Topological optimization lattice structure machine learning and regulation and control method for anisotropic characteristics of additive manufacturing

Abstract

The invention discloses a topological optimization lattice structure machine learning and regulation method for anisotropic characteristics of additive manufacturing. The method comprises the following steps: step 1, constructing a data set and dividing the data set into a training set and a test set; 2, constructing a dot matrix stretching structure data driving model: constructing the dot matrix stretching structure data driving model based on a Gaussian process regression algorithm, and training the dot matrix stretching structure data driving model by adopting the data set constructed in the step 1; step 3, obtaining anisotropy data of the target macrostructure: calculating the anisotropy data of each lattice structure included in the target macrostructure based on the trained lattice stretching structure data driving model; and fourthly, additive manufacturing process forming parameters and relative density of the target lattice structure are optimized. Therefore, the method can provide support for topological optimization design and forming parameter determination of the lattice structure under consideration of additive manufacturing forming constraints in combination with a machine learning method under a small number of experiments according to the anisotropic characteristics of additive manufacturing.

Description

Technical Field

[0001] The present invention relates to a topological optimization lattice structure machine learning and control method for anisotropic characteristics of additive manufacturing, belonging to the technical field of additive manufacturing. Background Art

[0002] Laser additive manufacturing technology has been rapidly developed in the field of material processing and material repair because it can take into account both the needs of forming and high performance. It uses a high-energy laser beam as a heat source and realizes the forming of three-dimensional solid parts through a layer-by-layer manufacturing process. However, the anisotropy of materials in the additive manufacturing process has always been a key factor restricting its performance improvement. Anisotropy describes the phenomenon that materials exhibit different physical and chemical properties in different directions. This property may lead to significant differences in the performance of materials in different directions, especially in the forming process. Specifically, the material may exhibit excellent performance in the forming direction, such as higher strength and stiffness, while the performance perpendicular to the forming direction is relatively weak. This imbalance in performance may cause problems in practical applications, especially when subjected to complex loads, the material may not be able to evenly disperse stress, resulting in unexpected deformation or even structural damage. At present, there is a lack of a topology optimization strategy that can fully consider the anisotropic properties of materials in the additive manufacturing process.

[0003] In addition, the existing regression problem algorithms for predicting the anisotropy of additive manufacturing lattice structures still have many defects. For example, the prediction of anisotropy using the random forest regression algorithm needs to be adjusted through methods such as cross-validation because the random forest algorithm has multiple hyperparameters, such as the number of trees and the number of features considered when splitting each node, which may require a lot of experiments and computing resources. The use of multi-scale topology optimization machine learning algorithms requires a deep understanding of physical phenomena at different scales, and machine learning models may not be able to fully capture these multi-scale effects, thereby reducing the accuracy of the algorithm. Summary of the invention

[0004] The purpose of the present invention is to provide a topological optimization lattice structure machine learning and control method for the anisotropic characteristics of additive manufacturing, so as to realize the anisotropic topological optimization design of the additive manufacturing process.

[0005] In order to achieve the above technical objectives, the present invention will adopt the following technical solutions:

[0006] A machine learning and control method for topological optimization lattice structure based on anisotropic characteristics of additive manufacturing, comprising the following steps:

[0007] Step 1: Build a data set and divide it into training set and test set:

[0008] The data set includes several elements, each of which includes corresponding additive manufacturing process molding parameters, relative density of the lattice structure, and anisotropy data of the lattice structure;

[0009] Step 2: Construct a data-driven model for lattice tensile structure:

[0010] A lattice stretching structure data-driven model is constructed based on a Gaussian process regression algorithm, and the lattice stretching structure data-driven model is trained using the data set constructed in step 1;

[0011] The input of the lattice tensile structure data-driven model is the additive manufacturing process molding parameters in the training set constructed in step 1 and the relative density of the corresponding lattice structure, and the output is the anisotropic data prediction value of the corresponding lattice structure;

[0012] During the training process, the K-fold cross-validation algorithm is used to obtain the mean square error of the anisotropic data prediction values ​​of all lattice structures in the training set relative to the anisotropic data of the lattice structure in the data set, and the minimum mean square error is used as the optimization goal. The Bayesian search algorithm is used to obtain the optimal hyperparameters of the lattice stretching structure data-driven model to complete the model training.

[0013] Step 3: Obtain anisotropic data of the target macrostructure:

[0014] Preset the initial values ​​of the additive manufacturing process molding parameters and the initial values ​​of the relative density for the target macrostructure;

[0015] Input the preset initial values ​​of the additive manufacturing process molding parameters and the initial values ​​of the relative density into the trained lattice tensile structure data-driven model, calculate the anisotropic data of each lattice structure included in the target macrostructure, and obtain the anisotropic elastic matrix of each lattice structure included in the target macrostructure;

[0016] Step 4: Optimize the additive manufacturing process parameters and relative density of the target lattice structure:

[0017] The anisotropic data of each lattice structure included in the target macrostructure calculated in step three is used as the input of the lattice topology optimization algorithm, and the load is applied to obtain the topological optimization configuration of the target macrostructure. The Bayesian method is used to reversely search the corresponding additive manufacturing process molding parameters and relative density, so as to determine the optimized values ​​of the additive manufacturing process molding parameters and relative density of the target macrostructure.

[0018] Preferably, in step 1, the construction of the data set specifically includes the following steps:

[0019] Step 1.1, obtaining a series of additive manufacturing process molding parameters, relative density of lattice structures, and anisotropy data of lattice structures that have a mapping relationship with each other;

[0020] Step 1.2, normalizing the additive manufacturing process molding parameters, the relative density of the lattice structure, and the anisotropy data of the lattice structure obtained in step 1;

[0021] Step 1.3: Divide the normalized additive manufacturing process parameters, the relative density of the lattice structure, and the anisotropy data of the lattice structure into two data sets, one of which is the algorithm input data set X = [x1, x2, …, x i ,…,x n ] m×n , and the other is the algorithm output data set Y = [y1, y2, ..., y i ,…,y m ] 1×m ; where: x i represents the i-th element of the algorithm input data set X, which is composed of the additive manufacturing process molding parameters and the relative density of the lattice structure; y i Represents the i-th element of the algorithm output data set Y, which is composed of anisotropic data of the lattice structure; i ranges from 1 to m, and m represents the number of samples in the training set.

[0022] Preferably, in step 4, the optimized values ​​of the additive manufacturing process molding parameters of the target macrostructure and the optimized values ​​of the relative density of the lattice structure included in the target macrostructure determined by the lattice topology optimization algorithm are specifically:

[0023]

[0024] Where: n is the number of finite elements in the macrostructure design domain, ρ i represents the relative density of the i-th lattice structure in the macrostructure; U is the node displacement matrix, F is the external load vector, and v i is the volume of the i-th lattice structure, V is the total volume of the macrostructure design domain; is the average volume of the macrostructure design domain, ρ min is the minimum relative density, ρ max Relatively dense

[0025] Maximum value of degree;

[0026] K a represents the overall stiffness matrix of the macrostructure, B is the elastic displacement matrix, Ω i represents the equivalent unit of the i-th lattice structure, is the elastic matrix of the i-th lattice structure;

[0027] The elastic matrix of any lattice structure calculated by the optimized lattice tensile structure data-driven model is expressed as:

[0028]

[0029] Among them, E1, E2, and E3 are the elastic moduli of the corresponding lattice structures, ν 12 , ν 13 , ν 23 、v 31 、v 32 、v 21 are the Poisson's ratios of the corresponding lattice structures respectively; the numbers 1, 2, 3 included in the subscripts of each elastic modulus, shear modulus and Poisson's ratio correspond to the three normal directions of the corresponding lattice structure.

[0030] Preferably, in step 2, a square exponential kernel function and automatic association determination are used to improve the generalization ability of the lattice tensile structure data-driven model; the kernel function is expressed as:

[0031]

[0032] Where: y represents the anisotropic data of the lattice structure in the training set, y' represents the predicted value of the anisotropic data of the corresponding lattice structure output by the lattice tensile structure data driven model, σ f Take 1, It reflects the size of white noise in the training data; Θ is a diagonal matrix whose diagonal elements are composed of the characteristic length of each structural and process parameter; δ(y,y') is the Kronecker function.

[0033] Preferably, in step 1, the data set is divided into a training set and a test set in a ratio of 8:2.

[0034] Another technical objective of the present invention is to provide a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned lattice topology optimization machine learning method for anisotropic features of additive manufacturing are implemented.

[0035] Another technical objective of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned lattice topology optimization machine learning method for anisotropic features of additive manufacturing.

[0036] Based on the above technical objectives, the present invention has the following advantages over the prior art:

[0037] ① By using the Bayesian search algorithm, we can efficiently explore the parameter space of the Gaussian process regression (SVR) algorithm and identify the optimal hyperparameter combination. The Bayesian method, with its probabilistic reasoning nature, can quickly converge to the optimal solution with fewer evaluations when faced with a complex search space. This process not only improves the efficiency of model training, but also significantly improves the accuracy of prediction results by accurately adjusting key hyperparameters such as kernel functions and regularization parameters;

[0038] ② In traditional machine learning model training, a large amount of data is usually required to ensure the generalization ability of the model. However, the SVR algorithm optimized by the Bayesian search algorithm can achieve high prediction accuracy even with only a small amount of data. This is because the optimized model can more effectively capture the potential patterns in the data, reducing the dependence on large-scale data sets, and thus making accurate predictions even when data is scarce;

[0039] ③Compared with the random forest regression algorithm, the Gaussian process regression algorithm with hyperparameter optimization is more economical in terms of data requirements. The random forest algorithm often requires a large number of training data sets for iterative calculations to improve the prediction accuracy of the model. In contrast, the SVR algorithm, with the help of Bayesian search, can achieve a prediction accuracy comparable to or even higher than that of the random forest even with less data. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of the lattice topology optimization machine learning method for anisotropic features of additive manufacturing according to the present invention;

[0041] Figure 2 It is a schematic diagram of a structural part obtained by performing printing experiments in different forming directions using the lattice topology optimization machine learning method of the present invention;

[0042] Figure 3 It is the transformation principle of the anisotropic elastic matrix in the lattice topology optimization machine learning method described in the present invention;

[0043] Figure 4 It is a modulus-relative density mapping diagram in the lattice topology optimization machine learning method described in the present invention;

[0044] Figure 5 is a topology optimization process convergence curve in the lattice topology optimization machine learning method of the present invention; DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means any limitation to the present invention and its application or use. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise specified, the relative arrangement, expressions and numerical values ​​of the components and steps described in these embodiments do not limit the scope of the present invention. The techniques, methods and equipment known to ordinary technicians in the relevant fields may not be discussed in detail, but in appropriate cases, the techniques, methods and equipment should be regarded as part of the specification. In all examples shown and discussed here, any specific value should be interpreted as merely exemplary, rather than as a limitation. Therefore, other examples of the exemplary embodiments may have different values.

[0046] For ease of description, spatially relative terms such as "above", "above", "on the upper surface of", "above", etc. may be used here to describe the spatial positional relationship between a device or feature and other devices or features as shown in the figure. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figure. For example, if the device in the accompanying drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below other devices or structures". Thus, the exemplary term "above" can include both "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations).

[0047] like Figure 1 As shown, the present invention provides a topology optimization lattice structure machine learning strategy for anisotropic features of additive manufacturing, which specifically includes the following steps:

[0048] Step 1: Build a data set and divide it into training set and test set:

[0049] Considering additive manufacturing process parameters (laser power, scanning speed) and lattice structure parameters (i.e., changing the relative density of the lattice structure) as input variables, by collecting the anisotropic data of the lattice structure formed by additive manufacturing, including the elastic modulus, shear modulus and Poisson's ratio in three directions, a data set is constructed and divided into a training set and a test set. The data set is divided into a training set and a test set in a ratio of 8:2. In other words, in the present invention, the data set includes several elements, each of which includes the corresponding additive manufacturing process forming parameters, the relative density of the lattice structure, and the anisotropic data of the lattice structure.

[0050] Step 2: Construct a data-driven model for lattice tensile structure:

[0051] A lattice stretching structure data-driven model is constructed based on a Gaussian process regression algorithm, and the lattice stretching structure data-driven model is trained using the data set constructed in step 1;

[0052] The input of the lattice tensile structure data-driven model is the additive manufacturing process molding parameters in the training set constructed in step 1 and the relative density of the corresponding lattice structure, and the output is the anisotropic data prediction value of the corresponding lattice structure;

[0053] During the training process, the K-fold cross-validation algorithm is used to obtain the mean square error of the anisotropic data prediction values ​​of all lattice structures in the training set relative to the anisotropic data of the lattice structure in the data set, and the minimum mean square error is used as the optimization goal. The Bayesian search algorithm is used to obtain the optimal hyperparameters of the lattice stretching structure data-driven model to complete the model training.

[0054] Specifically, this step constructs a lattice tensile structure data-driven model for AlSi10Mg structural parts. The model is constructed based on the Gaussian process regression algorithm and trained using the training set constructed in step 1. The input is the process parameters and relative density of the lattice structure in the training set constructed in step 1, and the output is the anisotropic data of the lattice structure. Specifically, it includes the following steps:

[0055] (1) In order to eliminate the dimensional influence between parameters and make the prediction results more accurate, the sample data in the training set are first normalized.

[0056] (2) The process parameters (laser power, scanning speed) and lattice structure parameters (i.e., changing the relative density of the lattice structure) of additive manufacturing forming lattice structure are used as algorithm input, and the normalized anisotropic data (including elastic modulus, shear modulus and Poisson's ratio in three directions) are used as algorithm output.

[0057] The basic single-task Gaussian process regression defines the joint probability distribution from the original space to the high-dimensional feature space by transforming φ(·) and adopts the prior distribution of the feature space as the Gaussian process. The corresponding covariance can be expressed in the form of a kernel function:

[0058]

[0059] (3) Based on experience and understanding of our own dataset, we use the squared exponential (SE) kernel function and automatic association determination (ARD) to improve the generalization ability of the model:

[0060]

[0061] Where: y represents the anisotropic data of the lattice structure in the training set, y' represents the predicted value of the anisotropic data of the corresponding lattice structure output by the lattice tensile structure data driven model, σ f Take 1, It reflects the size of white noise in the training data; Θ is a diagonal matrix whose diagonal elements are composed of the characteristic length of each structural and process parameter; δ(y,y') is the Kronecker function.

[0062] (4) For a group with l (where x i ∈R n is an n-dimensional feature vector) and a label vector y = [y1, y2, ..., y n ] T , the learning sample matrix X = {x1, x2, ..., x n}, given a new input X * and its corresponding output y * , y and y * Satisfies the joint normal distribution:

[0063]

[0064] Among them, y represents the mechanical properties output by the training set, y + represents the predicted mechanical properties of the training set, and x represents the structural and process parameters of the training set input.

[0065] From this joint distribution, we can take y * The marginal distribution of , we get the prediction form of Gaussian process regression:

[0066]

[0067] cov(y * )=κ(X * ,X * )-Σ(X * ,X)K -1 Σ(X,X* )

[0068] Multi-task Gaussian process regression defines a joint probability distribution by considering the correlation between multiple targets. D represents the number of target outputs, Represents the input of multiple tasks, Y = {y1,y2,...,y D} represents the output of multiple tasks. The multi-task covariance matrix is ​​defined as follows:

[0069]

[0070] Step 3: Obtain anisotropic data of the target macrostructure:

[0071] Preset the initial values ​​of the additive manufacturing process molding parameters and the initial values ​​of the relative density for the target macrostructure;

[0072] The preset initial values ​​of the additive manufacturing process molding parameters and the initial values ​​of the relative density are input into the trained lattice tensile structure data-driven model, and the anisotropic data of each lattice structure included in the target macrostructure are calculated to obtain the anisotropic elastic matrix of each lattice structure included in the target macrostructure, which is expressed as:

[0073]

[0074] Among them, E1, E2, and E3 are the elastic moduli of the corresponding lattice structures, G 23 , G 13 , G 12 are the shear modulus of the corresponding lattice structure, ν 12 , ν 13 , ν 23 、v 31 、v 32 、v 21 are the Poisson's ratios of the corresponding lattice structures respectively; the numbers 1, 2, 3 included in the subscripts of each elastic modulus, shear modulus and Poisson's ratio correspond to the three normal directions of the corresponding lattice structure.

[0075] Step 4: Optimize the additive manufacturing process parameters and relative density of the target lattice structure:

[0076] The anisotropic data of each lattice structure included in the target macrostructure calculated in step three is used as the input of the lattice topology optimization algorithm, and the load is applied to obtain the topological optimization configuration of the target macrostructure. The Bayesian method is used to reversely search the corresponding additive manufacturing process molding parameters and relative density, so as to determine the optimized values ​​of the additive manufacturing process molding parameters and relative density of the target macrostructure.

[0077] The optimized values ​​of the additive manufacturing process forming parameters of the target macrostructure and the optimized values ​​of the relative density of the lattice structure included in the target macrostructure determined by the lattice topology optimization algorithm are specifically:

[0078]

[0079] Where: n is the number of finite elements in the macrostructure design domain, ρ i represents the relative density of the i-th lattice structure in the macrostructure; U is the node displacement matrix, F is the external load vector, and v i is the volume of the i-th lattice structure, V is the total volume of the macrostructure design domain; is the average volume of the macrostructure design domain, ρ min is the minimum relative density, ρ max Relatively dense

[0080] Maximum value of degree;

[0081] K a represents the overall stiffness matrix of the macrostructure, B is the elastic displacement matrix, Ω i represents the equivalent unit of the i-th lattice structure, D i H (ρ i ) is the elastic matrix of the i-th lattice structure.

[0082] like Figure 3 As shown in the figure, by collecting the elastic modulus, shear modulus and Poisson's ratio in three directions, the anisotropic elastic matrix of the lattice structure is obtained, and then the effective elastic properties are calculated using these data. The results show that the BCC lattice structure exhibits the highest elastic modulus in the diagonal direction and the lowest elastic modulus in the X, Y and Z axis directions. Figure 4 As shown, the optimized Gaussian process regression algorithm is used as a proxy model for calculating the relative density-anisotropic mechanical properties of the lattice structure. The increase in relative density significantly improves the overall Young's modulus. In summary, the method of the present invention reversely searches for the optimal anisotropic mechanical properties under different relative densities through the Bayesian method, and derives the anisotropic elastic matrix and its corresponding process and structural parameters. The method of the present invention is different from the neural network prediction. With a small number of experiments, it combines machine learning methods to support the topological optimization design and forming parameter determination of the lattice structure considering the constraints of additive manufacturing. At the same time, the method of the present invention only requires a small training set to obtain accurate prediction results.

Claims

1. A machine learning and control method for topological optimization lattice structure of anisotropic characteristics of additive manufacturing, characterized in that: The steps include: Step 1: Build a data set and divide it into training set and test set: The data set includes several elements, each of which includes corresponding additive manufacturing process molding parameters, relative density of the lattice structure, and anisotropy data of the lattice structure; Step 2: Construct a data-driven model for lattice tensile structure: A lattice stretching structure data-driven model is constructed based on a Gaussian process regression algorithm, and the lattice stretching structure data-driven model is trained using the data set constructed in step 1; The input of the lattice tensile structure data-driven model is the additive manufacturing process molding parameters in the training set constructed in step 1 and the relative density of the corresponding lattice structure, and the output is the anisotropic data prediction value of the corresponding lattice structure; During the training process, the K-fold cross-validation algorithm is used to obtain the mean square error of the anisotropic data prediction values ​​of all lattice structures in the training set relative to the anisotropic data of the lattice structure in the data set, and the minimum mean square error is used as the optimization goal. The Bayesian search algorithm is used to obtain the optimal hyperparameters of the lattice stretching structure data-driven model to complete the model training. Step 3: Obtain anisotropic data of the target macrostructure: Preset the initial values ​​of the additive manufacturing process molding parameters and the initial values ​​of the relative density for the target macrostructure; Input the preset initial values ​​of the additive manufacturing process molding parameters and the initial values ​​of the relative density into the trained lattice tensile structure data-driven model, calculate the anisotropic data of each lattice structure included in the target macrostructure, and obtain the anisotropic elastic matrix of each lattice structure included in the target macrostructure; Step 4: Optimize the additive manufacturing process parameters and relative density of the target lattice structure: The anisotropic data of each lattice structure included in the target macrostructure calculated in step three is used as the input of the lattice topology optimization algorithm, and the load is applied to obtain the topological optimization configuration of the target macrostructure. The Bayesian method is used to reversely search the corresponding additive manufacturing process molding parameters and relative density, so as to determine the optimized values ​​of the additive manufacturing process molding parameters and relative density of the target macrostructure.

2. The method for topological optimization lattice structure machine learning and control based on anisotropic characteristics of additive manufacturing according to claim 1, characterized in that: In step 1, the construction of the data set includes the following steps: Step 1.1, obtaining a series of additive manufacturing process molding parameters, relative density of lattice structures, and anisotropy data of lattice structures that have a mapping relationship with each other; Step 1.2, normalizing the additive manufacturing process molding parameters, the relative density of the lattice structure, and the anisotropy data of the lattice structure obtained in step 1; Step 1.3: Divide the normalized additive manufacturing process parameters, the relative density of the lattice structure, and the anisotropy data of the lattice structure into two data sets, one of which is the algorithm input data set X = [x1, x2, ..., x i ,…,x n ] m×n , and the other is the algorithm output data set Y = [y1, y2, ..., y i ,…,y m ] 1×m ; Where: x i represents the i-th element of the algorithm input data set X, which is composed of the additive manufacturing process molding parameters and the relative density of the lattice structure; y i Represents the i-th element of the algorithm output data set Y, which is composed of anisotropic data of the lattice structure; i ranges from 1 to m, and m represents the number of samples in the training set.

3. The method for topological optimization lattice structure machine learning and control based on anisotropic characteristics of additive manufacturing according to claim 1, characterized in that: In step 4, the optimization values ​​of the additive manufacturing process molding parameters of the target macrostructure and the relative density optimization values ​​of the lattice structure included in the target macrostructure determined by the lattice topology optimization algorithm are specifically: Where: n is the number of finite elements in the macrostructure design domain, ρ i represents the relative density of the i-th lattice structure in the macrostructure; U is the node displacement matrix, F is the external load vector, and v i is the volume of the i-th lattice structure, V is the total volume of the macrostructure design domain, is the average volume of the design domain of the structure, ρ min is the minimum relative density, ρ max is the maximum relative density; K a represents the overall stiffness matrix of the macrostructure, B is the elastic displacement matrix, Ω i represents the equivalent unit of the i-th lattice structure, is the elastic matrix of the i-th lattice structure; The elastic matrix of any lattice structure calculated by the optimized lattice tensile structure data-driven model is expressed as: Among them, E1, E2, and E3 are the elastic moduli of the corresponding lattice structures, G 23 , G 13 , G 12 are the shear modulus of the corresponding lattice structure, ν 12 , ν 13 , ν 23 、v 31 、v 32 、v 21 are the Poisson's ratios of the corresponding lattice structures respectively; the numbers 1, 2, 3 included in the subscripts of each elastic modulus, shear modulus and Poisson's ratio correspond to the three normal directions of the corresponding lattice structure.

4. The method for topological optimization lattice structure machine learning and control based on anisotropic characteristics of additive manufacturing according to claim 1, characterized in that: In step 2, the square exponential kernel function and automatic association determination are used to improve the generalization ability of the lattice tensile structure data-driven model; the kernel function is expressed as: Where: y represents the anisotropic data of the lattice structure in the training set, y' represents the predicted value of the anisotropic data of the corresponding lattice structure output by the lattice tensile structure data driven model, σ f Take 1, It reflects the size of white noise in the training data; Θ is a diagonal matrix whose diagonal elements are composed of the characteristic length of each structural and process parameter; δ(y,y') is the Kronecker function.

5. The method for topological optimization lattice structure machine learning and control based on anisotropic characteristics of additive manufacturing according to claim 1, characterized in that: In step 1, the data set is divided into training set and test set in a ratio of 8:

2.

6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the machine learning and control method of topological optimization lattice structure for anisotropic features of additive manufacturing as described in any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the machine learning and control method of topological optimization lattice structure for additive manufacturing anisotropic features as described in any one of claims 1 to 5 are implemented.

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