Large casting and forging material research and development system

Abstract

The invention relates to a large casting and forging material research and development system, belongs to the technical field of material research and development, and solves the problems of low research and development efficiency, high cost, poor resource integration capability and difficulty in data collection during material research and development of large castings and forgings in the prior art. The system comprises an intelligent data center which is used for data acquisition, data storage and data model construction and updating in each large casting and forging material research and development project; the integrated calculation center is used for constructing and updating a simulation calculation model in each large casting and forging material research and development project; the research and development workbench is used for constructing a project research and development path of the large-scale casting and forging material research and development project, performing research and development operation based on the project research and development path and collecting and auditing research and development data; wherein the data model of the intelligent data center and the simulation calculation model of the integrated calculation center are adopted in the research and development operation process. The research and development efficiency of large casting and forging materials is improved, and data is efficiently collected.

Description

Technical Field

[0001] The invention relates to the technical field of material research and development, and in particular to a large casting and forging material research and development system. Background Art

[0002] In modern manufacturing, large castings and forgings, as core basic components in the field of equipment manufacturing, are widely used in many key industries such as aerospace, energy and electricity, transportation, petrochemicals, etc. Their performance and quality are directly related to the reliability, safety and service life of the entire equipment system. Large castings and forgings have the characteristics of single-piece, small-batch R&D and production methods. The traditional design and R&D process of large castings and forgings is highly dependent on the experience and trial and error of engineers, that is, large castings and forgings are a "trial + experience" R&D model, which has bottleneck problems such as long design cycle, high cost, low material utilization rate, and difficult to control product quality. The R&D of large castings and forgings involves multiple disciplines such as materials science, mechanical design, and manufacturing technology. The traditional R&D model is difficult to effectively integrate interdisciplinary resources and achieve collaborative innovation and resource sharing. At the same time, its single-piece and small-batch R&D and production methods are difficult to achieve the transformation of its R&D model with a process-based system establishment idea, and the existing system does not unify data processing and project management and data association, resulting in the high data processing cost, inconsistent data processing standards, and low data collection efficiency in the R&D process of large castings and forgings.

[0003] Therefore, the existing large castings and forgings have problems such as low R&D efficiency, high cost, poor resource integration capabilities and difficulty in data collection when conducting material research and development. Summary of the invention

[0004] In view of the above analysis, an embodiment of the present invention aims to provide a large casting and forging material research and development system to solve the problems of low research and development efficiency, high cost, poor resource integration ability and difficulty in data collection in the existing large casting and forging material research and development.

[0005] The embodiment of the present invention provides a large casting and forging material research and development system, including a research and development workbench, an intelligent data center and an integrated computing center;

[0006] The intelligent data center is used for data acquisition, data storage, data model construction and updating in various large-scale casting and forging material research and development projects;

[0007] The integrated computing center is used to construct and update simulation computing models in various large-scale casting and forging material research and development projects;

[0008] The R&D workbench is used to construct a project R&D path for a large casting and forging material R&D project, and then perform R&D operations based on the project R&D path and collect and review R&D data; wherein the data model of the intelligent data center and the simulation calculation model of the integrated computing center are used in the R&D operation process.

[0009] Based on the further improvement of the above system, the intelligent data center includes a data acquisition module, a data storage module and a data application module;

[0010] The data acquisition module is used to acquire the research and development data generated by each large casting and forging material research and development project in the research and development work of the research and development work platform;

[0011] The data storage module is used to store the R&D data based on the data relationship of the acquired R&D data and the project to which it belongs;

[0012] The data application module is used to establish or update the data model based on the stored R&D data for packaging and publishing.

[0013] Based on further improvements to the above system, the data relationships of the R&D data include the relationships between material composition data and performance data, process data and performance data, performance data and characterization data, and mechanical properties data and tissue performance data.

[0014] Based on the further improvement of the above system, the data model is obtained by training machine learning and empirical formulas through stored R&D data, and is updated by acquiring new R&D data.

[0015] Based on further improvements to the above system, the integrated computing center constructs simulation computing models for the research and development of large casting and forging materials based on material thermodynamics computing software and finite element simulation software, and also updates the simulation computing models based on the research and development data generated by the research and development operations.

[0016] Based on further improvements to the above system, the R&D workbench constructs a project R&D path based on task nodes based on the basic project information of the large casting and forging material R&D project; wherein the project R&D path includes at least one R&D path formed by sequentially connecting several task nodes.

[0017] Based on the further improvement of the above system, the task nodes include root-level, intermediate-level and final-level task nodes; the final-level task nodes include R&D forms; the R&D workbench performs R&D operations based on the project R&D path and collects and reviews R&D data, including:

[0018] Based on the path direction in the project R&D path, each of the terminal task nodes performs R&D operations according to the corresponding R&D form, and records the R&D data in the R&D form; when the task node in the project R&D path completes its own R&D task, all the R&D data generated or received by it are uploaded to the task node on the upper-level R&D path corresponding to the R&D path where the task node is located for review until all task nodes have completed the review.

[0019] Based on further improvements of the above system, the intelligent data center also includes a template storage module, including an R&D form template library and an R&D path template library, which are used to store and update R&D form templates and R&D path templates; and are also used to intelligently recommend R&D path templates and R&D form templates when constructing R&D paths and establishing R&D forms on the R&D workbench; wherein each R&D form template and R&D path template is obtained based on historical projects.

[0020] Based on further improvement of the above system, the R&D workbench is also used to view project progress, project data and project contributions of members through project R&D paths.

[0021] Based on the further improvement of the above system, the project development path is constructed by performing the following steps:

[0022] Obtain basic project information; the basic project information includes project name, project number, project background, project source, project research objectives, project research content, project leader, technical leader, responsible department, project cycle, assessment nodes, assessment indicators, and key technologies formed;

[0023] Construct a root-level task node, wherein the basic information of the project is used as the task information of the root-level task node;

[0024] Based on the task information of the root-level task node, each R&D path is constructed in sequence; the task node on the R&D path is an intermediate-level or final-level task node;

[0025] Among them, the task information of the intermediate and final task nodes includes task objectives, task content, workload ratio and task completion time node.

[0026] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0027] The present invention provides a large casting and forging material research and development system.

[0028] (1) The system includes an intelligent data center for data acquisition, data storage, data model construction and updating in various large-scale casting and forging material R&D projects, an integrated computing center for the construction and updating of simulation computing models in various large-scale casting and forging material R&D projects, and a R&D workbench for building project R&D paths for large-scale casting and forging material R&D projects, and then performing R&D operations based on the project R&D paths and collecting and reviewing R&D data. This system realizes the construction of an interdisciplinary collaborative work platform, integrates the various disciplines involved in the R&D of large-scale casting and forging materials, that is, integrates the R&D processes of various fields through the design of project R&D paths, and uniformly stores the data and models of various fields through the intelligent data center and the integrated computing center, realizing the deep integration and collaboration of design, process, material, and manufacturing fields, and improving the resource integration capability of large-scale castings and forgings in material R&D;

[0029] (2) The R&D workbench models and modularizes the R&D process to form a project R&D path, thereby improving the R&D efficiency of the large casting and forging industry with the characteristics of single-piece and small-batch R&D and production methods;

[0030] (3) Data is collected based on the project R&D path. The collection, classification and association of project-related data can be completed during the project execution process, solving the data collection difficulties in the R&D process of large castings and forgings, such as inconsistent data standards, incomplete data information, high data entry costs and low data collection efficiency. This enables efficient processing of project business and data, improves the project management level and project management effectiveness, and enhances the enterprise's project data processing efficiency.

[0031] (4) The intelligent data center can integrate and share multi-source data such as design data, test data, and production data;

[0032] (5) Through the data model in the intelligent data center and the simulation calculation model in the integrated computing center, high-precision simulation analysis technology and intelligent optimization algorithms can be used in R&D operations to achieve accurate prediction and optimization adjustment of the material performance of castings and forgings in R&D operations, thereby improving the quality of project R&D.

[0033] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0035] Figure 1 A schematic diagram of the structure of a large casting and forging material research and development system provided in Example 1 of the present invention;

[0036] Figure 2 A schematic diagram of a project development path for the R&D workbench provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0037] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0038] Example 1

[0039] A specific embodiment of the present invention discloses a large casting and forging material research and development system, such as Figure 1 As shown, it includes: R&D workbench, intelligent data center and integrated computing center;

[0040] The intelligent data center is used for data acquisition, data storage, data model construction and updating in various large-scale casting and forging material research and development projects;

[0041] The integrated computing center is used to construct and update simulation computing models in various large-scale casting and forging material research and development projects;

[0042] The R&D workbench is used to construct a project R&D path for a large casting and forging material R&D project, and then perform R&D operations based on the project R&D path and collect and review R&D data; wherein the data model of the intelligent data center and the simulation calculation model of the integrated computing center are used in the R&D operation process.

[0043] It is understandable that each large casting and forging material R&D project to be developed can construct a project R&D path through the R&D workbench, decompose the large casting and forging material and process R&D process, and complete the assembly of the entire R&D process of different types of large castings and forgings, so as to solve the problems of process and digitalization of the R&D process of single-piece and small-batch large castings and forgings.

[0044] When implemented, the intelligent data center includes a data acquisition module, a data storage module and a data application module;

[0045] The data acquisition module is used to acquire the research and development data generated by each large casting and forging material research and development project on the research and development work platform during the research and development operation.

[0046] The data storage module is used to store the R&D data based on the data relationship of the acquired R&D data and the project to which it belongs; wherein the data relationship of the R&D data includes the relationship between material composition data and performance data, process data and performance data, performance data and characterization data, and mechanical property data and organizational performance data. It can be understood that the data storage module meets the requirements of data structuring throughout the entire process of the R&D of large castings and forgings, and designs data elements and data relationships according to the enterprise standard of large castings and forgings data, thereby establishing a data relationship structure between various data, thereby constructing a data and data relationship system for the entire life cycle of the R&D of large castings and forgings materials. For example, a piece of material performance data can be associated with the process data of the material sample manufacturing process, the sample characterization process data, and the sample analysis process data, and can also be associated with the organizational data and composition data under the same sample conditions, thereby facilitating the construction and update of the data model.

[0047] The data application module is used to establish or update the data model based on the stored R&D data for packaging and publishing; wherein the data model is obtained by training machine learning and empirical formulas through the stored R&D data, and is updated by acquiring new R&D data. For example, a prediction data model for the yield strength of a material is formed, and when the R&D workbench is used, the yield strength can be obtained by inputting the material composition, quenching temperature, and tempering temperature.

[0048] Preferably, the data storage module provides a global search, and material and process selection is performed by setting search conditions.

[0049] During implementation, the integrated computing center constructs a simulation computing model for the R&D of large casting and forging materials based on material thermodynamics computing software and finite element simulation software, and also updates the simulation computing model based on the R&D data generated by the R&D operation. It is understandable that the updating of the simulation computing model is based on the R&D test data in the R&D data.

[0050] Specifically, the simulation calculation model includes a material high-throughput calculation model and a process automatic simulation model.

[0051] Specifically, the integrated computing center also stores a geometric model library, a material model library, and a boundary condition library for calling when the simulation computing model is applied.

[0052] Preferably, the data storage module and the data application module can also set permissions for the R&D data and data models; the integrated computing center can also set permissions for the simulation computing models. It is understandable that permissions can be set for the R&D data, data models and simulation computing models generated by each project according to its needs, increasing the flexibility of project sharing.

[0053] During implementation, the R&D workbench constructs a project R&D path based on the large casting and forging material R&D project, and the project R&D path based on the basic information of the large casting and forging material R&D project is constructed based on task nodes; wherein the project R&D path includes at least one R&D path formed by sequentially connecting a number of task nodes, and the task nodes include root-level, intermediate-level and final-level task nodes; the final-level task nodes include R&D forms;

[0054] Specifically, by constructing a project R&D path, the implementation plan writing process of large casting and forging material and process R&D projects can be displayed in a structured and graphical manner, which can more intuitively display the project progress and process.

[0055] In specific implementation, the project development path is constructed by executing the following steps:

[0056] S11. Obtain basic project information of large casting and forging material research and development projects; the basic project information includes project name, project number, project background, project source, project research objectives, project research content, project leader, technical leader, responsible department, project cycle, assessment nodes, assessment indicators, and key technologies formed.

[0057] Specifically, the project leader organizes the basic information of the project.

[0058] S12. Construct a root-level task node, wherein the basic information of the project is used as the task information of the root-level task node.

[0059] Specifically, the project leader is set as the person in charge of the root-level task node.

[0060] S13. Based on the task information of the root-level task node, construct each R&D path in sequence; the task node on the R&D path is an intermediate or final-level task node; wherein the task information of the intermediate and final-level task nodes includes task objectives, task content, workload ratio and task completion time node. wherein the workload ratio is the ratio of the workload of the task node to the total workload of the project.

[0061] In specific implementation, step S13 includes:

[0062] The root-level task node constructs a first-level research and development path of the task node based on the task information;

[0063] Determine whether each task node on the first-level R&D path can be directly executed.

[0064] If yes, the current task node is used as the final task node;

[0065] If not, the current task node constructs the second-level R&D path of the task node based on the task information;

[0066] Determine whether each task node on each level 2 R&D path can be directly executed.

[0067] If yes, the current task node is used as the final task node;

[0068] If not, the current task node constructs the third-level R&D path of the task node based on the task information;

[0069] And so on.

[0070] Until there are no task nodes that cannot be directly executed on the R&D paths at all levels.

[0071] It can be understood that, except for the root-level and last-level task nodes, all other task nodes are intermediate-level task nodes.

[0072] Specifically, the R&D path of each task node is constructed in the following ways:

[0073] The current task node decomposes the task into multiple subtasks according to the task information, and then connects the subtasks in the order of implementation to form the research and development path of the task node, and each subtask serves as a task node on the research and development path.

[0074] More specifically, the intermediate or final task nodes are provided with task leaders, and the task leaders may be the same or different.

[0075] Specifically, when the R&D task of a task node can directly carry out experiments and operations, the task node can be directly executed, such as hot and cold processing of materials, simulation calculations, physical and chemical testing, pilot tests, and various links of product production. The task node is taken as the final task node, and an R&D form is added; the R&D form includes the experimental material composition, experimental objectives, and R&D experimental design plan of the task node before the R&D operation, and also includes R&D data after the R&D operation.

[0076] Preferably, the intelligent data center also includes a template storage module, including a research and development form template library and a research and development path template library, which are used to store and update research and development form templates and research and development path templates; and are also used to intelligently recommend research and development path templates and research and development form templates when the research and development workbench is performing research and development path construction and research and development form establishment; wherein each research and development form template and research and development path template is obtained according to historical projects. When adding a research and development form to the final task node in the research and development path of the project in the research and development workbench, it can be directly established according to the task information, and the research and development form template can be obtained through intelligent recommendation in the research and development form template library according to the task information of the final task node, and the research and development form template can be directly used or modified to establish the research and development form of the final task node; when constructing a research and development path at a task node in the research and development path of the project in the research and development workbench, it can be directly constructed according to the task information, and the research and development path template can be obtained through intelligent recommendation in the research and development path template library according to the task information of the task node, and the research and development path template can be directly used or modified to establish the research and development path of the task node.

[0077] During implementation, the R&D workbench performs R&D operations based on the project R&D path and collects and reviews R&D data, including:

[0078] Based on the path direction in the project R&D path, each of the terminal task nodes performs R&D operations according to the corresponding R&D form, and records the R&D data in the R&D form; when the task node in the project R&D path completes its own R&D task, all the R&D data generated or received by it are uploaded to the task node on the upper-level R&D path corresponding to the R&D path where the task node is located for review until all task nodes have completed the review.

[0079] Specifically, when the final task node performs R&D operations according to the corresponding R&D form, the R&D operations of the final task node at the end of the project R&D path are based on the R&D data of the R&D form of the previous adjacent final task node. In other words, when the task node at the end of the R&D path is implemented, the next R&D operation is performed based on the R&D data of the R&D form of the previous adjacent task node.

[0080] Specifically, when performing R&D operations according to the R&D form, the data model in the intelligent data center and the simulation calculation model in the integrated computing center are used to perform the operations, and the data model and the simulation calculation model are updated according to the new R&D data. If the corresponding model does not exist, the data model or the simulation calculation model is constructed according to the content of the R&D form to perform the R&D operations, and stored in the intelligent data center and the integrated computing center; wherein the constructed or updated data and simulation calculation models are shared with other projects according to the set permissions, that is, if the permissions are public, other projects can call them when performing R&D operations on the R&D workbench; if the permissions are not public, other projects cannot call them when performing R&D operations on the R&D workbench.

[0081] For example, the R&D workbench needs to conduct R&D work on the heat treatment process test of a certain material, that is, the curve of the change of the material's yield strength with the quenching temperature. The data model describing the relationship between the quenching temperature, tempering temperature and yield strength of this type of material in the intelligent data center is called, and the yield strength can be obtained by inputting the material composition, quenching temperature and tempering temperature.

[0082] For example, if the R&D workbench needs to perform high-throughput computing on a certain material, the high-throughput computing model in the integrated computing center is called to obtain the material composition that meets the task requirements.

[0083] Specifically, the research and development data include literature data, test data and simulation calculation data according to the source, and also include material composition data, material processing technology data, material characterization data, material analysis process derived data, and material performance data according to the content.

[0084] The test data include composition data, process data, performance data, characterization data and processing data. The performance data include mechanical properties, thermophysical parameters, phase change properties. The process data include smelting, forging and heat treatment processes.

[0085] Exemplarily, the simulation calculation data include a temperature change curve over time, a stress-time curve, a tissue ratio-time curve at a focus position of the workpiece during heat treatment, and strain values ​​and temperature values ​​at a focus position during forging of the workpiece.

[0086] It is understandable that by establishing a project R&D path and reproducing the R&D process in a digital way, on the one hand, it is helpful to clarify the project ideas, personnel division of labor, and control the implementation of the project as planned; on the other hand, in the business process of project implementation, data collection is realized according to the R&D path, and data collection is closely integrated with business, so that data can be collected and queried in a project manner, and based on this, data sharing is carried out, which simplifies data entry and ensures data quality and integrity.

[0087] Preferably, when the R&D workbench performs R&D operations according to the R&D form, if the data model of the intelligent data center and the simulation calculation model of the integrated computing center do not have corresponding models or cannot meet the corresponding operation requirements, they can also be entrusted to professional departments.

[0088] Exemplarily, the intelligent data center provides a commission registration function for the sample processing process of small steel ingot smelting, forging, heat treatment, sample processing, and physical and chemical testing process. When small steel ingot smelting is required in the R&D work of the project in the R&D workbench, log in to the system and register the commission information of small steel ingot smelting at the small steel ingot smelting commission entrance, including the smelting target composition requirements, the principal, the time requirements, etc. After receiving the commission, the smelting department accepts the commission, formulates the smelting process, and enters or imports the smelting process in a structured manner into the system. Then, the commission information flows to the smelting process implementation department, and the small steel ingot is smelted according to the process provided by the smelting department, and a sample QR code is attached to the small steel ingot; after the smelting is successful, the small steel ingot forging commission is submitted, and the forging requirements are registered. The commission flows to the forging process department to design the forging process, and the process is entered or imported into the platform in a structured manner. After that, the commission flows to the forging process implementation department to forge the small steel ingot. After the completion, a new sample QR code is attached to the forging; then continue to submit the heat treatment test commission Entrust, design the heat treatment test process and register the heat treatment requirements, and enter or import the process into the platform in a structured manner. After that, the entrustment is transferred to the heat treatment process implementation department, and the corresponding person performs heat treatment of the samples according to the process. After the end, a new sample QR code is attached to the sample; entrust the sample processing information, register the sample processing drawings and register the processing requirements, and entrust it to the sample processing technology department to process the samples and attach a new sample QR code to the samples; finally, entrust the sample testing and register the sample testing requirements, and entrust it to the physical and chemical testing department to test the samples according to the testing requirements, and can read the relevant information in the sample processing process through the QR code. After the test is completed, the test personnel upload the test data to the corresponding R&D form of the R&D workbench by entering or importing, and obtain the sample process data, sample characterization and performance data through the entrusted business process system.

[0089] During implementation, when a task node in the project R&D path completes its own R&D task, all R&D data generated or received by it shall be uploaded to the task node on the upper-level R&D path corresponding to the R&D path where the task node is located for review until all task nodes have completed the review, specifically:

[0090] After the final task node completes the task based on the R&D form, the R&D data in the R&D form is reviewed, and after the review is passed, the R&D data in the R&D form is uploaded to the task node on the upper-level R&D path corresponding to the R&D path where the final task node is located for review;

[0091] The intermediate-level task node receives the R&D data in the R&D forms uploaded by each task node on the R&D path of the task node for review. When all task nodes on the R&D path of the task node have completed the review, the R&D data in all received R&D forms are uploaded to the task node on the upper-level R&D path corresponding to the R&D path where the task node is located for review;

[0092] The root-level task node receives the R&D data in the R&D form uploaded by each task node on the R&D path of the task node for review. When all task nodes on the R&D path of the task node complete the review, the review of the R&D data of all task nodes is completed.

[0093] Specifically, when the task node reviews the received or generated R&D form, if the R&D form data is complete and correct, the review is passed; otherwise, the R&D form is returned to the corresponding task node for modification.

[0094] Preferably, after reviewing the received or generated R&D forms, each task node will also generate a summary report, which is stored in the R&D data; the summary includes the research conclusions.

[0095] Preferably, the R&D workbench also includes a setting for each project whether to store the project R&D path and the included R&D form generation template into the template storage module of the intelligent data center after all task nodes have been reviewed and received; wherein, the R&D data is deleted when the R&D form generates the template.

[0096] Preferably, the R&D workbench also includes a setting for each project whether to share the R&D data generated in the project.

[0097] Preferably, the project leader and task leader view the project progress, project data and project contributions of members through the project R&D path, and simultaneously realize data processing, project management and R&D talent management.

[0098] Preferably, the R&D system also includes an integrated management and control center, and the integrated management and control R&D system manages the R&D workbench, intelligent data center and integrated computing center, and records statistical data, model upload and usage.

[0099] Compared with the prior art, this embodiment provides a large-scale casting and forging material research and development system, which includes an intelligent data center for data acquisition, data storage, data model construction and updating in each large-scale casting and forging material research and development project, an integrated computing center for construction and updating of simulation calculation models in each large-scale casting and forging material research and development project, and a research and development workbench for constructing project research and development paths for large-scale casting and forging material research and development projects, and then performing research and development operations based on the project research and development paths and collecting and reviewing research and development data, thereby realizing the construction of an interdisciplinary collaborative work platform, integrating various disciplines involved in the research and development of large-scale casting and forging materials, that is, integrating the research and development processes of various fields through the design of project research and development paths, and uniformly storing data and models of various fields through the intelligent data center and the integrated computing center, thereby realizing deep integration and collaboration in the fields of design, process, material, and manufacturing, and improving the resource integration capability of large castings and forgings when conducting material research and development; the research and development workbench integrates the research and development The development process is modeled and modularized to form a project development path, thereby improving the R&D efficiency of the large castings and forgings industry with single-piece and small-batch R&D and production characteristics; data is collected based on the project R&D path, and the collection, classification and association of project-related data can be completed during the project execution process, solving the data collection difficulties in the R&D process of large castings and forgings, such as inconsistent data standards, incomplete data information, high data entry costs, and low data collection efficiency, thereby achieving efficient processing of project business and data, improving the project management level and project management effectiveness, and improving the project data processing efficiency of the enterprise; through the intelligent data center, multi-source data such as design data, test data, and production data can be integrated and shared; through the data model in the intelligent data center and the simulation calculation model in the integrated computing center, high-precision simulation analysis technology and intelligent optimization algorithm can be called in R&D operations, so as to achieve accurate prediction and optimization adjustment of the material performance of castings and forgings in R&D operations, and improve the quality of project R&D.

[0100] Example 2

[0101] The present invention provides a specific embodiment, taking a material design project as an example, to illustrate the working process of the above embodiment system:

[0102] S1. The project leader enters basic project information on the R&D workbench, such as Figure 2 As shown, the R&D work platform builds the project R&D path:

[0103] The root-level task node is constructed based on the basic information of the project. The task information includes project name, project number, project background, project source, research objectives, research content, project leader, technical leader, responsible department, project cycle, assessment nodes and assessment indicators, and key technologies formed.

[0104] The intelligent data center makes intelligent recommendations on R&D paths based on the task information of the root-level task nodes, and decomposes the tasks into four sub-tasks: material calculation, material testing, pilot testing, and product production. The root-level task nodes thus constitute the first-level R&D path. The task information includes the task objectives, the proportion of the task workload to the total project workload, and the time node for task completion. You can view the corresponding task information and the designated person in charge by clicking on the corresponding task node.

[0105] The intelligent data center makes intelligent recommendations on R&D paths or R&D forms for each intermediate and final task node based on the task information of each intermediate and final task node on the first-level R&D path. Each intermediate and final task node establishes the R&D path and R&D form of the corresponding task node by establishing, selecting or modifying the recommended R&D path template and R&D form template; among them, material calculation constructs a second-level R&D path consisting of three final task nodes: literature data search, calculation of basic material properties, and high-throughput screening of materials; material experiments construct a second-level R&D path consisting of three final task nodes: high-throughput smelting and high-throughput heat treatment experiments.

[0106] S2. The final task node uses data models, simulation calculation models or delegates some tasks to professional departments in the R&D workbench to carry out R&D operations such as experiments or simulation calculations related to the R&D form. For example,

[0107] In the material calculation task, first search for literature in the literature database and export literature related to the project. Use literature reading tools to read important data in the literature. At the same time, use the statistical analysis data model in the intelligent data center to perform statistical analysis on the read data, so as to determine the focus of material research in this project.

[0108] In the task of calculating the basic properties of materials, the material thermodynamics software is selected in the integrated computing center to calculate the basic properties of materials, including material balance machine, CCT diagram, TTT diagram, etc., and the screening indicators for high-throughput calculation of materials are determined in combination with the results of literature data analysis. The focus of material design in this project is strength. The results of literature data analysis show that the performance of this material can basically meet the standard within the composition range of the standard specification, but the processing performance is poor. The key factor affecting the processing performance of the material is the precipitation of carbides. Therefore, when designing the composition, the main goal is to reduce the precipitation temperature of the carbide to below the process temperature range;

[0109] In the high-throughput calculation task, a high-throughput calculation model based on material thermodynamic calculation software is selected for high-throughput calculation. If the integrated computing center does not have a ready-made high-throughput model, secondary development is performed based on the material thermodynamic software to obtain a model that meets the high-throughput calculation and screening requirements of the project. Then use the high-throughput model; first, according to the overall design requirements, set the calculation interval value of each element within the material standard range, perform calculations, and obtain calculation results for 800 groups of different components. When processing the calculation results, set the screening conditions to carbon equivalent less than 0.3, strength greater than 700MPa, and a carbide precipitation temperature lower than 700°C, so as to obtain 10 groups of components that meet the conditions and complete the task. This task node enters the components that meet the requirements obtained by screening into the system R&D form and uploads the R&D data;

[0110] The material test task receives the R&D data of material calculation, and conducts high-throughput testing of materials based on the 10 components selected by calculation, including: high-throughput component smelting and high-throughput heat treatment, that is, smelting steel samples of 10 components at one time, and then conducting tests of different heat treatment processes after machining, so as to obtain samples of 10 components and multiple heat treatment processes. Then, the sample performance is tested to quickly obtain the appropriate material composition-process matching solution, which can then be submitted for pilot testing and production;

[0111] In pilot and production tasks, the system's intelligent recommendations can be used to understand the common processes of the component system and make modifications based on calculation and test results. At the same time, process simulation can be performed in the integrated computing center, that is, the small-scale laboratory sample data is transitioned to large-scale engineering test pieces or product data, so as to quickly develop pilot and production processes.

[0112] S3. Each task node receives the R&D data, verifies it and confirms or returns modification opinions;

[0113] S4. Generate R&D path templates and R&D form templates based on the project R&D path. The R&D path templates, R&D forms and R&D data involved in the project are aggregated and stored in the intelligent data center in the form of projects. Set the permissions of the R&D data, R&D path templates and R&D form templates in the project according to project requirements for sharing.

[0114] S5. Set permissions for data models and simulation models built in project R&D operations according to project requirements;

[0115] S6. Update and optimize the data model and simulation calculation model based on the project R&D data. Specifically, the R&D data generated by this project enters the intelligent data center, and the new R&D data is combined with historical data and data in the process specifications, and the built-in algorithm is used to update the data model and simulation calculation model to make the model prediction more accurate; when a similar model is used again on the R&D workbench, more accurate process parameters can be given.

[0116] S7. The R&D workbench tracks the project completion status, conducts statistical analysis of projects, personnel contributions, data, and models, and makes comprehensive evaluations.

[0117] S8. Relevant persons in charge of the company and project team members can view the progress of the project and conduct management, analysis and decision-making.

[0118] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0119] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A large casting and forging material research and development system, characterized in that: include: R&D workbenches, intelligent data centers, and integrated computing centers; The intelligent data center is used for data acquisition, data storage, data model construction and updating in various large-scale casting and forging material research and development projects; The integrated computing center is used to construct and update simulation computing models in various large-scale casting and forging material research and development projects; The R&D workbench is used to construct a project R&D path for a large casting and forging material R&D project, and then perform R&D operations based on the project R&D path and collect and review R&D data; wherein the data model of the intelligent data center and the simulation calculation model of the integrated computing center are used in the R&D operation process.

2. The large casting and forging material research and development system according to claim 1 is characterized in that: The intelligent data center includes a data acquisition module, a data storage module and a data application module; The data acquisition module is used to acquire the research and development data generated by each large casting and forging material research and development project in the research and development work of the research and development work platform; The data storage module is used to store the R&D data based on the data relationship of the acquired R&D data and the project to which it belongs; The data application module is used to establish or update the data model based on the stored R&D data for packaging and publishing.

3. The large casting and forging material research and development system according to claim 2 is characterized in that: The data relationships of the R&D data include the relationships between material composition data and performance data, process data and performance data, performance data and characterization data, and mechanical properties data and tissue performance data.

4. The large casting and forging material research and development system according to claim 2, characterized in that: The data model is obtained by training machine learning and empirical formulas through stored R&D data, and is updated by acquiring new R&D data.

5. The large casting and forging material research and development system according to claim 1 is characterized in that: The integrated computing center constructs simulation computing models for the research and development of large casting and forging materials based on material thermodynamic computing software and finite element simulation software, and also updates the simulation computing models based on research and development data generated by research and development operations.

6. The large casting and forging material research and development system according to claim 1, characterized in that: The R&D workbench constructs a project R&D path based on task nodes based on the basic project information of the large casting and forging material R&D project; wherein the project R&D path includes at least one R&D path formed by sequentially connecting a number of task nodes.

7. The large casting and forging material research and development system according to claim 6, characterized in that: The task nodes include root-level, intermediate-level and final-level task nodes; the final-level task nodes include R&D forms; the R&D workbench performs R&D operations based on the project R&D path and collects and reviews R&D data, including: Based on the path direction in the project R&D path, each of the terminal task nodes performs R&D operations according to the corresponding R&D form, and records the R&D data in the R&D form; when the task node in the project R&D path completes its own R&D task, all the R&D data generated or received by it are uploaded to the task node on the upper-level R&D path corresponding to the R&D path where the task node is located for review until all task nodes have completed the review.

8. The large casting and forging material research and development system according to claim 7, characterized in that: The intelligent data center also includes a template storage module, including an R&D form template library and an R&D path template library, which are used to store and update R&D form templates and R&D path templates; and are also used to intelligently recommend R&D path templates and R&D form templates when constructing R&D paths and establishing R&D forms on the R&D workbench; wherein each R&D form template and R&D path template is obtained based on historical projects.

9. The large casting and forging material research and development system according to claim 1, characterized in that: The R&D workbench is also used to view project progress, project data and members' project contributions through the project R&D path.

10. The large casting and forging material research and development system according to claim 6, characterized in that: Build a project development path by performing the following steps: Obtain basic project information; the basic project information includes project name, project number, project background, project source, project research objectives, project research content, project leader, technical leader, responsible department, project cycle, assessment nodes, assessment indicators, and key technologies formed; Construct a root-level task node, wherein the basic information of the project is used as the task information of the root-level task node; Based on the task information of the root-level task node, each R&D path is constructed in sequence; the task node on the R&D path is an intermediate-level or final-level task node; Among them, the task information of the intermediate and final task nodes includes task objectives, task content, workload ratio and task completion time node.

Images