Method for manufacturing heat sink and heat sink

Abstract

To provide a method for manufacturing a heat sink that enables the manufacture of a heat sink having a three-dimensional structure as a three-dimensional structure that reproduces a designed shape with high precision, and a heat sink that can be manufactured by this manufacturing method.SOLUTION: A method for manufacturing a heat sink having a heat dissipation element having a three-dimensional structure in which a set of nodes arranged three-dimensionally are connected by a set of edges includes a manufacturing data generation step, a modeling process analysis step, and a modeling defect diagnosis step, a molding limit setting step, and an additional manufacturing step.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method for manufacturing a heat sink used for dissipating heat in electronic devices such as computers, and to a heat sink that can be manufactured by this manufacturing method. [Background technology]

[0002] Conventional heat sinks have a structure in which multiple plate-like fins are provided parallel to one another on one surface of a base material, and are formed by casting, extrusion molding, machining, or other methods using materials with high thermal conductivity, such as aluminum (see, for example, Patent Documents 1 and 2). In these heat sinks, many fins of the same shape are arranged at regular intervals, which creates an air flow along the fins, contributing to improved heat dissipation efficiency.

[0003] In such conventional heat sinks, in order to obtain a specified heat dissipation efficiency, a large number of fins are arranged across one entire surface of the base material. Although there is space between adjacent fins, the heat sink essentially occupies a volume equivalent to that of a solid body with the fins at both ends as side walls and the base material as the bottom wall. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 57-193049 [Patent Document 2] Japanese Patent Application Publication No. 63-235031 Summary of the Invention [Problem to be solved by the invention]

[0005] In response to this, the inventors are actively conducting research into configuring heat dissipation elements as three-dimensional structures based on graph theory, with a set of edges (lines connecting points) connecting sets of nodes (points) arranged in a three-dimensional manner, in order to increase the amount of heat dissipation while keeping small the volume of the space in which the heat dissipation elements are placed.

[0006] Therefore, the present invention has an object to provide a manufacturing method of a heat sink that enables a heat sink having a three-dimensional structure to be manufactured as a three-dimensional structure that reproduces a design shape with high accuracy, and a heat sink that can be manufactured by this manufacturing method. Another object of the present invention is to provide a heat sink that can exhibit stable heat dissipation performance regardless of the position when installed, and a manufacturing method of this heat sink. [Means for solving the problem]

[0007] In order to solve the above problems, the manufacturing method of a heat sink of the present invention is a manufacturing method of a heat sink equipped with a heat dissipation element having a three-dimensional structure in which a set of three-dimensionally arranged nodes are connected by a set of edges, and includes a manufacturing data generation process for generating manufacturing data that can be modeled by additive manufacturing based on design values ​​of the nodes and edges, a modeling process analysis process for generating mesh structure data based on the manufacturing data and calculating a shape deviation between the mesh structure data and the manufacturing data, and a maximum principal stress value at a predetermined position on the mesh structure data, and a first predetermined range for the shape deviation and a second predetermined range for the maximum principal stress value, which are set to realize a three-dimensional structure corresponding to the design values ​​for the shape deviation and maximum principal stress value calculated in the modeling process analysis process. The method is characterized by comprising: a printing defect diagnosis process for predicting areas that will cause printing defects by exceeding the range; a printing limit setting process for setting a printing limit to be considered in the manufacturing data generation process in order to eliminate the printing defects predicted in the printing defect diagnosis process, based on the results of the printing process analysis process and a predetermined range set in advance for the printing limit; and an additive manufacturing process for, when a new printing limit is obtained in the printing limit setting process, repeating the manufacturing data generation process, the printing process analysis process, the printing defect diagnosis process, and the printing limit setting process, and, when both the shape deviation and the maximum principal stress value fall within the first and second predetermined ranges, respectively, in the printing defect diagnosis process, performing additive manufacturing based on the manufacturing data at that time to manufacture the heat sink.

[0008] The heat sink of the present invention is a heat sink equipped with a heat dissipation element, the heat dissipation element comprising a set of edges connecting a set of nodes arranged three-dimensionally, a part of the set of nodes being arranged on a substrate, the heat sink having a three-dimensional lattice structure in which a plurality of layers are stacked in the out-of-plane direction of the surface of the substrate, the edges being arranged more densely in the central portion than in the outer portions in the in-plane direction of the surface the further away from the substrate the layers are, a first ventilation hole is formed in the farthest layer farthest from the substrate, and a second ventilation hole is formed in the outer portion of a layer closer to the substrate than the farthest layer.

[0009] The heat sink of the present invention is a heat sink equipped with a heat dissipation element, the heat dissipation element comprising a set of edges connecting a set of nodes arranged three-dimensionally, a part of the set of nodes being arranged on a substrate, and having a three-dimensional lattice structure in which a plurality of layers are stacked in the out-of-plane direction of the surface of the substrate, the edges being arranged more densely in the central part than in the outer part in the in-plane direction of the surface as the layer is further from the substrate, a first air vent is formed in the farthest layer furthest from the substrate, and a second air vent is formed in the outer part of a layer closer to the substrate than the farthest layer, and depending on the posture of the heat sink, air heated by a heat source close to the heat sink is released from the first air vent or the second air vent which is located higher in the vertical direction, and air flows in from the first air vent or the second air vent which is located lower in the vertical direction. Effect of the Invention

[0010] According to the heat sink manufacturing method of the present invention, it is possible to manufacture a heat sink having a designed three-dimensional structure as a three-dimensional structure that reproduces the designed shape with high accuracy. This manufacturing method makes it possible to reduce the volume of the space in which the heat dissipation elements are arranged, while increasing the amount of heat dissipation, and to realize a heat sink that exhibits stable heat dissipation performance regardless of the installation position. [Brief description of the drawings]

[0011] [Figure 1] 4 is a flowchart showing a flow of a method for manufacturing a heat sink according to an embodiment of the present invention. [Diagram 2] FIG. 1 is a block diagram showing an example of the configuration of a computing device that can be used in an embodiment of the present invention. [Diagram 3] FIG. 13 is a diagram showing an example of a groove pattern engraved in a flow channel expressed by graph theory. [Figure 4] FIG. 2 is a diagram showing an example of a three-dimensional lattice structure in an embodiment of the present invention. [Diagram 5] 1 is a flowchart showing a procedure of optimization calculation in an embodiment of the present invention. [Figure 6] FIG. 2 is a diagram showing a model of a target region and boundary conditions of a thermal fluid analysis according to an embodiment of the present invention. [Figure 7] 1 is a graph in which a scatter diagram of sample data searched by an optimization calculation in an embodiment of the present invention is plotted in an objective function space. [Figure 8] This is a visualization of the structure of a ready-made heat sink and the generated flow field. [Figure 9] FIG. 1 is a visualization of the structure and generated flow field of sample 09-001, one of the heat sinks relating to a two-objective optimum solution with a three-dimensional lattice structure in an embodiment of the present invention. [Figure 10] FIG. 1 is a visualization of the structure and generated flow field of sample 08-001, one of the heat sinks relating to a two-objective optimum solution with a three-dimensional lattice structure in an embodiment of the present invention. [Figure 11] FIG. 1 is a visualization of the structure and generated flow field of sample 10-002, among the heat sinks relating to a two-objective optimum solution with a three-dimensional lattice structure in an embodiment of the present invention. [Figure 12] FIG. 2 is a diagram showing the appearance of a model used in a simulation for analyzing a modeling process. [Figure 13] FIG. [Figure 14] FIG. 13 is a diagram showing the maximum principal stress distribution during molding. [Figure 15] FIG. 13 is a diagram showing the maximum principal stress distribution during molding. [Figure 16] FIG. 1 is a diagram showing the appearance of a model used in a heat transfer performance experiment. [Figure 17] FIG. 1 is a visualization of the thermal boundary layer. [Figure 18] FIG. 13 is a diagram showing an apparatus configuration when a natural convection cooling experiment is performed as a heat transfer performance experiment with a heat sink arranged vertically. [Figure 19] FIG. 13 is a diagram showing an apparatus configuration for conducting a natural convection cooling experiment as a heat transfer performance experiment with a heat sink placed flat. [Figure 20]20 is a graph showing the relationship between the position in the X direction and the temperature, obtained in a natural convection cooling experiment in which the heat sink 44 was placed flat using the device shown in FIG. 19. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, a method for manufacturing a heat sink and a heat sink according to an embodiment of the present invention will be described in detail with reference to the drawings. Fig. 1 is a flowchart showing the flow of a method for manufacturing a heat sink according to this embodiment. Fig. 2 is a block diagram showing an example of the configuration of a computing device that can be used in this embodiment. In this embodiment, a heat sink is manufactured through each process shown in Fig. 1. Here, the flow of each process will be described with reference to Fig. 1, and experimental results of each process will be described later.

[0013] The heat dissipation elements of a heat sink can be designed by various methods, but here, a design based on graph theory will be taken as an example. In the design process (step S11), design values ​​are calculated for each of the nodes and edges as the heat dissipation elements of the heat sink. The heat dissipation elements of the heat sink have a three-dimensional structure with a set of edges connecting a set of three-dimensionally arranged nodes. The nodes are nodes or vertices that form the start and end points of each heat dissipation element. The edges are branch-like, side-like, linear, or columnar elements that connect two nodes that form one heat dissipation element. The thickness of the edges and the cross-sectional shape perpendicular to the direction in which the edges extend can be set according to the specifications of the heat sink, the tolerances in the manufacturing process, and the like.

[0014] The calculation of the design value is performed in the calculation device shown in FIG. 2, for example, a computer, based on the information input from the input unit 15 and the design information previously stored in the storage unit 14, under the control of the control circuit 11. The information input from the input unit 15, the design information used in the calculation, the progress and results of the calculation, etc. are displayed on the display unit 16. In the calculation, for example, the calculation circuit 12 selects nodes connected by edges, and the adjustment circuit 13 adjusts and optimizes the topology of the three-dimensional structure of the nodes using a predetermined optimization algorithm, for example, an optimization algorithm based on a population search. The calculation results by the calculation circuit 12 and the calculation results by the adjustment circuit 13 are stored in the storage unit 14 every time a calculation is performed, and are read out by the control circuit 11 during adjustment by the adjustment circuit 13.

[0015] Next, in a manufacturing data generation process (step S12), manufacturing data, for example CAD data, for modeling by additive manufacturing (AM modeling), for example modeling by a 3D printer, is generated based on the design values ​​of the nodes and edges calculated in the design process (step S11). The manufacturing data is generated by the arithmetic circuit 12 under the control of the control circuit 11, based on generation information previously stored in the storage unit 14, and the generated data is saved in the storage unit 14.

[0016] In the three-dimensional structure designed in the design process (step S11), an edge where one of the nodes is not supported by the base material of the heat sink or the edge of the lower layer constitutes an overhanging portion. For this reason, in the manufacturing data generation process (step S12), the overhanging portion is identified, and a supporting edge for supporting the overhanging portion by the base material or the edge of the lower layer is added to the manufacturing data. This ensures that the heat sink can be manufactured reliably in the subsequent additive manufacturing process (step S17).

[0017] Next, in the modeling process analysis step (step S13), mesh structure data is generated based on the manufacturing data generated in the manufacturing data generation step (step S12), and the shape deviation between the mesh structure data and the manufacturing data, and the maximum principal stress value at a predetermined position on the mesh structure data are calculated. The mesh structure data is generated by the arithmetic circuit 12 under the control of the control circuit 11 based on generation information previously stored in the storage unit 14, and the generated data is saved in the storage unit 14.

[0018] In the modeling process analysis step (step S13), the inherent strain method is used to calculate the shape deviation between the mesh structure data and the manufacturing data, and the maximum principal stress value at a predetermined position on the mesh structure data.

[0019] The next printing defect diagnosis step (step S14) predicts a portion that will cause a printing defect by exceeding a first predetermined range for the shape deviation and a second predetermined range for the maximum principal stress value, which are set to realize a three-dimensional structure corresponding to the design values, for the shape deviation and the maximum principal stress value calculated in the printing process analysis step (step S13). The printing defect diagnosis is performed by the arithmetic circuit 12 under the control of the control circuit 11 based on the diagnosis information previously stored in the memory unit 14, and the diagnosis result is stored in the memory unit 14.

[0020] If a modeling defect is diagnosed in the modeling defect diagnosis process (step S14) (YES in step S15), the modeling limit setting process (step S16) is executed, and then the design process (step S11), the manufacturing data generation process (step S12), the modeling process analysis process (step S13), and the modeling defect diagnosis process (step S14) are executed until the modeling defect is eliminated.

[0021] In the printing limit setting step (step S16), the printing limit to be considered in the manufacturing data generating step is set based on the result of the printing process analysis step and a predetermined range set in advance for the printing limit in order to eliminate the printing defect predicted in the printing defect diagnosis step (step S14). The printing limit is set by the arithmetic circuit 12 under the control of the control circuit 11 based on setting information stored in advance in the memory unit 14, and the setting result is stored in the memory unit 14.

[0022] If it is diagnosed that there is no printing defect in the above-mentioned printing defect diagnosis process (step S14) (NO in step S15), the additive manufacturing process (step S17) is executed. In the additive manufacturing process (step S17), additive manufacturing is performed based on the manufacturing data at that time to manufacture the heat sink. The manufacturing is performed, for example, by providing a manufacturing instruction signal corresponding to the manufacturing data to a 3D printer connected to the arithmetic device shown in FIG. 2 based on the control of the control circuit 11, and modeling is performed based on this signal. The overhang portion is removed after modeling (step S18).

[0023] Next, in the heat transfer performance experiment process (step S19), a heat transfer performance experiment is performed on the heat sink manufactured in the additive manufacturing process (step S17) using a heat source in a natural convection environment. In the subsequent heat transfer performance analysis process (step S20), the heat transfer performance obtained in the heat transfer performance experiment is analyzed, and the analysis results are fed back to the design process (step S11) to improve the prediction accuracy of the heat transfer performance in the design process. The heat transfer performance experiment is performed using an experimental device (FIGS. 18 and 19) described below, and the results are input to the calculation device shown in FIG. 2, the heat transfer performance analysis is performed by the calculation circuit 12, and the analysis results are stored in the memory unit 14.

[0024] Next, in an inherent strain test process (step S21), an inherent strain experiment is performed by heating and cooling a test piece made of the material used in the heat dissipation element. In the subsequent inherent strain analysis process (step S22), the inherent strain value obtained in the inherent strain experiment is analyzed, and the analysis results are fed back to the molding process analysis process (step S13) to improve the accuracy of defect diagnosis in the molding process analysis process (step S13) and the subsequent molding defect diagnosis process (step S14). The results of the inherent strain test are input to the calculation device shown in FIG. 2, and inherent strain analysis is performed by the calculation circuit 12, and the analysis results are stored in the memory unit 14.

[0025] Next, a performance convergence judgment (step S23) is executed. For example, the performance convergence judgment is performed by measuring the total heat transfer amount (amount of heat) and volume of the manufactured heat sink, comparing them with the total heat transfer amount (amount of heat) and volume as the objective function when the topology is optimized in the design process (step S11), and judging whether or not each of them converges within a preset range. The judgment is performed by changing the object to be measured, for example, the total heat transfer amount and the volume, according to the objective function in the design process (step S11). The performance convergence judgment is performed by the arithmetic circuit 12 under the control of the control circuit 11 based on judgment information including the set range pre-stored in the storage unit 14, and the diagnosis result is saved in the storage unit 14.

[0026] In the performance convergence judgment (step S23), if the total heat transfer amount and volume have converged within the set range (YES in step S24), it is determined that a heat sink with optimized topology has been manufactured, and the manufacturing process is terminated.

[0027] On the other hand, if the total heat transfer amount and volume have not converged within the set range in the performance convergence judgment (step S23) (NO in step S24), it is determined that the performance has not converged, and the design process (step S11) is performed again based on the measured total heat transfer amount and volume.

[0028] <Heat sink configuration> As described above, the heat sink according to the present embodiment has a three-dimensional structure, and has a three-dimensional lattice structure in which multiple layers are stacked in the out-of-plane direction of the surface of the substrate, for example, in the normal direction of the surface. In a configuration stacked along the normal direction, a set of nodes arranged on a surface perpendicular to the normal direction forms one layer. In the three-dimensional lattice structure, the edges in the central part have a higher edge arrangement density than the outer part in the plane direction of the surface as the layer is further away from the substrate. Here, when looking at the change in edge arrangement density from the layer closest to the substrate to the furthest layer, the edge arrangement density in adjacent layers above and below is also included. The arrangement density being the same means that the density has the same numerical value, as well as that the difference in density is small enough that the air inside the heat sink, which is warmed by being close to the heat source, does not change the direction or flow rate of the air flow flowing vertically upward.

[0029] In the three-dimensional lattice structure, the first ventilation hole is formed in the farthest layer farthest from the substrate, and the second ventilation hole is formed in the outer part of the layer closer to the substrate than the farthest layer. For example, in the case of a three-layer laminated structure, the second ventilation hole is provided in one or both of the layer closest to the substrate and the layer farthest from this layer. More specifically, in the three-dimensional lattice structure shown in Figs. 9 to 11, a plurality of voids serving as the first ventilation hole Gt are provided in the farthest layer L3 farthest from the substrate 20 in the normal direction Dn. Also, in the layer L1 closest to the substrate 20, voids serving as the second ventilation hole Gb are provided in the outer part, that is, in the ranges corresponding to each of the four sides of the substrate 20 that forms a square when viewed from above in the normal direction Dn. The first ventilation hole Gt is formed in quadrangular symmetry when viewed from above in the normal direction Dn, and the second ventilation hole Gb is also formed in quadrangular symmetry.

[0030] In a heat sink with a three-dimensional lattice structure as described above, air heated by a heat source close to the heat sink is released from the first ventilation opening Gt or the second ventilation opening Gb whichever is higher in the vertical direction, depending on the posture, which differs depending on the object, orientation, method, etc. of installation, and accordingly, surrounding air flows in from the first ventilation opening Gt or the second ventilation opening Gb whichever is lower in the vertical direction.

[0031] For example, when a heat sink is placed flat with the stacking direction of its multiple layers aligned vertically, air heated by a heat source close to the heat sink is released from the first ventilation opening Gt of the farthest layer, and air flows in through the second ventilation opening Gb.

[0032] On the other hand, when the heat sink is placed vertically so that the stacking direction of its multiple layers is horizontal, air heated by a heat source close to the heat sink is released from the first air vent Gt or the second air vent Gb, whichever is located higher in the vertical direction, and accordingly, air flows in from the first air vent Gt or the second air vent Gb, whichever is located lower in the vertical direction.

[0033] The overhang portion H is formed in any layer from the layer immediately above the layer closest to the substrate to the farthest layer, except for the layer closest to the substrate. The position of the overhang portion H corresponds to the position of the second ventilation port Gb, and the gap formed by the overhang portion H increases the opening area of ​​the second ventilation port Gb, thereby increasing the flow rate of air passing through the second ventilation port Gb.

[0034] <Design process (step S11)> The arrangement of a set of nodes connected by a set of edges can be performed by various methods. For example, by selecting a set of nodes and a set of edges based on the graph theory described below and using a population search-based optimization algorithm in combination with response surface methodology, it is possible to adjust the topology of a three-dimensional structure and optimize the topology.

[0035] Any algorithm can be used as the population search-based optimization algorithm. For example, in addition to the genetic algorithm described below, a memetic algorithm, a path recombination method, an ant colony method, a particle swarm optimization method, and a differential evolution method can be used.

[0036] The set of nodes may be a set of nodes distributed in a three-dimensional space, or may be a set of nodes arranged at any position on the substrate. The shape of the substrate is not particularly limited, but may be, for example, a plate with a flat surface, a curved surface, a sphere, a hemisphere, etc. In addition, arranging the nodes on the substrate may include arranging the nodes on the surface of the substrate, as well as on the back surface, side surface, or inner surface opposite the surface.

[0037] When some nodes are arranged on the substrate, the remaining nodes are arranged in a three-dimensional space away from the substrate. The edges are set to extend in any out-of-plane direction with respect to the surface of the substrate. Here, the out-of-plane direction of the substrate surface is the direction away from the surface, and more specifically, for a node arranged on the surface of the substrate, it is the direction away from the surface at the arrangement position. In addition, the out-of-plane direction can be the same for all nodes arranged on the surface of the substrate, but it may be different from each other for some or all of the nodes arranged on the surface.

[0038] In the above-mentioned configuration in which some of the nodes are arranged on the substrate, the remaining nodes can be arranged so that multiple layers are stacked in the out-of-plane direction of the substrate surface, forming a three-dimensional lattice structure as a whole. The number of layers can be set arbitrarily.

[0039] In addition, the shape of the substrate in plan view from the normal direction of the surface can be any shape as long as the topology of the three-dimensional structure is adjusted. Examples of such plan view shapes include a square, a triangle, other regular polygons, and a circle.

[0040] Furthermore, in a configuration in which some of the nodes are arranged on the substrate, the set of edges is composed of a first edge that starts from a node arranged on the surface of the substrate, and a second edge that is connected to the first edge directly or via another edge. In a three-dimensional lattice configuration in which the set of nodes is arranged so as to stack multiple layers in the out-of-plane direction of the surface of the substrate, the first edge and the second edge are each set to extend at an angle of less than a predetermined angle, for example, less than 45 degrees, with respect to the stacking direction.

[0041] The topology is adjusted to maximize or minimize based on an objective function related to the heat sink. The number of objective functions can be set arbitrarily according to the specifications of the heat sink. Examples of the objective functions include an index representing the heat dissipation efficiency of the heat sink, an index representing the material cost of the heat sink, and an index representing the thermal fluid performance related to the heat sink. These indexes can be used alone as the objective function, or two or more of them can be used in combination. When an index representing the heat dissipation efficiency of the heat sink is used, the topology is adjusted to maximize this index, and when an index representing the material cost of the heat sink is used, the topology is adjusted to minimize this index. The index representing the thermal fluid performance is adjusted to maximize, for example, the flow rate per unit time.

[0042] Examples of the index representing the heat dissipation efficiency of a heat sink and the thermal fluid performance of the fluid related to the heat sink include the total heat transfer amount Q of the heat sink, the temperature gradient, and the shape ratio (intake / exhaust ratio) of the vent at the bottom of the heat sink to the vent at the top of the heat sink, while examples of the index representing the material cost of the heat sink include the volume V of the material constituting the heat sink and the unit price of the material. The respective indexes are not limited to the above examples, and for example, the volume V of the material may be used as an index representing the heat dissipation efficiency of the heat sink. Also, each index may be combined in any ratio. The total heat transfer amount Q is evaluated, for example, with the base material as the heat source and the working fluid as air.

[0043] Here, when considering the manufacture of the heat sink, the topology adjustment preferably includes the identification of the manufacturing limit for the design value of the set of nodes and the set of edges. When the heat sink is manufactured by a 3D printer or other additive manufacturing, the identification of the manufacturing limit in the additive manufacturing is included, and for example, feedback based on the experimental results of the heat transfer performance and inherent distortion of the heat sink manufactured by additive manufacturing is included.

[0044] As the response surface methodology used for adjusting the topology, for example, the Kriging model can be used.

[0045] In this embodiment, a heat sink used to remove heat from electronic devices such as computers is designed by topology optimization. Although many previous studies have focused on improving the performance of heat sinks, here we also focus on material costs and tackle multi-objective optimization. In the past, the inventors have considered the two-dimensional extrusion structure of heat sink fins as a structure similar to natural objects (such as tree branches, plant veins, and bronchi), and expressed this using a recursive data format grammar called Lindenmayer Systems (L-systems). By using multi-objective optimization, they created a new heat sink that has equivalent performance to existing designs but can significantly reduce material costs.

[0046] In contrast to this, in this embodiment, the three-dimensional lattice structure of the heat sink is newly targeted and expressed by graph theory, thereby achieving multi-objective optimization of a more complex and fine heat sink.

[0047] <Graph Theory> Graph theory is a theory that abstracts how nodes are connected by a set of nodes (vertices) and a set of edges (branches) that connect them. For example, when looking at a route map for a train or bus, the issue is how stations (nodes) are connected by routes (edges), but the specific curves of the tracks are often not an essential issue. In other words, the main important information is how stations are connected, and this is the role of graph theory.

[0048] Here, an example of the pattern of grooves (micromixers) carved into a flow channel expressed by graph theory is shown in Figures 3(a) and (b). If the groove pattern shown in Figure 3(b) is expressed as edges connecting 3 × 3 = 9 nodes as shown in Figure 3(a), the total number of edges when all the nodes are connected is 9 C 2 = 36. This graph is represented using the following 9 × 9 adjacency matrix A:

[0049]

number

[0050] In the adjacency matrix A, A ij = 1 means that there is an edge connecting nodes i and j, and A ij =0 means there is no edge. Also, A ij The diagonal terms denoted by =· are meaningless. In this example, the edges that do not affect the mixing efficiency, specifically, the grooves parallel to the flow direction, are ij As the 9 edges indicated by =* can be excluded, 27 edges are ultimately left. In other words, the groove pattern can be expressed by 27 parameters (design variables).

[0051] <Definition of the optimization problem> In this embodiment, the three-dimensional lattice structure of the heat sink is expressed as edges connecting 7×7×3=147 nodes (node ​​spacing=8.3 mm) as shown in FIG. 4(a). In other words, the nodes arranged in 7×7 are laminated in three layers in the lamination direction Ds along the normal direction Dn of the surface 21 of the substrate 20. Considering the symmetry of the flow occurring in the flat-laid heat sink, the design area can be reduced to 1 / 8 (10 nodes, represented by light gray areas) in each layer (7×7=49 nodes) as shown in FIG. 4(b). Furthermore, the first and second layers (10×2=20 nodes, represented by large circles P1) and the second and third layers (10×2=20 nodes, represented by small circles P2) are designed independently.

[0052] As shown in Figure 4(c), each layer is connected. 20 C 2 Of the 190 edges, only 34 edges are selected that can be printed, i.e., the angle between the stacking direction Ds is 45 degrees or less, as shown in Fig. 4(d). As a result, 34 × 2 = 68 edges remain. Therefore, a 3D lattice structure can be expressed using 68 design variables.

[0053] The design variables are given as real numbers between 0 and 1, with 0 to 0.2 indicating that there is no edge, and 0.2 to 1 indicating that there is an edge. Each edge is replaced with a solid cylinder with a diameter of 2.6 mm. By cutting out the portion that protrudes from the design domain (50 mm x 50 mm x 15 mm) and attaching a base part 30 (50 mm x 50 mm x 6 mm) to the bottom, solid data such as that shown in Figure 4(e) can be obtained.

[0054] The objective function includes an index representing the heat dissipation efficiency as the performance of the heat sink and an index representing the material cost. In this embodiment, the topology is maximized based on the total heat transfer amount Q, and is minimized based on the lattice volume V (the volume of the three-dimensional lattice structure). The total heat transfer amount Q is evaluated by thermal fluid analysis, and the lattice volume V is replaced with the total edge length and analytically evaluated by a self-made code. In addition, a constraint is considered that all edges are connected to the base portion 30 so that all edges contribute to heat transfer.

[0055] In this embodiment, a genetic algorithm (hereinafter referred to as "GA"), which is a population search-based optimization algorithm that simulates the evolution of living organisms, is used as a calculation method for optimization. GA can be applied to various optimization problems regardless of the nature of the objective function (differentiability, nonlinearity, multimodality, etc.), and is expected to discover a global optimum solution without falling into a local optimum solution. GA is a powerful solution method for thermo-fluid dynamics problems described by nonlinear governing equations and optimization problems in which performance changes sensitively (i.e., multimodally) due to changes in the topology of the structure, which are the targets of this embodiment. A wide variety of GAs have been proposed so far, but here we use Non-Dominated Sorting Genetic Algorithm II (hereinafter referred to as "NSGA-II"), which is the most famous in the world and has many performance verifications and applications.

[0056] On the other hand, GA requires the evaluation of the objective function for a large number of solutions that make up the population, so the total cost required for optimization calculations becomes enormous. In particular, when evaluating the objective function through large-scale numerical calculations such as thermal fluid analysis, it is not realistic to use GA alone in terms of calculation costs. Therefore, in order to reduce calculation costs, a response surface (also known as a surrogate model) is used in combination (response surface methodology). A response surface is an approximation of the response of f(x), which is a black box, as the algebraic formula B shown below, by learning sample data of the output (objective function) f(x) relative to the input (design variable) x.

[0057]

number

[0058] The output value for any input value can be instantly estimated through algebraic formula B, which significantly reduces the calculation time required for objective function evaluation and the entire optimization. However, since the response surface is only an approximation of the objective function, and any errors that occur there affect the quality of the optimal solution that is ultimately obtained, it is necessary to handle the approximation errors carefully in the solution search process.

[0059] In this embodiment, the Kriging response surface is used as the response surface. While other response surfaces model only the algebraic expression B, which is an approximation value of the objective function f(x), Kriging can model both the approximation value (algebraic expression B) and its uncertainty, i.e., the approximation error (shown in the following equation).

[0060]

number

[0061] By referring to this uncertainty C, we can probabilistically identify the location on the response surface where the global optimum is expected to exist. Here, for the objective function f(x) to be maximized, the optimal value f max The expected improvement from the actual value, EI[f(x)] (Expected Improvement), is calculated using the following formula:

[0062]

number

[0063] Here, F is a probability distribution that follows a normal distribution with mean B and variance C, and PDF(F) is the probability density distribution of F. Instead of maximizing the original objective function f(x), a solution x* that maximizes the expected value EI[f(x)] on the Kriging response surface is searched for through optimization calculations. The objective function f(x*) evaluated at this x* is added as new sample data, and the Kriging response surface is then updated. By repeatedly adding sample data in this manner, it is possible to simultaneously search for a global optimum solution and improve the accuracy of the response surface.

[0064] The procedure of optimization calculation in this embodiment is shown in Fig. 5. The first objective function, the total heat transfer amount Q, is evaluated by thermal fluid analysis under conditions where sample data is given, and is approximately evaluated using a Kriging response surface under other conditions. The second objective function, the lattice volume V, is replaced with the total edge length and analytically evaluated using a self-made code.

[0065] 5, first, initial sample data (total of 98 points) is created uniformly in the 68 design variable space by Latin Hypercube Sampling (LHS) (step S31). Then, structural data of the heat sink corresponding to each point is created, and Q is evaluated by thermal fluid analysis. Then, a Kriging response surface that approximates Q is constructed (step S32).

[0066] Next, a Pareto optimal solution that maximizes EI[Q] estimated on the Kriging response surface and minimizes the volume V (analysis value of the entire edge length) is searched for using NSGA-II (population size 200, number of generations 200, mutation rate approximately 1.5%) (step S33). Here, a Pareto optimal solution means a solution that is not inferior to any other solution for all objective functions. From the innumerable Pareto optimal solution sets obtained in the two-objective function space, two limit solutions, the maximum solution of the expected value EI[Q] and the minimum solution of the volume V, and the three solutions closest to the centers of gravity when the remaining solution set is divided into three by the K-means method, are subjected to thermal fluid analysis, and these results (maximum of five points in total) are added to the sample data, and then the Kriging response surface is updated (step S34). By repeating the above update work, a Pareto optimal solution is efficiently searched for with the minimum number of thermal fluid analyses (i.e., the number of sample points).

[0067] Next, a convergence determination is performed (step S35). For example, the convergence determination is performed by determining whether or not EI[Q] has converged to be less than a predetermined threshold value, or whether or not the topology of the shape of the edge heat dissipation element has ceased to be updated.

[0068] In the convergence determination, if it is determined that convergence has not occurred (No in step S35), a sample is added to the current sample point to update the Kriging model (step S31).

[0069] On the other hand, if it is determined that the solution has converged (Yes in step S35), the optimization process ends. By the above procedure, the Pareto solution can be efficiently searched for with the minimum number of iterations.

[0070] <Thermofluid analysis> The thermal fluid analysis is performed in the region A1 and boundary conditions shown in FIG. 6 as a cube. The working fluid is air. The bottom surface of the heat sink (bottom surface Ab of region A1) is used as the heat source, and a constant temperature of 323.15K is given as the first boundary condition. Around region A1, a third boundary condition (293.15K) is set so that heat is sufficiently dissipated. Here, the Boussinesq approximation is used to model natural convection. The total heat transfer amount Q of the heat sink is evaluated using the pressure-based solver of the commercially available fluid analysis software "ANSYS Fluent 2019 R1.2". The governing equations are the continuity equation, the steady incompressible Navier-Stokes equation, and the steady energy equation. In addition, the pseudo-unsteady algorithm of the pressure-based coupled solver is enabled using the pseudo-transient method. As a result, an unsteady term is efficiently added to the governing equation, improving stability and convergence. Table 1 shows the calculation scheme used in this embodiment.

[0071] [Table 1]

[0072] The computational grid for thermal fluid analysis is generated by the cut cell method (minimum element size 0.2 mm). Compared to general object-fitting grid methods, the cut cell method can automatically generate grids even for complex structures explored in the topology optimization process. In addition, unlike the Cartesian grid method, which expresses the object surface with simple stepped cells, the cut cell method cuts cells that intersect with the object surface to extract a grid that follows the object, and has the characteristic that the conservation law is satisfied because the inspection volume is defined even next to walls.

[0073] <Results and Discussion> The scatter plot of the initial sample data (point P11) and the additional sample data searched by the optimization calculation is shown in the objective function space (horizontal axis: total heat transfer amount Q (unit: W), vertical axis: volume V (unit: m 3 )) is shown in Figure 7. The additional sample data are (a) the result (point P12) of two-objective optimization of maximizing the total heat transfer amount Q and minimizing the volume V (the response surface was updated 10 times), and (b) the result (point P13) of single-objective optimization of only maximizing the total heat transfer amount Q (the response surface was updated 18 times). In the two-objective optimization of (a) above, the response surface was updated 10 times, and in the single-objective optimization of (b) above, the response surface was updated 18 times.

[0074] Among these sample data, the heat sink of the embodiment that is the Pareto optimal solution is indicated by a small circle point P14. In addition, as a comparative example, a ready-made heat sink (21F50) having comb-tooth-shaped heat dissipation elements and a two-dimensional fin structure formed by extrusion molding, as shown in FIG. 8, is indicated by point P15. The heat sink indicated by point P15 has the same size as the heat sink of the embodiment that is the Pareto optimal solution, as shown by point P14, with a fin part of 50 mm x 50 mm x 15 mm and a base part of 50 mm x 50 mm x 6 mm. Furthermore, data for a structure in which lattices are arranged on all edges (full lattice structure) as shown in FIG. 4(e) is also plotted as point P16.

[0075] As shown in Figure 7, the two-objective optimal solution (point P14) has improved both the total heat transfer amount Q and the volume V compared to the initial sample data (P11). For example, the two-objective Pareto optimal solution with the largest Q (09-001, the first additional sample data obtained in the ninth update) (hereinafter referred to as the sample name "Lattice3") has improved (increased) the total heat transfer amount Q by 22% and reduced (decreased) the volume V by 53% compared to the ready-made product (P15). In addition, the distribution of the additional sample data (point P14) by two-objective optimization showed that there is a trade-off relationship between maximizing the total heat transfer amount Q and minimizing the volume V. Next, when we focus on the point (14-001) with the largest total heat transfer amount Q among the one-objective optimal solutions (point P13), it is located near the sample of the two-objective optimal solution (09-001) (the sample shown in Figure 9). From this, we can say that the two-objective optimal solution (09-001) has almost reached the design limit for maximizing the total heat transfer amount Q, in other words, it is impossible to maximize the total heat transfer amount Q any further.

[0076] Next, a representative optimum solution and a ready-made product will be described. The structure of the heat sink and visualization of the flow field generated in the heat sink are shown in Figs. 8 to 11. Fig. 8 shows a ready-made product (shown as sample name "21F50" in Fig. 7) in which a plurality of plate-like fins F are provided parallel to each other, and Figs. 9 to 11 show a two-objective optimum solution with a three-dimensional lattice structure. The sample shown in Figs. 9 to 11 has a three-dimensional lattice structure in which a heat dissipation element having a set of edges E is laminated in the normal direction Dn of the surface 21 of the substrate 20 in three layers L1, L2, and L3 from the substrate 20 side. Furthermore, when this sample is viewed along the normal direction Dn of the surface 21 of the substrate 20, it has a quadrilaterally symmetrical shape that is symmetrical with respect to four directions D1, D2, D3, and D4 corresponding to the four sides of the square substrate 20.

[0077] Figure 9 shows the first additional sample data obtained in the ninth update (shown as "sample name 09-001" in Figure 7), Figure 10 shows the first additional sample data obtained in the eighth update (shown as sample name "08-001" in Figure 7), and Figure 11 shows the second additional sample data obtained in the tenth update (shown as sample name "10-002" in Figure 7).

[0078] 8 to 11, (a) is a perspective view showing the overall shape of the sample, and (b) to (d) show the heat distribution in the range described below. The higher the density of the black, the higher the temperature.

[0079] In Figure 8, (b) shows the heat distribution in a height range close to the substrate 120 in the normal direction Dn of the substrate 120, (c) shows the heat distribution in an intermediate height range between the range shown in (b) and the range shown in (d), and (d) shows the heat distribution in the highest range.

[0080] In each of Figures 9 to 11, (b) shows the heat distribution in the lower layer of the lattice structure, i.e., the first layer L1 and second layer L2 on the substrate 20 side, (c) shows the heat distribution in the upper layer of the lattice structure, i.e., the second layer L2 and third layer L3, and (d) shows the heat distribution in the third layer L3 (the farthest layer).

[0081] In the ready-made product (sample name: 21F50) shown in Fig. 8, the surrounding cold air enters the heat sink along the gaps between adjacent fins F, i.e., only from two directions Da and Db along the direction in which the fins F extend, and as can be seen from (b), (c), and (d), the air that is heated by receiving heat from the heat sink rises away from the base material 120. In the ready-made product shown in Fig. 8, air is taken in only from the directions Da and Db corresponding to the two sides of the square-shaped base material 120 in a plan view seen along the normal direction Dn of the base material 120, so the heat absorption and dissipation performance is easily affected by the posture and direction in which the heat sink is attached.

[0082] On the other hand, in the dual-objective optimization solution shown in Figures 9 to 11, the heat sink structure is symmetrical in all four directions, D1, D2, D3, and D4, which correspond to the four sides of the base material 20. As shown in (b) and (c) of each figure, the air taken in is heated in the center and released from the third layer L3 as shown in (d). From this, it can be seen that the mounting direction has a smaller effect on the performance of the optimized heat sink shown in Figures 9 to 11 compared to the ready-made product shown in Figure 8, and it can be said that a robust design has been achieved.

[0083] Furthermore, the dual-objective optimal solutions in Figs. 9 to 11 are compared. A common configuration of these lattice structures is that there are gaps Gb (intakes, vents) on the sides of the first layer L1 to the second layer L2, which are the lower layers of the lattice structure, and the lattice is dense in the center of the second layer L2 to the third layer L3, which are the upper layers of the lattice structure. The arrangement density of the edges E is higher in the center than in the outer parts in the planar direction of the surface 21 of the substrate 20 in the layers farther from the substrate 20, that is, from the first layer L1 to the third layer L3. In addition, the third layer L3 has gaps Gt (vents) that open upward in the normal direction Dn of the substrate 20.

[0084] The above-mentioned intakes (gap Gb) make it possible to take in cold air from the surroundings from all four sides of the heat sink (four directions D1 to D4), which is thought to enable the air to reach the center of the heat sink sufficiently. In contrast, in the ready-made product shown in Fig. 8, the intakes are only at positions corresponding to two sides of the base material 120 that is rectangular in plan view, and depending on the surrounding air flow, it is expected that air will only flow in from one of the intakes, so it is not easy for the air taken in from the surroundings to reach the center of the heat sink.

[0085] In the heat sink of the dual-objective optimum solution shown in Figs. 9 to 11, dense lattices are concentrated in the center where most of the air passes so that the air taken in can sufficiently remove heat from the heat sink. This prevents the air that has entered from escaping through the gap Gb (intake) and makes it easier to form a flow toward the third layer L3, making it easier for the air that has removed heat and become warm inside the heat sink to be released from the third layer L3, which is considered to be a significant effect on improving heat dissipation performance. Furthermore, in the heat sink of the dual-objective optimum solution shown in Figs. 9 to 11, the layer below the lattice structure has a gap Gb (vent) as an intake, and while satisfying the configuration in which the lattices are densely arranged in the center of the third layer L3, the lattices can be appropriately thinned out to adjust the balance between performance (Q) and material cost (V). In addition, in the dual-objective optimum solution shown in Figs. 9 to 11, an overhang portion H is formed, which contributes to increasing the opening area of ​​the gap Gb.

[0086] In contrast, with the existing product shown in Figure 8, as described above, the air that flows in does not easily reach the center, and it does not have a structure to suppress the air flow in the center and make it easier for the air to escape upwards. Therefore, near the intake, the air that flows in from the periphery and the air that has been warmed inside the heat sink mix, causing the air flow to stagnate, making it difficult to achieve sufficient heat dissipation performance.

[0087] As described above, in this embodiment, multi-objective optimization was performed on the three-dimensional lattice structure of the heat sink, aiming at improving performance and reducing material costs. As a result, the optimization calculation could be efficiently realized by optimizing the lattice structure expressed by graph theory using GA and Kriging response surface in combination. Then, a three-dimensional lattice structure that can realize performance improvement and material cost reduction compared to existing products (two-dimensional extrusion fin structure) was found. This three-dimensional lattice structure is novel, achieving both heat dissipation performance and low material cost, which is not seen in conventional heat sinks. Furthermore, the characteristics of the lattice structure for performance improvement and the thermal fluid phenomenon that supports it were identified.

[0088] <Modeling process analysis (step S13), modeling defect diagnosis (step S14)> The simulation of the modeling process analysis (step S13) and the modeling defect diagnosis (step S14) will be described with reference to FIGS. In this embodiment, a modeling simulation calculation and analysis were performed for the case where a heat sink having a heat dissipation element made of an aluminum alloy was modeled by a 3D printer based on the manufacturing data generated in the manufacturing data generation process (step S12). For the simulation calculation and analysis, stress analysis software corresponding to the simulation of the metal additive manufacturing process was used.

[0089] 12(a) and (b) are diagrams showing the appearance of the model used in the above simulation, where (a) is a perspective view seen from the side, and (b) is a perspective view seen from above. This model is a model in which supports S are provided as supporting edges that support the overhanging portion H of the sample (09-001, Lattice3) shown in Fig. 9 from the substrate 20 side. The supports S are provided at 12 locations indicated by circled numbers in Fig. 12(b).

[0090] In the above stress analysis software, the shape of the external shape of the molded object is approximated by a voxel mesh (a collection of regular hexahedral elements). The voxel mesh in this embodiment was created with a size of 0.2 mm on one side. The total number of voxel meshes was approximately 2.83 million.

[0091] The simulation calculations were performed in the following order: (1) molding, (2) separation from the base plate, and (3) removal of support S.

[0092] (1) In the modeling, voxel meshes are layered from the surface of the substrate (base plate) 20. The inherent strain was determined by the inherent strain identification test and the value obtained by calibration using the above-mentioned stress analysis software.

[0093] (2) The separation from the substrate 20 was performed after the completion of the above-mentioned modeling. The separation position was the boundary between the model and the base plate. The effect of the wire diameter on the separation was not taken into consideration.

[0094] (3) The support removal was performed by removing the supports S after the shaped object was separated from the base material 20.

[0095] 13(a), (b), and (c) are perspective views showing the state of shaping. FIG. 13(a) is a diagram showing the state of shaping, and (b) is a diagram showing the shape after the removal of the supports S is completed, and is displayed with a deformation magnification of 10. FIG. 13(c) is a diagram comparing the shape in the state of FIG. 13(b) with the shape of the data (CAD data) generated in the manufacturing data generation process (step S12), superimposed, and the distribution of shape deviation is shown by color shading. In FIG. 13(c), the deviation is at most about 0.04 mm, and the amount of deformation is kept small compared to the manufacturing data. There was almost no change in the shape during the support removal process.

[0096] 14(a), (b), (c), (d), and 15 are diagrams showing the maximum principal stress distribution during modeling, in which the normal direction (stacking direction) of the substrate 20 is the Z direction, and the directions corresponding to two mutually perpendicular sides of the substrate 20 having a rectangular shape in plan view in a plane perpendicular to the Z direction are the X direction and the Y direction, respectively. FIG. 14(a) is a cross-sectional view of a cross-sectional area A perpendicular to the Y direction, (b) is an enlarged view of (a), (c) is a cross-sectional view of a cross-sectional area B perpendicular to the X direction, and (d) is an enlarged view of (c). FIG. 15 is an enlarged view of the cross-sectional area A as viewed along the arrow AL in FIG. 14(a), and the value of the maximum principal stress (tensile component) is shown in the figure. In FIG. 14(a), (b), (c), (d), and 15, the difference in strength of only the tensile component of the maximum principal stress is expressed by the color density.

[0097] By comparing the maximum value of the maximum principal stress with the tensile strength of the material, the possibility of breakage during molding can be investigated. In the molding of this embodiment, tensile stress exceeding the tensile strength (about 460 MPa) of a general AlSi10Mg alloy (JIS standard H5202) was generated in a part of the base material 20 (base) and the root of the edge E (branch). The value was a maximum of 574 MPa, which is 1.25 times the tensile strength, and it can be seen that the deformation also decreases with the decrease in inherent strain, and therefore the generation of tensile stress is also suppressed. In addition, the tensile stress is high only in a part of the root of the edge E (branch). Compared to the tensile strength (about 460 MPa) of a general AlSi10Mg alloy, it can be suppressed to 1.25 times.

[0098] As shown in Figure 15, it was confirmed that during printing, a maximum tensile stress of approximately 570 MPa, which exceeds the tensile strength of a typical AlSi10Mg alloy of 460 MPa, acts at the base of the edge E (branch) of the printed object.

[0099] It was found that areas where tensile stress is high have the following characteristics (1) to (3). (1) The root of edge E (branch) parallel to the XZ cross section. Due to the inherent strain used, it shrinks more in the Y direction than in the X direction. Therefore, the stress at the base parallel to the XZ cross section is higher than that at the YZ cross section.

[0100] (2) Overhang portion H is adjacent, and the distance to the intersection (node) between edges E and E is long. Overhang H is supported by a pillar called support S, which is thought to weaken its rigidity. Also, edge E of the corresponding area is a long distance to the node, so it is thought to have weaker rigidity than other areas.

[0101] (3) There is a free branch. There is an edge E that extends vertically in the center, but it is not connected to the node at the top and is in a free state. Furthermore, the edges E that extend individually from the center are long distances to the nodes. Therefore, it is thought that the rigidity is weaker than other parts.

[0102] From the above results, the shape deviation of the shape after the support S has been removed, i.e., the shape of the molded product, from the CAD shape (manufacturing data) was about 0.04 mm at maximum. It was found that if no breakage problems occur during molding, a shape equivalent to the CAD shape can be obtained. In addition, it was confirmed that during molding, a tensile stress of up to about 570 MPa, which exceeds the tensile strength of a typical AlSi10Mg alloy of 460 MPa, acts on a part of the base of the edge E of the molded product. Furthermore, except for the base of the base 20 and the edge E, the tensile stress was generally lower than the tensile strength. From the above, it can be inferred that the risk of breakage can be reduced by providing a rounded (R) to the base of the edge E attached to the base 20 (base).

[0103] <Heat transfer performance experiment (Step S19), heat transfer performance analysis (Step S20)> The heat transfer performance experiment (step S19) and the heat transfer performance analysis (step S20) will be described with reference to Figs. 16 to 20. Figs. 16(a) and (b) are diagrams showing the external appearance of a model that is the subject of the heat transfer performance experiment. The model shown in Figs. 16(a) and (b) corresponds to the sample (09-001, Lattice3) shown in Fig. 9.

[0104] Figures 17(a), (b), (c), and (d) are visualizations of the thermal boundary layer (temperature field in the internal space and surroundings of the heat sink). Figures 17(a) and (b) show the case of sample (09-001, Lattice3) shown in Figure 9, and (c) and (d) show the case of the ready-made heat sink (21F50) shown in Figure 8. Figures 17(a) and (c) are visualizations of the thermal boundary layer in the actual heat sink using an interferometer, and Figures 17(b) and (d) are the results obtained by numerical calculation. As can be seen from Figures 17(a), (b), (c), and (d), in sample Lattice3, high-temperature air is emitted from the center of the farthest layer (the top layer in Figures 17(a) and (b)). In addition, the outer edge of the layer on the substrate side (the bottom layer in Figures 17(a) and (b)) is at a low temperature, and it can be seen that outside air flows in from this part and is efficiently circulated by being released from the farthest layer.

[0105] Figures 18(a), (b), and (c) are diagrams showing the apparatus configuration when a natural convection cooling experiment is performed as a heat transfer performance experiment with a heat sink placed vertically, and Figure 19 is a diagram showing the apparatus configuration when a natural convection cooling experiment is performed as a heat transfer performance experiment with a heat sink placed flat. Figures 18(a) and 19 are side views of the internal configuration, Figure 18(b) is a front view (viewed from the right side of Figure 18(a)), and Figure 18(c) is a perspective view seen from above. In Figures 18(a), (b), (c), and 19, the vertical direction is indicated as the Z direction.

[0106] In the configurations of Figs. 18(a), (b), and (c), an aluminum block 42 made of aluminum is horizontally placed in a heat insulating material 41 that is horizontally placed, that is, arranged to extend in the X direction (the left-right direction in Fig. 18(a)). The aluminum block 42 is arranged so that a first end face 42a, which is one end face in the X direction, is flush with one end face 41b in the longitudinal direction of the heat insulating material 41, and a second end face 42b, which is the other end face, is placed inside the heat insulating material 41. A heat sink 44 is fixed to the first end face 42a of the aluminum block 42 in a vertically placed state, and a heater 43 is fixed to the second end face 42b as a heat source. The heater 43 generates heat by driving a power source 45 arranged outside the heat insulating material 41.

[0107] The aluminum block 42 has a TC 1 , T.C. 2 , T.C. 3 In addition, the thermocouples are arranged at two positions TC b , T.C. f TC ∞ The detection results from each thermocouple are output to a circuit (not shown).

[0108] 18(a), (b), and (c), airflow as natural convection is supplied to the heat sink 44 along the direction indicated by the arrows. The flow velocity of the airflow was set to 6.78±0.02 m / s.

[0109] The configuration shown in FIG. 19 is the same as the configuration shown in FIG. 18(a) except that the heat insulating material 41 is placed vertically so that the heat sink 44 is located vertically above, and the heat sink 44 is placed flat.

[0110] Fig. 20 is a graph showing the relationship between temperature and vertical position in Fig. 19, obtained in a natural convection cooling experiment with heat sink 44 laid flat using the device shown in Fig. 19. The position shown on the horizontal axis is set so that the value increases upward in Fig. 19, with the position where heater 43 is provided as the origin, and the position from 0 to 90 mm corresponds to aluminum block 42, and the position from 90 mm to approximately 123 mm corresponds to heat sink 44. In Fig. 20, the results of sample Lattice3 are indicated by circles, and the results of a ready-made heat sink (21F50) used as a reference are indicated by diamonds.

[0111] 20, in Sample Lattice 3, similar to the reference heat sink, no sudden change is observed between the temperature gradient in the aluminum block 42 and the temperature gradient in the heat sink 44. Therefore, it can be seen that in Sample Lattice 3, high heat transfer performance from the aluminum block 42 to the heat sink 44 is achieved under natural convection, at the same level as in the reference.

[0112] As shown in Fig. 19, the heat amount was 5.92 W when Sample Lattice3 was placed flat, and 5.18 W when the heat sink 44 was placed vertically as shown in Fig. 18. Although a graph like that of Fig. 20 is not shown here, it was confirmed that when the heat sink 44 was placed vertically as in Fig. 18, there was no sudden change in the temperature gradient in the aluminum block 42 and the temperature gradient in the heat sink 44, just like when it was placed flat, and the same level of heat transfer performance was achieved as when it was placed flat. This shows that Sample Lattice3 can achieve stable heat dissipation performance regardless of the installation position.

[0113] <Inherent strain test (step S21), inherent strain analysis (step S22)> In actual additive manufacturing, material shrinks due to heating by laser irradiation and cooling. This shrinkage rate is not constant, but changes in relation to the laser irradiation direction. In stress analysis software that supports simulations of metal additive manufacturing, this shrinkage rate is parameterized as "intrinsic strain" and calculations and analysis are performed by replacing simulations related to thermal phenomena with mechanical simulations.

[0114] Inherent strain varies depending on additive manufacturing parameters and materials. To perform accurate calculations and analyses, it is necessary to identify the inherent strain. Here, we conducted an experiment to identify the inherent strain to be used in calculations and analyses of actual molding (hereinafter referred to as "intrinsic strain tests") and obtained the inherent strain to be used in molding simulations.

[0115] The inherent strain test and inherent strain analysis can be carried out, for example, by the following steps (1) to (3). (1) A test specimen is created using additive manufacturing (AM), and the amount of deformation caused by residual strain is measured. (2) Using the above-mentioned stress analysis software, print the same test piece as in (1) under the same conditions as in (1), and calibrate the inherent strain value until the deformation amount matches the result in (1). (3) The inherent strain value obtained in (2) is incorporated into the material properties of the AlSi10Mg alloy and applied to the subsequent manufacturing process analysis of the heat sink shape.

[0116] In the inherent strain analysis, the shape of the test piece, the material properties, and the amount of deformation are given as inputs to the stress analysis software, and the inherent strain value is obtained as output by performing calibration using the stress analysis software. The obtained inherent strain value is fed back to the modeling process analysis step (step S13). [Explanation of symbols]

[0117] 11 Control circuit 12 Arithmetic circuit 13 Adjustment circuit 14 Storage section 15 Input section 16 Display 20, 120 base material 21 Surface of the substrate 30 Base 41 Insulation 41b End face of insulation 42 Aluminum block 42a 1st end face 42b 2nd end face 43 Heater (heat source) 44 Heat sink 45 Power supply A1 area Ab bottom D1, D2, D3, D4, Da, Db direction Dn Normal direction Ds Stacking direction E-edge F Fin Gb Air gap (secondary vent) Gt gap (first vent) H Overhang L1, L2, L3 3D lattice structure layers S Support (support edge)

Claims

1. A method for manufacturing a heat sink having a heat dissipation element having a three-dimensional structure in which a set of three-dimensionally arranged nodes are connected by a set of edges, comprising: A manufacturing data generation process for generating manufacturing data that can be modeled by additive manufacturing based on design values ​​of the nodes and the edges; a modeling process analysis step of generating mesh structure data based on the manufacturing data, and calculating a shape deviation between the mesh structure data and the manufacturing data, and a maximum principal stress value at a predetermined position on the mesh structure data; a molding defect diagnosis process for predicting a portion that will cause a molding defect due to the shape deviation and the maximum principal stress value calculated in the molding process analysis process exceeding a first predetermined range for the shape deviation and a second predetermined range for the maximum principal stress value, the first predetermined range and the second predetermined range for the maximum principal stress value being set in order to realize the three-dimensional structure corresponding to the design values; a printing limit setting step of setting a printing limit to be considered in the manufacturing data generating step in order to eliminate the printing defect predicted in the printing defect diagnosis step, based on the result of the printing process analysis step and a predetermined range set in advance for the printing limit; an additive manufacturing process in which, when a new printing limit is obtained in the printing limit setting process, the manufacturing data generating process, the printing process analyzing process, the printing defect diagnosing process, and the printing limit setting process are repeated, and when both the shape deviation and the maximum principal stress value fall within the first predetermined range and the second predetermined range, respectively, in the printing defect diagnosing process, additive manufacturing is performed based on the manufacturing data at that time to manufacture the heat sink; A method for manufacturing a heat sink comprising the steps of:

2. A heat sink having a heat dissipation element, The heat dissipation element includes a set of edges connecting a set of nodes arranged three-dimensionally, a portion of the set of nodes is arranged on a substrate, and has a three-dimensional lattice structure in which a plurality of layers are stacked in an out-of-plane direction of a surface of the substrate; the edge is arranged at a higher density in a central portion than in an outer portion in a planar direction of the surface in a layer farther from the base material; A first ventilation hole is formed in the farthest layer farthest from the base material, A heat sink, characterized in that a second ventilation hole is formed in the outer portion in a layer closer to the base material than the farthest layer.

3. A heat sink having a heat dissipation element, The heat dissipation element includes a set of edges connecting a set of nodes arranged three-dimensionally, a portion of the set of nodes is arranged on a substrate, and has a three-dimensional lattice structure in which a plurality of layers are stacked in an out-of-plane direction of a surface of the substrate; the edge is arranged at a higher density in a central portion than in an outer portion in a planar direction of the surface in a layer farther from the base material; A first ventilation hole is formed in the farthest layer farthest from the base material, a second ventilation hole is formed in the outer portion of a layer closer to the base material than the farthest layer, Depending on the attitude of the heat sink, Air heated by a heat source adjacent to the heat sink is discharged from one of the first and second vents that is located higher in the vertical direction, and A heat sink, wherein air flows in through one of the first vent hole and the second vent hole which is located lower in the vertical direction.

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