Automatic design method and system for injection mold cooling system
By automating the design of the injection mold cooling system and optimizing the cooling channels using temperature clustering and genetic algorithms, the problems of uneven cooling and long cycle times were solved, achieving uniform cooling and rapid molding of complex topological structures.
Patent Information
- Application Number
- CN202511576483.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-31
- Publication Date
- 2026-01-30
AI Technical Summary
Existing injection mold cooling system designs rely on manual experience and do not utilize the actual melt temperature distribution, resulting in uneven cooling and longer cooling cycles. Furthermore, traditional design methods fail to effectively handle complex topological structures.
An automated design method is adopted. By performing discrete mesh modeling on the 3D model of the plastic part, the temperature distribution at the melt front is obtained. The k-means algorithm is used for temperature clustering, and the cooling channel parameters are optimized by combining the genetic algorithm to generate Zig-Zag shaped cooling channel paths, thereby realizing parametric layout and manufacturability optimization.
It achieves uniform cooling based on the real molding thermal history, shortens the molding cycle, is suitable for injection molds with complex topologies, and reduces reliance on senior designers.
Smart Images
Figure CN121435720A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of mold design technology, and in particular to an automated design method and system for an injection mold cooling system. Background Technology
[0002] In injection molding, the molding cycle is mainly affected by the cooling stage, which typically accounts for 70% to 80% of the total cycle. Furthermore, the efficiency and uniformity of the cooling process directly affect the appearance, dimensional accuracy, and warpage of the part. Traditional linear cooling channels struggle to conform to complex free-form surfaces, often resulting in localized hot spots, uneven cooling, and longer cycles. Adaptive cooling channels, facilitated by additive manufacturing, can be arranged along the part's shape, significantly improving heat transfer and surface temperature distribution while shortening cooling time, thus becoming an industry trend.
[0003] However, many design processes in existing technologies still rely on experience or extensive manual intervention, or require designers to provide multiple process / geometric parameters in advance. Furthermore, some porous / crystalline schemes are prone to significant pressure drops and turbulence losses. More importantly, existing methods often fail to consider the actual temperature distribution at the end of filling as input for cooling system design, frequently substituting it with isothermal assumptions, resulting in significant deviations in complex topologies. To obtain high-quality and manufacturable cooling solutions, the industry urgently needs an automated design and verification method that integrates part geometry, molding thermal history, and manufacturing constraints. Summary of the Invention
[0004] In view of the above technical problems, the present invention provides an automated design method and system for injection mold cooling systems to solve the problems of existing cooling system designs being highly dependent on human experience, not utilizing actual melt temperature distribution, uneven cooling, and long cycle times, and to achieve parametric layout and manufacturability optimization based on real molding thermal history.
[0005] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part by practice of this disclosure.
[0006] According to one aspect of the present invention, an automated design method for an injection mold cooling system is proposed, the method comprising: Discrete mesh modeling is performed on the three-dimensional model of the plastic part, and the geometry of the plastic part is discretized into a three-dimensional discrete mesh model composed of multiple nodes and triangular facets; The temperature distribution of the melt front of the plastic part after injection molding is completed is obtained, and the melt front temperature value of the molten plastic is obtained at each node of the three-dimensional discrete mesh model. The k-means algorithm is used to perform temperature clustering on the melt front temperature value of each node, and nodes with similar temperatures are divided into the same cluster. Based on the temperature clustering results, a multi-dimensional data structure is generated, and the spatial coordinates of each node in the three-dimensional discrete mesh model are associated with the corresponding temperature clustering results to form a multi-dimensional discrete model for cooling channel design. Based on the multidimensional discrete model, the design parameters of the cooling system are optimized by a genetic algorithm. The design parameters include the inlet temperature of the cooling medium, the cooling time, the spacing between cooling channels, the diameter of the cooling channels, and the distance between the cooling channels and the mold surface. The cooling channel spacing, optimized using a genetic algorithm, is used to generate several parallel cross sections in the three-dimensional discrete mesh model of the plastic part, with the cooling channel spacing as the interval, and Zig-Zag shaped cooling channel paths are arranged along each cross section. Based on the optimized cooling channel diameter and the distance between the cooling channel and the mold surface, the Zig-Zag shaped cooling channel path is offset relative to the mold surface, and a circular cross-section is swept along the cooling channel path to construct a three-dimensional geometric model of the cooling channel.
[0007] Furthermore, the triangular facet is defined by three nodes, and the dimensional accuracy of the triangular facet in the three-dimensional discrete mesh model is set according to the wall thickness of the plastic part.
[0008] Furthermore, in obtaining the temperature distribution at the melt front, the following steps are included: The temperature distribution of the melt front is obtained by numerically simulating the injection mold cavity filling process of the plastic part. The three-dimensional discrete mesh model is imported into the injection molding simulation software, and the gate position, filling time, holding pressure time and cooling time are set to simulate the injection filling stage. The temperature value of the molten plastic front is obtained at each node of the three-dimensional discrete mesh model, and the temperature value is exported as the input parameter for subsequent design.
[0009] Furthermore, when using the k-means algorithm for temperature clustering, the method further includes: evaluating the clustering results for different numbers of clusters using the Davies-Bouldin index to determine the optimal number of clusters for temperature clustering, wherein the optimal number of clusters is limited to between 2 and 6.
[0010] Furthermore, the generated multidimensional data structure is a hybrid matrix. For each node of the three-dimensional discrete mesh model, the hybrid matrix records its Cartesian coordinates, the melt front temperature value, the identifier of the temperature cluster to which it belongs, and the average temperature of the cluster, thereby associating the spatial coordinates of each discrete location of the plastic part with the temperature clustering information.
[0011] Furthermore, during the genetic algorithm optimization, the cooling medium inlet temperature, cooling time, cooling channel spacing, and cooling channel diameter are set as global parameters applicable to the entire plastic part for optimization, while the distance between the cooling channel and the mold surface is optimized as a local parameter set independently for each temperature cluster, thereby obtaining the optimal parameter combination that takes into account both the overall cooling requirements and those of each temperature cluster.
[0012] Furthermore, the genetic algorithm employs a multi-objective optimization strategy, transforming the performance requirements of the injection mold cooling process into objective functions. These include reducing the temperature difference between different regions of the mold cavity surface and the target uniform temperature, shortening the cooling time, and reducing the average temperature difference of each temperature cluster after cooling. This achieves uniform cooling of the plastic part and shortens the injection molding cycle. During the optimization process, constraints are set for the design parameters, limiting the cooling channel spacing, cooling channel diameter, and distance between the cooling channel and the mold surface to within preset ranges to meet process requirements and ensure the manufacturability of the designed cooling channel structure.
[0013] Furthermore, by intersecting several parallel sections with the three-dimensional discrete mesh model according to the optimized cooling channel spacing, multiple section node sets are obtained; each section node set is classified according to the demolding direction of each region of the plastic part to determine the nodes located on the cavity side and the core side respectively; on each section, the cooling channel path in the form of a smooth curve is generated by connecting the nodes, and the cooling channel paths on adjacent sections are arranged alternately on the cavity side and the core side to form a Zig-Zag arrangement; all the cooling channel paths are connected in sequence to obtain a continuous Zig-Zag-shaped cooling channel path that runs through the cavity side and the core side of the plastic part.
[0014] Furthermore, based on the optimized distance parameters corresponding to each temperature cluster, the cooling channel path of the Zig-Zag shape is offset along the normal direction of the mold surface to a predetermined distance from the mold surface to determine the position of the cooling channel axis; a circular cross-section sweep model is performed along the axis according to the optimized cooling channel path to generate the three-dimensional geometric model, and the inlet and outlet positions of the cooling channel in the mold are determined.
[0015] According to a second aspect of this disclosure, an automated design system for an injection mold cooling system is provided, the system comprising: The mesh modeling module is used to perform discrete mesh modeling on the three-dimensional model of the plastic part, discretizing the geometry of the plastic part into a three-dimensional discrete mesh model composed of multiple nodes and triangular facets; A multidimensional discrete modeling module is used to obtain the melt front temperature distribution of the plastic part after injection molding and filling. The melt front temperature value of the molten plastic is obtained at each node of the three-dimensional discrete mesh model. The k-means algorithm is used to perform temperature clustering on the melt front temperature values of each node, and nodes with similar temperatures are divided into the same cluster. A multidimensional data structure is generated based on the temperature clustering results, and the spatial coordinates of each node in the three-dimensional discrete mesh model are associated with the corresponding temperature clustering results to form a multidimensional discrete model for cooling channel design. The optimization module is used to optimize the design parameters of the cooling system using a genetic algorithm based on the multidimensional discrete model. The design parameters include the inlet temperature of the cooling medium, the cooling time, the spacing between cooling channels, the diameter of the cooling channels, and the distance between the cooling channels and the mold surface. The cooling channel calculation module is used to generate several parallel cross sections in the three-dimensional discrete mesh model of the plastic part with the cooling channel spacing as the interval, using the cooling channel spacing as the interval, and to arrange Zig-Zag shaped cooling channel paths along each cross section. The generation module is used to offset and position the Zig-Zag shaped cooling channel path relative to the mold surface based on the optimized cooling channel diameter and the distance between the cooling channel and the mold surface, and to sweep the circular cross section along the cooling channel path to construct a three-dimensional geometric model of the cooling channel.
[0016] The technical solution disclosed herein has the following beneficial effects: By acquiring and utilizing temperature distribution information on the surface of the plastic part at the end of the filling process, combined with temperature clustering and multidimensional data structures, the system drives parametric optimization and geometry generation of the cooling system. This allows for targeted treatment of hot spots from the source, achieving more uniform cooling and a shorter molding cycle. The design process simultaneously embeds manufacturing and process boundaries (such as channel diameter, channel spacing, and channel-to-surface distance), providing complete 3D channel geometry and inlet / outlet positions, adapting to additive manufacturing mold making. The entire process can automatically complete dimensional design and scheme verification, reducing reliance on experienced mold designers and making it suitable for industrial parts with complex topologies such as deep cavities, ribs, and varying wall thicknesses. Attached Figure Description
[0017] Figure 1 This is a flowchart illustrating an automated design method for an injection mold cooling system as described in the embodiments of this specification. Figure 2This is a structural block diagram of an automated design system for an injection mold cooling system, as described in one of the embodiments of this specification. Detailed Implementation
[0018] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.
[0019] Furthermore, the accompanying drawings are merely illustrative of this disclosure. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.
[0020] This invention provides an automated design method for a product injection mold cooling system. (Refer to...) Figure 1 The diagram shown is a flowchart illustrating an automated design method for an injection mold cooling system according to an embodiment of the present invention. This method can be applied to electronic devices such as personal computers and servers. The method can be executed by a device, which can be implemented by software and / or hardware. Specifically, the method may include the following steps S101-S105: In step S101, the three-dimensional model of the plastic part is discretized into a three-dimensional discrete mesh model composed of multiple nodes and triangular facets.
[0021] The triangular facet is defined by three nodes, and the dimensional accuracy of the triangular facet in the three-dimensional discrete mesh model is set according to the wall thickness of the plastic part.
[0022] The process uses a 3D virtual model of a plastic part as input, representing it as a geometric solid located in 3D Euclidean space. The geometry is discretized to obtain a three-dimensional discrete mesh model. The three-dimensional discrete mesh consists of two basic elements: one is nodes, denoted as... One is used to record the spatial position of each discrete point in the Cartesian coordinate system; the other is a surface patch, which in this embodiment is limited to a triangular surface patch, denoted as . Each triangular facet is uniquely defined by three nodes, forming a local approximation of the plastic part's surface. Through the combination of these nodes and triangular facets, complex freeform surfaces can be stably represented with a finite number of planar elements without altering the original topology, facilitating subsequent discrete geometric analysis and mapping of thermal parameters. For ease of understanding, this embodiment interprets nodes as vertices of a 3D discrete mesh, the smallest geometric entity storing spatial coordinates; triangular facets as the smallest surface element composed of three adjacent nodes; and the 3D discrete mesh model as a polygonal surface model formed by topologically connecting the aforementioned nodes and triangular facets, its purpose being to carry geometric and manufacturing-related information in a discrete and computationally friendly manner. Furthermore, although discretization can be extended to other polygon types, this embodiment uses triangular facets as a unified unit to achieve better surface approximation stability and robustness of downstream algorithms.
[0023] The dimensional accuracy of the triangular facets is set by the wall thickness of the plastic part: when the wall thickness is small or the size of local detailed features is small, a higher mesh accuracy is selected to ensure that geometric details are fully captured; when the wall thickness is large or the surface change is gradual, the average facet size can be increased accordingly to improve discretization efficiency. Therefore, this embodiment introduces the concept of mesh accuracy in discretization modeling, matching the average side length of the triangular facets with the set accuracy, thereby adaptively controlling the mesh density globally with the wall thickness as a reference. This approach avoids computational redundancy caused by excessive subdivision in thick-walled areas while ensuring geometric fidelity in critical areas such as thin walls and corners.
[0024] In step S102, the melt front temperature distribution of the plastic part after injection molding is completed is obtained, and the melt front temperature value of the molten plastic is obtained at each node of the three-dimensional discrete mesh model. The k-means algorithm is used to perform temperature clustering on the melt front temperature value of each node, and nodes with similar temperatures are divided into the same cluster. Based on the temperature clustering results, a multi-dimensional data structure is generated, and the spatial coordinates of each node in the three-dimensional discrete mesh model are associated with the corresponding temperature clustering results to form a multi-dimensional discrete model for cooling channel design.
[0025] The process of obtaining the temperature distribution at the melt front includes: obtaining the temperature distribution at the melt front by numerically simulating the injection mold cavity filling process of the plastic part; importing the three-dimensional discrete mesh model into injection molding simulation software; setting the gate position, filling time, holding time, and cooling time; simulating the injection filling stage; obtaining the temperature value of the molten plastic front at each node of the three-dimensional discrete mesh model; and exporting the temperature value as an input parameter for subsequent design.
[0026] When using the k-means algorithm for temperature clustering, the method further includes: evaluating the clustering results for different numbers of clusters using the Davies-Bouldin index to determine the optimal number of clusters for temperature clustering, wherein the optimal number of clusters is limited to between 2 and 6.
[0027] The generated multidimensional data structure is a hybrid matrix. For each node of the three-dimensional discrete mesh model, the hybrid matrix records its Cartesian coordinates, the melt front temperature value, the identifier of the temperature cluster to which it belongs, and the average temperature of the cluster, thereby associating the spatial coordinates of each discrete position of the plastic part with the temperature clustering information.
[0028] First, the melt front temperature distribution at the completion of injection filling is obtained based on a three-dimensional discrete mesh model. To this end, the discrete mesh model obtained in step S101 is used as input and imported into the injection molding numerical simulation environment to establish a transient thermal-flow analysis scenario for the filling stage. Based on the part and gate design, the Cartesian coordinates of the injection point and the gate geometry (e.g., denoted as...) are set in the model. ), and fill time is given according to molding process. Holding time Cooling time and the melt temperature related to the material Mold temperature Demolding temperature Process parameters such as maximum injection pressure. After numerical calculation, at each node of the discrete mesh... The temperature of the molten plastic front at the corresponding moment is obtained and denoted as . (Unit: °C), and the temperature field is exported in a readable format such as ASCII as input for subsequent temperature clustering and data structure construction. Based on the above results, the geometric coordinates of each node and the melt front temperature at the node are used to form the basic columns of the mixing matrix, specifically: .
[0029] This expression corresponds to a four-dimensional data structure with nodes as rows and geometric-thermal properties as columns, where node coordinates are used to identify spatial locations and melt front temperature values are used to characterize the thermal history of the filling end.
[0030] The obtained node temperature sequences were used as clustering samples, and the k-means algorithm was used to perform temperature clustering. Nodes with similar temperatures were grouped into the same cluster based on temperature similarity. The average temperature of each cluster sample was used as the cluster center temperature. In this process, the objective is to minimize the sum of squares of temperature deviations within the cluster, and its mathematical objective function is: ; To reasonably determine the number of clusters, the Davies-Bouldin (DB) index is introduced to evaluate cluster quality, and it is defined as follows: ; in The ratio of intra-cluster to inter-cluster distance is calculated as follows: ; in This represents the average distance from each point within cluster u to the centroid of cluster u. This represents the average distance from each point within cluster v to the centroid of cluster v. This represents the Euclidean distance between the centroids of clusters u and v. A smaller DB exponent indicates better clustering performance. In this embodiment, the number of candidate clusters is limited to between 2 and 6, and the cluster number corresponding to the minimum DB exponent is taken as the optimal cluster number. For ease of implementation, the number of candidate clusters can be organized into a vector. : ; and = 2, = 6. After selecting the optimal number of clusters and performing clustering, the mixture matrix is expanded by adding a temperature cluster identifier to each node record. With average cluster temperature Two columns expand the node data from four dimensions to six dimensions, forming a multidimensional discrete model for subsequent design and analysis, specifically: ; In the above description, temperature clustering refers to unsupervised grouping based solely on the melt front temperature at the end of the filling process; cluster identifiers are used to map nodes to their respective temperature level regions; and the cluster average temperature... The temperature serves as the representative temperature of this temperature level region; while the multidimensional discrete model (a six-dimensional form of a hybrid matrix) carries both spatial geometry and thermal-topological labels at the node granularity, realizing point-by-point association between spatial coordinates and temperature clustering information.
[0031] In step S103, the design parameters of the cooling system are optimized by a genetic algorithm based on the multidimensional discrete model. The design parameters include the inlet temperature of the cooling medium, the cooling time, the spacing between cooling channels, the diameter of the cooling channels, and the distance between the cooling channels and the mold surface.
[0032] When performing the genetic algorithm optimization, the cooling medium inlet temperature, the cooling time, the cooling channel spacing and the cooling channel diameter are set as global parameters applicable to the entire plastic part for optimization, while the distance between the cooling channel and the mold surface is set as a local parameter independently for each temperature cluster for optimization, thereby obtaining the optimal parameter combination that takes into account both the overall cooling needs and the cooling needs of each temperature cluster.
[0033] The genetic algorithm employs a multi-objective optimization strategy, transforming the performance requirements of the injection mold cooling process into objective functions. These include reducing the temperature difference between different regions of the mold cavity surface and the target uniform temperature, shortening the cooling time, and reducing the average temperature difference of each temperature cluster after cooling. This achieves uniform cooling of the plastic part and shortens the injection molding cycle. Furthermore, during the optimization process, constraints are set for the design parameters, limiting the cooling channel spacing, cooling channel diameter, and distance between the cooling channel and the mold surface to within preset ranges to meet process requirements and ensure that the designed cooling channel structure is manufacturable.
[0034] In this step, a genetic algorithm is used to optimize the design parameters of the cooling system based on the established multidimensional discrete model. The design parameters include the inlet temperature of the cooling medium, cooling time, cooling channel spacing, cooling channel diameter, and the distance between the cooling channels and the mold surface. The first four parameters are optimized as global parameters (i.e., uniform values are used throughout the entire cooling system), while the distance between the cooling channels and the mold surface is used as a local parameter, with optimal values set for different temperature clusters (temperature clusters refer to several regions divided according to the temperature field distribution of the plastic part, with each cluster corresponding to a local cooling design parameter). The genetic algorithm in this step employs a multi-objective optimization strategy, using improving the uniformity of the mold temperature field, shortening the cooling time, and reducing the temperature difference within the same cluster as the optimization objective functions. Mathematically, this can be expressed as: Temperature uniformity optimization objective: ; Among them, among them, This represents the temperature of the i-th region on the surface of the mold cavity. Indicates the target uniform temperature Cooldown time optimization goal: ; Optimization objective for temperature difference within clusters: ; in, Represents clusters The number of nodes in For nodes The melt front temperature at that point For clusters The average temperature.
[0035] At the same time, in order to ensure that the optimization results can be put into actual manufacturing, reasonable value ranges should be set in advance for each design parameter as constraints.
[0036] In step S104, the cooling channel spacing obtained by the genetic algorithm is used to generate several parallel cross sections in the three-dimensional discrete mesh model of the plastic part with the cooling channel spacing as the interval, and Zig-Zag shaped cooling channel paths are arranged along each cross section.
[0037] By intersecting several parallel cross-sections with the three-dimensional discrete mesh model according to the optimized cooling channel spacing, multiple cross-section node sets are obtained; each cross-section node set is classified according to the demolding direction of each region of the plastic part to determine the nodes located on the cavity side and the core side respectively; on each cross-section, the cooling channel path in the form of a smooth curve is generated by connecting the nodes, and the cooling channel paths on adjacent cross-sections are arranged alternately on the cavity side and the core side to form a Zig-Zag arrangement; all the cooling channel paths are connected in sequence to obtain a continuous Zig-Zag-shaped cooling channel path that runs through the cavity side and the core side of the plastic part.
[0038] In simple terms, this step uses the cooling channel spacing optimized in step S103 as a basis. Multiple parallel cross-sectional planes are set along the demolding direction of the plastic part (i.e., the direction of mold opening and closing) in the three-dimensional discrete mesh model of the plastic part. The spacing between each plane is the channel spacing optimized above. Intersecting each cross-sectional plane with the discrete mesh model yields a series of discretely distributed nodes on the surface of the plastic part. Then, based on the demolding direction of the plastic part and the mold structure, the nodes obtained from each cross-sectional plane are grouped according to their spatial orientation, categorized as nodes located on the cavity side and nodes located on the core side (i.e., closer to the cavity plate and core plate, respectively). Next, within each cross-sectional plane, a group of nodes belonging to the same side (cavity side or core side) are sequentially connected to form a smooth curved path, and the curved paths formed by adjacent cross-sectional planes are arranged alternately between the cavity side and the core side—for example, the path of the previous cross-section is located on the cavity side, and the path of the next cross-section is located on the core side, thus forming an overall zig-zag cooling channel layout. Finally, all the curved path segments generated in the cross-sectional plane are connected end to end in sequence to obtain a continuous cooling channel path that runs through the entire plastic part.
[0039] In step S105, based on the optimized cooling channel diameter and the distance between the cooling channel and the mold surface, the Zig-Zag shaped cooling channel path is offset relative to the mold surface, and a circular cross-section is swept along the cooling channel path to construct a three-dimensional geometric model of the cooling channel.
[0040] Based on the optimized distance parameters corresponding to each temperature cluster, the cooling channel path of the Zig-Zag shape is offset along the normal direction of the mold surface to a predetermined distance from the mold surface to determine the position of the cooling channel axis; according to the optimized cooling channel path, a circular cross-section sweep model is performed along the axis to generate the three-dimensional geometric model, and the inlet and outlet positions of the cooling channel in the mold are determined.
[0041] In step S105, based on the optimal distance between the cooling channel corresponding to each temperature cluster and the mold surface, the obtained Zig-Zag cooling path is offset and positioned at that distance along the normal direction of the mold surface to determine the axial position of the cooling channel. For the path segments corresponding to different temperature cluster regions on the plastic part surface, they are offset in a direction perpendicular to the plastic part surface according to the optimized distance, so that the cooling channel path is located at an appropriate depth from the cavity or core surface in each region, forming a three-dimensional channel axis conforming to the shape of the plastic part. Subsequently, using the optimized cooling channel diameter as the cross-sectional dimension, a circular cross-section is swept across the above axis to generate a three-dimensional solid geometric model of the cooling channel. Finally, the inlet and outlet positions of the cooling channel on the mold are determined and set. Exemplarily, the two ends of the channel are located at opposite ends of the plastic part and connected to the inlet and outlet outside the mold, thereby completing the design of a conforming cooling channel that runs through the interior of the plastic part.
[0042] Based on the same line of thought, such as Figure 2 The diagram shown is a structural block diagram of an automated design system for an injection mold cooling system according to an embodiment of the present invention. The system includes: Mesh modeling module 201 is used to perform discrete mesh modeling on the three-dimensional model of the plastic part, and to discretize the geometry of the plastic part into a three-dimensional discrete mesh model composed of multiple nodes and triangular facets. The multidimensional discrete modeling module 202 is used to obtain the melt front temperature distribution of the plastic part after injection molding and filling. It obtains the melt front temperature value of the molten plastic at each node of the three-dimensional discrete mesh model. The k-means algorithm is used to perform temperature clustering on the melt front temperature values of each node, and nodes with similar temperatures are divided into the same cluster. Based on the temperature clustering results, a multidimensional data structure is generated, and the spatial coordinates of each node in the three-dimensional discrete mesh model are associated with the corresponding temperature clustering results to form a multidimensional discrete model for cooling channel design. The optimization module 203 is used to optimize the design parameters of the cooling system using a genetic algorithm based on the multidimensional discrete model. The design parameters include the inlet temperature of the cooling medium, the cooling time, the spacing between cooling channels, the diameter of the cooling channels, and the distance between the cooling channels and the mold surface. The cooling channel calculation module 204 is used to generate several parallel cross sections in the three-dimensional discrete mesh model of the plastic part with the cooling channel spacing as the interval, using the cooling channel spacing as the interval, and to arrange Zig-Zag shaped cooling channel paths along each cross section. The generation module 205 is used to offset and position the Zig-Zag shaped cooling channel path relative to the mold surface based on the optimized cooling channel diameter and the distance between the cooling channel and the mold surface, and to sweep the circular cross section along the cooling channel path to construct a three-dimensional geometric model of the cooling channel.
[0043] The specific details of the above system have been described in detail in the method section of the implementation plan. For any undisclosed details, please refer to the implementation plan of the method section, and therefore will not be repeated here.
[0044] This system acquires and utilizes temperature distribution information on the surface of the plastic part at the end of the filling process. Combining temperature clustering and multidimensional data structures, it drives the parametric optimization and geometric generation of the cooling system. This allows for targeted treatment of hot spots from the source, achieving more uniform cooling and a shorter molding cycle. During the design process, manufacturing and process boundaries (such as channel diameter, channel spacing, and channel-to-surface distance) are simultaneously embedded, providing complete 3D channel geometry and inlet / outlet positions, adapting to additive manufacturing mold making. The entire process can automatically complete dimensional design and scheme verification, reducing reliance on experienced mold designers. It is suitable for industrial parts with complex topologies such as deep cavities, ribs, and varying wall thicknesses.
[0045] The accompanying drawings are merely illustrative of the processes included in the methods according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the drawings do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.
[0046] It should be noted that although several modules or units of the system have been mentioned in the detailed description above, this division is not mandatory. In fact, according to exemplary embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.
[0047] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.
[0048] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.
Claims
1. A method of automated design of an injection mold cooling system, characterized in that, The method comprises: discrete grid modeling on a three-dimensional model of a plastic part, discretizing the geometric shape of the plastic part into a three-dimensional discrete grid model composed of multiple nodes and triangular facets; obtaining the melt front temperature distribution of the plastic part after the completion of injection filling, obtaining the melt front temperature value of the molten plastic at each node of the three-dimensional discrete grid model; using a k-means algorithm to perform temperature clustering on the melt front temperature value of each node, dividing nodes with similar temperatures into the same cluster; generating a multi-dimensional data structure based on the temperature clustering result, associating the spatial coordinates of each node in the three-dimensional discrete grid model with the corresponding temperature clustering result, forming a multi-dimensional discrete model for cooling channel design; performing genetic algorithm optimization on cooling system design parameters according to the multi-dimensional discrete model, the design parameters including cooling medium inlet temperature, cooling time, cooling channel spacing, cooling channel diameter, and distance between cooling channel and mold surface; using the cooling channel spacing obtained by genetic algorithm optimization, generating a plurality of parallel cross sections in the three-dimensional discrete grid model of the plastic part at intervals of the cooling channel spacing, and arranging a Zig-Zag-shaped cooling channel path along each cross section; based on the optimized cooling channel diameter and the distance between the cooling channel and the mold surface, biasing and positioning the Zig-Zag-shaped cooling channel path relative to the mold surface, and performing circular cross-section sweeping along the cooling channel path to construct a three-dimensional geometric model of the cooling channel.
2. The automated design method of injection mold cooling system according to claim 1, wherein, The triangular facet is defined by three nodes, and the triangular facet size precision of the three-dimensional discrete grid model is set according to the wall thickness of the plastic part.
3. The automated design method of injection mold cooling systems of claim 1, wherein, When obtaining the melt front temperature distribution, it includes: obtaining the melt front temperature distribution by numerically simulating the filling process of the injection cavity of the plastic part, importing the three-dimensional discrete grid model into an injection molding simulation software, setting the gate position, filling time, holding time and cooling time, simulating the injection filling stage, obtaining the temperature value of the molten plastic front at each node of the three-dimensional discrete grid model, and exporting the temperature value as an input parameter for subsequent design.
4. The automated design method of injection mold cooling systems of claim 1, wherein, When performing temperature clustering using the k-means algorithm, it further includes: evaluating the clustering results under different cluster numbers by Davies-Bouldin index to determine the optimal cluster number of temperature clustering, which is limited between 2 and 6.
5. The automated design method of injection mold cooling systems of claim 1, wherein, The generated multi-dimensional data structure is a hybrid matrix, which records the Cartesian coordinates, melt front temperature value, temperature clustering cluster identification and cluster average temperature of each node of the three-dimensional discrete grid model, thereby associating the spatial coordinates of each discrete position of the plastic part with the temperature clustering information.
6. The automated design method of injection mold cooling systems of claim 1, wherein, During the optimization of the genetic algorithm, the cooling medium inlet temperature, the cooling time, the cooling channel spacing and the cooling channel diameter are set as global parameters applicable to the whole plastic part for optimization, and the distance between the cooling channel and the mold surface is set as a local parameter independently set for each temperature clustering cluster for optimization, so as to obtain an optimal parameter combination considering the overall and cooling requirements of each temperature clustering cluster.
7. The automated design method of injection mold cooling systems of claim 1, wherein, The genetic algorithm adopts a multi-objective optimization strategy, converts the performance requirements of the injection mold cooling process into objective functions, including reducing the difference between the temperature of each region of the mold cavity surface and the target uniform temperature, shortening the cooling time, and reducing the average temperature difference of each temperature clustering cluster after cooling, so as to realize uniform cooling of the plastic part and shorten the injection molding cycle; and during the optimization process, the design parameters are set with constraints, limiting the cooling channel spacing, the cooling channel diameter and the distance between the cooling channel and the mold surface within a predetermined range to meet the process requirements and ensure that the designed cooling channel structure is manufacturable.
8. The automated design method of injection mold cooling systems of claim 1, wherein, By intersecting a plurality of parallel cross sections with the three-dimensional discrete grid model according to the optimized cooling channel spacing, a plurality of cross section node sets are obtained; each cross section node set is classified according to the demolding direction of each region of the plastic part to determine the nodes located on the cavity side and the core side, respectively; the cooling channel path in the form of a smooth curve is generated on each cross section by connecting the nodes, and the cooling channel paths on adjacent cross sections are alternately arranged on the cavity side and the core side to form a Zig-Zag arrangement; all the cooling channel paths are sequentially connected to obtain a continuous Zig-Zag-shaped cooling channel path through the cavity side and the core side of the plastic part.
9. The automated design method of injection mold cooling systems of claim 1, wherein, Based on the optimized distance parameters corresponding to each temperature clustering cluster, the Zig-Zag-shaped cooling channel path is offset to a predetermined distance from the mold surface along the normal direction of the mold surface to determine the position of the cooling channel axis; the three-dimensional geometric model is generated by performing circular cross-section sweeping modeling along the axis according to the optimized cooling channel path, and the inlet and outlet positions of the cooling channel in the mold are determined.
10. An automatic design system of an injection mold cooling system, the system comprising: a grid modeling module for discretely modeling a three-dimensional model of a plastic part, and discretizing the geometric shape of the plastic part into a three-dimensional discrete grid model composed of a plurality of nodes and triangular facets; a multi-dimensional discrete modeling module for obtaining a melt front temperature distribution of the plastic part after injection filling is completed, and obtaining a melt front temperature value of the molten plastic at each node of the three-dimensional discrete grid model; a k-means algorithm is used to perform temperature clustering on the melt front temperature values of each node, and nodes with similar temperatures are divided into the same clustering cluster; a multi-dimensional data structure is generated based on the temperature clustering result, the spatial coordinates of each node in the three-dimensional discrete grid model are associated with the corresponding temperature clustering result, and a multi-dimensional discrete model for cooling channel design is formed; an optimization module configured to perform a genetic algorithm optimization on cooling system design parameters including cooling medium inlet temperature, cooling time, cooling channel spacing, cooling channel diameter, and cooling channel distance from mold surface based on the multi-dimensional discrete model; a cooling channel calculation module configured to generate a plurality of mutually parallel cross sections in the three-dimensional discrete mesh model of the plastic part at the cooling channel spacing obtained by the genetic algorithm optimization, and arrange a Zig-Zag shaped cooling channel path along each cross section; a generation module configured to position the Zig-Zag shaped cooling channel path relative to the mold surface based on the cooling channel diameter and the cooling channel distance from mold surface obtained by the optimization, and perform a circular cross section sweep along the cooling channel path to construct a three-dimensional geometric model of the cooling channel.