Component determination method of titanium alloy and preparation method of titanium alloy

Abstract

The invention discloses a method for determining components of a titanium alloy. The method comprises the following steps that the adjusting ranges and the step lengths of Ti, Al, V, Mo and Si are obtained; obtaining a plurality of groups of components according to the adjusting ranges and step lengths of Ti, Al, V, Mo and Si; simulating and generating a plurality of phase structures based on each group of components, and calculating mole fractions of key phases in the phase structures; and inputting the mole fractions corresponding to each group of components into a model to obtain a plurality of performances corresponding to each group of components, so as to determine an optimal component according to the plurality of performances. According to the scheme provided by the invention, a brand-new correlation model among components, tissues and performance is constructed, the influence of the components on the phase tissues is quickly predicted through thermodynamic calculation, and a prediction model of the tissues on the performance is constructed in combination with a machine learning algorithm, so that quick feedback from the components to the tissues to the performance is realized; and the existing titanium alloy components are effectively improved. According to the method, the research and development period and the test cost can be greatly shortened, and the scientificity and the accuracy of material design can be improved.

Description

Technical Field

[0001] The present invention relates to the field of alloys, and in particular to a method for determining the composition of a titanium alloy and a method for preparing the titanium alloy. Background Art

[0002] The traditional method of titanium alloy composition design is to determine the corresponding component combination that meets different performance requirements through repeated experiments. This method requires a large number of experiments and comprehensive analysis of the test results to roughly obtain the optimal combination that meets the corresponding performance requirements. The accuracy of the optimal combination is not high, the entire process is time-consuming and costly, and the efficiency is low, which makes it difficult to meet the needs of modern industry for titanium alloys. Summary of the Invention

[0003] In view of this, in order to overcome at least one aspect of the above problems, an embodiment of the present invention provides a method for determining the composition of a titanium alloy, comprising the following steps: Get the adjustment range and step size of Ti, Al, V, Mo, and Si; Multiple groups of components are obtained according to the adjustment range and step size of Ti, Al, V, Mo, and Si; Generate multiple phase structures based on each component simulation and calculate the mole fractions of α and β phases; The mole fraction corresponding to each component is input into the model to obtain multiple properties corresponding to each component, so as to determine the optimal composition according to the multiple properties.

[0004] In some embodiments, obtaining the adjustment range and step size of Ti, Al, V, Mo, and Si further includes: The adjustment ranges and step sizes of Al, V, Mo, and Si are set as follows: the adjustment range of Al is 4~7%, with a step size of 0.03%; the adjustment range of V is 0~5%, with a step size of 0.05%; the adjustment range of Mo is 0~2%, with a step size of 0.02%; the adjustment range of Si is 0~1%, with a step size of 0.01%, and Ti is used as a supplementary element.

[0005] In some embodiments, before inputting the mole fraction corresponding to each component group into the model to obtain the performance corresponding to each component group, the method further includes: Acquire multiple sets of actual data of the titanium alloy, each set of the actual data including the composition and properties of the titanium alloy, and obtain the mole fraction of the corresponding key phase based on the composition of the titanium alloy; The model is trained using the mole fraction of the key phase corresponding to each component as a sample and the performance as a label.

[0006] In some embodiments, the model is trained using the mole fraction of the key phase corresponding to each group of components as a sample and the performance as a label, further comprising: Divide multiple sets of real data into training sets, validation sets, and test sets; The model is trained using the mole fraction of the key phase corresponding to each component in the training set as samples and the performance as labels; The model is validated using the mole fraction of the key phase corresponding to each component in the validation set as a sample and the performance as a label; The model is tested using the mole fraction of the key phase corresponding to each component in the test set as a sample and the performance as a label.

[0007] In some embodiments, obtaining multiple sets of data of the titanium alloy further includes: Each set of data is processed with missing values, unified data format, and vectorized.

[0008] In some embodiments, obtaining the adjustment range and step size of Ti, Al, V, Mo, and Si further includes: The adjustment range and step size of Ti, Al, V, Mo, and Si are determined according to the composition and properties of each set of actual data.

[0009] In some embodiments, obtaining multiple groups of components according to the adjustment ranges and step sizes of Ti, Al, V, Mo, and Si further includes: Grid searches were performed based on the adjustment ranges and step sizes corresponding to Ti, Al, V, Mo, and Si to obtain multiple groups of components with different proportions.

[0010] In some embodiments, the mole fraction corresponding to each component is input into a model to obtain the properties corresponding to each component, so as to determine the optimal composition according to the properties, further comprising: The obtained multiple performances are weighted and calculated to obtain the final performance; The optimal composition is determined based on the final properties.

[0011] In some embodiments, simulating and generating multiple phase structures based on each group of components and calculating the mole fraction of the key phase therein further includes: The influence of Ti, Al, V, Mo, and Si on the key phases is determined based on the mole fraction of the key phases of each component.

[0012] Based on the same inventive concept, according to another aspect of the present invention, an embodiment of the present invention further provides a method for preparing a titanium alloy, comprising: Determine the composition ratio of Ti, Al, V, Mo, and Si using the method described in any of the above embodiments; Titanium alloy is prepared according to the composition ratio of Ti, Al, V, Mo and Si.

[0013] The present invention has the following beneficial technical effects: The proposed solution establishes a new correlation model between composition, structure, and performance. Through thermodynamic calculations, it rapidly predicts the influence of composition on phase structure. Combined with machine learning algorithms, it constructs a predictive model of structure-performance. This enables rapid feedback from composition to structure and then to performance, effectively improving the composition of existing titanium alloys. This approach not only significantly shortens R&D cycles and testing costs, but also improves the scientific nature and precision of material design. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other embodiments can be obtained based on these drawings without paying any creative work.

[0015] Figure 1 A schematic flow chart of a method for determining the composition of a titanium alloy provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0017] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are for distinguishing two non-identical entities with the same name or non-identical parameters. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present invention. Subsequent embodiments will not explain this one by one.

[0018] According to one aspect of the present invention, an embodiment of the present invention provides a method for determining the composition of a titanium alloy, such as Figure 1 As shown, it may include the steps of: Get the adjustment range and step size of Ti, Al, V, Mo, and Si; Multiple groups of components are obtained according to the adjustment range and step size of Ti, Al, V, Mo, and Si; Generate multiple phase structures based on each component simulation and calculate the mole fractions of α and β phases; The mole fraction corresponding to each component is input into the model to obtain multiple properties corresponding to each component, so as to determine the optimal composition according to the multiple properties.

[0019] The proposed solution establishes a new model for the correlation between composition, structure, and performance. Using thermodynamic calculations to rapidly predict the influence of composition on phase structure, combined with machine learning algorithms, a predictive model for the effect of structure on performance is constructed. This allows for rapid feedback from composition to structure and then to performance, effectively improving the composition of existing titanium alloys. This approach not only significantly shortens R&D cycles and testing costs, but also improves the scientific nature and precision of material design.

[0020] In some embodiments, obtaining the adjustment range and step size of Ti, Al, V, Mo, and Si further includes: The adjustment ranges and step sizes of Al, V, Mo, and Si are set as follows: the adjustment range of Al is 4~7%, with a step size of 0.03%; the adjustment range of V is 0~5%, with a step size of 0.05%; the adjustment range of Mo is 0~2%, with a step size of 0.02%; the adjustment range of Si is 0~1%, with a step size of 0.01%, and Ti is used as a supplementary element.

[0021] In some embodiments, before inputting the mole fraction corresponding to each component group into the model to obtain the performance corresponding to each component group, the method further includes: Acquire multiple sets of actual data of the titanium alloy, each set of the actual data including the composition and properties of the titanium alloy, and obtain the mole fraction of the corresponding key phase based on the composition of the titanium alloy; The model is trained using the mole fraction of the key phase corresponding to each component as a sample and the performance as a label.

[0022] Specifically, titanium alloys include TC4, TC11, etc. The composition-related data of titanium alloys include the types and contents of alloying elements, the weight and content of impurity elements, alloying elements include aluminum, vanadium, molybdenum, tin, etc., impurity elements include iron, oxygen, nitrogen, carbon, etc., and the performance-related data of titanium alloys include mechanical property-related data, chemical property-related data, and physical property-related data, etc. Among them, mechanical properties include strength, plasticity, and toughness. Titanium alloys are composed of multiple components and have multiple properties. Therefore, there is a corresponding relationship between the composition-related data and performance-related data of different titanium alloys, and multiple composition-related data correspond to multiple performance-related data. Before constructing the dataset, the composition-related data and performance-related data need to be preprocessed, and the missing value processing, data format unification, and vectorization processing of the data are completed in sequence. For example, the data can be standardized through Python, and then the dataset is constructed based on the processed data.

[0023] In some embodiments, the model is trained using the mole fraction of the key phase corresponding to each group of components as a sample and the performance as a label, further comprising: Divide multiple sets of real data into training sets, validation sets, and test sets; The model is trained using the mole fraction of the key phase corresponding to each component in the training set as samples and the performance as labels; The model is validated using the mole fraction of the key phase corresponding to each component in the validation set as a sample and the performance as a label; The model is tested using the mole fraction of the key phase corresponding to each component in the test set as a sample and the performance as a label.

[0024] Specifically, the model is trained using the mole fractions of the key phases corresponding to each component in the training set as samples and the performance as labels. During this process, the model continuously adjusts its parameters to minimize the error between the predicted performance results and the actual performance labels. A loss function (such as mean squared error or cross-entropy loss) is typically used to measure the difference between the predicted and true values. An optimization algorithm (such as gradient descent) is then used to update the model parameters to minimize the loss function.

[0025] The model is validated using the mole fractions of key phases corresponding to each component in the validation set as samples and performance as labels. At each stage of model training or after a certain number of training steps, the validation set is used to evaluate model performance. By analyzing evaluation metrics on the validation set (such as accuracy, recall, and mean squared error), it is possible to determine whether the model is overfitting or underfitting, and to adjust the model's hyperparameters to improve its generalization ability.

[0026] The model is tested using the mole fractions of key phases corresponding to each component in the test set as samples and performance as labels. After model training and hyperparameter tuning are complete, the test set is used to evaluate the model's final performance. Because the test set data is not used during model training and validation, the evaluation results on the test set more accurately reflect the model's performance in real-world applications.

[0027] In some embodiments, obtaining multiple sets of data of the titanium alloy further includes: Each set of data is processed with missing values, unified data format, and vectorized.

[0028] Specifically, the collected data can be normalized, including missing value processing, format unification, and vectorized representation, to provide a basic data set for subsequent analysis. For example, Python can be used to normalize the above-mentioned component-related data and performance-related data, and complete missing value processing, data format unification, and vectorization processing in sequence. Using Python's pandas library, first conduct a comprehensive inspection of the data to identify the fields or records where missing values are located. Based on the characteristics of the data, appropriate missing value processing strategies are used to delete, fill, and mark missing values. Use Python to unify the format of the data, such as data type conversion, date and time format processing, and text data processing. Finally, vectorize all features and construct a data set based on the processed data.

[0029] In some embodiments, obtaining the adjustment range and step size of Ti, Al, V, Mo, and Si further includes: The adjustment range and step size of Ti, Al, V, Mo, and Si are determined according to the composition and properties of each set of actual data.

[0030] Specifically, you can check the content ranges of Ti, Al, V, Mo, and Si in all actual data. Find the minimum and maximum values of Ti, Al, V, Mo, and Si, which will preliminarily determine their possible adjustment ranges. For example, if the Ti content in the actual data is between 10%-30% and the Al content is between 5%-15%, then the initial adjustment range can be set to slightly larger than this range, such as Ti is 5%-35% and Al is 0%-20%, to include possible changes. You can also analyze the sensitivity of performance to changes in Ti and Al content. If the performance is sensitive to changes in Ti and Al content, that is, a small change in content will lead to a large change in performance, then the step size should be selected as a smaller value, such as 0.05% or 0.1%, to more accurately capture the relationship between performance changes and composition.

[0031] In some embodiments, obtaining multiple groups of components according to the adjustment ranges and step sizes of Ti, Al, V, Mo, and Si further includes: Grid searches were performed based on the adjustment ranges and step sizes corresponding to Ti, Al, V, Mo, and Si to obtain multiple groups of components with different proportions.

[0032] In some embodiments, the mole fraction corresponding to each component is input into a model to obtain the properties corresponding to each component, so as to determine the optimal composition according to the properties, further comprising: The obtained multiple performances are weighted and calculated to obtain the final performance; The optimal composition is determined based on the final properties.

[0033] In some embodiments, simulating and generating multiple phase structures based on each group of components and calculating the mole fraction of the key phase therein further includes: The influence of Ti, Al, V, Mo, and Si on the key phases is determined based on the mole fraction of the key phases of each component.

[0034] Specifically, with key phases as the optimization target, adjustments are made to components with properties close to the target values (e.g., strength >= 850 MPa, elongation >= 20%). By simulating the changes in the proportions of the main alloying elements, the influence of the mole fraction of the key phases can be determined. By focusing on analyzing the changing trends of the key phases and identifying the extreme points, the optimal composition combination that meets the project's performance requirements can be selected.

[0035] The proposed solution establishes a new model for the correlation between composition, structure, and performance. Using thermodynamic calculations to rapidly predict the influence of composition on phase structure, combined with machine learning algorithms, a predictive model for the effect of structure on performance is constructed. This allows for rapid feedback from composition to structure and then to performance, effectively improving the composition of existing titanium alloys. This approach not only significantly shortens R&D cycles and testing costs, but also improves the scientific nature and precision of material design.

[0036] Based on the same inventive concept, according to another aspect of the present invention, an embodiment of the present invention further provides a method for preparing a titanium alloy, comprising: Determine the composition ratio of Ti, Al, V, Mo, and Si using the method described in any of the above embodiments; Titanium alloy is prepared according to the composition ratio of Ti, Al, V, Mo and Si.

[0037] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope of the embodiments disclosed in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any particular order. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless expressly limited to the singular.

[0038] It should be understood that, as used herein, the singular forms "a" and "an" are intended to include the plural forms as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" is intended to include any and all possible combinations of one or more of the associated listed items.

[0039] The serial numbers of the embodiments disclosed in the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0040] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to limit the scope of the disclosure of the present invention (including the claims) to these examples. Within the spirit of the present invention, the technical features of the above embodiments or different embodiments may be combined, and many other variations exist in different aspects of the above embodiments, which are not provided in detail for the sake of clarity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining the composition of a titanium alloy, characterized in that: The following steps are involved: Get the adjustment range and step size of Ti, Al, V, Mo, and Si; Multiple groups of components are obtained according to the adjustment range and step size of Ti, Al, V, Mo, and Si; Generate multiple phase structures based on each component simulation and calculate the mole fractions of α and β phases; The mole fraction corresponding to each component is input into the model to obtain multiple properties corresponding to each component, so as to determine the optimal composition according to the multiple properties.

2. The method according to claim 1, wherein Obtaining the adjustment range and step size of Ti, Al, V, Mo, and Si further includes: The adjustment ranges and step sizes of Al, V, Mo, and Si are set as follows: the adjustment range of Al is 4~7%, with a step size of 0.03%; the adjustment range of V is 0~5%, with a step size of 0.05%; the adjustment range of Mo is 0~2%, with a step size of 0.02%; the adjustment range of Si is 0~1%, with a step size of 0.01%, and Ti is used as a supplementary element.

3. The method according to claim 1, wherein Before inputting the mole fraction corresponding to each component into the model to obtain the performance corresponding to each component, the method further includes: Acquire multiple sets of actual data of the titanium alloy, each set of the actual data including the composition and properties of the titanium alloy, and obtain the mole fraction of the corresponding key phase based on the composition of the titanium alloy; The model is trained using the mole fraction of the key phase corresponding to each component as a sample and the performance as a label.

4. The method according to claim 3, wherein The model is trained using the mole fraction of the key phase corresponding to each component as a sample and the performance as a label, further comprising: Divide multiple sets of real data into training sets, validation sets, and test sets; The model is trained using the mole fraction of the key phase corresponding to each component in the training set as samples and the performance as labels; The model is validated using the mole fraction of the key phase corresponding to each component in the validation set as a sample and the performance as a label; The model is tested using the mole fraction of the key phase corresponding to each component in the test set as a sample and the performance as a label.

5. The method according to claim 3, wherein Acquiring multiple sets of data of the titanium alloy further includes: Each set of data is processed with missing values, unified data format, and vectorized.

6. The method according to claim 3, wherein Obtaining the adjustment range and step size of Ti, Al, V, Mo, and Si further includes: The adjustment range and step size of Ti, Al, V, Mo, and Si are determined according to the composition and properties of each set of actual data.

7. The method according to claim 1, wherein According to the adjustment range and step size of Ti, Al, V, Mo, and Si, multiple groups of components are obtained, further including: Grid searches were performed based on the adjustment ranges and step sizes corresponding to Ti, Al, V, Mo, and Si to obtain multiple groups of components with different proportions.

8. The method according to claim 1, wherein Inputting the mole fraction corresponding to each component into the model to obtain the properties corresponding to each component, so as to determine the optimal composition according to the properties, further comprising: The obtained multiple performances are weighted and calculated to obtain the final performance; The optimal composition is determined based on the final properties.

9. The method according to claim 1, wherein Generate multiple phase structures based on each component simulation and calculate the mole fraction of the key phase, further including: The influence of Ti, Al, V, Mo, and Si on the key phases is determined based on the mole fraction of the key phases of each component.

10. A method for preparing a titanium alloy, characterized in that: include: Determining the composition ratio of Ti, Al, V, Mo, and Si using the method according to any one of claims 1 to 9; Titanium alloy is prepared according to the composition ratio of Ti, Al, V, Mo and Si.

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