Method and device for constructing additive manufacturing material database based on in-situ synchrotron radiation data

Abstract

The invention provides a method and device for constructing an additive manufacturing material database based on in-situ synchrotron radiation data, and relates to the technical field of material databases. The method comprises the steps that a relational database of laser additive manufacturing is constructed based on the synchrotron radiation principle of laser additive manufacturing, transient behaviors in the additive manufacturing process are captured in real time, the dynamic, defect evolution and phase change processes of a molten pool are monitored from microsecond to millisecond, and meanwhile a graphical user interface function is established based on the relational database of laser additive manufacturing. And a user is supported to carry out defect dynamic behavior analysis. According to the method, in-situ capture and data communication of defect dynamic behaviors in the laser additive manufacturing process can be realized remarkably through structured storage, correlation analysis and visual display, and material design, process optimization and quality control are supported.

Description

Technical Field

[0001] This invention relates to the field of materials database technology, and in particular to a method and apparatus for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data. Background Technology

[0002] Laser additive manufacturing (such as LPBF and DED) is increasingly used in critical component applications, and its R&D process is highly data-driven. Internationally, a diverse ecosystem of materials data platforms has emerged. For example, Citrine focuses on end-to-end data governance in materials R&D, Materials Commons supports traceable workflows for collaborative research, MaterialsProject and AiiDA specialize in high-throughput computing and automated computational processes, respectively, and the Senvol database provides engineering selection support. These platforms collectively validate the value of the "high-quality data + traceable workflow" architecture. However, additive manufacturing involves highly complex non-equilibrium physical metallurgical processes, such as laser-metal interaction, rapid solidification of moving molten pools, and thermal stress evolution under cyclic conditions. Traditional materials databases primarily handle static "composition-structure-property" relationships, making it difficult to capture the dynamic characteristics of "process as material" in laser additive manufacturing. Synchrotron radiation in-situ characterization technology has unique advantages in dynamic, real-time, and high-resolution characterization. Technologies that incorporate the most critical defect dynamics information in additive manufacturing into databases to provide data support for quality control and process optimization are relatively scarce. Summary of the Invention

[0003] To address the technical problem of existing technologies that struggle to integrate multi-dimensional, heterogeneous in-situ and ex-situ data and construct a dedicated database reflecting the "process as material" characteristic of laser additive manufacturing, this invention provides a technical solution. The technical solution is as follows:

[0004] On the one hand, a method for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data is provided. This method is implemented by an apparatus for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data, and includes: S1: Based on the synchrotron radiation principle of laser additive manufacturing, define the full-link data type and construct a relational database for laser additive manufacturing. The synchrotron radiation principle of laser additive manufacturing includes using high-energy, high-throughput X-rays to penetrate metal materials and capture transient behaviors in the additive manufacturing process in real time. The real-time capture of transient behaviors in the additive manufacturing process includes monitoring the dynamics of the molten pool, defect evolution, and phase transition process at the microsecond to millisecond scale through ultrafast imaging and diffraction technology. S2: Based on the relational database, collect material data, including composition data, process data, synchrotron radiation in-situ data, ex-situ microstructure data, and performance data; S3: Convert the material data into multiple data entity tables, construct a unified public key for each data entity table, and associate them based on the unified public key to obtain a laser additive manufacturing process entity table group; S4: Based on the entity table group of the laser additive manufacturing process, establish a graphical user interface function. The graphical user interface function includes data overview, data import, data retrieval, data classification, data analysis, data extraction, data deletion, data modification, and defect dynamic behavior analysis. The defect dynamic behavior analysis includes the analysis of defect dynamic behavior factors, which include, but are not limited to, the number of pores, crack area, and porosity.

[0005] Preferably, the method further includes: The relational database of laser additive manufacturing is used for material design, specifically including: establishing a data-driven paradigm by constructing the correlation between in-situ synchrotron radiation data and processes, microstructures, and properties; Additive manufacturing mechanism analysis based on data-driven paradigm; Based on the analysis of additive manufacturing mechanisms, process parameters are back-engineered and optimized to achieve precise quality control. Construct a relational database for laser additive manufacturing; Machine learning models for additive manufacturing are constructed based on experimental data. These models include, but are not limited to, machine learning models for additive manufacturing of high-temperature alloys, additive manufacturing of stainless steel, and additive manufacturing of titanium alloys and aluminum-magnesium alloys.

[0006] Preferably, the synchrotron radiation in-situ data includes: Synchrotron radiation in-situ characterization technology was used to collect information on the dynamic behavior of materials during the laser additive manufacturing process, obtain multi-dimensional and heterogeneous in-situ and ex-situ data, and obtain data on the synchrotron radiation configuration and in-situ tissue parameters. The synchrotron radiation configuration and the in-situ tissue parameter data are processed and integrated to align with the key process data of the material, thereby obtaining synchrotron radiation in-situ data.

[0007] Preferably, S1, based on the synchrotron radiation principle of laser additive manufacturing, defines a full-link data type and constructs a relational database for laser additive manufacturing. The synchrotron radiation principle of laser additive manufacturing includes the use of high-energy, high-flux X-rays to penetrate metallic materials and capture transient behaviors in the additive manufacturing process in real time. This real-time capture of transient behaviors includes monitoring the dynamics of the molten pool, defect evolution, and phase transition processes at the microsecond to millisecond scale using ultrafast imaging and diffraction techniques. S11: Utilizes ultrafast imaging and diffraction technology for monitoring at the microsecond to millisecond scale; S12: Synchrotron radiation uses high-energy, high-flux X-rays to penetrate metallic materials, capture transient behavior in the additive manufacturing process in real time, and obtain synchrotron radiation in-situ time-series images, diffraction spectra, and ex-situ microstructure data. S13: Spatially-temporally align synchrotron radiation in-situ time-series images, diffraction patterns and ex-situ tissue structure data; S14: Data on molten pool dynamics, defect evolution, and phase transition processes are obtained based on monitoring at the microsecond to millisecond scale; S15: Define end-to-end data types based on data from molten pool dynamics, defect evolution, and phase transition processes; S16: Based on the full-link data type, construct a relational database for laser additive manufacturing.

[0008] Preferably, step S2 involves collecting material data based on the relational database. This material data includes composition data, process data, synchrotron radiation in-situ data, ex-situ microstructure data, and performance data, including: S21: Based on the relational database, perform systematic data acquisition of the laser additive manufacturing process; S22: Collect and record the chemical composition ratio of the materials to obtain composition data; S23: Record the process parameter settings during additive manufacturing to obtain process data; S24: Record and characterize the real-time dynamic evolution behavior during the manufacturing process to obtain in-situ synchrotron radiation data; S25: Record the physical and mechanical properties of laser additive manufacturing to obtain performance data; S26: The composition data, process data, synchrotron radiation in-situ data, ex-situ tissue data, and performance data constitute the material data.

[0009] Preferably, step S3 involves converting the material data into multiple data entity tables, constructing a unified public key for each data entity table, and linking them based on the unified public key to obtain a laser additive manufacturing process entity table group, including: S31: Construct a data entity table based on the material data, wherein the material data includes data from the entire additive manufacturing process; S32: A unified public key is constructed using a unified public key, and field conventions are performed to obtain a unified public key dataNo. The construction of the unified public key includes setting dataNo as a cross-table primary key. The dataNo has a unique identifier characteristic, which is used to realize the identification information for traceable association of the component-process-organization-performance link. S33: Based on the SQL relational architecture, a database schema with dataNo as the core identifier is obtained through schema design. The entity table group of the laser additive manufacturing process includes a unified definition of the entity tables and meets the requirements of normalization, consistency and scalability. S34: Adopting the genealogical modeling concept, data organization and link design are carried out. Through equi-joins, a traceable data genealogy is obtained. The genealogical modeling concept includes the modeling concept of the AiiDA platform. The equi-joins include cross-table joins based on dataNo and facilitate cross-domain queries and data-driven modeling. S35: Using dataNo as the cross-table primary key, perform task-oriented slicing to obtain a target data subset. The target data subset includes data extracted according to the link tracing requirements and used for data-driven modeling or cross-domain querying.

[0010] Preferably, in step S4, a graphical user interface (GUI) function is established based on the entity table group of the laser additive manufacturing process. This GUI function includes data overview, data import, data retrieval, data classification, data analysis, data extraction, data deletion, data modification, and defect dynamic behavior analysis. The defect dynamic behavior analysis includes the analysis of defect dynamic behavior factors, which include, but are not limited to, the number of pores, crack area, and porosity. S41: Establish data overview and data import functions, which allow users to view the overall status of the database and import external data into the system; S42: Establish data retrieval and data classification tools for users to search and organize various types of manufacturing data according to specific conditions; S43: Establish data analysis and data extraction components to support in-depth data value mining and export of required information for use; S44: Establish data deletion and data modification permissions, allowing authorized users to maintain and update specific entries in the database; S45: Construct a defect dynamic behavior analysis function to study the evolution law of defects in the additive manufacturing process; S46: Construct a defect dynamic behavior analysis function, which mainly performs quantitative analysis of defect dynamic behavior factors to evaluate the internal quality status of the material. The defect dynamic behavior factors cover a number of key indicators, including but not limited to the number of pores, crack area and porosity. S47: Based on the above functions, establish a complete graphical user interface to realize the visual operation and management of the entity table group of the laser additive manufacturing process.

[0011] On the other hand, an apparatus for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data is provided. This apparatus is applied to a method for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data. The apparatus includes: Database module: Based on the synchrotron radiation principle of laser additive manufacturing, this module defines full-link data types and constructs a relational database for laser additive manufacturing. The synchrotron radiation principle of laser additive manufacturing includes the use of high-energy, high-flux X-rays to penetrate metallic materials and capture transient behaviors in the additive manufacturing process in real time. The real-time capture of transient behaviors in the additive manufacturing process includes monitoring the dynamics of the molten pool, defect evolution, and phase transition processes at the microsecond to millisecond scale through ultrafast imaging and diffraction techniques. Data acquisition module: used to acquire material data based on the relational database, the material data including composition data, process data, synchrotron radiation in-situ data, ex-situ microstructure data and performance data; Entity Table Module: This module is used to convert the material data into multiple data entity tables, construct a unified public key for each data entity table, and associate them based on the unified public key to obtain a laser additive manufacturing process entity table group. Interface Function Module: Used to establish a graphical user interface function based on the entity table group of the laser additive manufacturing process. The graphical user interface function includes data overview, data import, data retrieval, data classification, data analysis, data extraction, data deletion, data modification, and defect dynamic behavior analysis. The defect dynamic behavior analysis includes the analysis of defect dynamic behavior factors, which include, but are not limited to, the number of pores, crack area, and porosity.

[0012] On the other hand, an apparatus for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data is provided. The apparatus for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data includes: a processor; a memory storing computer-readable instructions, which, when executed by the processor, implement any of the methods described above for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data.

[0013] On the other hand, a computer-readable storage medium is provided, characterized in that program code is stored in the computer-readable storage medium.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: This invention integrates discrete, multi-source data from the entire laser additive manufacturing process into a structured, traceable data chain through a unified SQL relational architecture and public keys, solving the problem of data silos. It also provides a graphical interface, offering a complete closed loop from data import and management to analysis and visualization, significantly lowering the barrier to data use. This database particularly emphasizes the correlation between in-situ synchrotron radiation data and processes, microstructure, and performance, incorporating the most difficult-to-obtain "process truth" into the data backbone. This provides a solid data foundation for a deeper understanding of additive manufacturing mechanisms, enabling reverse optimization of process parameters, precise quality control, and training data-driven models, effectively promoting the transformation of additive manufacturing technology towards a data-driven paradigm. This invention utilizes synchrotron radiation data to construct a database, successfully achieving dynamic correlation analysis of defects in the manufacturing process and guiding process optimization based on data-driven methods, demonstrating the practical value of this system in supporting the research and development and quality control of laser additive manufacturing materials. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a method for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data, provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of the interface functions of an additive manufacturing material database provided in an embodiment of the present invention; Figure 3 This is a block diagram of an apparatus for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data, provided in an embodiment of the present invention. Figure 4 This is a schematic diagram of the structure of a device for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data, provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] This invention provides a method for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data. This method can be implemented by a device for constructing such a database, which can be a terminal or a server. Figure 1 The flowchart shown illustrates a method for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data. This method's processing flow may include the following steps:

[0023] Based on the synchrotron radiation principle of laser additive manufacturing, a full-link data type is defined, and a relational database for laser additive manufacturing is constructed. The synchrotron radiation principle of laser additive manufacturing includes the use of high-energy, high-flux X-rays to penetrate metallic materials and capture transient behaviors in the additive manufacturing process in real time. The real-time capture of transient behaviors in the additive manufacturing process includes monitoring the dynamics of the molten pool, defect evolution, and phase transition process at the microsecond to millisecond scale through ultrafast imaging and diffraction technology. Preferably, the relational database of laser additive manufacturing is used for material design, including: The relational database of laser additive manufacturing is used for material design, specifically including: establishing a data-driven paradigm by constructing the correlation between in-situ synchrotron radiation data and processes, microstructures, and properties; Additive manufacturing mechanism analysis based on data-driven paradigm; Based on the analysis of additive manufacturing mechanisms, process parameters are back-engineered and optimized to achieve precise quality control. Construct a relational database for photoaddition manufacturing; Machine learning models for additive manufacturing are constructed based on experimental data. These models include, but are not limited to, machine learning models for additive manufacturing of high-temperature alloys, additive manufacturing of stainless steel, and additive manufacturing of titanium alloys and aluminum-magnesium alloys.

[0024] Preferably, the in-situ synchrotron radiation data includes: Synchrotron radiation in-situ characterization technology was used to collect information on the dynamic behavior of materials during the laser additive manufacturing process, obtain multi-dimensional and heterogeneous in-situ and ex-situ data, and obtain data on the synchrotron radiation configuration and in-situ tissue parameters. The synchrotron radiation configuration and the in-situ tissue parameter data are processed and integrated to align with the key process data of the material, thereby obtaining synchrotron radiation in-situ data.

[0025] Preferably, based on the synchrotron radiation principle of laser additive manufacturing, a full-link data type is defined, and a relational database for laser additive manufacturing is constructed. The synchrotron radiation principle of laser additive manufacturing includes the use of high-energy, high-flux X-rays to penetrate metallic materials and capture transient behaviors in the additive manufacturing process in real time. This real-time capture of transient behaviors includes monitoring the dynamics of the molten pool, defect evolution, and phase transition processes at the microsecond to millisecond scale using ultrafast imaging and diffraction techniques. Monitoring on the microsecond to millisecond scale is achieved by utilizing ultrafast imaging and diffraction technology; Synchrotron radiation utilizes high-energy, high-flux X-rays to penetrate metallic materials, capture transient behavior in the additive manufacturing process in real time, and obtain in-situ time-series images, diffraction spectra, and ex-situ microstructure data of synchrotron radiation. Spatial-temporal alignment of synchrotron radiation in-situ time-series images, diffraction patterns and ex-situ tissue structure data; Data on molten pool dynamics, defect evolution, and phase transition processes are obtained based on monitoring at the microsecond to millisecond scale; Based on data on molten pool dynamics, defect evolution, and phase transition processes, define end-to-end data types; Based on end-to-end data types, a relational database for laser additive manufacturing is constructed.

[0026] In some embodiments, the full-chain data type covers the entire material process: basic information (sample number, grade, etc.), chemical composition, powder characteristics (particle size, absorptivity), process parameters (power, speed, etc.), synchrotron radiation configuration (current intensity, exposure time, etc.), in-situ microstructure (porosity, cracks, etc.), ex-situ microstructure (melt pool geometry, grain boundary ratio, etc.), and mechanical properties (yield strength, tensile strength, etc.). The synchrotron radiation configuration and in-situ microstructure parameter data are from the fourth-generation in-situ synchrotron radiation facility at the Institute of High Energy Physics, Chinese Academy of Sciences.

[0027] Specifically, the alloy chemical composition; the average particle size and laser absorptivity of the powder; the laser power and scanning speed of additive manufacturing; the synchrotron radiation configuration such as flux intensity and exposure time; the ex-situ microstructure parameters such as molten pool geometry and grain boundary ratio; the in-situ microstructure parameters such as porosity and crack area; and the mechanical properties such as yield strength and tensile strength.

[0028] It should be noted that synchrotron radiation utilizes high-energy, high-flux X-rays to penetrate metallic materials and capture transient behaviors during additive manufacturing in real time. Through ultrafast imaging and diffraction techniques, it monitors molten pool dynamics, defect evolution, and phase transitions on a microsecond-millisecond scale. For example, high-speed X-ray imaging can record porosity formation and movement, while Laue diffraction is used to track single-crystal epitaxial growth and impurity crystal formation. This technology breaks through the spatiotemporal limitations of traditional characterization, providing a dynamic and high-resolution observation method for understanding the "process truth" of additive manufacturing.

[0029] Based on the relational database, material data is collected, including composition data, process data, synchrotron radiation in-situ data, ex-situ microstructure data, and performance data; Preferably, material data is collected based on the relational database. This material data includes composition data, process data, synchrotron radiation in-situ data, ex-situ microstructure data, and performance data, including: Based on the relational database, systematic data acquisition of the laser additive manufacturing process is performed; The chemical composition and ratio of the recorded materials are used to obtain composition data. Record the process parameter settings during additive manufacturing to obtain process data; Record the real-time dynamic evolution behavior during the manufacturing process to obtain synchrotron radiation in-situ data; The physical and mechanical properties of laser additive manufacturing are recorded to obtain performance data; The composition data, process data, synchrotron radiation in-situ data, ex-situ tissue data, and performance data constitute the material data.

[0030] In some embodiments, you first need to install the Anaconda distribution to configure a Python environment of version 3.9.13 or later. To support graphical interface and database connection, you need to install the PyQt6 and PyMySQL libraries using the command `pip install pyqt6 pymysql`.

[0031] The database service uses MySQL Community 8.0.43. After installation, you can verify it by executing `mysql --version` in the command prompt. The server character set needs to be configured to utf8mb4, the username to root, the password to ****, and the service name to MySQL 80, ensuring support for transaction processing and high-concurrency queries.

[0032] Extract the database installation package ihep_DB.zip to the server. The recommended path is F:\. After extraction, you will see the main directory structure. This directory contains the functional directories DB-GUI for the four material sub-libraries (Alloy_Al, Alloy_Co, Alloy_Fe, Alloy_Ti) and technical documentation.

[0033] The material data is converted into multiple data entity tables. A unified public key is constructed for each data entity table, and the tables are associated based on the unified public key to obtain a laser additive manufacturing process entity table group. Preferably, the material data is converted into multiple data entity tables, a unified public key is constructed for each data entity table, and associations are performed based on the unified public key to obtain a laser additive manufacturing process entity table group, including: Based on the material data, construct a data entity table, where the material data includes data from the entire additive manufacturing process. A unified public key is constructed using a unified public key, and field conventions are performed to obtain a unified public key dataNo. The construction of the unified public key includes setting dataNo as a cross-table primary key. The dataNo has a unique identifier characteristic, which is used to realize the identification information for traceable association of the component-process-organization-performance link. Based on the SQL relational architecture, a database schema with dataNo as the core identifier was obtained through schema design. The entity table group of the laser additive manufacturing process includes a unified definition of the entity tables and meets the requirements of normalization, consistency and scalability. The data organization and link design are carried out by adopting the genealogical modeling concept. Through equi-joins, a traceable data genealogy is obtained. The genealogical modeling concept includes the modeling concept of the AiiDA platform. The equi-joins include cross-table joins based on dataNo, which facilitates cross-domain queries and data-driven modeling. Using dataNo as the cross-table primary key, task-oriented slicing is performed to obtain a target data subset. The target data subset includes data extracted according to the link tracing requirements and used for data-driven modeling or cross-domain queries.

[0034] In some embodiments, following the four elements of material composition, process, microstructure, and properties, a relational architecture with dataNo as the public key is constructed. Data is divided into eight core tables: 1_Info (meta-information), 2_Comp (composition), 3_Powder (powder properties), 4_Manufact (process parameters), 5_SynRadiate (synchrotron radiation configuration), 6_MicroExsitu (exposed microstructure), 7_MicroInsitu (in-situ microstructure), and 8_Property (mechanical properties), achieving a fully traceable data chain. A standardized import process is designed for the eight types of heterogeneous data. Data is categorized and imported into corresponding sub-tables through dedicated interfaces, employing a "divide and conquer" strategy to ensure data standardization and clear table structure. The dataNo key is used to realize logical relationships between multiple tables, establishing a complete data foundation for complex queries and analyses.

[0035] The dataNo method enables traceable association of the "composition-process-structure-performance" chain, supporting wide table flattening and task-based slicing. This design draws on the genealogical modeling concept of platforms such as AiiDA, meeting the requirements of normalization, consistency, and scalability, and facilitating cross-domain queries and data-driven modeling.

[0036] It is important to explain the principle of synchrotron radiation data and database integration. Synchrotron radiation in-situ data (such as time-series images and diffraction spectra) are recorded in pairs through a configuration table (SynRadiate) and an observation table (MicroInsitu), and are associated with process parameters, ex-situ microstructures, and performance data via dataNo. After CSV template validation, unit standardization, and deduplication, the data is batch-entered into the database, forming a traceable chain of "parameters → in-situ process → ex-situ microstructure → performance." This model achieves a closed-loop modeling and analysis across scales, from pixel-level dynamics to component-level performance.

[0037] Based on the entity table group of the laser additive manufacturing process, a graphical user interface function is established. The graphical user interface function includes data overview, data import, data retrieval, data classification, data analysis, data extraction, data deletion, data modification, and defect dynamic behavior analysis. The defect dynamic behavior analysis includes the analysis of defect dynamic behavior factors, which include, but are not limited to, the number of pores, crack area, and porosity.

[0038] Preferably, based on the entity table group of the laser additive manufacturing process, a graphical user interface (GUI) function is established. This GUI function includes data overview, data import, data retrieval, data classification, data analysis, data extraction, data deletion, data modification, and defect dynamic behavior analysis. The defect dynamic behavior analysis includes the analysis of defect dynamic behavior factors, which include, but are not limited to, the number of pores, crack area, and porosity. Establish data overview and data import functions so that users can view the overall status of the database and import external data into the system; Establish data retrieval and data classification tools to enable users to search and organize various types of manufacturing data according to specific conditions; Establish data analysis and data extraction components to support in-depth data value mining and export of required information for use; Establish data deletion and data modification permissions, allowing authorized users to maintain and update specific entries in the database; Build a dynamic behavior analysis function for defects to study the evolution of defects in the additive manufacturing process; The system constructs a defect dynamic behavior analysis function, which mainly performs quantitative analysis of defect dynamic behavior factors to assess the internal quality status of materials. These defect dynamic behavior factors cover a number of key indicators, including but not limited to the number of pores, crack area, and porosity. Based on the above functions, a complete graphical user interface is established to realize the visual operation and management of the entity table group of the laser additive manufacturing process.

[0039] In some embodiments, the following describes the usage process of the database using high-temperature alloy database and titanium alloy database as examples. Additive Manufacturing High-Temperature Alloy Database Usage Process: Click the main interface ( Figure 2 The additive manufacturing high-temperature alloy database allows users to access a sub-database interface. Clicking the "Data Overview" button brings up the primary interface, displaying all database data in a wide table format. The top of the interface shows total statistics, while the bottom table automatically adjusts column width. Users can switch between "Calculated Data" and "Experimental Data" tabs for a comprehensive overview of the entire data chain.

[0040] The data import function provides a standardized CSV import entry point: users place the CSV file in the inputs folder of the sub-library function directory, and then import it into the CSV file. Figure 2 In the interface shown, select the file "co_exp_2025.csv" and execute the import. The system will automatically perform format validation, deduplication check, and write the data specifications to each sub-table according to dataNo.

[0041] The data retrieval function supports precise location of a single record by dataNo. For example... Figure 2 As shown, after entering the number "co_exp_3", the interface outputs the full-link information of the eight data tables of the sample across in the format of "field name: value", which facilitates quick tracing and detailed verification.

[0042] The data categorization function supports range filtering based on key indicators. For example... Figure 2As shown, users can set interval conditions for performance or organizational characteristics (such as tensile strength and yield strength), and the system returns a list of samples that meet the conditions, which can be used to quickly identify target data clusters.

[0043] The data analysis capabilities offer powerful 3D visualization features. For example... Figure 2 As shown, users can select their own data type and specify the parameters represented by the X, Y, and Z axes (such as melt pool depth, melt pool width, and yield strength) to generate a rotatable and scalable 3D scatter plot, which intuitively reveals the intrinsic relationship between "process-defect-performance".

[0044] The data extraction function is used to export wide table data for a single record. (For example...) Figure 2 After entering dataNo in the interface shown, the system will export all fields of the sample as a CSV file and save it in the exports folder of the sub-database for subsequent offline analysis or sharing.

[0045] Data deletion and data modification functions (interfaces are as follows) Figure 2 (As shown) provides the ability to maintain data within the database. Both operate around dataNo; the former is used to clean up the entire chain of records for a single sample, while the latter allows for precise field-level updates and corrections by selecting tables and fields through dropdown menus, ensuring data accuracy and consistency.

[0046] Additive Manufacturing Titanium Alloy Database Usage Procedure: Click on "Additive Manufacturing Titanium Alloy Database" on the main interface to enter the sub-database interface, then click the "Data Overview" button, as shown below. Figure 2 It displays all the calculated and collected experimental data for titanium alloys, using a wide-screen table to hold the entire database data, summarize key counts, and provide commonly used search and export interfaces.

[0047] The data import function provides a unified entry point for one-click import of standard CSV files into various sub-databases. After the user selects a file, the system verifies the column names and order according to the template, supports import with add or overwrite strategies, and checks and prompts for required fields, data types, numerical ranges, and duplicate records.

[0048] The data retrieval function supports quickly viewing detailed views of individual records by number, and allows switching between calculation and experimental data. Figure 2 As shown. Entering the complete dataNo will retrieve the full chain of information for this sample, covering eight data tables: from basic information to performance indicators, achieving a seamless output of "parameters → process → characterization → performance", allowing users to fully grasp the sample information.

[0049] The data classification function supports batch filtering of samples based on range criteria, such as... Figure 2As shown, users can quickly define a sample set that meets the criteria by setting thresholds for key indicators such as elastic modulus, microhardness, and phase fraction. The results area displays dataNo and core indicators in a compact list, arranged in order of number for easy comparison and replication.

[0050] The data analysis function provides 3D scatter plots for intuitive observation of the "process-microstructure-property" relationship, such as... Figure 2 As shown. Users can select the data type and specify X, Y, and Z axis data (such as process, phase content, performance). After system verification, a 3D scatter plot is generated, with colors changing along the Z axis and an accompanying color bar.

[0051] The data extraction function is used to export selected records to a standard CSV file. After the user selects the data type and enters the number, the system locates the records and exports the wide table data. The file is named dataNo and saved to the specified folder.

[0052] The data deletion function is used to clean up the entire chain of records for a single sample, such as... Figure 2 As shown. Users select a data type and enter `dataNo`. After system verification, the corresponding record is precisely deleted from eight data tables. Successful submission is acknowledged with feedback; errors are displayed with a notification. Data modification function: After entering `dataNo`, users select the data table and field from the dropdown menu, view the old values, and then enter the new values ​​to submit the update.

[0053] The above is an introduction to the method embodiments. The following describes the solution described in this application through device embodiments.

[0054] Figure 3 This is a block diagram of an apparatus for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data, according to an exemplary embodiment. The apparatus is used for a method of constructing an additive manufacturing materials database based on in-situ synchrotron radiation data. (Refer to...) Figure 3 The device includes a database module, a data acquisition module, an entity table module, and an interface function module.

[0055] Database module: Based on the synchrotron radiation principle of laser additive manufacturing, this module defines full-link data types and constructs a relational database for laser additive manufacturing. The synchrotron radiation principle of laser additive manufacturing includes the use of high-energy, high-flux X-rays to penetrate metallic materials and capture transient behaviors in the additive manufacturing process in real time. The real-time capture of transient behaviors in the additive manufacturing process includes monitoring the dynamics of the molten pool, defect evolution, and phase transition processes at the microsecond to millisecond scale through ultrafast imaging and diffraction techniques. Data acquisition module: used to acquire material data based on the relational database, the material data including composition data, process data, synchrotron radiation in-situ data, ex-situ microstructure data and performance data; Entity Table Module: This module is used to convert the material data into multiple data entity tables, construct a unified public key for each data entity table, and associate them based on the unified public key to obtain a laser additive manufacturing process entity table group. Interface Function Module: Used to establish a graphical user interface function based on the entity table group of the laser additive manufacturing process. The graphical user interface function includes data overview, data import, data retrieval, data classification, data analysis, data extraction, data deletion, data modification, and defect dynamic behavior analysis. The defect dynamic behavior analysis includes the analysis of defect dynamic behavior factors, which include, but are not limited to, the number of pores, crack area, and porosity.

[0056] An apparatus for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data, the apparatus comprising: a processor; and a memory storing computer-readable instructions, wherein when executed by the processor, the computer-readable instructions implement any of the methods described above for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data.

[0057] A computer-readable storage medium, characterized in that the computer-readable storage medium stores program code, the program code being invoked by a processor to execute the method as described in any one of claims 1 to 7.

[0058] Figure 4 This is a schematic diagram of the structure of a device for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data, as provided in an embodiment of the present invention. Figure 4 As shown, the equipment for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data may include the above-mentioned... Figure 3 The apparatus shown is for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data. Optionally, the apparatus 410 for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data may include a first processor 2001.

[0059] Optionally, the device 410 for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data may also include a memory 2002 and a transceiver 2003.

[0060] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0061] The following is combined Figure 4 A detailed description is given of each component of the equipment 410 for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data: The first processor 2001 is the control center of the device 410 for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0062] Optionally, the first processor 2001 can perform various functions of the device 410 for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0063] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 4 CPU0 and CPU1 are shown in the diagram.

[0064] In a specific implementation, as one example, the device 410 for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data may also include multiple processors, such as... Figure 4 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor or a multi-core processor. Here, a processor can refer to one or more devices, circuits, and / or processing cores used to process data (such as computer program instructions).

[0065] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0066] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or exist independently, and may be connected to the interface circuit of the device 410 for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data. Figure 4 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0067] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0068] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 4 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0069] Optionally, the transceiver 2003 can be integrated with the first processor 2001 or exist independently, and can be used as an interface circuit for the device 410 that constructs an additive manufacturing materials database based on in-situ synchrotron radiation data. Figure 4 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0070] It should be noted that, Figure 4 The structure of the device 410 shown in the diagram, which constructs an additive manufacturing materials database based on in-situ synchrotron radiation data, does not constitute a limitation on the router. Actual knowledge structure identification devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0071] Furthermore, the technical effects of the device 410 for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data can be referred to the technical effects of the method for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data described in the above method embodiments, and will not be repeated here.

[0072] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0073] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0074] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0075] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0076] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0077] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0078] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0079] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0080] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0081] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0082] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0083] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0084] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data, characterized in that, The method includes: S1: Based on the synchrotron radiation principle of laser additive manufacturing, define the full-link data type and construct a relational database for laser additive manufacturing. The synchrotron radiation principle of laser additive manufacturing includes using high-energy, high-throughput X-rays to penetrate metal materials and capture transient behaviors in the additive manufacturing process in real time. The real-time capture of transient behaviors in the additive manufacturing process includes monitoring the dynamics of the molten pool, defect evolution, and phase transition process at the microsecond to millisecond scale through ultrafast imaging and diffraction technology. S2: Based on the relational database, collect material data, including composition data, process data, synchrotron radiation in-situ data, ex-situ microstructure data, and performance data; S3: Convert the material data into multiple data entity tables, construct a unified public key for each data entity table, and associate them based on the unified public key to obtain a laser additive manufacturing process entity table group; S4: Based on the entity table group of the laser additive manufacturing process, establish a graphical user interface function. The graphical user interface function includes data overview, data import, data retrieval, data classification, data analysis, data extraction, data deletion, data modification, and defect dynamic behavior analysis. The defect dynamic behavior analysis includes the analysis of defect dynamic behavior factors, which include, but are not limited to, the number of pores, crack area, and porosity.

2. The method for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data according to claim 1, characterized in that, The method further includes: Using the relational database of laser additive manufacturing for material design specifically includes: By constructing the correlation between in-situ synchrotron radiation data and process, microstructure, and performance, a data-driven paradigm is established. Additive manufacturing mechanism analysis based on data-driven paradigm; Based on the analysis of additive manufacturing mechanisms, process parameters are back-engineered and optimized to achieve precise quality control. Construct a relational database for laser additive manufacturing; Machine learning models for additive manufacturing are constructed based on experimental data. These models include, but are not limited to, machine learning models for additive manufacturing of high-temperature alloys, additive manufacturing of stainless steel, and additive manufacturing of titanium alloys and aluminum-magnesium alloys.

3. The method for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data according to claim 1, characterized in that, The synchrotron radiation in-situ data includes: Synchrotron radiation in-situ characterization technology was used to collect information on the dynamic behavior of materials during the laser additive manufacturing process, obtain multi-dimensional and heterogeneous in-situ and ex-situ data, and obtain data on the synchrotron radiation configuration and in-situ tissue parameters. The synchrotron radiation configuration and the in-situ tissue parameter data are processed and integrated to align with the key process data of the material, thereby obtaining synchrotron radiation in-situ data.

4. The method for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data according to claim 1, characterized in that, S1, based on the synchrotron radiation principle of laser additive manufacturing, defines end-to-end data types and constructs a relational database for laser additive manufacturing, including: S11: Utilizes ultrafast imaging and diffraction technology for monitoring at the microsecond to millisecond scale; S12: Synchrotron radiation uses high-energy, high-flux X-rays to penetrate metallic materials, capture transient behavior in the additive manufacturing process in real time, and obtain synchrotron radiation in-situ time-series images, diffraction spectra, and ex-situ microstructure data. S13: Spatially-temporally align synchrotron radiation in-situ time-series images, diffraction patterns and ex-situ tissue structure data; S14: Data on molten pool dynamics, defect evolution, and phase transition processes are obtained based on monitoring at the microsecond to millisecond scale; S15: Define end-to-end data types based on data from molten pool dynamics, defect evolution, and phase transition processes; S16: Based on the full-link data type, construct a relational database for laser additive manufacturing.

5. The method for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data according to claim 1, characterized in that, S2 collects material data based on the relational database. This material data includes composition data, process data, synchrotron radiation in-situ data, ex-situ microstructure data, and performance data, including: S21: Based on the relational database, perform systematic data acquisition of the laser additive manufacturing process; S22: Collect and record the chemical composition ratio of the materials to obtain composition data; S23: Record the process parameter settings during additive manufacturing to obtain process data; S24: Record and characterize the real-time dynamic evolution behavior during the manufacturing process to obtain in-situ synchrotron radiation data; S25: Record the physical and mechanical properties of laser additive manufacturing to obtain performance data; S26: The composition data, process data, synchrotron radiation in-situ data, ex-situ tissue data, and performance data constitute the material data.

6. The method for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data according to claim 1, characterized in that, S3 involves converting the material data into multiple data entity tables, constructing a unified public key for each data entity table, and linking them based on the unified public key to obtain a laser additive manufacturing process entity table group, including: S31: Construct a data entity table based on the material data, wherein the material data includes data from the entire additive manufacturing process; S32: A unified public key is constructed using a unified public key, and field conventions are performed to obtain a unified public key dataNo. The construction of the unified public key includes setting dataNo as a cross-table primary key. The dataNo has a unique identifier characteristic, which is used to realize the identification information for traceable association of the component-process-organization-performance link. S33: Based on the SQL relational architecture, a database schema with dataNo as the core identifier is obtained through schema design. The entity table group of the laser additive manufacturing process includes a unified definition of the entity tables and meets the requirements of normalization, consistency and scalability. S34: Adopting the genealogical modeling concept, data organization and link design are carried out. Through equi-joins, a traceable data genealogy is obtained. The genealogical modeling concept includes the modeling concept of the AiiDA platform. The equi-joins include cross-table joins based on dataNo and facilitate cross-domain queries and data-driven modeling. S35: Using dataNo as the cross-table primary key, perform task-oriented slicing to obtain a target data subset. The target data subset includes data extracted according to the link tracing requirements and used for data-driven modeling or cross-domain querying.

7. The method for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data according to claim 1, characterized in that, The S4 function, which establishes a graphical user interface based on the entity table group of the laser additive manufacturing process, includes: S41: Establish data overview and data import functions, which allow users to view the overall status of the database and import external data into the system; S42: Establish data retrieval and data classification tools for users to search and organize various types of manufacturing data according to specific conditions; S43: Establish data analysis and data extraction components to support in-depth data value mining and export of required information for use; S44: Establish data deletion and data modification permissions, allowing authorized users to maintain and update specific entries in the database; S45: Construct a defect dynamic behavior analysis function to study the evolution law of defects in the additive manufacturing process; S46: Construct a defect dynamic behavior analysis function, which mainly performs quantitative analysis of defect dynamic behavior factors to evaluate the internal quality status of the material. The defect dynamic behavior factors cover a number of key indicators, including but not limited to the number of pores, crack area and porosity. S47: Based on the above functions, establish a complete graphical user interface to realize the visual operation and management of the entity table group of the laser additive manufacturing process.

8. An apparatus for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data, wherein the apparatus is used to implement the method for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data as described in any one of claims 1-7, characterized in that... The device includes: Database module: Based on the synchrotron radiation principle of laser additive manufacturing, this module defines full-link data types and constructs a relational database for laser additive manufacturing. The synchrotron radiation principle of laser additive manufacturing includes the use of high-energy, high-flux X-rays to penetrate metallic materials and capture transient behaviors in the additive manufacturing process in real time. The real-time capture of transient behaviors in the additive manufacturing process includes monitoring the dynamics of the molten pool, defect evolution, and phase transition processes at the microsecond to millisecond scale through ultrafast imaging and diffraction techniques. Data acquisition module: used to acquire material data based on the relational database, the material data including composition data, process data, synchrotron radiation in-situ data, ex-situ microstructure data and performance data; Entity Table Module: This module is used to convert the material data into multiple data entity tables, construct a unified public key for each data entity table, and associate them based on the unified public key to obtain a laser additive manufacturing process entity table group. Interface Function Module: Used to establish a graphical user interface function based on the entity table group of the laser additive manufacturing process. The graphical user interface function includes data overview, data import, data retrieval, data classification, data analysis, data extraction, data deletion, data modification, and defect dynamic behavior analysis. The defect dynamic behavior analysis includes the analysis of defect dynamic behavior factors, which include, but are not limited to, the number of pores, crack area, and porosity.

9. A device for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data, characterized in that, The processor for constructing an additive manufacturing materials database based on in-situ synchrotron radiation data; and a memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 7.

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