge pixel point, the direction of the edge, etc.; the temperature correlation matrix reflects the degree of correlation between different positions in the sub-region based on temperature. The fusion method can adopt various methods, such as vector splicing. The edge feature and the temperature correlation matrix are respectively converted into vector form, and then spliced in order to form a new vector, which is the temperature feature representation of the sub-region. The weighted summation method can also be used, and the weights are assigned according to the importance of the edge feature and the temperature correlation matrix to the temperature feature, and the corresponding elements are weighted and summed to obtain a new matrix or vector as the temperature feature representation.

[0124] Step 4042, integrating the temperature feature representation of the sub-region to obtain the global temperature feature representation.

[0125] Further, the mold cooling device collects the temperature feature representations of all sub-regions, and since the dimensions of the temperature feature representations of each sub-region are the same, these temperature feature representations of the sub-regions can be arranged or combined in a certain order to form a larger matrix or vector as the global temperature feature representation. For example, the temperature feature representation vectors of all sub-regions can be spliced into a long vector in turn, or arranged into a two-dimensional matrix, each row of which represents the temperature feature representation of a sub-region. In this way, the global temperature feature representation contains temperature-related feature information of each sub-region on the entire mold surface.

[0126] Step 4043, performing clustering analysis on the global temperature feature representation to cluster sub-regions with similar temperature features into the same class, and obtaining the clustering result.

[0127] Further, the mold cooling device performs clustering analysis on the global temperature feature representation. The purpose of clustering analysis is to divide sub-regions with similar temperature features into the same class. Common clustering algorithms include K-Means algorithm, DBSCAN algorithm, etc. Taking the K-Means algorithm as an example, the number of classes K for clustering needs to be determined first (which can be determined according to experience or experiment). Then K points are randomly selected as initial cluster centers, the distance (such as Euclidean distance) between the temperature feature representation vector of each sub-region and the K cluster centers is calculated, and each sub-region is assigned to the class of the nearest cluster center. Then the cluster center of each class is recalculated, i.e. the mean vector of the temperature feature representation vectors of all sub-regions in the class. The above process is repeatedly performed until the cluster center no longer changes significantly or the preset number of iterations is reached, and finally the clustering result is obtained, i.e. the class label to which each sub-region belongs.

[0128] Step 4044, mapping the clustering result to the first mold surface image to represent different temperature feature categories with different colors to obtain the first image analysis result.

[0129] Further, the mold cooling device maps the clustering result back to the first mold surface image. According to the category to which each sub-region belongs, a different color is assigned to it. For example, sub-regions belonging to category 1 are displayed in red in the image, sub-regions belonging to category 2 are displayed in blue, and sub-regions belonging to category 3 are displayed in green. In this way, on the first mold surface image, the regions of different temperature feature categories are intuitively presented in different colors. At the same time, the temperature range or other related temperature feature information corresponding to each category can also be labeled on the image, thereby obtaining a comprehensive and intuitive first image analysis result.

[0130] In an embodiment, on the first mold surface image, the color value of all sub-region pixel points marked as category 1 is modified to red (such as RGB value (255, 0, 0)), the color value of sub-region pixel points of category 2 is modified to blue (such as RGB value (0, 0, 255)), and the color value of sub-region pixel points of category 3 is modified to green (such as RGB value (0, 255, 0)). Then label in the corner or blank area of the image: the temperature range of the red region is 50-60 degrees Celsius, the temperature range of the blue region is 60-70 degrees Celsius, and the temperature range of the green region is 70-80 degrees Celsius, forming a complete first image analysis result image.

[0131] The embodiment of the present application can systematically and intuitively analyze the temperature distribution of the mold surface, so as to quickly and accurately identify the difference in the temperature distribution of the mold surface, provide an intuitive and reliable basis for adjusting the initial cooling condition, and help to improve the precision and effectiveness of the cooling process, reduce the quality problems caused by uneven temperature of the mold, and effectively improve the production quality and production efficiency of the 3D printing mold cooling process.

[0132] Further, the 3D printing mold cooling device based on dynamic visual feedback provided by the present application is described below, and the 3D printing mold cooling device based on dynamic visual feedback described below can be correspondingly referred to the 3D printing mold cooling method based on dynamic visual feedback described above.

[0133] Optionally, referring to Figure 2 , Figure 2 is a structural diagram of the 3D printing mold cooling device based on dynamic visual feedback provided by the present application, and the 3D printing mold cooling device based on dynamic visual feedback comprises:

[0134] The initialization module 210 is configured to determine the initial cooling condition of the 3D printing mold in the cooling process based on the parameter information obtained from the design drawing of the 3D printing mold and the thermal physical properties of the 3D printing material.

[0135] The mold initial cooling module 220 is used to respond to the printing start command of the 3D printing mold and perform initial cooling on the 3D printing mold during the printing process based on the initial cooling conditions.

[0136] The dynamic visual feedback module 230 is used to acquire images of the surface of the first mold during the initial cooling process of the 3D printed mold based on a visual camera device, and to perform feature analysis on the surface images of the first mold to obtain the first image analysis results.

[0137] The mold re-cooling module 240 is used to adjust the initial cooling conditions based on the first image analysis results and the preset target surface temperature if it is determined based on the first image analysis results that the surface temperature of the 3D printed mold has not reached the preset target surface temperature, so as to obtain the optimized cooling conditions, and to re-cool the 3D printed mold during the printing process based on the optimized cooling conditions.

[0138] The dynamic feedback cooling module 250 acquires a second mold surface image of the 3D printed mold during the re-cooling process using a vision camera device, and adjusts the optimized cooling conditions based on the second image analysis results of the second mold surface image until the surface temperature of the 3D printed mold is cooled to the preset target surface temperature.

[0139] This invention utilizes a visual camera to capture real-time images of the 3D printed mold's surface. Therefore, regardless of the mold's complexity, the temperature distribution on its surface can be directly observed. By combining image analysis with a preset target surface temperature, optimized cooling conditions for various parts of the mold's surface can be dynamically generated. This ensures uniform cooling across the mold's surface, effectively addressing uneven cooling and improving overall performance and lifespan. Furthermore, continuous monitoring and optimization throughout the process ensure that the temperature of each part of the mold surface remains within a suitable range until the preset target surface temperature is reached, preventing over- or under-cooling and improving cooling efficiency. This, in turn, enhances mold quality and production efficiency.

[0140] Please see Figure 3 , Figure 3 An embodiment diagram of an electronic device provided in accordance with the present invention. For example... Figure 3 As shown, this embodiment of the invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, it performs the following steps:

[0141] Based on the parameter information obtained from the design drawings of the 3D printed mold and combined with the thermophysical properties of the 3D printed material, the initial cooling conditions of the 3D printed mold during the cooling process are determined.

[0142] In response to the printing start command of the 3D printing mold, the 3D printing mold is initially cooled during the printing process based on the initial cooling conditions;

[0143] The first mold surface image of the 3D printed mold during the initial cooling process is acquired using a visual camera device, and feature analysis is performed on the first mold surface image to obtain the first image analysis result;

[0144] If the surface temperature of the 3D printed mold does not reach the preset target surface temperature based on the first image analysis result, the initial cooling conditions are adjusted based on the first image analysis result and the preset target surface temperature to obtain optimized cooling conditions, and the 3D printed mold is cooled again during the printing process based on the optimized cooling conditions.

[0145] The second mold surface image of the 3D printed mold is acquired by a vision camera device during the re-cooling process. The optimized cooling conditions are then adjusted based on the second image analysis results of the second mold surface image until the surface temperature of the 3D printed mold is cooled to the preset target surface temperature.

[0146] Please see Figure 4 , Figure 4 An embodiment diagram of a computer-readable storage medium provided in accordance with an embodiment of the present invention is shown. Figure 4 As shown, this embodiment provides a computer-readable storage medium 400 on which a computer program 311 is stored. When the computer program 311 is executed by a processor, it performs the following steps:

[0147] Based on the parameter information obtained from the design drawings of the 3D printed mold and combined with the thermophysical properties of the 3D printed material, the initial cooling conditions of the 3D printed mold during the cooling process are determined.

[0148] In response to the printing start command of the 3D printing mold, the 3D printing mold is initially cooled during the printing process based on the initial cooling conditions;

[0149] The first mold surface image of the 3D printed mold during the initial cooling process is acquired using a visual camera device, and feature analysis is performed on the first mold surface image to obtain the first image analysis result;

[0150] If the surface temperature of the 3D printed mold does not reach the preset target surface temperature based on the first image analysis result, the initial cooling conditions are adjusted based on the first image analysis result and the preset target surface temperature to obtain optimized cooling conditions, and the 3D printed mold is cooled again during the printing process based on the optimized cooling conditions.

[0151] The second mold surface image of the 3D printing mold in the recooling process is collected based on the visual camera equipment, and the optimized cooling condition is adjusted according to the second image analysis result of the second mold surface image, until the surface temperature of the 3D printing mold is cooled to the preset target surface temperature.

[0152] In another aspect, the present application also provides a computer program product, the computer program product comprising a computer program, the computer program being stored on a non-transitory computer readable storage medium, and the computer program being executable by a processor to cause the computer to execute the 3D printing mold cooling method based on dynamic visual feedback provided by the above-mentioned methods, the method comprising:

[0153] The initial cooling condition of the 3D printing mold in the cooling process is determined based on the parameter information obtained from the design drawing of the 3D printing mold and the thermal physical properties of the 3D printing material;

[0154] The 3D printing mold in the printing process is preliminarily cooled based on the initial cooling condition in response to the printing start instruction of the 3D printing mold;

[0155] The first mold surface image of the 3D printing mold in the preliminary cooling process is collected based on the visual camera equipment, and feature analysis is performed on the first mold surface image to obtain a first image analysis result;

[0156] If it is determined based on the first image analysis result that the surface temperature of the 3D printing mold does not reach the preset target surface temperature, the initial cooling condition is adjusted based on the first image analysis result and the preset target surface temperature to obtain an optimized cooling condition, and the 3D printing mold in the printing process is re-cooled based on the optimized cooling condition;

[0157] The second mold surface image of the 3D printing mold in the recooling process is collected based on the visual camera equipment, and the optimized cooling condition is adjusted according to the second image analysis result of the second mold surface image, until the surface temperature of the 3D printing mold is cooled to the preset target surface temperature.

[0158] The device embodiments described above are only schematic, wherein the units shown as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0159] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and necessary general hardware platforms through the description of the above embodiments, and of course, the implementation can also be through hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, and the computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the method of each embodiment or some parts of the embodiment.

[0160] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for some technical features thereof; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A cooling method for 3D printed molds based on dynamic visual feedback, characterized in that, Within the printing working area of ​​the 3D printing equipment, multiple visual cameras are evenly arranged around the printing position of the mold; the 3D printing mold cooling method based on dynamic visual feedback includes: Based on the parameter information obtained from the design drawings of the 3D printed mold and combined with the thermophysical properties of the 3D printed material, the initial cooling conditions of the 3D printed mold during the cooling process are determined. In response to the printing start command of the 3D printing mold, the 3D printing mold is initially cooled during the printing process based on the initial cooling conditions; The first mold surface image of the 3D printed mold during the initial cooling process is acquired using a visual camera device, and feature analysis is performed on the first mold surface image to obtain the first image analysis result; If, based on the first image analysis result, it is determined that the surface temperature of the 3D printing mold has not reached the preset target surface temperature, then the initial cooling conditions are adjusted based on the first image analysis result and the preset target surface temperature to obtain optimized cooling conditions, and the 3D printing mold is cooled again during the printing process based on the optimized cooling conditions. The second mold surface image of the 3D printed mold is acquired by a visual camera device during the re-cooling process, and the optimized cooling conditions are adjusted according to the second image analysis results of the second mold surface image until the surface temperature of the 3D printed mold is cooled to the preset target surface temperature. The parameter information includes size and shape; the parameter information obtained from the design drawings of the 3D printed mold, combined with the thermophysical properties of the 3D printing material, determines the initial cooling conditions of the 3D printed mold during the cooling process, including: The original thermal diffusivity of the 3D printing material is corrected based on its thermophysical properties to obtain the corrected thermal diffusivity of the 3D printing material. Based on the mold size and shape of the 3D printed mold and the corrected thermal diffusivity, the heat flux is estimated to obtain the initial heat flux of the mold surface of the 3D printed mold; the mold surface of the 3D printed mold is a uniform heat dissipation surface. Based on the pipe structure of the cooling system and the flow characteristics of the coolant in the cooling system, the convective heat transfer coefficient between the coolant in the cooling system and the mold surface of the 3D printed mold is determined. The initial cooling conditions are determined based on the initial heat flux and the convective heat transfer coefficient; the initial cooling conditions include the initial cooling temperature and initial cooling flow rate of the coolant in the cooling system; The step of performing feature analysis on the surface image of the first mold to obtain the first image analysis result includes: Convert the first mold surface image from a color image to a grayscale image; The grayscale image is divided into multiple sub-regions, and edge detection is performed on each sub-region to obtain the edge features of each sub-region; A temperature correlation matrix for each sub-region is constructed based on the edge features of each sub-region. The matrix elements in the temperature correlation matrix represent the degree of temperature correlation between different locations within the sub-region based on the edge features. The first image analysis result is obtained by performing a fusion analysis based on the edge features and temperature correlation matrix of each sub-region. The step of adjusting the initial cooling conditions based on the first image analysis results and the target surface temperature to obtain optimized cooling conditions includes: The surface of the 3D printed mold is divided into multiple hot zones, and the thermal field distribution difference analysis of each hot zone is performed based on the temperature data in the first image analysis result to obtain the temperature difference coefficient between each hot zone and the preset target surface temperature. Based on the temperature difference coefficient of each hot zone and the mold volume corresponding to each hot zone, combined with the heat exchange principle between the mold and the coolant, the heat capacity adjustment amount of the coolant in each hot zone is calculated. Based on the coolant mass and specific heat capacity of the coolant flowing through each hot zone, and combined with the corresponding heat capacity adjustment amount of the coolant in each hot zone, the initial temperature adjustment value of the coolant in each hot zone is determined. The initial cooling conditions are adjusted based on the temperature difference coefficient of each hot zone and the initial temperature adjustment value to obtain the optimized cooling conditions.

2. The 3D printing mold cooling method based on dynamic visual feedback according to claim 1, characterized in that, The process of adjusting the initial cooling conditions based on the temperature difference coefficients of each hot zone and the initial temperature adjustment value to obtain the optimized cooling conditions includes: The initial temperature adjustment value of each hot zone is adjusted based on the temperature difference coefficient of each hot zone to obtain the target temperature adjustment value of each hot zone. The initial cooling flow rate of the coolant in each hot zone is adjusted based on the target temperature adjustment value of each hot zone to obtain the optimized flow rate of the coolant in each hot zone. Based on the optimized flow rate of each hot zone and the cross-sectional area of ​​the cooling channel corresponding to each hot zone, the flow rate adjustment of the coolant in each hot zone is determined. The optimized flow rate and flow rate adjustment of the coolant in each hot zone are determined as the optimized cooling conditions.

3. The 3D printing mold cooling method based on dynamic visual feedback according to claim 1, characterized in that, Determining the initial cooling conditions based on the initial heat flux and the convective heat transfer coefficient includes: Based on the initial heat flux, the convective heat transfer coefficient, the mold mass and specific heat capacity of the mold material of the 3D printed mold, and the contact surface area between the coolant in the cooling system and the mold surface of the 3D printed mold, the energy transfer is predicted to obtain the heat reduction. Based on the heat reduction and the preset target surface temperature of the 3D printed mold, the initial cooling temperature of the coolant in the cooling system is determined. Based on the initial heat flux, the convective heat transfer coefficient, the preset target surface temperature, and the initial cooling temperature, combined with the density, cooling rate, and specific heat capacity of the coolant in the cooling system, the initial cooling flow rate of the coolant in the cooling system is predicted.

4. The 3D printing mold cooling method based on dynamic visual feedback according to claim 1, characterized in that, The first image analysis result is obtained by performing a fusion analysis based on the edge features and temperature correlation matrix of each sub-region, including: The edge features and temperature correlation matrix of each sub-region are fused to obtain the temperature feature representation of each sub-region; The temperature feature representations of each sub-region are integrated to obtain the global temperature feature representation; Cluster analysis is performed on the global temperature feature representation to cluster sub-regions with similar temperature features into the same class, thus obtaining the clustering results; The clustering results are mapped onto the image of the first mold surface, and different colors are used to represent different temperature feature categories to obtain the first image analysis results.

5. A 3D printing mold cooling device based on dynamic visual feedback, characterized in that, The 3D printing mold cooling device based on dynamic visual feedback, as described in any one of claims 1 to 4, comprises a plurality of visual cameras uniformly arranged around the printing position of the mold within the printing working area of ​​the 3D printing equipment; the 3D printing mold cooling device based on dynamic visual feedback includes: An initialization module is used to determine the initial cooling conditions of the 3D printing mold during the cooling process based on the parameter information obtained from the design drawings of the 3D printing mold and the thermophysical properties of the 3D printing material. The mold initial cooling module is used to respond to the printing start command of the 3D printing mold and perform initial cooling on the 3D printing mold during the printing process based on the initial cooling conditions; The dynamic visual feedback module is used to acquire a first mold surface image of the 3D printed mold during the initial cooling process based on a visual camera device, and to perform feature analysis on the first mold surface image to obtain the first image analysis result. The mold re-cooling module is used to adjust the initial cooling conditions based on the first image analysis results and the preset target surface temperature if the surface temperature of the 3D printed mold does not reach the preset target surface temperature, thereby obtaining optimized cooling conditions, and then re-cooling the 3D printed mold during the printing process based on the optimized cooling conditions. The dynamic feedback cooling module is used to acquire a second mold surface image of the 3D printed mold during the re-cooling process based on a visual camera device, and to adjust the optimized cooling conditions based on the second image analysis results of the second mold surface image until the surface temperature of the 3D printed mold is cooled to the preset target surface temperature. The parameter information includes size and shape; the parameter information obtained from the design drawings of the 3D printed mold, combined with the thermophysical properties of the 3D printing material, determines the initial cooling conditions of the 3D printed mold during the cooling process, including: The original thermal diffusivity of the 3D printing material is corrected based on its thermophysical properties to obtain the corrected thermal diffusivity of the 3D printing material. Based on the mold size and shape of the 3D printed mold and the corrected thermal diffusivity, the heat flux is estimated to obtain the initial heat flux of the mold surface of the 3D printed mold; the mold surface of the 3D printed mold is a uniform heat dissipation surface. Based on the pipe structure of the cooling system and the flow characteristics of the coolant in the cooling system, the convective heat transfer coefficient between the coolant in the cooling system and the mold surface of the 3D printed mold is determined. The initial cooling conditions are determined based on the initial heat flux and the convective heat transfer coefficient; the initial cooling conditions include the initial cooling temperature and initial cooling flow rate of the coolant in the cooling system; The step of performing feature analysis on the surface image of the first mold to obtain the first image analysis result includes: Convert the first mold surface image from a color image to a grayscale image; The grayscale image is divided into multiple sub-regions, and edge detection is performed on each sub-region to obtain the edge features of each sub-region; A temperature correlation matrix for each sub-region is constructed based on the edge features of each sub-region. The matrix elements in the temperature correlation matrix represent the degree of temperature correlation between different locations within the sub-region based on the edge features. The first image analysis result is obtained by performing a fusion analysis based on the edge features and temperature correlation matrix of each sub-region. The step of adjusting the initial cooling conditions based on the first image analysis results and the target surface temperature to obtain optimized cooling conditions includes: The surface of the 3D printed mold is divided into multiple hot zones, and the thermal field distribution difference analysis of each hot zone is performed based on the temperature data in the first image analysis result to obtain the temperature difference coefficient between each hot zone and the preset target surface temperature. Based on the temperature difference coefficient of each hot zone and the mold volume corresponding to each hot zone, combined with the heat exchange principle between the mold and the coolant, the heat capacity adjustment amount of the coolant in each hot zone is calculated. Based on the coolant mass and specific heat capacity of the coolant flowing through each hot zone, and combined with the corresponding heat capacity adjustment amount of the coolant in each hot zone, the initial temperature adjustment value of the coolant in each hot zone is determined. The initial cooling conditions are adjusted based on the temperature difference coefficient of each hot zone and the initial temperature adjustment value to obtain the optimized cooling conditions.

6. An electronic device, comprising: Memory, used to store computer software programs; A processor for reading and executing the computer software program, characterized in that, when the computer software program is executed by the processor, it implements the 3D printing mold cooling method based on dynamic visual feedback as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium storing a computer software program, characterized in that, When the computer software program is executed by the processor, it implements the 3D printing mold cooling method based on dynamic visual feedback as described in any one of claims 1 to 4.

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