Injection mold cooling detection method and system based on data analysis

Abstract

The present invention discloses a method and system for detecting cooling of injection molds based on data analysis, and relates to the technical field of injection molds. The method and system for detecting cooling of injection molds based on data analysis include: obtaining the circulating fluid trajectory of the coolant in the cooling circuit, and evaluating the cooling uniformity of the injection mold based on the cooling parameter distribution of the coolant flowing through each cooling channel; using a boundary element model to analyze the heat transfer performance when the injection mold and the cooling insert are in contact; combining the heat transfer performance and cooling uniformity to generate the optimal cooling detection result of the injection mold; comparing the difference between the optimal cooling detection result and the actual cooling detection result with a preset threshold. The present invention can accurately simulate the flow of coolant in a complex cooling circuit, and form a circulating fluid trajectory by simulating the collision reaction between the coolant and the cooling channel wall, thereby providing a basis for an in-depth understanding of the coolant fluid behavior, and then providing accurate cooling uniformity detection data.

Description

Technical Field

[0001] The present invention relates to the technical field of injection molds, and in particular to a method and system for detecting cooling of injection molds based on data analysis. Background Art

[0002] An injection mold is a tool used for injection molding. It is used to produce plastic parts in the plastic processing industry. It injects molten plastic into the mold cavity through an injection molding machine, and forms the required plastic parts after cooling and solidification. The design and manufacture of injection molds is a precise technology involving multiple fields such as material science, mechanical engineering, precision machining and heat treatment.

[0003] Cooling testing of injection molds can ensure the effectiveness of the mold cooling system and maintain high quality and efficiency during the production process. The mold must maintain a constant temperature during the injection molding process to prevent warping or internal stress in the product. Effective cooling can shorten the cycle time and improve production efficiency.

[0004] During the cooling inspection of injection molds, the coolant flows through the cooling channel of the mold to remove heat energy and help the mold maintain a suitable temperature range. This temperature control is essential to ensure the dimensional stability of plastic products and reduce internal stress. However, because the flow rate of the coolant may slow down due to changes in direction at the corners of the cooling channel, the kinetic energy loss of the fluid at the corners will be converted into pressure loss, increasing the energy consumption of the entire cooling system. If problems at the corners lead to uneven cooling or reduced efficiency, it may take longer cooling time to meet product quality requirements, and it will affect the cooling inspection results of the injection mold.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In view of the problems in the related art, the present invention proposes an injection mold cooling detection method and system based on data analysis to overcome the above-mentioned technical problems existing in the existing related art.

[0007] To this end, the specific technical solution adopted by the present invention is as follows:

[0008] According to one aspect of the present invention, a method for detecting cooling of an injection mold based on data analysis is provided. The method for detecting cooling of an injection mold based on data analysis comprises:

[0009] Obtain the circulation fluid trajectory of the coolant in the cooling circuit, and evaluate the cooling uniformity of the injection mold based on the cooling parameter distribution of the coolant flowing through each cooling channel;

[0010] Boundary element models are constructed for the injection mold and cooling insert respectively, and the boundary element model is used to analyze the heat transfer performance when the injection mold and cooling insert are in contact;

[0011] Generate the optimal cooling test result of the injection mold by combining heat transfer performance and cooling uniformity, and calculate the difference between the optimal cooling test result and the actual cooling test result;

[0012] The difference between the optimal cooling detection result and the actual cooling detection result is compared with a preset threshold, and the cooling efficiency of the injection mold is evaluated based on the comparison result.

[0013] Preferably, obtaining the circulating fluid trajectory of the coolant in the cooling circuit and evaluating the cooling uniformity of the injection mold based on the cooling parameter distribution of the coolant flowing through each cooling channel includes:

[0014] The lattice Boltzmann technique is used to simulate the circulating fluid motion of the coolant in the cooling circuit, and the circulating fluid trajectory of the coolant is formed by combining the collision reaction between the coolant and each cooling channel;

[0015] Based on the circulation fluid trajectory of the coolant, the cooling parameters of the coolant flowing through each cooling channel are collected, and the cooling parameter distribution trend is generated to identify the bottleneck area between each cooling channel;

[0016] allocating a local resistance flow to the bottleneck area, and identifying the blocking flow loss caused by the bottleneck area based on the allocated local resistance flow;

[0017] The choking flow loss is eliminated from the cooling parameter distribution to obtain the optimized cooling parameter distribution, and the cooling uniformity of the injection mold is evaluated based on the cooling parameter distribution.

[0018] Preferably, the simulating the circulating fluid motion of the coolant in the cooling circuit by using the lattice Boltzmann technique and forming the circulating fluid trajectory of the coolant in combination with the collision reaction between the coolant and each cooling channel comprises:

[0019] Define the initial geometric conditions of each cooling channel in the cooling circuit, and set the initial fluid parameters and initial distribution function of the coolant;

[0020] In each time step, the collision effect approximation algorithm is used to calculate the distribution function of the coolant when it flows through the current cooling channel and transfers to the adjacent cooling channel, and the macro variables of the coolant between the adjacent cooling channels are obtained, and the macro variables are used as the initial conditions for the next time step;

[0021] A rebound rule is established to obtain the collision reaction between the coolant and each cooling channel, and the collision and fluid movement of the coolant between adjacent cooling channels are continuously repeated to form a circulating fluid trajectory of the coolant in the cooling circuit.

[0022] Preferably, allocating a local resistance flow to the bottleneck area, and identifying the blocking flow loss caused by the bottleneck area based on the allocated local resistance flow includes:

[0023] Mark the bottleneck area between each cooling channel and assign unique indexes to the bottleneck area and cooling channel respectively;

[0024] Based on the cooling difference at the bottleneck area between the cooling channels, an initial local resistance flow is allocated to each bottleneck area;

[0025] Combined with the initial local resistance flow at the bottleneck area, the total coolant flow is distributed to each cooling channel using the network flow algorithm to determine whether the flow of each cooling channel meets the maximum carrying flow;

[0026] If the flow rate of the cooling channel does not meet the maximum carrying flow rate, the bottleneck area between the current cooling channels is weighted and the weighted distribution local resistance flow rate is generated;

[0027] The sum of the weighted distribution of the local resistance flows in each bottleneck area is taken as the blocking flow loss caused by the bottleneck area.

[0028] Preferably, the weighted allocation of the bottleneck areas between the current cooling channels includes:

[0029] Establish a multidimensional linear equation group of bottleneck area weights between cooling channels, and solve the multidimensional linear equation group to obtain the subjective weight of each bottleneck area;

[0030] Based on the subjective weight of each bottleneck area, the standard deviation of each bottleneck area is calculated to obtain the objective weight of each bottleneck area;

[0031] An integrated weighted model is constructed based on the ratio of subjective weight and objective weight, and the integrated weighted vector is solved based on the principle of maximizing the target value of the bottleneck area, and the integrated weighted vector is used as the weight of the bottleneck area between the current cooling channels.

[0032] Preferably, the combining the heat transfer performance and the cooling uniformity to generate the optimal cooling test result of the injection mold, and calculating the difference between the optimal cooling test result and the actual cooling test result comprises:

[0033] The heat transfer performance and cooling uniformity of the injection mold are used as cooling variables, and the game equilibrium model is used to obtain the optimal cooling variable combination of the injection mold.

[0034] Based on the optimal cooling variable combination, a parameter index trend chart is formulated to obtain the optimal cooling parameter indexes of the heat transfer performance and cooling uniformity of the injection mold, and the optimal cooling test results of the injection mold are obtained through a limited number of cycles;

[0035] The optimal cooling test result of the injection mold is calculated with the predefined actual cooling test result to obtain the difference calculation result.

[0036] Preferably, the heat transfer performance and cooling uniformity of the injection mold are used as cooling variables respectively, and the optimal cooling variable combination of the injection mold is obtained by using a game equilibrium model, including:

[0037] The heat transfer performance and cooling uniformity of the injection mold are respectively used as game parties, each game party corresponds to a cooling variable, and a cooling variable set is set for each game party;

[0038] Randomly generate initial strategy combinations in each cooling variable set and define the payoff function for each player;

[0039] Iteratively optimize each player based on its profit function, continuously generate the latest strategy of each player in the iterative optimization, and calculate the difference between the new and old strategies;

[0040] Update the iterative optimization results based on the difference between the new and old strategies, and integrate the iterative optimization results to obtain the optimal strategy combination. The optimal strategy combination should meet the constraints of cooling uniformity and maximum heat transfer performance.

[0041] Determine whether the optimal strategy combination meets the convergence criteria. If so, the game ends; if not, continue to perform the iterative optimization process;

[0042] When the optimal strategy combination meets the convergence criterion, the optimal strategy combination is output to obtain the optimal cooling variable combination based on the injection mold.

[0043] Preferably, comparing the difference between the optimal cooling detection result and the actual cooling detection result with a preset threshold, and evaluating the cooling efficiency of the injection mold based on the comparison result includes:

[0044] Comparing the difference calculation result with a preset threshold value;

[0045] If the difference calculation result is greater than or equal to the preset threshold, it means that the cooling effect of the injection mold is poor; if the difference calculation result is less than the preset threshold, it means that the cooling effect of the injection mold is strong;

[0046] Fault tree analysis is used to identify and record the causes of poor cooling of injection molds, and the injection molds are optimized based on the causes of the failures.

[0047] Preferably, the calculation formula for the difference between the optimal cooling detection result and the actual cooling detection result is:

[0048]

[0049] Wherein, μ(Y, X) represents the difference between the optimal cooling test result and the actual cooling test result; Y represents the optimal cooling test result; X represents the actual cooling test result; n represents the number of indicators associated with each test result; k represents the number of each ... optimal cooling test result; X represents the actual cooling test result; n represents the optimal cooling test result; k represents the kth optimal cooling test result; X(k) represents the kth actual cooling test result corresponding to the optimal cooling test result; μ(Y k , X(k)) represents the difference between the kth optimal cooling detection result and the actual cooling detection result.

[0050] According to another aspect of the present invention, there is also provided an injection mold cooling detection system based on data analysis, the injection mold cooling detection system based on data analysis comprising:

[0051] A cooling uniformity evaluation module is used to obtain the circulation fluid trajectory of the coolant in the cooling circuit and evaluate the cooling uniformity of the injection mold based on the cooling parameter distribution of the coolant flowing through each cooling channel;

[0052] Heat transfer performance analysis module, which is used to build boundary element models for injection molds and cooling inserts respectively, and use boundary element models to analyze the heat transfer performance when the injection mold and cooling insert are in contact;

[0053] A cooling test result generation module is used to generate the optimal cooling test result of the injection mold by combining the heat transfer performance and cooling uniformity, and calculate the difference between the optimal cooling test result and the actual cooling test result;

[0054] The cooling efficiency evaluation module is used to compare the difference between the optimal cooling detection result and the actual cooling detection result with a preset threshold value, and evaluate the cooling efficiency of the injection mold based on the comparison result.

[0055] The beneficial effects of the present invention are:

[0056] 1. The present invention can accurately simulate the flow of coolant in a complex cooling circuit, and form a circulating fluid trajectory by simulating the collision reaction between the coolant and the cooling channel wall, thereby providing a basis for in-depth understanding of the coolant fluid behavior. The coolant fluid trajectory and cooling parameter distribution obtained by simulation can identify the bottleneck area in the cooling circuit, and redistribute and adjust the local resistance flow in these areas, which can optimize the flow distribution of the entire cooling system, and then provide accurate cooling uniformity detection data.

[0057] 2. The present invention allocates appropriate local resistance flow in the bottleneck area, which can help manage and reduce the additional resistance and pressure loss caused by changes in flow direction, and helps maintain the overall efficiency of the cooling system. By adjusting the local resistance in the bottleneck area, the flow rate can be controlled in the bottleneck area, thereby reducing the additional loss caused by too fast or too slow flow rate. At the same time, combined with weighted allocation technology, the flow rate in these areas is reduced to be too high or too low, avoiding eddy currents and local heat accumulation caused by fluid separation. By effectively managing the flow characteristics of the bottleneck area, the mold failure rate due to uneven cooling or excessive thermal stress is reduced.

[0058] 3. The present invention uses heat transfer performance and cooling uniformity as cooling variables to obtain more accurate cooling detection results, and uses a game equilibrium model to dynamically adjust cooling parameters to ensure that cooling uniformity is achieved while maintaining optimal heat transfer performance. This is crucial to improving the quality of the mold. Through the optimized combination of cooling variables, product defects caused by uneven cooling or insufficient heat transfer can be effectively reduced. The difference between the optimal cooling detection results and the actual results is calculated and updated in real time, making the adjustment of the injection mold cooling system more accurate and timely. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0060] Figure 1 is a flow chart of a method for detecting cooling of an injection mold based on data analysis according to an embodiment of the present invention;

[0061] Figure 2 The invention is a block diagram of a cooling detection system for injection molds based on data analysis according to an embodiment of the invention.

[0062] In the figure:

[0063] 1. Cooling uniformity evaluation module; 2. Heat transfer performance analysis module; 3. Cooling test result generation module; 4. Cooling efficiency evaluation module. DETAILED DESCRIPTION

[0064] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0065] According to an embodiment of the present invention, a method and system for detecting cooling of an injection mold based on data analysis are provided.

[0066] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to an injection mold cooling detection method based on data analysis according to an embodiment of the present invention, the injection mold cooling detection method based on data analysis includes:

[0067] S1. Obtain the circulation fluid trajectory of the coolant in the cooling circuit, and evaluate the cooling uniformity of the injection mold based on the cooling parameter distribution of the coolant flowing through each cooling channel.

[0068] Among them, obtaining the circulation fluid trajectory of the coolant in the cooling circuit and evaluating the cooling uniformity of the injection mold based on the cooling parameter distribution of the coolant flowing through each cooling channel includes:

[0069] The lattice Boltzmann technique is used to simulate the circulating fluid motion of the coolant in the cooling circuit, and the circulating fluid trajectory of the coolant is formed by combining the collision reaction between the coolant and each cooling channel.

[0070] It should be noted that the lattice Boltzmann technique is a computational fluid dynamics simulation technology that is widely used to simulate fluid motion under complex boundary conditions, and is particularly suitable for simulating the fluid motion of coolants in the cooling circuit of injection molds. The lattice Boltzmann technique is used to simulate the fluid motion of coolant circulation, including:

[0071] First, the geometry and dimensions of the cooling circuit need to be set in the calculation model, including inlet and outlet locations, corners, branches and other complex structures.

[0072] Distribute the initial fluid density, velocity and other macro variables and the corresponding discrete velocity distribution function in the entire computational domain.

[0073] At each time step, the fluid particles update their velocity distribution functions at each grid point according to the collision rules, which usually include local conservation laws such as conservation of mass and momentum.

[0074] The updated velocity distribution function is used to calculate the macroscopic properties of the fluid (such as velocity and pressure) and transfer these properties to neighboring grid points.

[0075] At the boundaries of the model, such as solid walls or flow inlets, specific boundary conditions are applied to ensure correct fluid behavior.

[0076] The above process is repeated until the fluid reaches a steady state, that is, the macroscopic properties of the fluid no longer change significantly over time, or the predetermined simulation time step is reached.

[0077] It should be noted that LBM (Lattice Boltzmann) is particularly suitable for dealing with fluid flow problems with complex geometric structures because it can easily handle the interaction between solid boundaries and flow boundaries. Due to the locality of LBM in space and time, it is very suitable for high-performance parallel computing and can effectively simulate large-scale complex systems. While maintaining high efficiency, LBM can also provide accuracy that matches traditional fluid dynamics methods. It is particularly suitable for simulating low-speed and incompressible fluid flows.

[0078] Among them, the lattice Boltzmann technology is used to simulate the circulating fluid movement of the coolant in the cooling circuit, and the circulating fluid trajectory of the coolant is formed by combining the collision reaction between the coolant and each cooling channel, including:

[0079] Define the initial geometric conditions of each cooling channel in the cooling circuit, and set the initial fluid parameters and initial distribution function of the coolant;

[0080] In each time step, the collision effect approximation algorithm is used to calculate the distribution function of the coolant when it flows through the current cooling channel and transfers to the adjacent cooling channel. The macro variables of the coolant between adjacent cooling channels are obtained and used as the initial conditions for the next time step.

[0081] It should be noted that the collision effect approximation algorithm is used to calculate the distribution function of the coolant when it flows through the current cooling channel and transfers to the adjacent cooling channel, and the macro variables of the coolant between the adjacent cooling channels are obtained, and the macro variables are used as the initial conditions of the next time step, including:

[0082] Define the geometry and meshing of the cooling circuit, set the grid points and boundary conditions of each cooling channel, and set the initial distribution function for each grid point. These distribution functions reflect the probability distribution of the coolant in each discrete velocity direction.

[0083] At each time step, the distribution function is calculated at each grid point using the collision effect approximation algorithm. This process involves calculating the distribution function f after the local collision i post , the BGK (Bhatnagar-Gross-Krook) approximation is usually used, and its expression is:

[0084]

[0085] In the formula, f i post Represents the distribution function after a local collision, which is used to calculate the change in particle distribution after a period of collision process; Indicates the intensity of the collision or interaction; f i represents the initial distribution function; f i eqIt represents the equilibrium distribution function. By calculating the deviation between the initial distribution and the equilibrium distribution, it tries to reduce the difference between the initial distribution and the equilibrium distribution and gradually move closer to the equilibrium state.

[0086] The distribution function obtained after the collision is used to calculate the flow to the adjacent grid points, and its expression is:

[0087] f i (b+c i Δt, t+Δt)=f i post (b, t);

[0088] In the formula, c i represents the i-th discrete velocity direction; t represents the time variable; Δt represents the time step; b represents the spatial position vector.

[0089] A rebound rule is established to obtain the collision reaction between the coolant and each cooling channel, and the collision and fluid movement of the coolant between adjacent cooling channels are continuously repeated to form a circulating fluid trajectory of the coolant in the cooling circuit.

[0090] It should be noted that for each contact point between the fluid and the cooling channel boundary, the bounce-back rule is applied, that is, the incident distribution function bounces in the opposite direction, simulating the collision between the coolant and the cooling channel boundary, and for the grid points on the boundary, the distribution function value in the incident direction is set to the distribution function value in the reflection direction, which means that the fluid particles "bounce" in the opposite direction after hitting the boundary.

[0091] After the collision and rebound processing is completed, the distribution function of all grid points is propagated, that is, the position is updated according to the velocity vector in each direction, and the macro variables of each grid point are recalculated using the updated distribution function. These macro variables will reflect the physical state after the coolant collides with the cooling channel boundary.

[0092] Assume a simple rectangular cooling loop with four solid boundaries. The coolant flow in this loop is simulated and the rebound rule is applied as follows:

[0093] Set a 20×5 grid, set all boundaries to solid boundaries, and set the initial velocity of the coolant to the right, at the left entrance of the water channel.

[0094] Conventional collision calculations are performed at the interior grid points, and the distribution function value for each incident direction is bounced to the corresponding reflection direction at the boundary grid points of the solid boundary.

[0095] Perform fluid propagation on all grid points, update and calculate the macro variables at each grid point.

[0096] Based on the circulating fluid trajectory of the coolant, the cooling parameters of the coolant flowing through each cooling channel are collected, and the cooling parameter distribution trend is generated to identify the bottleneck area between the cooling channels.

[0097] It should be noted that in the cooling system, the bend (bottleneck area) does have a significant impact on the pressure of the coolant. This impact is mainly determined by the principles of fluid dynamics and is specifically reflected in the following aspects:

[0098] 1. Increased pressure loss:

[0099] When the coolant flows in the cooling channel and passes through the bend, the flow velocity distribution and flow direction will change due to the change in the flow channel direction. This change in direction will cause fluid dynamics loss, mainly manifested as pressure reduction. This pressure loss is mainly caused by the following factors:

[0100] Flow separation: On the outside of a bend, the fluid may separate from the pipe wall, forming a vortex.

[0101] Flow velocity variations: Flow velocity may be uneven around bends, leading to the formation of localized acceleration and deceleration areas.

[0102] 2. Turbulence effect:

[0103] Flow at bends is usually more prone to turbulence, especially at high flow rates or sudden bends. The generation of turbulence will increase the frictional resistance of the fluid and further cause pressure drop.

[0104] 3. Energy dispersion:

[0105] The flow changes caused by corners require additional energy to maintain the dynamic balance of the fluid. This energy adjustment is usually manifested in the form of pressure energy, which is manifested as the redistribution of energy among different fluid layers, resulting in a drop in pressure at the corners and behind the corners.

[0106] At the bends in the circuit, the direction of coolant flow changes, which usually leads to an increase in flow resistance, which will cause a slowdown in flow velocity and pressure loss, affecting the flow distribution of the coolant. Flow separation may occur at the corners, resulting in a local decrease in flow velocity and the formation of dead zones, thereby affecting the cooling effect on the cooling parameters of the coolant as it flows through each cooling channel.

[0107] Identify the changing trends of fluid parameters, especially in the critical turning areas of the system; by generating a distribution trend graph of the parameters, visually display the behavior pattern of the coolant in the entire cooling system. For example, if a significant drop in pressure or an abnormal increase in temperature is observed at a certain corner, this may indicate a design bottleneck or fluid dynamics problem in that area.

[0108] It should be noted that based on the circulating fluid trajectory of the coolant, the cooling parameters of the coolant flowing through each cooling channel are collected, and the cooling parameter distribution trend is generated to identify the bottleneck area between each cooling channel, including:

[0109] A local resistance flow is allocated to the bottleneck area, and the blocking flow loss caused by the bottleneck area is identified based on the allocated local resistance flow.

[0110] Among them, allocating local resistance flow to the bottleneck area, and identifying the blocking flow loss caused by the bottleneck area based on the allocated local resistance flow includes:

[0111] Mark the bottleneck area between each cooling channel and assign unique indexes to the bottleneck area and cooling channel respectively;

[0112] Based on the cooling difference at the bottleneck areas among the cooling channels, an initial local resistance flow is allocated to each bottleneck area.

[0113] It should be noted that, based on the cooling difference at the bottleneck area between the cooling channels, the initial local resistance flow rate is allocated to each bottleneck area, including:

[0114] Combined with the initial local resistance flow at the bottleneck area, the network flow algorithm is used to distribute the total coolant flow to each cooling channel to determine whether the flow of each cooling channel meets the maximum carrying flow.

[0115] It should be noted that, combined with the initial local resistance flow at the bottleneck area, the total coolant flow is distributed to each cooling channel using the network flow algorithm, including:

[0116] Using the network flow algorithm to distribute the total coolant flow to each cooling channel and determine whether the flow of each channel meets its maximum carrying flow can follow a series of specific steps. This process involves modeling, algorithm selection, calculating flow distribution, and verifying whether the flow meets the design constraints, such as:

[0117] Define network nodes: Define the inlet and outlet of each cooling channel as nodes in the network.

[0118] Define edges and capacities: Define the connections between water channels as edges and assign a maximum flow capacity to each edge, which is the maximum carrying flow of each cooling channel.

[0119] Select Algorithm: Select a suitable network flow algorithm, such as Ford-Fulkerson algorithm, Edmonds-Karp algorithm, or Dinic algorithm, to calculate the maximum flow.

[0120] Algorithm implementation: The maximum flow from the total flow source point to the sink point is calculated using the selected algorithm to determine the flow distribution for each edge (cooling channel).

[0121] Check flow limits: Verify whether the calculated flow of each edge exceeds its maximum carrying flow.

[0122] Adjust and optimize: If the flow in some waterways exceeds the maximum carrying capacity, adjust the network model or reconfigure local resistances to change the flow distribution.

[0123] In specific applications, the flow distribution of the cooling system includes:

[0124] Consider a simple cooling system consisting of three cooling channels, each connected to a central collection point, and each channel has a different carrying capacity limit.

[0125] Nodes: waterway entrances A, B, C, and central gathering point D.

[0126] Side and capacity: A→D (maximum load 10 units), B→D (maximum load 15 units), C→D (maximum load 8 units).

[0127] Calculate the maximum flow from A, B, C to D, ensuring that the flow distribution does not exceed the capacity limit of each edge.

[0128] Assume that the initial flow calculation results are A→D=10, B→D=12, and C→D=6.

[0129] Verify that the flow rate of all water channels does not exceed their maximum carrying capacity, and adjust the settings of each water channel based on the calculation results to achieve the ideal flow distribution.

[0130] If the flow rate of the cooling channel does not meet the maximum carrying flow rate, the bottleneck area between the current cooling channels is weighted and the weighted distributed local resistance flow rate is generated.

[0131] Among them, weighted allocation of bottleneck areas between current cooling channels includes:

[0132] Establish a multidimensional linear equation group of bottleneck area weights between cooling channels, and solve the multidimensional linear equation group to obtain the subjective weight of each bottleneck area;

[0133] Based on the subjective weight of each bottleneck area, the standard deviation of each bottleneck area is calculated to obtain the objective weight of each bottleneck area;

[0134] An integrated weighted model is constructed based on the ratio of subjective weight and objective weight, and the integrated weighted vector is solved based on the principle of maximizing the target value of the bottleneck area, and the integrated weighted vector is used as the weight of the bottleneck area between the current cooling channels.

[0135] It should be noted that an integrated weighted model based on the ratio of subjective weight and objective weight should be constructed, and the integrated weighted vector should be solved according to the principle of maximizing the target value of the bottleneck area, and then this vector is used as the weight of the bottleneck area between cooling channels, which specifically includes:

[0136] It is determined by combining expert opinions, preference surveys or decision makers' judgments, reflecting the importance that decision makers attach to each factor; it is obtained through data analysis or algorithms (such as hierarchical analysis method, entropy weight method, etc.), reflecting the statistical importance of each factor in the actual data.

[0137] Use linear weighting, geometric mean or other suitable integration methods to integrate subjective weights and objective weights into a comprehensive weight, and determine the proportion of subjective weights and objective weights in the integrated weight based on the specific needs and background of the project.

[0138] A weighted model is constructed using the integrated weights, which is used to evaluate and compare the performance of the bottleneck area between the cooling channels, and an objective function is established according to the target value of the bottleneck area (such as minimizing the temperature increase or maximizing the heat transfer efficiency).

[0139] Linear programming, nonlinear programming, multi-objective optimization and other methods are used to solve the integrated weighted vector according to the established objective function, and various parameters and constraints are adjusted to seek the optimal solution of the objective function.

[0140] The integrated weighted vector obtained by solving the problem is used as the weight of the bottleneck area between each cooling channel. Based on the weight result, the flow distribution in the cooling system is adjusted to optimize the performance of the bottleneck area.

[0141] The sum of the weighted distribution of the local resistance flows in each bottleneck area is taken as the blocking flow loss caused by the bottleneck area.

[0142] The choking flow loss is eliminated from the cooling parameter distribution to obtain the optimized cooling parameter distribution, and the cooling uniformity of the injection mold is evaluated based on the cooling parameter distribution.

[0143] S2. Construct boundary element models for the injection mold and the cooling insert respectively, and use the boundary element model to analyze the heat transfer performance when the injection mold and the cooling insert are in contact.

[0144] It should be noted that the boundary element models are constructed for the injection mold and the cooling insert respectively, and the boundary element model is used to analyze the heat transfer performance when the injection mold and the cooling insert are in contact, including:

[0145] The detailed geometry of the mold and insert is drawn using CAD software and simplified when necessary to suit the needs of boundary element analysis.

[0146] The surfaces of the mold and inserts are properly meshed. The boundary element method focuses on the boundaries and improves the calculation efficiency and accuracy through reasonable meshing.

[0147] Set the temperature distribution and boundary conditions at the beginning of the simulation, such as ambient temperature and specific operating conditions, and define the thermal contact impedance of the contact interface between the mold and the insert, as well as any special conditions that may affect heat transfer.

[0148] The boundary element analysis software is used to perform heat transfer analysis on the model, and the accuracy and convergence of the solution are ensured through iterative calculation. Parameters or meshes may need to be adjusted during the solution process.

[0149] Analyze the heat transfer efficiency at the interface between the mold and the cooling insert, focusing on how the heat is transferred from the insert to the mold, and evaluate the temperature distribution to ensure that the temperature is evenly distributed inside the mold, thereby improving product quality.

[0150] S3. Generate an optimal cooling test result of the injection mold by combining heat transfer performance and cooling uniformity, and calculate the difference between the optimal cooling test result and the actual cooling test result.

[0151] Among them, the optimal cooling test results of the injection mold are generated by combining heat transfer performance and cooling uniformity, and the difference between the optimal cooling test results and the actual cooling test results is calculated, including:

[0152] The heat transfer performance and cooling uniformity of the injection mold are taken as cooling variables respectively, and the game equilibrium model is used to obtain the optimal cooling variable combination of the injection mold.

[0153] Among them, the heat transfer performance and cooling uniformity of the injection mold are used as cooling variables respectively, and the game equilibrium model is used to obtain the optimal cooling variable combination of the injection mold, including:

[0154] The heat transfer performance and cooling uniformity of the injection mold are respectively used as game parties, each game party corresponds to a cooling variable, and a cooling variable set is set for each game party;

[0155] Randomly generate initial strategy combinations in each cooling variable set and define the payoff function for each player;

[0156] Iteratively optimize each player based on its profit function, continuously generate the latest strategy of each player in the iterative optimization, and calculate the difference between the new and old strategies;

[0157] Update the iterative optimization results based on the difference between the new and old strategies, and integrate the iterative optimization results to obtain the optimal strategy combination. The optimal strategy combination should meet the constraints of cooling uniformity and maximum heat transfer performance.

[0158] Determine whether the optimal strategy combination meets the convergence criteria. If so, the game ends; if not, continue to perform the iterative optimization process;

[0159] When the optimal strategy combination meets the convergence criterion, the optimal strategy combination is output to obtain the optimal cooling variable combination based on the injection mold.

[0160] It should be noted that the game equilibrium model is an important concept in game theory, which is used to analyze the strategic choices and results of participants (called "game parties") with mutually competitive or interdependent behaviors under certain rules. For example, in the optimization of injection mold cooling, the game equilibrium model can be used to find the best operating point so that all relevant factors can achieve the optimal balance.

[0161] In the Nash equilibrium state, no player can obtain a better result by unilaterally changing its strategy, and each player's strategy is the best response to the strategies of other players.

[0162] Games usually involve multiple players (players), each of whom can choose multiple strategies. In the case of injection molds, heat transfer performance and cooling uniformity can be considered as two players.

[0163] Each player has its own payment function (or revenue function), which is based on the player's strategy choice and the strategy choices of other players. Specifically, it includes:

[0164] Identify the parties involved in the game (such as heat transfer performance and cooling uniformity), and define a set of possible strategies for each party (such as cooling channel layout, flow rate, temperature, etc.).

[0165] For injection mold applications, heat transfer performance may be targeted at maximizing heat transfer efficiency, while cooling uniformity may be targeted at minimizing temperature differences within the mold.

[0166] Find a Nash equilibrium and determine the optimal strategy for each player given the strategies of the other players.

[0167] Based on the optimal cooling variable combination, a parameter index trend chart is formulated to obtain the optimal cooling parameter indexes of the heat transfer performance and cooling uniformity of the injection mold, and the optimal cooling test results of the injection mold are obtained through a limited number of cycles;

[0168] It should be noted that the parameter indicator trend graphs can be drawn based on the collected data using charting software or programming tools (such as Python's Matplotlib library). These graphs should show how the cooling parameters affect the heat transfer performance and cooling uniformity.

[0169] Based on the trend graph analysis, determining the optimal parameter combination for improving heat transfer efficiency and cooling uniformity may require multiple rounds of iterations, each time adjusting the parameters based on the results of the previous round until the best balance is found.

[0170] The optimal cooling test result of the injection mold is calculated with the predefined actual cooling test result to obtain the difference calculation result.

[0171] The calculation formula for the difference between the optimal cooling test result and the actual cooling test result is:

[0172]

[0173] Wherein, μ(Y, X) represents the difference between the optimal cooling test result and the actual cooling test result; Y represents the optimal cooling test result; X represents the actual cooling test result; n represents the number of indicators associated with each test result; k represents the number of each ... optimal cooling test result; X represents the actual cooling test result; n represents the optimal cooling test result; k represents the kth optimal cooling test result; X(k) represents the kth actual cooling test result corresponding to the optimal cooling test result; μ(Y k , X(k)) represents the difference between the kth optimal cooling test result and the actual cooling test result. Its calculation focuses on the comparison of a single test result, and the average value of these comparison results gives the overall average difference, which can be used to evaluate the deviation between the overall performance of the cooling system and the optimal performance.

[0174] S4. Compare the difference between the optimal cooling test result and the actual cooling test result with a preset threshold, and evaluate the cooling efficiency of the injection mold based on the comparison result.

[0175] Among them, comparing the difference between the optimal cooling test result and the actual cooling test result with a preset threshold, and evaluating the cooling efficiency of the injection mold based on the comparison result includes:

[0176] Comparing the difference calculation result with a preset threshold value;

[0177] If the difference calculation result is greater than or equal to the preset threshold, it means that the cooling effect of the injection mold is poor; if the difference calculation result is less than the preset threshold, it means that the cooling effect of the injection mold is strong;

[0178] Fault tree analysis is used to identify and record the causes of poor cooling of injection molds, and the injection molds are optimized based on the causes of the failures.

[0179] It should be noted that FTA is a systematic and analytical method to determine the various factors that may cause a specific failure in a system, product or process. In the cooling system of the injection mold, the use of FTA can help identify the causes of poor cooling effect and optimize it accordingly, including:

[0180] Identify the top event for the fault tree analysis, which is the least desirable event in the system, such as "poor injection mold cooling."

[0181] Start with the top-level event and work backwards to the direct and secondary causes that may have led to that event.

[0182] Use logic gates (such as AND gates, OR gates) to represent the logical relationship between fault events. For example, poor cooling effect may be caused by "cooling channel blockage" and "cooling water flow rate is too low" (AND relationship).

[0183] Base events are the bottom-level causes in a fault tree. These are the causes of failure that cannot be further decomposed. For example, a cooling channel blockage may be caused by "deposits accumulation" or "manufacturing defects."

[0184] The probability of occurrence of each basic event is assessed. This can be based on historical data, empirical estimates, or other reliable sources, and the probability of occurrence of the top events is calculated to determine the overall risk of poor cooling effectiveness.

[0185] Perform effect analysis on each fault cause identified in the fault tree to determine their specific impact on the production process, and give priority to those fault causes with the greatest impact and highest probability of occurrence.

[0186] like Figure 2 As shown, according to another embodiment of the present invention, there is also provided an injection mold cooling detection system based on data analysis, the injection mold cooling detection system based on data analysis comprising:

[0187] A cooling uniformity evaluation module 1 is used to obtain the circulation fluid trajectory of the coolant in the cooling circuit and evaluate the cooling uniformity of the injection mold based on the cooling parameter distribution of the coolant flowing through each cooling channel;

[0188] A heat transfer performance analysis module 2 is used to construct boundary element models for the injection mold and the cooling insert respectively, and use the boundary element model to analyze the heat transfer performance when the injection mold and the cooling insert are in contact;

[0189] A cooling test result generating module 3 is used to generate an optimal cooling test result of the injection mold by combining the heat transfer performance and the cooling uniformity, and calculate the difference between the optimal cooling test result and the actual cooling test result;

[0190] The cooling efficiency evaluation module 4 is used to compare the difference between the optimal cooling detection result and the actual cooling detection result with a preset threshold value, and evaluate the cooling efficiency of the injection mold based on the comparison result.

[0191] In summary, with the help of the above technical solution of the present invention, the present invention can accurately simulate the flow of coolant in a complex cooling circuit, and form a circulating fluid trajectory by simulating the collision reaction between the coolant and the cooling channel wall, thereby providing a basis for in-depth understanding of the coolant fluid behavior. Through the simulated coolant fluid trajectory and cooling parameter distribution, the bottleneck area in the cooling circuit can be identified, and the local resistance flow rate in these areas can be redistributed and adjusted, which can optimize the flow distribution of the entire cooling system, thereby providing accurate cooling uniformity detection data. The present invention allocates appropriate local resistance flow in the bottleneck area to help manage and reduce the additional resistance and pressure loss caused by changes in flow direction, which helps to maintain the overall efficiency of the cooling system, and by adjusting the local resistance in the bottleneck area, the flow rate can be controlled in the bottleneck area, thereby reducing the additional loss caused by too fast or too slow flow rate, and at the same time, combined with the weighted allocation technology to reduce the flow rate in these areas from being too high or too low, avoiding eddy currents and local heat accumulation caused by fluid separation, and effectively managing the flow characteristics of the bottleneck area, thereby reducing the mold failure rate caused by uneven cooling or excessive thermal stress. The present invention uses heat transfer performance and cooling uniformity as cooling variables to obtain more accurate cooling detection results, and uses a game equilibrium model to dynamically adjust cooling parameters to ensure that cooling uniformity is achieved while maintaining optimal heat transfer performance. This is crucial to improving the quality of the mold. Through the optimized combination of cooling variables, product defects caused by uneven cooling or insufficient heat transfer can be effectively reduced. The difference between the optimal cooling detection results and the actual results is calculated and updated in real time, making the adjustment of the injection mold cooling system more accurate and timely.

[0192] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting cooling of injection molds based on data analysis, characterized in that: The method includes: S1. Obtain the circulation fluid trajectory of the coolant in the cooling circuit, and evaluate the cooling uniformity of the injection mold based on the cooling parameter distribution of the coolant flowing through each cooling channel; S2. construct boundary element models for the injection mold and the cooling insert respectively, and use the boundary element model to analyze the heat transfer performance when the injection mold and the cooling insert are in contact; S3, generating an optimal cooling test result of the injection mold by combining heat transfer performance and cooling uniformity, and calculating the difference between the optimal cooling test result and the actual cooling test result; S4, comparing the difference between the optimal cooling test result and the actual cooling test result with a preset threshold, and evaluating the cooling efficiency of the injection mold based on the comparison result; The S1 includes: The lattice Boltzmann technique is used to simulate the circulating fluid motion of the coolant in the cooling circuit, and the circulating fluid trajectory of the coolant is formed by combining the collision reaction between the coolant and each cooling channel; Based on the circulation fluid trajectory of the coolant, the cooling parameters of the coolant flowing through each cooling channel are collected, and the cooling parameter distribution trend is generated to identify the bottleneck area between each cooling channel; allocating a local resistance flow to the bottleneck area, and identifying the blocking flow loss caused by the bottleneck area based on the allocated local resistance flow; The choking flow loss is eliminated from the cooling parameter distribution to obtain the optimized cooling parameter distribution, and the cooling uniformity of the injection mold is evaluated based on the cooling parameter distribution.

2. The method for detecting cooling of injection molds based on data analysis according to claim 1, characterized in that: The method of simulating the circulating fluid motion of the coolant in the cooling circuit by using the lattice Boltzmann technique and forming the circulating fluid trajectory of the coolant by combining the collision reaction between the coolant and each cooling channel includes: Define the initial geometric conditions of each cooling channel in the cooling circuit, and set the initial fluid parameters and initial distribution function of the coolant; In each time step, the collision effect approximation algorithm is used to calculate the distribution function of the coolant when it flows through the current cooling channel and transfers to the adjacent cooling channel, and the macro variables of the coolant between the adjacent cooling channels are obtained, and the macro variables are used as the initial conditions for the next time step; A rebound rule is established to obtain the collision reaction between the coolant and each cooling channel, and the collision and fluid movement of the coolant between adjacent cooling channels are continuously repeated to form a circulating fluid trajectory of the coolant in the cooling circuit.

3. The method for detecting cooling of injection mold based on data analysis according to claim 1, characterized in that: The method of allocating a local resistance flow to the bottleneck area and identifying the blocking flow loss caused by the bottleneck area based on the allocated local resistance flow includes: Mark the bottleneck area between each cooling channel and assign unique indexes to the bottleneck area and cooling channel respectively; Based on the cooling difference at the bottleneck area between the cooling channels, an initial local resistance flow is allocated to each bottleneck area; Combined with the initial local resistance flow at the bottleneck area, the total coolant flow is distributed to each cooling channel using the network flow algorithm to determine whether the flow of each cooling channel meets the maximum carrying flow; If the flow rate of the cooling channel does not meet the maximum carrying flow rate, the bottleneck area between the current cooling channels is weighted and the weighted distribution local resistance flow rate is generated; The sum of the weighted distribution of the local resistance flows in each bottleneck area is taken as the blocking flow loss caused by the bottleneck area.

4. The method for detecting cooling of injection molds based on data analysis according to claim 3, characterized in that: The weighted allocation of the bottleneck areas between the current cooling channels includes: Establish a multidimensional linear equation group of bottleneck area weights between cooling channels, and solve the multidimensional linear equation group to obtain the subjective weight of each bottleneck area; Based on the subjective weight of each bottleneck area, the standard deviation of each bottleneck area is calculated to obtain the objective weight of each bottleneck area; An integrated weighted model is constructed based on the ratio of subjective weight and objective weight, and the integrated weighted vector is solved based on the principle of maximizing the target value of the bottleneck area, and the integrated weighted vector is used as the weight of the bottleneck area between the current cooling channels.

5. The method for detecting cooling of injection molds based on data analysis according to claim 1, characterized in that: The method of generating an optimal cooling test result of the injection mold by combining the heat transfer performance and the cooling uniformity, and calculating the difference between the optimal cooling test result and the actual cooling test result includes: The heat transfer performance and cooling uniformity of the injection mold are used as cooling variables, and the game equilibrium model is used to obtain the optimal cooling variable combination of the injection mold. Based on the optimal cooling variable combination, a parameter index trend chart is formulated to obtain the optimal cooling parameter indexes of the heat transfer performance and cooling uniformity of the injection mold, and the optimal cooling test results of the injection mold are obtained through a limited number of cycles; The optimal cooling test result of the injection mold is calculated with the predefined actual cooling test result to obtain the difference calculation result.

6. The method for detecting cooling of injection molds based on data analysis according to claim 5, characterized in that: The heat transfer performance and cooling uniformity of the injection mold are used as cooling variables respectively, and the optimal cooling variable combination of the injection mold is obtained by using the game equilibrium model, including: The heat transfer performance and cooling uniformity of the injection mold are respectively used as game parties, each game party corresponds to a cooling variable, and a cooling variable set is set for each game party; Randomly generate initial strategy combinations in each cooling variable set and define the payoff function for each player; Iteratively optimize each player based on its profit function, continuously generate the latest strategy of each player in the iterative optimization, and calculate the difference between the new and old strategies; Update the iterative optimization results based on the difference between the new and old strategies, and integrate the iterative optimization results to obtain the optimal strategy combination. The optimal strategy combination should meet the constraints of cooling uniformity and maximum heat transfer performance. Determine whether the optimal strategy combination meets the convergence criteria. If so, the game ends; if not, continue to perform the iterative optimization process; When the optimal strategy combination meets the convergence criterion, the optimal strategy combination is output to obtain the optimal cooling variable combination based on the injection mold.

7. The method for detecting cooling of injection molds based on data analysis according to claim 6, characterized in that: The step of comparing the difference between the optimal cooling detection result and the actual cooling detection result with a preset threshold value, and evaluating the cooling efficiency of the injection mold based on the comparison result includes: Comparing the difference calculation result with a preset threshold value; If the difference calculation result is greater than or equal to the preset threshold, it means that the cooling effect of the injection mold is poor; if the difference calculation result is less than the preset threshold, it means that the cooling effect of the injection mold is strong; Fault tree analysis is used to identify and record the causes of poor cooling of injection molds, and the injection molds are optimized based on the causes of the failures.

8. The method for detecting cooling of injection molds based on data analysis according to claim 7, characterized in that: The calculation formula for the difference between the optimal cooling test result and the actual cooling test result is: Wherein, μ(Y, X) represents the difference between the optimal cooling test result and the actual cooling test result; Y represents the optimal cooling test result; X represents the actual cooling test result; n represents the number of indicators associated with each test result; k represents the number of each ... optimal cooling test result; X represents the actual cooling test result; n represents the optimal cooling test result; k represents the kth optimal cooling test result; X(k) represents the kth actual cooling test result corresponding to the optimal cooling test result; μ(Y k , X(k)) represents the difference between the kth optimal cooling detection result and the actual cooling detection result.

9. An injection mold cooling detection system based on data analysis, used to implement the injection mold cooling detection method based on data analysis as described in any one of claims 1 to 8, characterized in that: The data analysis-based injection mold cooling detection system includes: A cooling uniformity evaluation module is used to obtain the circulation fluid trajectory of the coolant in the cooling circuit and evaluate the cooling uniformity of the injection mold based on the cooling parameter distribution of the coolant flowing through each cooling channel; Heat transfer performance analysis module, which is used to build boundary element models for injection molds and cooling inserts respectively, and use the boundary element model to analyze the heat transfer performance when the injection mold and cooling insert are in contact; A cooling test result generation module is used to generate the optimal cooling test result of the injection mold by combining the heat transfer performance and cooling uniformity, and calculate the difference between the optimal cooling test result and the actual cooling test result; The cooling efficiency evaluation module is used to compare the difference between the optimal cooling detection result and the actual cooling detection result with a preset threshold value, and evaluate the cooling efficiency of the injection mold based on the comparison result.

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