A method and device for verifying an analytical model of a process / structure / performance

Abstract

The present application provides a method and device for verifying an analytical model of process / structure / performance. The method provided by the present application includes: determining the process conditions to be measured for the analytical model of process / structure / performance; determining the laser power level and scanning speed level based on the process conditions to be measured; fabricating a sample to be scanned; screening candidate scanning regions from the sample to be scanned; extracting grain feature information based on the candidate scanning region images; calculating the material hardness according to the grain size information; combining the material hardness and the grain hardness information with the corresponding process conditions to be measured to obtain an evaluation data set, and evaluating the performance of the analytical model based on the evaluation data set. The method and device for verifying the analytical model of process / structure / performance provided by the present application are used to verify the performance of the analytical model of process / structure / performance, and ensure the reliability and accuracy of the analytical model of process / structure / performance in actual process production applications.

Description

Technical Field

[0001] This application relates to the field of additive manufacturing technology, and particularly to a method and device for verifying an analytical model of process / structure / performance. Background Art

[0002] In the fields of manufacturing and materials science, the P-S-P (Process-Structure-Property) analytical model is widely used to understand and predict the relationships among the processing technology, microstructure, and final properties of materials. Traditional P-S-P analytical models often rely on complex numerical models and data-driven models to simulate and optimize these processes. First, numerical models require a large amount of computing resources and time, especially when dealing with complex multi-physics problems. For occasions where process parameters need to be frequently adjusted, they are not suitable for real-time optimization and online control. Second, data-driven models rely on a large amount of historical data, but the reliability and coverage of the data may be insufficient, and there are overfitting problems, making it difficult to handle unknown process conditions. In addition, traditional P-S-P analytical models are usually static and cannot adapt to dynamic production environments.

[0003] To solve these problems, a P-S-P analytical model constructed based on the continuous growth limiting factor, interdependence model, and Hall-Petch relationship provides a new solution. Compared with traditional numerical models, the analytical model provided by this application greatly improves the calculation efficiency, can quickly make predictions under different process parameters, and is suitable for real-time adjustment and optimization in online production. In addition, combining the interdependence model further improves the prediction accuracy, can better simulate the interaction between process parameters, and is applicable to various materials and process conditions. However, although the P-S-P analytical model of this application has significant advantages in calculation efficiency and adaptability, in order to ensure its reliability and accuracy in practical applications, its performance must be verified, especially in new process and material systems.

[0004] Therefore, there is an urgent need for a method to verify the performance of the analytical model of process / structure / performance to ensure the reliability and accuracy of the analytical model of process / structure / performance in actual process production applications. Summary of the Invention

[0005] In view of this, this application provides a method and device for verifying an analytical model of process / structure / performance to verify the performance of the analytical model of process / structure / performance and ensure the reliability and accuracy of the analytical model of process / structure / performance in actual process production applications.

[0006] Specifically, this application is implemented through the following technical solutions:

[0007] The first aspect of the present application provides a method for verifying an analytical model of a process / structure / performance, the method comprising:

[0008] Determine the process conditions to be measured for the analytical model of the process / structure / performance, wherein there are multiple process conditions to be measured, and the analytical model is used to output the material hardness based on the process conditions to be measured;

[0009] Determine the laser power level and the scanning speed level based on the process conditions to be measured, wherein the laser power level and the scanning speed level for each process condition to be measured are different, predict the microscopic structure change values under each process condition to be measured based on the analytical model, and match the corresponding laser power level and scanning speed level based on the difference in microscopic structure change values under different process conditions to be measured;

[0010] Fabricate a sample to be scanned;

[0011] Screen candidate scanning regions from the sample to be scanned, wherein, based on the microscopic structure change values predicted by the analytical model, locate the candidate regions of the molten pool boundary, and determine the scanning step size and the observation regions corresponding to the scanning points based on the influence degree of each process condition to be measured on the sample to be scanned;

[0012] Extract grain feature information based on the images of the candidate scanning regions; the grain feature information at least includes grain size information and grain hardness information;

[0013] Calculate the material hardness according to the grain size information;

[0014] Combine the material hardness, the grain hardness information with the corresponding process conditions to be measured to obtain an evaluation data set, and evaluate the performance of the analytical model based on the evaluation data set.

[0015] The second aspect of the present application provides an apparatus for verifying an analytical model of a process / structure / performance, the apparatus comprising a determination module, an acquisition module, a screening module, a calculation module and an evaluation module; wherein,

[0016] The determination module is configured to determine the process conditions to be measured for the analytical model of the process / structure / performance, wherein there are multiple process conditions to be measured, and the analytical model is used to output the material hardness based on the process conditions to be measured;

[0017] The determination module is further configured to determine the laser power level and the scanning speed level based on the process conditions to be measured, wherein the laser power level and the scanning speed level for each process condition to be measured are different, predict the microscopic structure change values under each process condition to be measured based on the analytical model, and match the corresponding laser power level and scanning speed level based on the difference in microscopic structure change values under different process conditions to be measured;

[0018] The acquisition module is used to produce a sample to be scanned;

[0019] The screening module is used to screen candidate scanning regions from the sample to be scanned. Among them, the candidate region of the molten pool boundary is located according to the microstructural change value predicted by the analysis model, and the scanning step length and the observation region corresponding to the scanning points are determined based on the influence degree of each process condition to be measured on the sample to be scanned;

[0020] The acquisition module is further used to extract grain feature information based on the candidate scanning region image; the grain feature information at least includes grain size information and grain hardness information;

[0021] The calculation module is used to calculate the material hardness according to the grain size information;

[0022] The evaluation module is used to combine the material hardness, the grain hardness information with the corresponding process conditions to be measured to obtain an evaluation data set, and evaluate the performance of the analysis model based on the evaluation data set.

[0023] The method and device for verifying the analytical model of process / structure / performance provided by the present application. In the first aspect, by comparing and analyzing the material hardness predicted by the analytical model of process / structure / performance with the grain hardness information obtained through experiments, the performance of the analytical model of process / structure / performance can be fully evaluated. First, the material hardness predicted by the analytical model of process / structure / performance is calculated based on the grain size information through the Hall-Petch law, which reflects the modeling ability of the analytical model for the relationship between microscopic features and macroscopic properties. The grain hardness information is experimental data obtained by direct measurement, with high authenticity and accuracy. The comparison between the two can intuitively reflect the deviation and applicability of the analytical model in predicting material properties. By analyzing whether the trend predicted by the analytical model is consistent with the experimental results, the rationality of the analytical model can be further verified. This evaluation method can not only verify the accuracy of the analytical model but also discover the potential limitations of the analytical model, providing a scientific basis for process optimization and model improvement, thereby improving the reliability and wide applicability of the analytical model in practical applications. In the second aspect, since the sample to be scanned usually contains multiple different regions, the grain structures and properties of these regions may vary significantly due to factors such as processing conditions, heat conduction, and material composition. The present application locates the candidate regions of the molten pool boundary according to the predicted values of the microscopic structure changes by the analytical model, which can focus on the representative and significantly changing regions, making the observation range concentrated on the key regions, and can comprehensively capture the influence of process conditions on the microscopic structure, thus clearly reflecting the overall picture of the structural changes. In this way, it can effectively avoid data deviation caused by the interference of irrelevant regions, improve the accuracy of the observation results, and ensure the high adaptability of the experimental data to the verification of the analytical model. Secondly, by determining the scanning step size and the position of the scanning points based on the different degrees of influence of each process condition to be measured on the sample, efficient and accurate scanning can be achieved. The optimized setting of the scanning step size and the scanning points can reduce the invalid regions of the scanning while ensuring the accuracy of the experimental data, thereby greatly improving the experimental efficiency. In addition, this accurate positioning method can avoid waste of resources, reduce the complexity of experimental data processing, and make the entire verification process more efficient and reliable. By selecting representative candidate scanning regions, data distortion caused by local special phenomena can be avoided, ensuring that the experimental data can reflect the typical features of the entire sample or the processing process, thereby enhancing the reliability of the verification of the analytical model. At the same time, according to the verification requirements of the analytical model of process / structure / performance, only some key regions need to be focused on, such as the weld center region, the heat-affected zone, or the grain-to-grain connection region. These regions are usually the sensitive points of material property changes and the key points of process optimization. Selecting these regions as candidate scanning regions can not only effectively reduce the experimental workload and improve the experimental efficiency but also ensure that the collected data better matches the verification target of the analytical model, avoiding the interference of irrelevant data on the experimental results.Thirdly, verification data is obtained based on the laser power level and scanning speed level determined according to the process conditions to be measured, ensuring that the verification process of the analytical model is highly relevant to the actual production environment. This avoids the gap between laboratory conditions and industrial applications that may exist in traditional experimental methods, making the verification of the analytical model closer to the real production scenario. During the process of making the sample to be scanned and screening the candidate scanning areas, the reliability and accuracy of the experimental data are further enhanced by ensuring the actual representativeness of the sample and selecting appropriate areas for testing. This process not only improves the accessibility of the data by combining image extraction of grain feature information (such as grain size and hardness information), but also provides a real and efficient basis for subsequent hardness calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flowchart of the method for verifying the analytical model of process / structure / performance provided in Embodiment 1 of the present application;

[0025] Figure 2 is a schematic structural diagram of the device for verifying the analytical model of process / structure / performance provided in Embodiment 2 of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application.

[0027] The terms used in the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "the" and "said" used in the present application are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0028] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0029] Specific embodiments are given below to introduce the technical solutions of the present application in detail.

[0030] Figure 1The flowchart of the analysis model verification method for the process / structure / performance provided in the first embodiment of this application. Please refer to Figure 1 , the method provided in this embodiment may include:

[0031] S101. Determine the process conditions to be measured for the analysis model of the process / structure / performance. Among them, there are multiple process conditions to be measured, and the analysis model is used to output the material hardness based on the process conditions to be measured.

[0032] Specifically, the analysis model of the process / structure / performance is a multi-level and multi-variable analysis framework, which is used to explore and understand the mutual relationship between the process conditions, the resulting micro or macro structure, and the final performance of materials or products during the processing process. The analysis model of the process / structure / performance is used to optimize the processing process conditions to obtain the required product performance. The analysis model of the process / structure / performance establishes a quantitative relationship between the process, structure, and performance through a mathematical model. Since this quantitative relationship is usually complex and involves the interaction of multiple factors, a large amount of experimental data is required to verify the performance of the analysis model of the process / structure / performance.

[0033] Furthermore, the analysis model of the process / structure / performance uses continuous growth limiting factors to replace the traditional grain growth hypothesis, uses an interdependent model to characterize the growth relationship between grains, and uses the Hall-Petch law to replace the traditional performance prediction method; the analysis model is used to determine the material hardness based on the grain size information corresponding to different process conditions to be measured, and adjust the process conditions to be measured based on the difference between the material hardness and the target hardness.

[0034] The analysis model of the process / structure / performance associates the process conditions to be measured with the grain size information, and the continuous growth limiting factor is inversely proportional to the grain size information; the Hall-Petch law associates the grain size information with the material hardness, and there is a linear change between the reciprocal square root of the grain size information and the material hardness.

[0035] Specifically, the analytical model of process / structure / performance is constructed based on continuous growth limiting factors, interdependent models, and the Hall-Petch law. The analytical model of process / structure / performance predicts the hardness of materials based on grain size information under different process conditions to be measured, and adjusts and optimizes the process conditions to be measured based on the difference between the material hardness and the target hardness corresponding to the processing requirements. The continuous growth limiting factors refer to the limiting factors that affect the growth of grains under specific process conditions, including temperature, time, stress state, etc., which are the conditions that limit grain growth during heat treatment or other processing processes. The continuous growth limiting factors will affect the final size of the material grains, usually by restricting the growth rate of grains to control the grain size, thereby affecting the final material properties. The traditional grain growth hypothesis is usually based on simple grain growth laws or empirical formulas, without fully considering the dynamic effects of external conditions and various limiting factors on grain growth.

[0036] The continuous growth limiting factors describe the change of the cooling rate on the solidification path, and the tangent of the continuous growth limiting factor curve represents the initial development rate of the cooling rate at different nucleation sites. The continuous growth limiting factors can be expressed as:

[0037] ;

[0038] ;

[0039] ;

[0040] Among them, the is the continuous growth limiting factor; the is the liquidus slope; the is the solute concentration; the is the distribution coefficient; the is the solidification path; the is the diffusion coefficient of the solute in the liquid; the is the solidification rate; the is the length of solute diffusion in front of the S / L interface; the is the laser scanning speed; the is the angle between the laser scanning direction and the grain growth direction.

[0041] Furthermore, due to the limitations of the traditional Q model (traditional growth restriction factor Q model), there are limitations in constructing the process-structure module based on the traditional Q model. First, the temperature field and cooling rate are assumed to be uniform: The traditional Q model assumes that the temperature and cooling rate are uniform throughout the molten pool, ignoring the differences in local temperature gradients and cooling rates and unable to reflect the solidification characteristics of local areas in the molten pool. Second, the effects of alloy composition changes and multiphase structures are ignored: Factors such as the composition, phase transformation, and grain morphology of the alloy on the solidification process are not considered. Third, the description of grain growth is overly simplified: The traditional model simplifies grain growth to a single restriction factor Q, ignoring the interaction of multiple factors such as nucleation rate and cooling rate. Fourth, the dynamic changes during the solidification process are not considered: The traditional Q model does not capture the effects of dynamic changes such as temperature and flow on grain growth. To overcome the limitations of the traditional Q model, the continuous growth restriction factor is used to replace Q in the traditional Q model, and the average grain size of the solidified structure in the molten pool is developed into a grain size that varies with the solidification path , that is:

[0042] ;

[0043] ;

[0044] wherein, the is the average grain size; the is the diffusion coefficient of the solute in the liquid; the is the solidification velocity; the is the continuous growth restriction factor; the is the undercooling increment required to activate subsequent nucleation events; the is the critical nucleation undercooling of the material; the is the solute concentration; the is the partition coefficient; the is the additional distance to the next most effective nucleating particle; the is the solute distribution on the liquid side in front of the solid-liquid interface ( less than 1).

[0045] It should be noted that in the traditional Q model, Q, as the growth limiting factor, mainly relies on the assumption of the uniformity of the cooling rate. However, it fails to accurately capture the dynamic changes of process parameters during solidification, especially the influence of factors such as local temperature gradient and molten pool flow. To overcome this limitation, introducing Qc as a function of the cooling rate is an effective improvement based on the same theoretical foundation. The core similarity between Qc and Q is that they both reflect the influence of the cooling rate on the grain growth process. However, Qc takes into account the characteristics of the cooling rate changing along the solidification path, rather than a fixed growth limiting factor. This enables Qc to be more flexible and accurate when describing the dynamic evolution of grain growth during solidification. Moreover, there is a significant linear relationship between the grain size and 1 / Qc, and Qc can more precisely reflect the change of the grain size, avoiding the overly simplified assumptions in the traditional Q model. By introducing the interdependence theory and the description of the continuous solidification path, the adaptability of Qc is further enhanced. The interdependence theory indicates that during solidification, factors such as temperature, flow, and composition interact with each other, and changing any one of them will affect the behavior of grain growth. By taking these dynamic changes into account, Qc can more realistically capture the change of the grain size under different process conditions, rather than just the influence of a single cooling rate. Compared with the traditional Q model, Qc can not only more accurately describe the influence of the cooling rate on the grain size, but also consider the non-uniformity of the local temperature and composition of the molten pool, further improving the prediction accuracy. By constructing a "P-S" module with Qc, it is possible to optimize the grain size and microstructure according to different process parameters, achieving the preparation of materials with higher performance. As an improved form of the growth limiting factor, Qc not only theoretically inherits the functions of the traditional Q model, but also provides a more accurate tool by considering the dynamic cooling rate, the change of the solidification path, and the interaction of multiple factors, which is particularly suitable for the modeling and prediction of grain growth behavior under complex process conditions.

[0046] Furthermore, the interdependence model describes the complex relationships between grains, including grain competition, co-growth, spatial constraints, etc., and can more accurately reflect the growth behavior of grains in a complex process environment. Most traditional models regard the influence between grains as independent or only model it in a simple competitive way.

[0047] The Hall-Petch law characterizes the quantitative relationship between the hardness of a material and the grain size. There is an inverse relationship between the material hardness and the grain size, that is, the smaller the grain size, the greater the material hardness. Most traditional performance prediction methods estimate the relationship between the grain size and performance based on linear or empirical formulas, ignoring the non-linear influence of the microstructure on the macroscopic performance. The Hall-Petch law can be expressed as:

[0048] ;

[0049] wherein, the is the material hardness; the is the inherent hardness; the is the Hall-Petch coefficient; the is the grain size; and are determined by experiments and are fixed values (the Hall-Petch coefficient and the fixed hardness corresponding to each material remain unchanged).

[0050] In specific implementation, the analytical model of process / structure / performance first determines the corresponding grain size information according to different process conditions (such as temperature, cooling rate, time, etc.). Based on the Hall-Petch law, the process / structure / performance model calculates the material hardness using the calculated grain size information, and adjusts the process conditions (such as heating time, cooling rate, pressure, etc.) through the feedback of the predicted material hardness, so as to control the microstructure of the material and finally achieve the required material properties. By predicting the microstructure evolution and material hardness of the material under the process conditions to be measured through the analytical model of process / structure / performance, the production process is optimized.

[0051] Furthermore, there are multiple process conditions to be measured, including material characteristics, process parameters, structural characteristics, performance requirements, etc. Among them, material characteristics include the selected material type (such as metal, polymer, ceramic, etc.), the initial properties of the material (such as hardness, elastic modulus, thermal conductivity, etc.), and the treatment method of the material (such as heat treatment, surface treatment, etc.). Process parameters include the type of processing process (such as milling, laser cutting, 3D printing), the setting of process conditions (such as temperature, pressure, time, etc.), processing speed, feed rate, cutting force, etc. Structural characteristics include the geometric shape of the product (such as size, thickness, surface roughness, etc.), and the complexity of the structure (such as multi-layer structure, pore structure, grid structure, etc.). Performance requirements include target performance indicators (such as mechanical properties, thermal properties, electrical properties, optical properties, etc.), and specific requirements such as strength, wear resistance, corrosion resistance, thermal stability, etc.

[0052] In specific implementation, when determining the process conditions to be measured, it is necessary to comprehensively consider multiple aspects such as material characteristics, process parameters, structural characteristics, and performance requirements. Therefore, there are multiple determined process conditions to be measured. When collecting experimental data (grain characteristic information) based on the process conditions to be measured in the subsequent embodiments, experiments also need to be carried out separately under multiple process conditions to be measured, that is, the experimental environment is controlled under multiple groups of process conditions to be measured to ensure a comprehensive analysis of the performance of the analytical model of process / structure / performance under different process conditions to be measured.

[0053] It should be noted that since there are many process conditions involved in additive manufacturing, in this embodiment, only the laser power level and the scanning speed level are taken as the main research objects for illustration, and other process conditions are controlled to be consistent.

[0054] The method provided in this embodiment effectively establishes the relationship among process conditions, grain size, and material properties through an analytical model of process / structure / performance constructed by introducing continuous growth limiting factors. Moreover, this relationship is based on the same physical basis, which endows the analytical model with higher accuracy and applicability in practical applications. First, the continuous growth limiting factor is used to replace the fixed growth limiting factor in the traditional Q model, and the continuous growth limiting factor is adopted to describe the dynamic characteristics of grain growth. This enables the analytical model to more accurately capture the influence of factors such as temperature, time, and stress state on grain growth. By considering the changes in cooling rate and solidification path, the continuous growth limiting factor can adapt to grain growth under different process conditions, ensuring an accurate connection among process, microstructure, and properties. Second, the interdependent model further enhances the practical adaptability of the analytical model. It takes into account the mutual influence among grains and the dynamic behavior of grain boundaries. This refined modeling method can effectively reflect the complex interaction of multiple factors during grain growth. The Hall-Petch law provides reliable theoretical support for material property prediction by quantifying the relationship between grain size and material hardness. This physical relationship makes hardness prediction more accurate and avoids the limitations of traditional empirical formulas. The analytical model can adjust process conditions based on the difference between the material hardness and the target hardness, thereby optimizing process parameters to meet the expected material property requirements. By accurately predicting the grain size under different process conditions, the analytical model can flexibly adjust processing conditions according to the material hardness prediction results, avoiding the fixed assumptions and over-simplifications in traditional methods. Different from traditional grain growth assumptions, the analytical model is based on a more dynamic and detailed theory, which can capture the comprehensive influence of process conditions on grain growth and provide more accurate material property regulation. In summary, the analytical model of process / structure / performance not only breaks through the limitations of traditional methods but also provides a more flexible and accurate process optimization means by introducing a dynamic model based on physical basis, endowing it with higher prediction ability and practical guiding significance in practical applications.

[0055] S102. Determine the laser power level and scanning speed level based on the process conditions to be measured.

[0056] Among them, the laser power level and scanning speed level of each process condition to be measured are different. Predict the values of microstructure changes under each process condition to be measured based on the analytical model, and match the corresponding laser power level and scanning speed level according to the difference in the values of microstructure changes under different process conditions to be measured.

[0057] Specifically, the laser power level and the scanning speed level are different ranges or categories of two important process parameters in the laser processing process, which directly affect the processing effect of the material. Among them, the laser power level refers to the power output by the laser. In laser processing, the power size determines the energy density of the laser beam, which in turn affects processes such as heating, melting, cutting, or surface treatment of the material. The laser power level is usually characterized by being divided into several levels based on different ranges of laser power. For example, the laser power can be divided into low, medium, high, etc. levels according to different power ranges. The scanning speed level refers to the speed at which the laser beam moves along the processing path, and is usually divided into different speed levels. The scanning speed level is usually classified according to different ranges of the scanning speed. For example, the scanning speed is divided into low, medium, and high speed levels, indicating the moving distance of the laser beam per unit time. The speed of the scanning directly affects the interaction time between the laser and the material (i.e., the action time), which in turn affects the processing effect, the shape of the molten pool, the size of the heat affected zone, etc.

[0058] It should be noted that, combined with the above description, since there are multiple process conditions to be measured, therefore, the laser power levels and scanning speed levels determined for the process conditions to be measured should also be multiple groups, and the laser power levels and scanning speed levels corresponding to each process condition to be measured are different.

[0059] Specifically, when implementing, collect the parameter information of all process conditions to be measured as input conditions. Use the analysis model of process / structure / performance to perform simulation calculations on each process condition to be measured, and obtain the corresponding microstructure change values under each process condition to be measured. Calculate the difference in the microstructure change values corresponding to different process conditions to be measured to obtain the difference in the microstructure change values between different process conditions to be measured. Further, based on the calculated difference in the microstructure change values, according to the mapping relationship between the preset difference in the microstructure change values and the laser power level and the scanning speed level, match the corresponding laser power level and scanning speed level one by one to obtain the laser power level and scanning speed level corresponding to each process condition to be measured. It should be noted that the mapping relationship between the difference in the microstructure change values and the laser power level and the scanning speed level is determined through experiments. Use the analysis model of process / structure / performance to simulate the microstructure change values under different combinations of laser power levels and scanning speed levels to generate a simulation data set. Through regression analysis or fitting methods, determine the corresponding relationship between the difference in the microstructure change values and the laser power level and the scanning speed level, and establish a mapping relationship.

[0060] The method provided in this embodiment takes into account that due to different process conditions to be measured, the impact of each process condition to be measured on the microstructure of the material is also different. Therefore, different laser power levels and scanning speed levels are matched for different process conditions to be measured to meet the requirements of these changing conditions. By adjusting the process parameters specifically, the actual microstructure changes under different process conditions to be measured can be better presented, ensuring the diversity and representativeness of experimental data, thereby providing more comprehensive support for verifying the analysis model of process / structure / performance. This differential setting can reflect the sensitivity of process parameters to the microstructure and performance, providing a more powerful basis for the optimization and improvement of the analysis model. Secondly, when the analysis model has not reached high precision, directly using the model prediction value may lead to inaccurate matching due to errors. By using the relative change amount (difference) of the microstructure change value for matching, the inaccurate part that may exist in the model prediction can be effectively offset. The relative change amount pays more attention to the trend differences under different process conditions to be measured rather than the accuracy of absolute values. This processing method reduces the impact of model prediction errors on the matching result and improves the reliability and robustness of the matching. Using this method, reasonable parameter matching can still be achieved at the stage when the model prediction is not yet perfect, providing a feasible experimental basis and result verification for further optimization of the analysis model.

[0061] Specifically, determining the laser power level and scanning speed level based on the process condition to be measured includes: predicting the microstructure change value under each process condition to be measured based on the analysis model; the microstructure change value includes grain size and grain hardness; based on the influence of the laser power level on the microstructure change value and the influence of the scanning speed level on the microstructure change value, and the microstructure change values under each process condition to be measured, determining the laser power level and scanning speed level under each process condition to be measured; the laser power level is directly proportional to the grain size and inversely proportional to the hardness; the scanning speed level is inversely proportional to the grain size and directly proportional to the grain hardness.

[0062] Specifically, the microstructure change value includes grain size and grain hardness. Both the laser power level and the scanning speed level will affect the grain size and grain hardness in the microstructure change. Specifically, as the laser power level increases, the molten pool temperature rises, the cooling rate slows down, and thus larger grains are formed, the grain size increases, and the grain hardness decreases. As the scanning speed level increases, the cooling rate increases, the molten pool temperature decreases, and thus finer grains are formed, the grain size decreases, and the grain hardness increases.

[0063] In specific implementation, an analytical model of process / structure / performance is used to predict the values of microstructure changes under each process condition to be measured, including grain size and grain hardness. Based on experimental data, the influence of the laser power level on the values of microstructure changes (grain size and grain hardness) is analyzed, and it is confirmed that it is directly proportional to the grain size and inversely proportional to the grain hardness; the influence of the scanning speed level on the values of microstructure changes is analyzed, and it is confirmed that it is inversely proportional to the grain size and directly proportional to the grain hardness. A mapping relationship between the laser power level and the grain size and grain hardness, and a mapping relationship between the scanning speed level and the grain size and grain hardness are constructed. For each process condition to be measured, according to the predicted grain size and grain hardness, the corresponding laser power level and scanning speed level are calculated in combination with the mapping relationship. According to the grain size information, the laser power level proportional to it is selected, and the scanning speed level inversely proportional to it is selected. According to the grain hardness information, the laser power level inversely proportional to it is selected, and the scanning speed level proportional to it is selected. Considering the change values of the grain size and grain hardness comprehensively, the laser power level and scanning speed level under each process condition to be measured are output.

[0064] It should be noted that with the difference of the process conditions to be measured, the laser power level and the scanning speed level change dynamically within a range. For example, in this embodiment, the laser power level changes dynamically within the range of 1000W - 3000W, and the scanning speed level changes dynamically within the range of 4 mm / s - 6 mm / s. In this embodiment, the laser power level is set to three levels of 1400W, 2100W, and 2600W, and the scanning speed level is set to three levels of 4.8 mm / s, 5.3 mm / s, and 5.9 mm / s.

[0065] S103. Fabricate the sample to be scanned.

[0066] Specifically, the sample to be scanned is a material sample processed under specific process conditions (such as laser power level, scanning speed level, etc.). The sample to be scanned will be used for subsequent scanning and analysis to extract characteristic information such as grain size and grain hardness, and verify the performance of the analytical model of process / structure / performance through these characteristic information.

[0067] In specific implementation, the fabricating of the sample to be scanned includes: using the concentric powder feeding method, based on the laser power level and the scanning speed level, performing layer-by-layer deposition on the powder material to obtain a multi-layer sample; vertically cutting the weld center area in the multi-layer sample based on the laser scanning direction to obtain a sample distributed in a two-dimensional plane; the weld is the connection area between each layer of the sample; mechanically polishing and grinding the sample, and chemically etching it with aqua regia to obtain the sample to be scanned.

[0068] Specifically, the distribution along the two-dimensional plane refers to the distribution along the x-y plane, that is, the plane perpendicular to the substrate where the laser scanning path lies. The weld refers to the connection area between each layer of the multi-layer sample. Aqua regia refers to a mixed solution obtained by mixing 3 parts of hydrochloric acid and 1 part of nitric acid, which has strong chemical corrosiveness.

[0069] In specific implementation, select a suitable powder material and set the working parameters of the laser according to the determined laser power level and scanning speed level. Use the concentric powder feeding method to deposit the powder material layer by layer under the irradiation of the laser beam. The thickness of each layer, the laser power level, and the scanning speed level are controlled according to the process requirements to form a multi-layer sample with a multi-layer structure. Each layer is combined by laser melting the powder and during the solidification process to form a weld area. Further, determine the laser scanning direction and cut the weld center area in the multi-layer sample according to the laser scanning path. When cutting, cut along the direction perpendicular to the laser scanning direction to obtain a sample distributed along the two-dimensional plane. The cut sample exposes the weld area between each layer. Finally, mechanically polish the cut sample using appropriate polishing tools and polishing paste to remove the rough substances on the sample surface and make its surface smooth and shiny. Further, continue grinding to further improve the smoothness of the sample surface and ensure that the microscopic structure features of the sample can be clearly observed. Finally, put the ground sample into aqua regia for chemical etching to remove the unnecessary oxide layer on the surface of the metal sample and obtain the sample to be scanned.

[0070] For example, when conducting experiments in this embodiment, Deloro60 nickel-based alloy powder is selected as the powder material. The main components of this powder are Ni and Cr, and it contains 4.3% Si as a solute, which is simplified to a binary alloy system. The substrate is a 42CrMo steel plate, whose chemical composition includes carbon, chromium, silicon, molybdenum, manganese, and iron, and has good heat resistance and strength characteristics. The laser used in the experiment is a continuous-wave CO2 laser. Using the concentric powder feeding method, deposit the Deloro60 nickel-based alloy powder on the 42CrMo steel substrate to form single-particle and single-layer samples layer by layer. After the deposition is completed, vertically cut the center area of the weld along the laser scanning direction to obtain a sample along the x-y plane. Conduct fine mechanical polishing and grinding on the sample, and chemically etch it with aqua regia to enhance the microscopic structure microscopic features and obtain the sample to be scanned.

[0071] S104. Screen candidate scanning areas from the sample to be scanned.

[0072] Among them, locate the candidate area of the molten pool boundary according to the predicted value of the microscopic structure change by the analytical model, and determine the scanning step size and the observation area corresponding to the scanning points based on the influence degree of each of the process conditions to be measured on the sample to be scanned.

[0073] Specifically, the candidate scanning regions refer to the regions selected on the sample to be scanned. These regions are representative and suitable for subsequent scanning analysis to extract relevant grain feature information (such as grain size information, grain hardness information, etc.). The candidate scanning regions are selected from the entire sample to be scanned through specific criteria to ensure the accuracy and representativeness of the verification results of the process / structure / performance analysis model.

[0074] It should be noted that since the sample to be scanned usually contains multiple different regions, the grain structure and performance of each region may be different. By selecting representative candidate scanning regions, it can be ensured that the scanning results can reflect the typical features of the entire sample or the processing process, rather than local special phenomena. Moreover, according to the verification requirements of the process / structure / performance analysis model, it may only be necessary to focus on certain specific regions (such as the weld center region, heat affected zone or the connection region between grains). Therefore, selecting these regions as candidate scanning regions can better match the model verification target and ensure the relevance of the experiment.

[0075] In specific implementation, use the process / structure / performance analysis model to predict the sample to be scanned, and obtain the distribution of the microstructural change values under different process conditions to be measured. According to the predicted distribution of the microstructural change values, identify the demarcation points with significant microstructural change values, and determine the candidate regions of the molten pool boundary (the candidate regions of the molten pool boundary are usually the positions where significant changes in grain features occur, such as the weld center region or the heat affected zone). According to the influence degree of each process condition to be measured on the microstructure of the sample to be scanned, calculate its influence range within the candidate region of the molten pool boundary, including the microstructural change gradient and distribution characteristics. Based on the microstructural change gradient within the candidate region of the molten pool boundary, combined with the resolution requirements of the experiment, calculate an appropriate scanning step size (a smaller scanning step size is used in the region with a larger change gradient, and the scanning step size can be appropriately increased in the region with a smaller change gradient). Within the candidate region of the molten pool boundary, evenly distribute scanning points according to the set scanning step size, and divide the observation regions according to the position of each scanning point to ensure that each observation region covers the characteristic change range of the microstructure. According to the determined scanning points and observation regions, generate a complete candidate scanning region.

[0076] The method provided in this embodiment takes into account that since the sample to be scanned usually contains multiple different regions, the grain structures and properties of these regions may vary significantly due to factors such as processing conditions, heat conduction, and material composition. The present application locates the candidate regions of the molten pool boundary according to the predicted values of the microstructure changes by the analytical model, which can focus on the representative and significantly changing regions, concentrate the observation range on the key regions, comprehensively capture the influence of process conditions on the microstructure, and thus clearly reflect the overall picture of the structural changes. In this way, it can effectively avoid data deviation caused by the interference of irrelevant regions, improve the accuracy of the observation results, and ensure the high adaptability of the experimental data to the verification of the analytical model. Secondly, by determining the scanning step size and the position of the scanning points according to the different degrees of influence of each process condition to be measured on the sample, efficient and accurate scanning can be achieved. The optimized setting of the scanning step size and the position can reduce the invalid regions of the scanning on the premise of ensuring the accuracy of the experimental data, thus greatly improving the experimental efficiency. In addition, this method of precise positioning can avoid waste of resources, reduce the complexity of experimental data processing, and make the whole verification process more efficient and reliable. By selecting representative candidate scanning regions, data distortion caused by local special phenomena can be avoided, ensuring that the experimental data can reflect the typical characteristics of the whole sample or the processing process, thus enhancing the reliability of the verification of the analytical model.

[0077] Optionally, the screening of the candidate scanning regions from the sample to be scanned includes: observing the microstructure of the sample to be scanned to determine the molten pool morphology and the grain distribution; determining the molten pool boundary based on the change demarcation points of the molten pool morphology and the grain distribution; determining the size of the candidate scanning regions based on the coverage range of the molten pool boundary; determining the scanning step size based on the grain size of the molten pool boundary and the resolution requirement; and determining the candidate scanning regions based on the molten pool boundary, the size of the candidate scanning regions, and the scanning step size.

[0078] Specifically, an electron backscatter diffraction (EBSD) instrument and a scanning electron microscope (SEM) are used to observe the sample to be scanned, and its microstructure image is obtained. Through the microstructure image, the morphology of the molten pool and the grain distribution in the sample to be scanned are analyzed to identify the area of the molten pool and the boundaries of the grains. Through the analysis of the microstructure image, the morphological characteristics of the molten pool area are determined, and the edge of the molten pool is marked. At the same time, the grain distribution is observed, and the places where the grain size changes greatly, that is, the areas where the grain distribution is uneven, are identified, and these change points are used as the identification and reference points of the molten pool boundary. Based on the determined change points, the molten pool boundary is drawn. By analyzing the molten pool boundary, the area range covered by the molten pool boundary is calculated, and the size of the candidate scanning area is determined. The size of the candidate scanning area needs to cover the entire area range covered by the molten pool boundary and the parts around it that may affect the microstructure. It should be noted that the size of the candidate scanning area can be appropriately adjusted according to experimental needs and process requirements to ensure that sufficient area is included to extract effective grain characteristics. Further, according to the grain size within the molten pool boundary and the resolution requirements of the scanning device, a suitable scanning step is calculated. The scanning compensation is dynamically variable. If the grain size is large, the scanning step can be slightly larger; if the grain size is small, the scanning step needs to be reduced to improve the resolution and accuracy. Considering the molten pool boundary, the size of the candidate scanning area, and the scanning step, the final candidate scanning area is determined. The candidate scanning area can completely cover the molten pool area, and at the same time, the selection of the scanning step can also meet the precise extraction requirements of grain characteristics.

[0079] Specifically, determining the scanning step based on the grain size and resolution requirements of the molten pool boundary includes: determining an initial scanning step based on the grain size of the molten pool boundary, where the grain size is positively correlated with the scanning step; adjusting the initial scanning step based on the resolution requirements to determine the scanning compensation; where the resolution requirement is negatively correlated with the scanning step.

[0080] S105. Extract grain feature information based on the candidate scanning area image; the grain feature information includes at least grain size information and grain hardness information.

[0081] Specifically, the grain feature information characterizes the microstructure of the material and reflects the overall performance of the material and the influence of the process on the material structure. The grain feature information includes grain size information and grain hardness information, which respectively characterize different material properties and qualities. Among them, the grain size information refers to the size of the grains (such as diameter, side length, or area, etc.), which is an important parameter for measuring the microstructure of crystalline materials, and the grain size has a significant impact on the material properties. The grain hardness information refers to the hardness characteristics exhibited by the grains in the material.

[0082] In specific implementation, extracting the grain feature information based on the candidate scanning area image includes: identifying the candidate scanning area image through an electron backscatter diffraction instrument to extract grain size information; analyzing the candidate scanning area to determine the solidification path; determining the depth to be measured based on the solidification path, and determining the grain hardness information of the candidate scanning area at the depth to be measured through micro-Vickers hardness measurement.

[0083] Specifically, the solidification path refers to the direction and morphology of crystal growth when liquid metal (or other materials) gradually transforms into a solid state during the solidification process of the material. The solidification path is usually affected by factors such as temperature, cooling rate, and external force fields (such as electromagnetic fields or laser irradiation directions). In the molten pool, the solidification process of the metal is not uniform, but regular structures are formed along certain specific directions. The solidification path reflects information such as the grain growth direction, grain boundary distribution, and grain arrangement.

[0084] In specific implementation, observe the candidate scanning area through an electron backscatter diffraction instrument (EBSD) and a scanning electron microscope (SEM). Through the candidate scanning area image, identify the grain boundaries in the sample. Grain boundaries usually correspond to regions with different crystal orientations. According to the distribution and shape of the grain boundaries, extract grain size information (such as grain diameter, area, aspect ratio, etc.). Based on the solidification characteristics of the material, analyze the solidification path on the sample surface, and determine the solidification direction through features such as texture and morphology observed in the candidate scanning area image. Combine processing process parameters (such as laser scanning direction, temperature gradient, etc.) and the stripes or linear patterns along the laser scanning direction to determine the position and morphology of the solidification path. Further, based on the solidification path and the hierarchical structure of the sample, determine the depth to be measured based on the thermal gradient and temperature change during the solidification process (the solidification path is usually closely related to the thermal history and grain growth direction of the material). And when determining the depth to be measured, also select an area perpendicular or parallel to the solidification path. Finally, at the determined depth to be measured, use a micro-Vickers hardness tester to test the hardness of the sample, apply a certain load and observe the indentation using a microscope, and calculate the hardness value. Record and extract the hardness value of each grain, and obtain the grain hardness information according to the hardness value distribution.

[0085] By analyzing the degree of bending of the solidification path, the directional characteristics of grain growth can be identified, and then adjust the process conditions and process parameters to be measured (such as laser power level and scanning speed level) to achieve grain refinement and hardness optimization. A larger degree of bending may indicate the existence of a strong temperature gradient or non-uniformity of the molten pool boundary, and this feature is closely related to the orientation and morphology of the grains. Combining the information of the molten pool temperature gradient, the grain growth rate and potential grain refinement regions can be further deduced to determine the microstructure evolution under different process conditions to be measured.

[0086] S106. Calculate the material hardness according to the grain size information.

[0087] When specifically implemented, calculating the material hardness according to the grain size information includes: extracting a preset number of grain size information and grain hardness information from the grain feature information; fitting the preset number of grain size information and the grain hardness information based on the Hall-Petch law to determine the Hall-Petch coefficient and the intrinsic hardness in the Hall-Petch law; using the fitted Hall-Petch law and based on the remaining number of grain size information in the grain feature information, determining the material hardness.

[0088] Specifically, combined with the above description, the Hall-Petch law characterizes the quantitative relationship between the material hardness and the grain size. There is an inverse relationship between the material hardness and the grain size, that is, the smaller the grain size, the greater the material hardness. When the Hall-Petch coefficient and the intrinsic hardness are determined, inputting the grain size information into the Hall-Petch law can calculate the material hardness. The preset number is set according to actual needs and is not limited in this embodiment. It should be noted that the preset number of grain size information and grain hardness information are used to determine the Hall-Petch coefficient and the intrinsic hardness in the Hall-Petch law.

[0089] When specifically implemented, select a certain number of grains as samples from the grain feature information extracted from the candidate scanning area. For each selected grain, extract its grain size information and grain hardness information to obtain a database containing grain size information and grain hardness information. Further, substitute the extracted grain size information and the corresponding grain hardness information into the Hall-Petch law, and use the least squares method or other fitting methods to fit the Hall-Petch coefficient and the intrinsic hardness. In each fitting, repeatedly adjust the Hall-Petch coefficient and the intrinsic hardness based on the results of each fitting until the number of fittings reaches a large value. Finally, use the fitted Hall-Petch law (the Hall-Petch coefficient and the intrinsic hardness are determined), combined with the grain size information corresponding to the remaining number of grains not selected, substitute the grain size information into the Hall-Petch law, and calculate the corresponding material hardness.

[0090] S107. Combine the material hardness, the grain hardness information with the corresponding process conditions to be measured to obtain an evaluation data set, and evaluate the performance of the analysis model based on the evaluation data set.

[0091] Specifically, the material hardness is the overall hardness value of the material predicted by the process / structure / property analysis model based on the process conditions to be measured, and it is an estimation of the ability of the process / structure / property analysis model to resist plastic deformation at the macroscopic level of the material. The grain hardness information is obtained by directly measuring the hardness of individual grains through microhardness testing (such as Vickers hardness). To ensure the reliability and accuracy of the process / structure / property analysis model in practical applications, it is necessary to compare the material hardness predicted by the process / structure / property model with the grain hardness information obtained through direct experiments to verify the performance of the process / structure / property analysis model.

[0092] When specifically implemented, combining the material hardness, the grain hardness information with the corresponding process conditions to be measured to obtain an evaluation dataset, and evaluating the performance of the analysis model based on the evaluation dataset includes: plotting a first hardness curve and a second hardness curve based on the material hardness and the grain hardness information under different process conditions to be measured in the evaluation dataset; wherein, the first hardness curve represents the change trend of the material hardness with the grain size information, and the second hardness curve represents the change trend of the grain hardness information with the grain size information; the material hardness is the predicted data obtained based on the analysis model, and the grain hardness information is the measured data obtained by scanning the image of the candidate area; comparing the first hardness curve and the second hardness curve, and determining the performance of the analysis model based on the change trends of the first hardness curve and the second hardness curve and the difference between the first hardness curve and the second hardness curve at any grain size information.

[0093] Specifically, in combination with the above description, different process conditions to be measured respectively correspond to different material hardnesses (predicted by the process / structure / property analysis model) and different grain hardness information (directly measured through microhardness testing). The first hardness curve is plotted based on different process conditions to be measured and the corresponding material hardness, and the second hardness curve is plotted based on different process conditions to be measured and the corresponding grain hardness information.

[0094] In specific implementation, for different process conditions to be measured, with the process conditions to be measured as the abscissa, under different process conditions to be measured, record the corresponding material hardness, and associate it with the process conditions to be measured to form a series of points, and connect these points to draw the first hardness curve. For different process conditions to be measured, with the process conditions to be measured as the abscissa, under different process conditions to be measured, record the corresponding grain hardness information, and associate it with the grain size information to form a series of points, and connect these points to draw the second hardness curve. Compare the first hardness curve with the second hardness curve to check the change trends of the two curves. Compare the change rules of the two curves under different process conditions to be measured, including the slope, shape, etc. of the curves. Compare the hardness value differences between the first hardness curve and the second hardness curve under each corresponding process condition to be measured. For each process condition to be measured, calculate the difference between the two curves. Based on the change trends and differences of the two curves, evaluate the performance of the analytical model of process / structure / performance.

[0095] Combined with the above description, the analytical model of process / structure / performance is used to predict the material hardness based on the process conditions to be measured, and the material hardness is inversely proportional to the process conditions to be measured (corresponding grain size information). When the change trends of the two curves are both reverse changes, that is, as the grain size information decreases, the hardness increases. And, based on the comparison between the grain size information corresponding to the analytical model of process / structure / performance under different process conditions to be measured and the actual grain size information, determine the accuracy of the analytical model of process / structure / performance based on the difference in grain size information.

[0096] For example, if the change trends of the two curves are similar and the differences under different grain size information are small, it indicates that the analytical model of process / structure / performance has high prediction accuracy and good performance. If there are significant differences in the change trends of the two curves or the differences are large, it may indicate that there is a large deviation between the prediction results of the analytical model of process / structure / performance and the experimental data, the performance is poor, and adjustment or optimization is required.

[0097] The method provided in this embodiment can fully evaluate the performance of the analysis model of process / structure / property by comparing and analyzing the material hardness predicted by the analysis model of process / structure / property with the grain hardness information obtained through experiments. First, the material hardness predicted by the analysis model of process / structure / property is calculated based on the grain size information through the Hall-Petch law, which reflects the modeling ability of the analysis model for the relationship between microscopic features and macroscopic properties. The grain hardness information is experimental data obtained through direct measurement, with high authenticity and accuracy. The comparison between the two can intuitively reflect the deviation and applicability of the analysis model in predicting material properties. By analyzing whether the trend predicted by the analysis model is consistent with the experimental results, the rationality of the analysis model can be further verified. This evaluation method can not only verify the accuracy of the analysis model, but also discover the potential limitations of the analysis model, providing a scientific basis for process optimization and model improvement, thereby improving the reliability and wide applicability of the analysis model in practical applications.

[0098] The analytical model verification method for process / structure / performance provided in this embodiment, in the first aspect, can fully evaluate the performance of the analytical model of process / structure / performance by comparing and analyzing the material hardness predicted by the analytical model of process / structure / performance with the grain hardness information obtained by experiment. First, the material hardness predicted by the analytical model of process / structure / performance is calculated based on the grain size information through the Hall-Page law, which reflects the modeling ability of the analytical model for the relationship between microscopic features and macroscopic performance, while the grain hardness information is the experimental data directly measured, which has high authenticity and accuracy. The comparison between the two can intuitively reflect the deviation and applicability of the analytical model in predicting material properties, and further verify the rationality of the analytical model by analyzing whether the trend predicted by the analytical model is consistent with the experimental results. This evaluation method can not only verify the accuracy of the analytical model, but also discover the potential limitations of the analytical model, provide a scientific basis for process optimization and model improvement, thereby improving the reliability and wide applicability of the analytical model in practical applications. On the second aspect, since the sample to be scanned usually contains multiple different regions, the grain structure and performance of these regions may be significantly different due to factors such as processing conditions, heat conduction, and material composition. This application locates the candidate area of ​​the molten pool boundary according to the microstructure change value predicted by the analytical model, and can focus on the representative and significantly changed area, so that the observation range is concentrated in the key area, and can fully capture the influence of the process conditions on the microstructure, so as to clearly reflect the overall picture of the structural change. In this way, the data deviation caused by the interference of irrelevant areas can be effectively avoided, the accuracy of the observation results can be improved, and the high adaptability of the experimental data to the analytical model verification can be ensured. Secondly, by determining the scanning step length and the position of the scanning point based on the different degrees of influence of each process condition to be tested on the sample, efficient and accurate scanning can be achieved. The optimized setting of the scanning step length and the point position can reduce the invalid area of ​​the scan while ensuring the accuracy of the experimental data, thereby greatly improving the experimental efficiency. In addition, this method of precise positioning can avoid waste of resources, reduce the complexity of experimental data processing, and make the entire verification process more efficient and reliable. By selecting a representative candidate scanning area, data distortion caused by local special phenomena can be avoided, ensuring that the experimental data can reflect the typical characteristics of the entire sample or processing process, thereby enhancing the reliability of analytical model verification. At the same time, according to the verification requirements of the analytical model of process / structure / performance, it is only necessary to focus on certain key areas, such as the center area of ​​the weld, the heat-affected zone or the inter-grain connection area, which are usually sensitive points of material performance changes and the focus of process optimization. Selecting these areas as candidate scanning areas can not only effectively reduce the experimental workload and improve the experimental efficiency, but also ensure that the collected data better matches the analytical model verification target and avoids the interference of irrelevant data on the experimental results.In a third aspect, verification data is obtained based on the laser power level and scanning speed level determined according to the process conditions to be measured, ensuring that the verification process of the analytical model is highly relevant to the actual production environment. This avoids the gap between laboratory conditions and industrial applications that may exist in traditional experimental methods, making the verification of the analytical model closer to the real production scenario. During the process of fabricating the sample to be scanned and screening the candidate scanning areas, the reliability and accuracy of the experimental data are further enhanced by ensuring the actual representativeness of the sample and selecting appropriate areas for testing. This process not only improves the accessibility of the data by combining image extraction of grain feature information (such as grain size and hardness information), but also provides a real and efficient basis for subsequent hardness calculation. In a fourth aspect, the grain size information and grain hardness information are used as important input parameters for the prediction of the analytical model. By combining with the Hall-Petch law, an effective way is provided to predict the material hardness. This transformation from the grain characteristics at the microscale to the macroscopic hardness helps to reveal the internal relationship between grain size and material properties, thereby enhancing the prediction ability of the analytical model. Moreover, by comparing the material hardness calculated by the analytical model with the actually measured grain hardness information, the applicability and accuracy of the analytical model under different process conditions to be measured can be systematically evaluated. This comparative analysis not only provides a feedback mechanism for the optimization of the model, but also helps to identify and adjust potential biases in the model, improving its prediction accuracy. In addition, by flexibly setting process parameters (such as laser power level, scanning speed level, etc.), the verification of the analytical model can cover a variety of process scenarios, which enhances the adaptability of the analytical model and the possibility of wide application. And through image analysis and data extraction, the entire verification process of the analytical model is automated, improving the verification efficiency and reducing manual intervention and errors, thus ensuring the high efficiency and accuracy of the experiment. Combining these advantages, this application systematically combines and verifies the relationship between process, structure, and performance, not only ensuring the reliability of the analytical model, but also promoting the efficient progress of process optimization, and can provide practical technical support in actual manufacturing.

[0099] Corresponding to the embodiment of the method for verifying an analytical model of a process / structure / performance described above, this application also provides an embodiment of an apparatus for verifying an analytical model of a process / structure / performance.

[0100] Figure 2 It is a schematic structural diagram of the apparatus for verifying an analytical model of a process / structure / performance provided in the second embodiment of this application. Please refer to Figure 2 The apparatus provided in this embodiment includes a determination module 210, an acquisition module 220, a screening module 230, a calculation module 240, and an evaluation module 250; wherein,

[0101] The determining module 210 is configured to determine process conditions to be measured for an analytical model of a process / structure / property. Among them, there are multiple process conditions to be measured, and the analytical model is used to output material hardness based on the process conditions to be measured.

[0102] The determining module 210 is further configured to determine a laser power level and a scanning speed level based on the process conditions to be measured. Among them, the laser power level and the scanning speed level of each process condition to be measured are different. Predict the microstructure change values under each process condition to be measured based on the analytical model, and match the corresponding laser power level and scanning speed level based on the difference in microstructure change values under different process conditions to be measured.

[0103] The obtaining module 220 is configured to fabricate a sample to be scanned.

[0104] The screening module 230 is configured to screen candidate scanning regions from the sample to be scanned. Among them, the candidate regions of the molten pool boundary are located according to the predicted microstructure change values of the analytical model, and the scanning step size and the observation regions corresponding to the scanning points are determined based on the influence degree of each process condition to be measured on the sample to be scanned.

[0105] The obtaining module 220 is further configured to extract grain feature information based on the candidate scanning region image. The grain feature information at least includes grain size information and grain hardness information.

[0106] The calculating module 240 is configured to calculate the material hardness according to the grain size information.

[0107] The evaluating module 250 is configured to combine the material hardness, the grain hardness information with the corresponding process conditions to be measured to obtain an evaluation data set, and evaluate the performance of the analytical model based on the evaluation data set.

[0108] The device of this embodiment can be used to execute Figure 1 the steps of the method embodiment shown. The specific implementation principle and process are similar and will not be elaborated here.

[0109] For the specific implementation process of the functions and roles of each unit in the above device, please refer to the implementation process of the corresponding steps in the above method, which will not be elaborated here.

[0110] For the apparatus embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial descriptions of the method embodiments. The apparatus embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this application. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0111] The above are only the preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A process / structure / performance analytical model verification method, characterized in that: The method comprises: Determine the process conditions to be tested of the analytical model of process / structure / performance, wherein there are multiple process conditions to be tested, and the analytical model is used to output the material hardness based on the process conditions to be tested; Determine the laser power level and the scanning speed level based on the process conditions to be tested, wherein the laser power level and the scanning speed level of each process condition to be tested are different, predict the microstructure change value under each process condition to be tested based on the analytical model, and match the corresponding laser power level and the scanning speed level based on the difference of the microstructure change values ​​under different process conditions to be tested; Prepare samples to be scanned; Screening candidate scanning areas from the sample to be scanned, wherein the candidate molten pool boundary area is located according to the microstructure change value predicted by the analytical model, and the scanning step length and the observation area corresponding to the scanning point are determined based on the influence of each of the process conditions to be tested on the sample to be scanned; Extracting grain feature information based on the candidate scanning area image; the grain feature information at least includes grain size information and grain hardness information; calculating the material hardness based on the grain size information; Combining the material hardness, the grain hardness information and the corresponding process conditions to be tested to obtain an evaluation data set, and evaluating the performance of the analytical model based on the evaluation data set; The step of combining the material hardness, the grain hardness information and the corresponding process conditions to be tested to obtain an evaluation data set, and evaluating the performance of the analytical model based on the evaluation data set, comprises: Based on the material hardness and the grain hardness information under different process conditions to be tested in the evaluation data set, a first hardness curve and a second hardness curve are drawn; wherein the first hardness curve represents the change trend of the material hardness with the process conditions to be tested, and the second hardness curve represents the change trend of the grain hardness information with the process conditions to be tested; the material hardness is the predicted data obtained based on the analytical model, and the grain hardness information is the measured data obtained by scanning the candidate area image; The first hardness curve and the second hardness curve are compared, and the performance of the analytical model is determined based on the change trends of the first hardness curve and the second hardness curve and the difference between the first hardness curve and the second hardness curve under any process condition to be tested.

2. The method according to claim 1, characterized in that The calculating the material hardness according to the grain size information comprises: Extracting a preset amount of grain size information and grain hardness information from the grain characteristic information; Fitting the preset number of grain size information and the grain hardness information based on the Hall-Page law to determine the Hall-Page coefficient and the intrinsic hardness in the Hall-Page law; The material hardness is determined by using the Hall-Page law obtained by fitting and based on the remaining grain size information in the grain characteristic information.

3. The method according to claim 1, characterized in that The step of extracting grain feature information based on the candidate scanning area image comprises: Identifying the candidate scanning area image by electron backscatter diffractometer to extract grain size information; Analyzing the candidate scanning area to determine a coagulation path; A depth to be measured is determined based on the solidification path, and grain hardness information of the candidate scanning area at the depth to be measured is determined by micro-Vickers hardness measurement.

4. The method according to claim 1, characterized in that The step of screening a candidate scanning area from the sample to be scanned includes: Observe the microstructure of the sample to be scanned to determine the molten pool morphology and grain distribution; Determining the molten pool boundary based on the change demarcation point of the molten pool morphology and the grain distribution; Determining the size of the candidate scanning area based on the coverage of the molten pool boundary; Determine the scanning step length based on the grain size and resolution requirements of the molten pool boundary; A candidate scanning area is determined based on the molten pool boundary, the size of the candidate scanning area, and the scanning step size.

5. The method according to claim 1, characterized in that The step of preparing a sample to be scanned comprises: Using a concentric powder feeding method, based on the laser power level and the scanning speed level, the powder material is deposited layer by layer to obtain a multi-layer sample; Based on the laser scanning direction, the central area of ​​the weld in the multi-layer sample is vertically cut to obtain a sample distributed along a two-dimensional plane; the weld is the connection area between each layer of samples; The sample is mechanically polished and ground, and chemically etched using aqua regia to obtain a sample to be scanned.

6. The method according to claim 1, characterized in that The step of determining the laser power level and the scanning speed level based on the process conditions to be measured includes: Predicting the microstructure change value under each process condition to be tested based on the analytical model; the microstructure change value includes grain size and grain hardness; Based on the influence of laser power level on microstructure change value, the influence of scanning speed level on microstructure change value, and the microstructure change value under each process condition to be tested, the laser power level and scanning speed level under each process condition to be tested are determined; the laser power level is directly proportional to the grain size and inversely proportional to the hardness; the scanning speed level is inversely proportional to the grain size and directly proportional to the grain hardness.

7. The method according to claim 1, characterized in that The analytical model of process / structure / performance uses a continuous growth limiting factor to replace the traditional grain growth assumption, uses an interdependence model to characterize the growth relationship between grains, and uses the Hall-Page law to replace the traditional performance prediction method; the analytical model is used to determine the material hardness based on the grain size information corresponding to different process conditions to be tested, and adjust the process conditions to be tested based on the difference between the material hardness and the target hardness.

8. The method according to claim 7, characterized in that The analytical model of process / structure / performance associates the process conditions to be tested with the grain size information, and the continuous growth limiting factor is inversely proportional to the grain size information; the Hall-Page law associates the grain size information with the material hardness, and the reciprocal square root of the grain size information varies linearly with the material hardness.

9. A process / structure / performance analytical model verification device, characterized in that: The device includes a determination module, an acquisition module, a screening module, a calculation module and an evaluation module; wherein, The determination module is used to determine the process conditions to be tested of the analytical model of process / structure / performance, wherein there are multiple process conditions to be tested, and the analytical model is used to output the material hardness based on the process conditions to be tested; The determination module is further used to determine the laser power level and the scanning speed level based on the process conditions to be tested; wherein the laser power level and the scanning speed level of each process condition to be tested are different, and the microstructure change value under each process condition to be tested is predicted based on the analytical model, and the corresponding laser power level and the scanning speed level are matched based on the difference of the microstructure change values ​​under different process conditions to be tested; The acquisition module is used to prepare a sample to be scanned; The screening module is used to screen candidate scanning areas from the sample to be scanned; wherein, the candidate area of ​​the molten pool boundary is located according to the microstructure change value predicted by the analytical model, and the scanning step length and the observation area corresponding to the scanning point are determined based on the influence of each of the process conditions to be tested on the sample to be scanned; The acquisition module is further used to extract grain feature information based on the candidate scanning area image; the grain feature information at least includes grain size information and grain hardness information; The calculation module is used to calculate the material hardness according to the grain size information; The evaluation module is used to combine the material hardness, the grain hardness information and the corresponding process conditions to be tested to obtain an evaluation data set, and evaluate the performance of the analytical model based on the evaluation data set; The step of combining the material hardness, the grain hardness information and the corresponding process conditions to be tested to obtain an evaluation data set, and evaluating the performance of the analytical model based on the evaluation data set, comprises: Based on the material hardness and the grain hardness information under different process conditions to be tested in the evaluation data set, a first hardness curve and a second hardness curve are drawn; wherein the first hardness curve represents the change trend of the material hardness with the process conditions to be tested, and the second hardness curve represents the change trend of the grain hardness information with the process conditions to be tested; the material hardness is the predicted data obtained based on the analytical model, and the grain hardness information is the measured data obtained by scanning the candidate area image; The first hardness curve and the second hardness curve are compared, and the performance of the analytical model is determined based on the change trends of the first hardness curve and the second hardness curve and the difference between the first hardness curve and the second hardness curve under any process condition to be tested.

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