Coil structure optimization method and device

Abstract

The invention provides a coil structure optimization method and device. The method comprises the following steps: acquiring a to-be-optimized region and a target function for optimizing the to-be-optimized region; the to-be-optimized area can drive a preset area of the conductive workpiece to enable the preset area to have a speed of rushing to the substrate, and the objective function is maximization of the speed; the numerical value of the target function is used as the fitness value of particles in the particle swarm algorithm, the structural parameters of the to-be-optimized area are used as the positions of the particles, and the particle swarm algorithm is subjected to iterative optimization until the iterative optimization stopping condition is met; and taking the structure parameters meeting the iterative optimization stopping condition as the structure parameters obtained by final optimization. According to the method, the discretization particle swarm optimization is adopted to optimize the coil, so that the energy consumption required when the coil obtained through final optimization is used for welding is remarkably reduced, and the strength of a joint generated by welding is higher. The device can realize the method.

Description

Technical Field

[0001] The invention relates to the technical field of electromagnetic pulse welding, and in particular to a coil structure optimization method and a device thereof. Background Art

[0002] Electromagnetic pulse welding technology is a special welding technology that has emerged with the development and progress of electromagnetic pulse forming technology. This technology is suitable for connecting dissimilar materials, especially pipes and thin plate materials. The magnetic pulse welding (MPW) process in a general sense is usually considered to be the combined effect of electromagnetic field, stress field and thermal field. However, since the welding discharge process is extremely short and the resistance of the coil is also very small. Therefore, the heat energy generated by the current is relatively small, and the impact on the entire welding process can be ignored. Please refer to Figure 1 The principle is to use high voltage capacitors c The electric energy is stored, and after the vacuum switch is closed, the electromagnetic coil is discharged instantly, a pulse current is formed, and electromagnetic induction is used to generate eddy current in the conductive workpiece, and an instantaneous strong magnetic field and a strong magnetic pulse force are generated, which converts the electric energy into the kinetic energy of the conductive workpiece, resulting in the relatively high-speed movement and collision of the two workpieces (such as the conductive workpiece and the substrate), and finally realizes the metallurgical bonding between the two workpieces (such as the conductive workpiece and the substrate). Its outstanding features are that the weldability of dissimilar metal materials is good, the applicability is wide, the production efficiency is high, it has good precision controllability and repeatability, and the welding process is easy to automate. Due to the high quality of the welded joints obtained by this technology and the environmental protection and pollution-free, it has been applied in nuclear energy engineering, automobile manufacturing, aviation manufacturing and other fields.

[0003] In electromagnetic pulse welding technology, the coil acts as a conductor for transmitting current, forming a magnetic field and driving the conductive workpiece. Its structure determines the welding performance and is one of the key components of magnetic pulse equipment. Therefore, optimizing the structure of the coil is not only in line with the development of the digital industry, but also in line with the research of magnetic pulse welding technology, which is of great significance. However, the existing coils are basically traditional I-shaped coils, and the structural parameters of the coils cannot be changed according to different materials or working conditions. There are differences in the welding windows between different materials, which makes the strength of the welded joints of different materials uneven. When the welding joint effect is poor, it is usually only possible to continuously increase the energy level of the magnetic pulse output, which results in problems such as high energy consumption and low efficiency when using traditional coils for welding.

[0004] Therefore, it is necessary to improve the above-mentioned deficiencies in the prior art. Summary of the invention

[0005] The main technical problem solved by the present invention is how to systematically optimize the flat coil in electromagnetic pulse welding.

[0006] According to the first aspect, an embodiment provides a coil structure optimization method, comprising: Acquire an area to be optimized and an objective function for optimizing the area to be optimized; wherein the area to be optimized includes a main working area of ​​the flat coil, the main working area is an area where the proportion of the conductor per unit area or unit volume exceeds a preset proportion threshold, the area to be optimized can drive a preset area of ​​the conductive workpiece so that the preset area has a speed that rushes toward the substrate, the preset area includes a target area in the conductive workpiece that is combined with the substrate through the speed, and the objective function is to maximize the speed; The value of the objective function is used as the fitness value of the particle in the particle swarm algorithm, and the structural parameters of the area to be optimized are used as the position of the above particles. The particle swarm algorithm performs iterative optimization until the iterative optimization stop condition is reached; The structural parameters after reaching the conditions for stopping iterative optimization are taken as the structural parameters obtained by the final optimization.

[0007] In one embodiment, the objective function is used as the fitness value of the particle in the particle swarm algorithm, the structural parameter of the region to be optimized is used as the position of the particle, and the particle swarm algorithm performs iterative optimization until the iterative optimization stop condition is reached, including: Step S210: Grid the area to be optimized to obtain D grid units; wherein the properties of the above grid units are conductors or insulators; D The target search space is composed of N particles form a particle swarm, each particle represents a feasible solution to the structural parameters. i The position of a particle is expressed as: X i =( x i1 , x i2 , …, x iD ), i =1, 2, …, N ;No. i The velocity of a particle is expressed as: V i =( v i1 , v i2 , …, v iD ); x i1 , x i2 , …, x iD Respectively represent the 1st to the DThe probability that the attribute of a grid cell is a conductor; Step S220: When the number of iterations k When it is equal to 0, it is initialized according to the initial area to be optimized. X i ,and V 0 i，η =0; where the initial area to be optimized 𝜻 There is a boundary between the area composed of conductors and the area composed of insulators. η express ζ Neither ζ a Nor does it belong to ζ d The grid cells, ζ a represents the grid cells within the preset range of the boundary in the region composed of the above-mentioned insulator, ζ d Represents a grid cell within a preset range of the boundary in the region formed by the conductor; Step S230: Calculate the value of the objective function corresponding to each particle at the current number of iterations, and use the value of the objective function corresponding to each particle as the fitness value of each particle; Step S240: Calculate the particle optimal fitness value of each particle, calculate the population optimal fitness value of the particle swarm, and assign the position corresponding to the population optimal fitness value to the current population extreme value; Step S250: updating the speed and position of each particle; Step S260: determine whether the conditions for stopping iterative optimization are met; if the conditions for stopping iterative optimization are not met, continue to execute steps S230 to S260; if the conditions for stopping iterative optimization are met, the particle swarm algorithm stops iterating.

[0008] In one embodiment, the step S300: taking the structural parameters after reaching the conditions for stopping iterative optimization as the structural parameters finally optimized includes: The population extreme value after reaching the conditions for stopping iterative optimization is taken as the final optimized structural parameter.

[0009] In one embodiment, the objective function is expressed as: ; ; in, V Ω Represents the mass point in the target area Ω d The speed of Ω, Represents each particle in the target area Ω dThe mean value of the velocity of Ω, S Ω Indicates the area of ​​the target area; The constraints of the iterative optimization include: S ψ≤ S ζ; S ψ and S ζ are the area of ​​the conductor in the region to be optimized and a preset conductor area threshold.

[0010] In one embodiment, the constraints of the iterative optimization further include: The conductor in the region to be optimized is a simply connected domain; when ΣV k i,ζ>0 When ζ a The properties of the grid cells are defined as conductors; when ΣV k i,ζ<0 When ζ d The properties of the mesh cells are defined as insulators; where Σ k V i,ζ Indicates k After iterations v i1 , v i2 ,…and v iD sum.

[0011] In one embodiment, updating the speed and position of each particle includes: updating the speed and position of each particle according to the following expression: X k+1 i = X k i + V k+1 i ; V k+1 i = ωV k i + c 1 r 1 ( X k i，best - X k i )+ c 2 r2 ( X k gbest - X k i ); in, X k i Indicates i The particle in k The position after iterations, V k i Indicates i Particles in the k The speed after iterations, V k+1 i Indicates i Particles in the k +1 speed after iteration, X k+1 i Indicates i Particles in the k +The position after 1 iteration, ω represents the inertia weight, X k i，best Indicates i Particles in the k The position corresponding to the particle's optimal fitness value after iterations, X k gbest Indicates k The population extreme value after iterations is c 1 and c 2 represent the local learning factor and partial learning factor, respectively. r 1 and r 2 Represent the first random number and the second random number respectively, 0 <r 1 < 1,0 <r 2 < 1; in,- V max <V k+1 i <V max , V max Indicates the preset maximum speed; when ΣV i,ζ k+1 >0 and r5 >( k / k max ) b hour, V k +1 i，j = r 3 , j ∈ ζ a ; When ΣV i,ζ k+1 < 0 and r 5 >( k / k max ) b hour, V k+1 i，j = r 4 , j ∈ ζ d , k max Indicates the preset maximum number of iterations. b Indicates the preset index, r 3 , r 4 and r 5 Represent the third random number, the fourth random number and the fifth random number respectively, 0 <r 3 < 1,0 <r 4 < 1,0 <r 5 < 1.

[0012] In one embodiment, the updating of the speed and position of each particle further includes: using the following expression X k+1 i To filter: X k+1 i，e =Σ j∈Γ H e，j X k+1 i，j / Σ j∈ΓH e，j ; H e，j = max (0, r min -Δ( e , j ) Where Γ represents ζ a or ζ d Located in the e The grid unit is the center of the circle and the radius is r min The set of all grid cells within the circle, X k+1 i，e Indicates i The particle corresponding to e The grid cell in k +The position after 1 iteration, H e，j Denotes the weight factor for the e The influence factor of each grid unit, Δ( e , j ) indicates the e grid cells to j The distance between grid cells is 1≤ r min ≤3.

[0013] According to the second aspect, an embodiment provides a coil structure optimization device, comprising: A preprocessing unit is configured to obtain a region to be optimized and an objective function for optimizing the region to be optimized; The area to be optimized includes the main working area of ​​the flat coil, the main working area is an area where the proportion of the conductor per unit area or unit volume exceeds a preset proportion threshold, the area to be optimized can drive the preset area of ​​the conductive workpiece so that the preset area has a speed that rushes toward the substrate, the preset area includes a target area in the conductive workpiece that is combined with the substrate through the speed, and the objective function is to maximize the speed; The structural optimization unit is configured to use the value of the objective function as the fitness value of the particle in the particle swarm algorithm, and the structural parameter of the area to be optimized as the position of the above particle, and the particle swarm algorithm performs iterative optimization until a condition for stopping iterative optimization is reached; The output unit is configured to use the structural parameters after reaching the conditions for stopping iterative optimization as the structural parameters obtained by final optimization.

[0014] According to the third aspect, an embodiment provides a computer-readable storage medium, characterized in that it includes a program, and the program can be executed by a processor to implement the method as described in any embodiment of the present invention.

[0015] According to a fourth aspect, in one embodiment, a computer program product is provided, comprising a computer program and / or instructions, wherein when the computer program and / or instructions are executed by a processor, the method described in any one of the embodiments herein is implemented.

[0016] The beneficial effects of this application are: The coil structure optimization method includes: obtaining an area to be optimized and an objective function for optimizing the area to be optimized; wherein the area to be optimized includes a main working area of ​​a flat coil, the main working area is an area where the proportion of conductors per unit area or unit volume exceeds a preset proportion threshold, the area to be optimized can drive a preset area of ​​a conductive workpiece so that the preset area has a speed that rushes toward a substrate, the preset area includes a target area in the conductive workpiece that is combined with the substrate through the speed, and the objective function is the maximization of the speed; the value of the objective function is used as the fitness value of a particle in a particle swarm algorithm, the structural parameters of the area to be optimized are used as the position of the above-mentioned particle, and the particle swarm algorithm is iteratively optimized until a condition for stopping iterative optimization is reached; the structural parameters after reaching the condition for stopping iterative optimization are used as the structural parameters obtained by the final optimization; since the coil structure optimization method of the present application uses a discretized particle swarm algorithm to optimize the coil, compared with the flat coil used in the past, the energy consumption required for welding using the above-mentioned coil obtained by the final optimization is significantly reduced, and the strength of the welded joint is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the principle of magnetic pulse welding according to an embodiment; Figure 2 A schematic diagram of a flow chart of a coil structure optimization method according to an embodiment; Figure 3 A schematic structural diagram of performing finite element analysis on a coil according to an embodiment; Figure 4 A schematic diagram of a process of iterative optimization of a particle swarm algorithm according to an embodiment; Figure 5 is a schematic diagram of operating parameters of an embodiment; Figure 6 A schematic diagram of a single connectivity constraint of an embodiment; Figure 7 A collection of embodiments Г Schematic diagram of Figure 8 A schematic diagram of a coil finally optimized in one embodiment; Fig. 9 A schematic diagram of a module of a coil structure optimization device according to an embodiment. DETAILED DESCRIPTION

[0018] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, this is to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0019] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0020] The serial numbers assigned to the components herein, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in this application, unless otherwise specified, include direct and indirect connections (couplings).

[0021] The coils currently used in electromagnetic pulse welding technology are basically traditional I-shaped coils, or are slightly adjusted according to the requirements of the welding structure, but the structural parameters of the coils cannot be adaptively optimized and adjusted according to the material or working conditions of the conductive workpiece; in addition, there is no systematic method for optimizing the coil structure.

[0022] The technical concept of the present application is to systematically optimize coils (such as flat coils in magnetic pulse welding) by using a discretized particle swarm algorithm.

[0023] The technical solution of the present application will be described in detail below in conjunction with embodiments.

[0024] Please refer to Figure 2 , the present application provides a coil structure optimization method, the method comprising: Step S100: obtaining the region to be optimized and the objective function; Step S200: introducing a particle swarm algorithm, which performs iterative optimization until a condition for stopping iterative optimization is reached; Step S300: The structural parameters that reach the conditions for stopping iterative optimization are used as the structural parameters obtained by the final optimization.

[0025] Specifically, step S100 includes: obtaining a region to be optimized and an objective function for optimizing the region to be optimized. Figure 3 , the area to be optimized (such as Figure 3 The rectangular area pointed by the corresponding arrow of the "area to be optimized" in the figure represents the area for optimizing the flat coil. The area to be optimized includes the main working area of ​​the flat coil. The flat coil is generally composed of only conductors (such as copper wires) and insulators. The main working area is an area where the proportion of conductors per unit area or unit volume exceeds the preset proportion threshold. Personnel in this field can determine the preset proportion threshold by themselves according to actual needs. Personnel in this field can also expand the area to be optimized according to actual needs. For example, the area to be optimized can also include other parts of the area near the above-mentioned main working area. The area to be optimized can drive the preset area of ​​the conductive workpiece so that the preset area has a speed that rushes toward the substrate. The current in the above-mentioned main working area of ​​the flat coil is the most concentrated and the voltage is the largest, which can provide the maximum Lorentz force for the conductive workpiece. It can be understood that the area to be optimized is the area in the flat coil that plays a major role in driving the preset area of ​​the conductive workpiece. Personnel in this field can determine the preset area of ​​the conductive workpiece and the above-mentioned main working area by themselves according to actual needs. The preset area includes a target area in the conductive workpiece that is combined with the substrate (such as metallurgical combination) at this speed. The target area can be determined by the skilled person according to actual needs. The preset area can also include an area within a preset distance range around the target area. For example, the preset area can be the entire conductive workpiece. The preset distance range can be determined by the skilled person. Please refer to Figure 3 , during welding, the conductive workpiece (such as Figure 3 The dotted box area pointed by the corresponding arrow of the "conductive workpiece" in the figure) is placed near the main working area where concentrated current flows.

[0026] In some embodiments, since each iteration of the optimization process requires finite element analysis of the optimized coil model (such as the above-mentioned area to be optimized), and if each analysis simulates the collision between the conductive workpiece and the substrate, it will inevitably waste a lot of computing resources. Therefore, in order to simplify the calculation, the present application attempts to simplify the coupling analysis of the electromagnetic field and the force field.

[0027] In some embodiments, a planar coil commonly used in magnetic pulse welding is used as a starting point for optimization. Figure 3 , the current from Figure 3The middle area of ​​the conductor is input from Figure 3 Since the welding quality of the welded joint (such as the above-mentioned target area) is positively correlated with the speed at which the conductive workpiece rushes toward the substrate, the present application sets the objective function to maximize the speed at which the preset area rushes toward the substrate.

[0028] In some embodiments, the method does not perform a collision analysis between the conductive workpiece and the substrate after each iteration, but performs an analysis of the velocity field of the conductive workpiece after each iteration.

[0029] In some embodiments, those skilled in the art may also use other planar coils as a starting point for structural optimization.

[0030] In some embodiments, since the objective function is mainly affected by the conductor portion of the coil located near the welding joint (such as the main working area), and the shape of the coil is usually axisymmetric, the area to be optimized may include the main working area of ​​the flat coil. In the actual optimization process, only the left half or the right half of the long straight coil segment of the flat coil divided by the symmetry axis of the flat coil may be optimized. In other words, the area to be optimized may be determined as the left half or the right half of the main working area divided by the symmetry axis of the flat coil. After the optimization is completed, the left half or the right half may be mirrored to obtain the structural parameters of the entire area to be optimized.

[0031] In some embodiments, the area to be optimized includes the left half or the right half of the main working area divided by the symmetry axis of the flat coil. The area to be optimized may also include an area in the left half or the right half within a preset distance range of the main working area. Those skilled in the art may determine the preset distance range according to actual needs.

[0032] Specifically, step S200: taking the value of the objective function as the fitness value of the particle in the particle swarm algorithm, taking the structural parameters of the region to be optimized as the position of the particle, and performing iterative optimization by the particle swarm algorithm until a condition for stopping iterative optimization is reached.

[0033] In some embodiments, the electromagnetic field model of the entire magnetic pulse welding process can be simulated by using the commercial software LS-DYNA (a general nonlinear finite element program). Figure 1 When high-frequency pulse current is transmitted in the coil, the conductive workpiece (such as an aluminum flyer of 25mm×120mm×1mm) and the substrate (such as a steel substrate of 25mm×120mm×1mm) will collide under the driving force of the huge Lorentz force. Therefore, in some embodiments, the coil (300×170×10mm) can be discretized into hexahedral solid units.

[0034] In some embodiments, the speed of the conductive workpiece is calculated by magnetic coupling finite element method. The expression of the objective function is: ; ; in, V Ω Represents the mass point in the target area Ω d The speed of Ω, Represents each particle in the target area Ω d The mean value of the velocity of Ω, S Ω Indicates the area of ​​the target region.

[0035] In some embodiments, the target area may be a side surface of a conductive workpiece. The target area may also be a designated area on a side surface of a conductive workpiece.

[0036] For some examples, please refer to Figure 4 , step S200: taking the value of the objective function as the fitness value of the particle in the particle swarm algorithm, taking the structural parameter of the region to be optimized as the position of the above particle, and the particle swarm algorithm performs iterative optimization until the iterative optimization stop condition is reached, including: Step S210: Grid the area to be optimized to obtain D grid cells; Step S220: When the number of iterations k When it is equal to 0, it is initialized according to the initial area to be optimized. X i ,and V 0 i，η =0; Step S230: Calculate the value of the objective function corresponding to each particle at the current number of iterations, and use the value of the objective function corresponding to each particle as the fitness value of each particle; Step S240: Calculate the particle optimal fitness value of each particle, calculate the population optimal fitness value of the particle swarm, and assign the position corresponding to the population optimal fitness value to the current population extreme value; Step S250: updating the speed and position of each particle; Step S260: determine whether the conditions for stopping iterative optimization are met; if the conditions for stopping iterative optimization are not met, continue to execute steps S230 to S260; if the conditions for stopping iterative optimization are met, the particle swarm algorithm stops iterating.

[0037] In some embodiments, the above structural parameters may include: DThe shape and size of the mesh cells and the properties of each mesh cell (such as conductor or insulator).

[0038] In some embodiments, the above structural parameters may further include: a proportion of mesh cells belonging to conductors and a proportion of mesh cells belonging to insulators.

[0039] In some embodiments, in step S210, those skilled in the art may determine the specific method of meshing the area to be optimized according to actual scene requirements. The properties of the mesh units are conductors or insulators. D The target search space is composed of N particles form a particle swarm, each particle represents a feasible solution to the structural parameters. i The position of a particle is expressed as: X i =( x i1 , x i2 , …, x iD ), i =1, 2, …, N ;No. i The velocity of a particle is expressed as: V i =( v i1 , v i2 , …, v iD ); x i1 , x i2 , …, x iD Respectively represent the 1st to the D The probability that the attribute of a grid cell is a conductor. It can be understood that V i It is only used to change during iterative optimization. X i An intermediate variable.

[0040] In some embodiments, in step S220, please refer to Figure 3 The initial area to be optimized may include the left half or the right half of the main working area divided by the symmetry axis of the planar coil.

[0041] In some embodiments, in step S220, the initial area to be optimized may include the left half or the right half of the main working area divided by the symmetry axis of the planar coil and an area in the left half or the right half within a preset distance range of the main working area. Those skilled in the art may determine the specific value of the preset distance range according to actual needs.

[0042] In some embodiments, step S230, the process of calculating the value of the objective function corresponding to each particle at the current iteration number may include: first determining the structural parameters of the region to be optimized corresponding to each particle at the current iteration number, and the working condition parameters of the region to be optimized, the conductive workpiece, and the substrate, please refer to Figure 5 The operating parameters may include material name, conductivity, constitutive relationship, Young's modulus, Poisson's ratio and density. Those skilled in the art may add or delete the operating parameters according to actual needs. For example, the operating parameters may also include dimensions. Then, based on the above parameters, the electromagnetic field generated in the area to be optimized and the electromagnetic field driving the conductive workpiece so that the conductive workpiece has a speed toward the substrate are simulated to obtain the corresponding objective function.

[0043] It should be noted that since the construction of the electromagnetic field model and the simulation process of the speed of the conductive workpiece during the entire magnetic pulse welding process belong to the existing technology in this field, they will not be described in detail here.

[0044] In some embodiments, in step S240, after calculating the particle optimal fitness value of each particle, the population optimal fitness value may be determined, and the position corresponding to the population optimal fitness value may be assigned to the current population extreme value. The population optimal fitness value is the largest one among the current particle optimal fitness values ​​of each particle; k The optimal fitness value of the population and the population extreme value when equal to 0 are set as the initial values ​​of the optimal fitness value of the population and the population extreme value, respectively. The above calculation of the optimal fitness value of each particle may include: respectively comparing the current fitness value of the particle with the optimal fitness value of the particle, and if the fitness value of the particle is greater than the optimal fitness value of the particle, assigning the fitness value of the particle to the optimal fitness value of the particle. The above determination of the optimal fitness value of the population may include: respectively comparing the individual optimal fitness of each particle with the optimal fitness value of the population, and if the individual optimal fitness is greater than the optimal fitness value of the population, assigning the individual optimal fitness to the optimal fitness value of the population.

[0045] In some embodiments, step S250: updating the speed and position of each particle includes: Step S251: Update the speed and position of each particle according to the following expression: X k+1i = X k i + V k+1 i ; V k+1 i = ωV k i + c 1 r 1 ( X k i，best - X k i )+ c 2 r 2 ( X k gbest - X k i ); in, X k i Indicates i The particle in k The position after iterations, V k i Indicates i Particles in the k The speed after iterations, V k+1 i Indicates i Particles in the k +1 speed after iteration, X k+1 i Indicates i Particles in the k +The position after 1 iteration, ω Inertia weight is used to represent the inertia weight of the i The particle is affected by its velocity since the previous iteration. Studies have shown that ω The larger the value of can make the entire iterative process converge faster, and ω The smaller the value of , the more accurate the search can be in the entire target search space, ensuring that no local optimal solution is missed. X k i，best Indicates i Particles in the kThe position corresponding to the particle's optimal fitness value after the iteration, i.e. i The historical optimal topological structure of particles, X k gbest Indicates k The population extreme value after iterations, that is, the topological structure of the global optimal individual in the particle swarm, c 1 and c 2 represent the local learning factor and partial learning factor, respectively. r 1 and r 2 Represent the first random number and the second random number respectively. Personnel in this field can determine r 1 and r 2 ,For example, r 1 and r 2 Can be a random value between 0 and 1.

[0046] It should be noted that step S250 may also adopt other existing methods to update the speed and position of each particle.

[0047] In some embodiments, in step S250, in order for the entire iterative optimization process to converge, the speed of the particle needs to be constrained, that is, the speed of the particle needs to be less than V max . V max It indicates the preset maximum speed, that is, the maximum value of the above probability change in one iteration. It is understandable that the transition between the grid cells belonging to conductors and the grid cells belonging to insulators is a process. People in this field can determine it according to the actual scene requirements. V max For example, V max can be equal to 10%*1; that is, to avoid premature convergence of the particle swarm algorithm, -0.1 <V< 0.1. It can be understood that since the grid cells belonging to the conductor can become the grid cells belonging to the insulator, and the grid cells belonging to the insulator can also become the grid cells belonging to the conductor, V The value of can be positive or negative.

[0048] In some embodiments, in step S260, the condition for stopping the iterative optimization is: k = k maxOr the difference between the optimal fitness values ​​of the population in two adjacent iterations is less than the preset difference. k max Or preset difference.

[0049] In some embodiments, in step S260, the condition for stopping the iterative optimization may also be: when the optimal fitness value of the population has not been updated for more than a preset threshold number of times. In other words, when the optimal fitness value of the particle swarm has not been updated after the preset threshold number of times, the particle swarm algorithm will stop the iterative optimization.

[0050] In some embodiments, the constraints of the iterative optimization include: S ψ≤ S ζ; S ψ and S ζ are the area of ​​the conductor in the region to be optimized and a preset conductor area threshold.

[0051] It should be noted that the purpose of setting the constraint condition here is to make the total area of ​​the conductor in the region to be optimized finally optimized smaller than the preset conductor area threshold. Those skilled in the art can determine the specific value of the conductor area threshold according to actual scene requirements.

[0052] Due to the skin effect of the current, the current will be conducted along the inner and outer edges of the coil. If there is a cavity in the coil generated by the above iterative optimization, although the coil with the cavity can save some materials, the coil with the cavity cannot improve the quality of the welded joint, but will significantly increase the difficulty of processing the coil. Therefore, the calculation can be further simplified by defining the conductor in the area to be optimized as a simply connected domain. In some embodiments, the constraint condition of the iterative optimization also includes: the conductor in the area to be optimized is a simply connected domain.

[0053] In some embodiments, in step S220, please refer to Figure 6 In order to ensure that the above single connectivity constraints are maintained during the iterative optimization process, k = 0, the initial region to be optimized (such as the coil as the starting point of the structural optimization) can be defined as a simply connected region; the initial region to be optimized ζ The area composed of conductors (such as Figure 6 The medium gray grid cells, i.e. Figure 6 The grid cells except the white grid cells in the figure) and the area composed of the insulator (such as Figure 6 There is a boundary between the white grid cells in the figure. ζ a represents the grid cells within the preset range of the boundary in the region composed of the above-mentioned insulator, ζ d represents the grid cells in the region composed of the conductors within the preset range of the boundary, η Indicates the area to be optimized ζ Neither ζ a Nor does it belong to ζ d All grid cells of . Those skilled in the art can determine ζ a and ζ d ,For example, ζ d It can be a grid unit in the region composed of the above conductors that is in direct contact with the boundary through its side length, ζ a It can be a grid unit in the region composed of the above-mentioned insulator, which is in direct contact with the boundary through its side length.

[0054] In some embodiments, when ΣV k i,ζ>0 When ΣV k i,ζ>0 When ζ a The properties of the grid cells are defined as conductors; when ΣV k i,ζ<0 When ζ d The properties of the mesh cells are defined as insulators; where ΣV k i,ζ Indicates k After iterations v i1 , v i2 ,…and v iD It is understandable that by changing the adaptive area (such as ζ a or ζ d ), in each iteration, only the properties of the grid cells in the adaptive region will change, thereby maintaining the above-mentioned single connectivity constraint.

[0055] In addition, although the initial position of each particle provides a certain degree of randomness, and the speed of the particle is only affected by the global and local optimal solutions in the current state, some potential excellent solutions of the above structural parameters may still be ignored in the above iterative optimization process. In order to increase the randomness of the above iterative optimization process, the present application introduces a technology similar to gene mutation. At the same time, in order to ensure the convergence of iterative optimization, the probability of this mutation will decrease with the increase of the number of iterations. Therefore, in some embodiments, step S250: updating the speed and position of each particle also includes: When ΣV i,ζ k+1 >0 and r 5> ( k / k max ) b hour, V k+1 i，j = r 3 , j ∈ ζ a ; When ΣV i,ζ k+1 <0 and r 5> ( k / k max ) b hour, V k+1 i，j = r 4 , j ∈ ζ d ; in, k max Indicates the preset maximum number of iterations. b Indicates a preset index. r 3 , r 4 and r 5 Respectively represent the third random number, the fourth random number and the fifth random number. Personnel in this field can determine according to actual needs k max 、b、r 3 , r 4 and r5 ,For example, r 3 , r 4 and r 5 can be a random value between 0 and 1. That is, if the above mutation conditions (such as ΣV i,ζ k+1 >0 and r 5> ( k / k max ) b ) was established, ζ a and ζ d Middle j The speed of each grid cell (e.g. V k+1 i，j ) will no longer be determined by the original speed calculation formula (such as the speed calculation formula in step S251).

[0056] It is understandable that with further iterative optimization, the probability of the above mutations will drop sharply. b = 0.1, in k = 1, the threshold of mutation ( k / k max ) b is 0.676, while the threshold of mutation at the end of iterative optimization ( k / k max ) b If it is 1, the speed of each particle will no longer change suddenly.

[0057] It is understandable that in step S251, the movement behavior of each particle is not only affected by the best performance of its corresponding objective function in the entire evolution process (such as X k i，best ) and is also affected by the best performance of the entire particle swarm (e.g. X k gbest ). In this case, the topological structure of the region to be optimized can converge to the global optimum of the objective.

[0058] In some embodiments, despite the introduction of the above connectivity constraints, each particle may still generate a discontinuous conductor during the entire iterative optimization process, that is, a conductor that is not a simply connected domain. For example, please refer to Figure 7 , when removing some grid cells with conductor attributes along the rectangular boundary, a pair of diagonally connected grid cells may be left. In the classic topology optimization algorithm, filtering the chessboard is indispensable. In order to ensure a reasonable circuit, filtering the grid cells based on position can be described as follows: Step S252: Use the following expression to filter X k+1 i To filter: X k+1 i，e =Σ j∈Γ H e，j X k+1 i，j / Σ j∈Γ H e，j ; H e，j = max (0, r min -Δ( e , j ) Where Γ represents ζ a or ζ d Located in the e The grid unit is the center of the circle and the radius is r min The set of all grid cells within the circle, that is, only the cells in this set are considered during the filtering process Г The grid cells within e The influence of grid cells. X k+1 i，e Indicates i The particle corresponding to e The grid cell in k +The position after 1 iteration, H e，j Indicates j The grid cells are e The influence factor of each grid unit, Δ( e , j ) indicates the e grid cells to j The distance between grid cells. It is understandable that only when the j The grid cell is currently being filtered. e The distance between grid cells is less than r minThe impact factor is only present when , otherwise the impact factor is 0; the size of the impact factor is inversely proportional to the above distance, the distance Δ ( e , j ) is larger, the smaller the impact factor is. X k+1 i，e The weight / influence factor can be normalized to ensure that its range is always between 0 and 1. The essence of the above expression for filtering can be understood as follows: if there are more grid cells with conductor properties around a grid cell, the grid cell is more likely to be a conductor; conversely, the grid cell is more likely to be an insulator. It should be noted that r min Usually between 1 and 3; usually, r min Smaller values ​​produce rougher edges, while r min Larger values ​​produce smoother edges.

[0059] In some embodiments, step S300: taking the structural parameter after reaching the condition for stopping the iterative optimization as the structural parameter finally optimized includes: taking the population extreme value after reaching the condition for stopping the iterative optimization as the structural parameter finally optimized.

[0060] Since the particle swarm algorithm has parallelism and each particle has memory, at the end of the algorithm iteration, not only the optimal value (such as the population extreme value) but also the suboptimal value (such as the population suboptimal extreme value, etc.) can be obtained. Therefore, in some embodiments, the population suboptimal extreme value or other extreme value after reaching the conditions for stopping iterative optimization can be used as the structural parameter finally optimized.

[0061] For some examples, please refer to Figure 8 , Figure 8 It represents the coil corresponding to the structural parameters finally optimized. Figure 8 The dotted rectangular box in the figure represents the conductive workpiece.

[0062] It can be seen that in some embodiments, the coil structure optimization method of the present application adopts a discretized particle swarm algorithm to optimize the flat coil in magnetic pulse welding. Compared with the flat coils used in the past, the energy consumption required for welding using the coil corresponding to the structural parameters obtained by the above-mentioned final optimization is significantly reduced, and the joints produced by welding (such as the above-mentioned target area) can form a more effective connection, and the mechanical properties of the joint (such as strength, etc.) are higher.

[0063] It can be seen that in some embodiments, the coil structure optimization method of the present application can optimize the working condition parameters that have been determined for the coil, the conductive workpiece and the substrate, and optimize the coil that is compatible with the working condition parameters of the conductive workpiece and the substrate, thereby solving some special materials and specific working condition problems that conventional coils could not solve in the past (for example, reducing the energy level required for welding between metal materials and non-metallic materials, while previously an energy level that exceeded the energy limit of the equipment was required). It can be understood that the coil structure optimization method of the present application can generate a corresponding coil to handle each determined working condition (such as a conductive workpiece with a determined material, a coil with a determined initial size, and a substrate with a determined size, etc.) to maximize the welding efficiency of the coil finally optimized. In other words, the coil structure optimization method of the present application can customize the required coil for specific materials and working conditions, and the optimization process uses determined parameters (such as the above-mentioned working condition parameters, etc.) to obtain the optimal coil for the specific material and working condition; and the use of the corresponding optimal coil can effectively improve the energy utilization rate, effectively reduce costs, and the joint strength produced by welding will also be higher than the joint produced by the unoptimized traditional coil, thereby improving the quality of welding.

[0064] The above is some description of a coil structure optimization method. Fig. 9 In some embodiments of the present application, a coil structure optimization device is also disclosed, including: The pre-processing unit 100 is configured to obtain a region to be optimized and an objective function for optimizing the region to be optimized; wherein the region to be optimized includes a main working region of the flat coil, the main working region is a region where the proportion of the conductor per unit area or per unit volume exceeds a preset proportion threshold, the region to be optimized can drive a preset region of the conductive workpiece so that the preset region has a speed that rushes toward the substrate, the preset region includes a target region in the conductive workpiece that is combined with the substrate through the speed, and the objective function is to maximize the speed; The structure optimization unit 200 is configured to use the value of the objective function as the fitness value of the particle in the particle swarm algorithm, and the structural parameter of the area to be optimized as the position of the above particle, and the particle swarm algorithm performs iterative optimization until the iterative optimization stop condition is reached; The output unit 300 is configured to use the structural parameters after reaching the conditions for stopping the iterative optimization as the structural parameters finally optimized.

[0065] It should be noted that the specific processing procedures and technical effects of the preprocessing unit 100, the structure optimization unit 200 and the output unit 300 are basically the same as the specific processing procedures and technical effects of step S100, step S200 and step S300 in the aforementioned coil structure optimization method, so they will not be repeated here.

[0066] The above is some description about a coil structure optimization device. In some embodiments of the present application, a computer-readable storage medium is also disclosed, including a program, which can be executed by a processor to implement a method as in any embodiment of the present invention.

[0067] In some embodiments of the present application, a computer program product is also disclosed, including a computer program and / or instructions, and when the computer program and / or instructions are executed by a processor, a method as described in any embodiment of the present invention is implemented.

[0068] This document is described with reference to various exemplary embodiments. However, those skilled in the art will recognize that changes and modifications may be made to the exemplary embodiments without departing from the scope of this document. For example, various operating steps and components for performing the operating steps may be implemented in different ways (e.g., one or more steps may be deleted, modified, or combined into other steps) depending on the specific application or considering any number of cost functions associated with the operation of the system.

[0069] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. In addition, as understood by those skilled in the art, the principles of this article can be reflected in a computer program product on a computer-readable storage medium, which is pre-installed with a computer-readable program code. Any tangible, non-temporary computer-readable storage medium can be used, including magnetic storage devices (hard disks, floppy disks, etc.), optical storage devices (CD to ROM, DVD, Blu Ray disks, etc.), flash memory and / or the like. These computer program instructions can be loaded onto a general-purpose computer, a special-purpose computer, or other programmable data processing device to form a machine, so that these instructions executed on a computer or other programmable data processing device can generate a device that implements a specified function. These computer program instructions can also be stored in a computer-readable memory, which can instruct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory can form a manufactured product, including an implementation device that implements a specified function. Computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operating steps are performed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device can provide steps for implementing a specified function.

[0070] Although the principles of this invention have been shown in various embodiments, many modifications of structures, arrangements, proportions, elements, materials and components particularly suitable for specific environments and operational requirements can be used without departing from the principles and scope of this invention. The above modifications and other changes or amendments will be included in the scope of this invention.

[0071] The foregoing specific description has been described with reference to various embodiments. However, those skilled in the art will recognize that various modifications and changes can be made without departing from the scope of the present disclosure. Therefore, the consideration of the present disclosure will be illustrative rather than restrictive, and all these modifications will be included in its scope. Similarly, the advantages, other advantages and solutions to the problems of various embodiments have been described above. However, the benefits, advantages, solutions to the problems and any elements that can produce these, or make them more clear, should not be interpreted as critical, necessary or necessary. The term "include" and any other variants used in this article are all non-exclusive inclusions, so that the process, method, article or device including the list of elements not only includes these elements, but also includes other elements that are not explicitly listed or do not belong to the process, method, system, article or device. In addition, the term "coupled" and any other variants used in this article refer to physical connections, electrical connections, magnetic connections, optical connections, communication connections, functional connections and / or any other connections.

[0072] Those skilled in the art will appreciate that many changes may be made to the details of the above-described embodiments without departing from the basic principles of the invention. Therefore, the scope of the present invention should be determined solely by the claims.

Claims

1. A coil structure optimization method, characterized in that: include: Acquire an area to be optimized and an objective function for optimizing the area to be optimized; wherein the area to be optimized includes a main working area of ​​the flat coil, the main working area is an area where the proportion of the conductor per unit area or unit volume exceeds a preset proportion threshold, the area to be optimized can drive a preset area of ​​the conductive workpiece so that the preset area has a speed that rushes toward the substrate, the preset area includes a target area in the conductive workpiece that is combined with the substrate through the speed, and the objective function is to maximize the speed; The value of the objective function is used as the fitness value of the particle in the particle swarm algorithm, and the structural parameters of the area to be optimized are used as the position of the above particles. The particle swarm algorithm performs iterative optimization until the iterative optimization stop condition is reached; The structural parameters after reaching the conditions for stopping iterative optimization are taken as the structural parameters obtained by the final optimization.

2. The method according to claim 1, characterized in that The objective function value is used as the fitness value of the particle in the particle swarm algorithm, the structural parameter of the area to be optimized is used as the position of the particle, and the particle swarm algorithm performs iterative optimization until the iterative optimization stop condition is reached, including: Step S210: Grid the area to be optimized to obtain D grid units; wherein the properties of the above grid units are conductors or insulators; D The target search space is composed of N particles form a particle swarm, each particle represents a feasible solution to the structural parameters. i The position of a particle is expressed as: X i =( x i1 , x i2 , …, x iD ), i =1, 2, …, N ;No. i The velocity of a particle is expressed as: V i =( v i1 , v i2 , …, v iD ); x i1 , x i2 , …, x iD Respectively represent the 1st to the D The probability that the attribute of a grid cell is a conductor; Step S220: When the number of iterations k When it is equal to 0, it is initialized according to the initial area to be optimized. X i ,and V 0 i，η =0; where the initial area to be optimized ζ There is a boundary between the area composed of conductors and the area composed of insulators. η express ζ Neither ζ a Nor does it belong to ζ d The grid cells, ζ a represents the grid cells within the preset range of the boundary in the region composed of the above-mentioned insulator, ζ d Represents a grid cell within a preset range of the boundary in the region formed by the conductor; Step S230: Calculate the value of the objective function corresponding to each particle at the current number of iterations, and use the value of the objective function corresponding to each particle as the fitness value of each particle; Step S240: Calculate the particle optimal fitness value of each particle, calculate the population optimal fitness value of the particle swarm, and assign the position corresponding to the population optimal fitness value to the current population extreme value; Step S250: updating the speed and position of each particle; Step S260: determine whether the conditions for stopping iterative optimization are met; if the conditions for stopping iterative optimization are not met, continue to execute steps S230 to S260; if the conditions for stopping iterative optimization are met, the particle swarm algorithm stops iterating.

3. The method according to claim 2, characterized in that The structural parameters after reaching the conditions for stopping iterative optimization are used as the structural parameters obtained by final optimization, including: The population extreme value after reaching the conditions for stopping iterative optimization is taken as the final optimized structural parameter.

4. The method according to claim 2, characterized in that The expression of the objective function is: ； ； in, V Ω Represents the mass point in the target area Ω d The speed of Ω, Represents each particle in the target area Ω d The mean value of the velocity of Ω, S Ω Indicates the area of ​​the target area; The constraints of the iterative optimization include: S ψ≤ S g; S ψ and S ζ are the area of ​​the conductor in the region to be optimized and a preset conductor area threshold.

5. The method according to claim 4, characterized in that The constraints of this iterative optimization also include: The conductor in the region to be optimized is a simply connected domain; Among them, when ΣV k i,ζ>0 When ζ a The properties of the grid cells are defined as conductors; when ΣV k i,ζ<0 When ζ d The properties of the grid cells are defined as insulators; k V i,ζ Indicates k After iterations v i1 , v i2 ,…and v iD sum.

6. The method according to claim 5, characterized in that The updating of the speed and position of each particle includes: updating the speed and position of each particle according to the following expression: X k+1 i = X k i + V k+1 i ； V k+1 i = ωV k i + c 1 r 1 （ X k i，best - X k i ）+ c 2 r 2 （ X k gbest - X k i ）； in, X k i Indicates i The particle in k The position after iterations, V k i Indicates i Particles in the k The speed after iterations, V k+1 i Indicates i Particles in the k +1 speed after iteration, X k+1 i Indicates i Particles in the k +The position after 1 iteration, ω represents the inertia weight, X k i，best Indicates i Particles in the k The position corresponding to the particle's optimal fitness value after iterations, X k gbest Indicates k The population extreme value after iterations is c 1 and c 2 represent the local learning factor and partial learning factor, respectively. r 1 and r 2 Represent the first random number and the second random number respectively, 0 <r 1 < 1,0 <r 2 < 1; in,- V max <V k+1 i <V max , V max Indicates the preset maximum speed; when ΣV i,ζ k+1 >0 and r 5 >( k / k max ) b hour, V k+1 i，j = r 3 , j ∈ ζ a ; When ΣV i,ζ k+1 < 0 and r 5 >( k / k max ) b hour, V k+1 i，j = r 4 , j ∈ ζ d ; k max Indicates the preset maximum number of iterations. b Indicates the preset index, r 3 , r 4 and r 5 Represent the third random number, the fourth random number and the fifth random number respectively, 0 <r 3 < 1,0 <r 4 < 1,0 <r 5 < 1.

7. The method according to claim 6, characterized in that The updating of the speed and position of each particle also includes: using the following expression to update X k+1 i To filter: X k+1 i，e =S j∈Γ H e，j X k+1 i，j / S j∈Γ H e，j ; H e，j = max (0, r min -D( e , j )); Where Γ represents ζ a or ζ d Located in the e The grid unit is the center of the circle and the radius is r min The set of all grid cells within the circle of X k+1 i，e Indicates i The particle corresponding to e The grid cell in k +The position after 1 iteration, H e，j Indicates j The grid cells are e The influence factor of each grid unit, Δ( e , j ) indicates the e grid cells to j The distance between grid cells is 1≤ r min ≤3.

8. A coil structure optimization device, characterized in that: include: A preprocessing unit is configured to obtain an area to be optimized and an objective function for optimizing the area to be optimized; wherein the area to be optimized includes a main working area of ​​the flat coil, the main working area is an area where the proportion of the conductor per unit area or unit volume exceeds a preset proportion threshold, the area to be optimized can drive a preset area of ​​the conductive workpiece so that the preset area has a speed that rushes toward the substrate, the preset area includes a target area in the conductive workpiece that is combined with the substrate through the speed, and the objective function is to maximize the speed; The structural optimization unit is configured to use the value of the objective function as the fitness value of the particle in the particle swarm algorithm, and the structural parameter of the area to be optimized as the position of the above particle, and the particle swarm algorithm performs iterative optimization until a condition for stopping iterative optimization is reached; The output unit is configured to use the structural parameters after reaching the conditions for stopping iterative optimization as the structural parameters obtained by final optimization.

9. A computer-readable storage medium, characterized in that: The method comprises a program which can be executed by a processor to implement the method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program and / or instructions, characterized in that: When the computer program and / or the instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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