Medium-entropy steel manufacturing method, device, equipment, medium and product

Abstract

The invention discloses a medium-entropy steel manufacturing method, device and equipment, a medium and a product. The method comprises the steps that laser metal deposition equipment is controlled to conduct medium-entropy steel manufacturing based on initial manufacturing parameters, and a surface image and a test result of a formed medium-entropy steel product are obtained; when the test result does not meet the design requirement, determining defect information of the medium-entropy steel product according to the surface image and the test result; and the initial manufacturing parameters are corrected based on the defect information, the laser metal deposition equipment is controlled to conduct medium-entropy steel manufacturing based on the corrected manufacturing parameters, the step of obtaining the surface image and the test result continues to be executed till the design requirement is met, and the final manufacturing parameters are obtained. By establishing a closed-loop feedback control mechanism, real-time monitoring and adjustment are achieved in the LMD machining process, manufacturing parameters are dynamically optimized, the size precision and shape consistency of the medium-entropy steel product are remarkably improved, and the precision defect problem in traditional LMD manufacturing is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of metal material design, and in particular, to a method, device, equipment, medium and product for manufacturing medium-entropy steel. Background Art

[0002] As a new type of alloy material, medium-entropy steel has received extensive attention in the fields of aerospace, automotive manufacturing and energy due to its excellent mechanical properties and thermal stability. Compared with traditional high-entropy alloys and low-entropy alloys, medium-entropy steel not only has a higher strength-toughness balance, but also exhibits a lower density and good corrosion resistance. These characteristics make medium-entropy steel an important research object in the field of advanced manufacturing.

[0003] The prior art usually uses Laser Metal Deposition (LMD) for the additive manufacturing of medium-entropy steel. LMD occupies an important position in the manufacturing of complex metal components due to its high precision, high efficiency and flexibility. When preparing medium-entropy steel by LMD technology, near-net shaping of parts can be achieved, thereby reducing material waste and subsequent processing costs.

[0004] However, the high energy input, rapid cooling and complex melt pool dynamic behavior involved in the LMD process are likely to cause defects in the shape accuracy, dimensional accuracy and microstructure distribution of the manufactured parts. Summary of the Invention

[0005] The present invention provides a method, device, equipment, medium and product for manufacturing medium-entropy steel to optimize the manufacturing parameters of medium-entropy steel and reduce the occurrence rate of manufacturing defects.

[0006] According to the first aspect of the present invention, a method for manufacturing medium-entropy steel is provided, and the method includes:

[0007] Controlling a laser metal deposition device to manufacture medium-entropy steel based on initial manufacturing parameters, and obtaining a surface image and test results of the formed medium-entropy steel part;

[0008] When the test results do not meet the design requirements, determining defect information of the medium-entropy steel part according to the surface image and the test results;

[0009] Modifying the initial manufacturing parameters based on the defect information, and controlling the laser metal deposition device to manufacture medium-entropy steel based on the modified manufacturing parameters, and continuing to execute the step of obtaining the surface image and the test results until the design requirements are met, and finally obtaining the final manufacturing parameters.

[0010] According to the second aspect of the present invention, a device for manufacturing medium-entropy steel is provided, including:

[0011] A result acquisition module, configured to control a laser metal deposition device to manufacture medium-entropy steel based on initial manufacturing parameters, and acquire a surface image and test results of the formed medium-entropy steel workpiece;

[0012] A defect determination module, configured to determine defect information of the medium-entropy steel workpiece according to the surface image and the test results when the test results do not meet the design requirements;

[0013] A parameter determination module, configured to correct the initial manufacturing parameters based on the defect information, and control the laser metal deposition device to manufacture medium-entropy steel based on the corrected manufacturing parameters, and continue to execute the steps of acquiring the surface image and the test results until the design requirements are met, and obtain the final manufacturing parameters.

[0014] According to a third aspect of the present invention, there is provided an electronic device, the electronic device includes:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the medium-entropy steel manufacturing method according to any embodiment of the present invention.

[0018] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium, the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the medium-entropy steel manufacturing method according to any embodiment of the present invention when executed by a processor.

[0019] According to a fifth aspect of the present invention, an embodiment of the present invention further provides a computer program product, the computer program product includes a computer program, and the computer program implements the medium-entropy steel manufacturing method according to any embodiment of the present invention when executed by a processor.

[0020] In the technical solution of the embodiment of the present invention, the laser metal deposition equipment is controlled based on the initial manufacturing parameters to manufacture medium-entropy steel, and the surface image and test results of the formed medium-entropy steel workpiece are obtained; when the test results do not meet the design requirements, the defect information of the medium-entropy steel workpiece is determined according to the surface image and the test results; the initial manufacturing parameters are corrected based on the defect information, and the laser metal deposition equipment is controlled based on the corrected manufacturing parameters to manufacture medium-entropy steel, and the steps of obtaining the surface image and the test results are continued until the design requirements are met, and the final manufacturing parameters are obtained. By establishing a closed-loop feedback control mechanism, real-time monitoring and adjustment are realized during the LMD processing, the manufacturing parameters are dynamically optimized, the dimensional accuracy and shape consistency of the medium-entropy steel workpiece are significantly improved, and the accuracy defect problem in traditional LMD manufacturing is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flowchart of a method for manufacturing medium-entropy steel according to Embodiment 1 of the present invention;

[0022] Figure 2 is an example diagram of defects in a method for manufacturing medium-entropy steel according to Embodiment 1 of the present invention;

[0023] Figure 3 is an example flowchart of a method for manufacturing medium-entropy steel according to Embodiment 1 of the present invention

[0024] Figure 4 is a schematic structural diagram of a medium-entropy steel manufacturing device according to Embodiment 2 of the present invention;

[0025] Figure 5 is a schematic structural diagram of an electronic device for implementing the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] Next, in combination with the drawings and specific embodiments, the present invention will be further described. It should be noted that, on the premise of no conflict, the following-described embodiments or technical features can be combined arbitrarily to form new embodiments.

[0027] Embodiment 1

[0028] Figure 1 is a flowchart of a method for manufacturing medium-entropy steel provided in Embodiment 1 of the present invention. This embodiment is applicable to the dynamic optimization of medium-entropy steel manufacturing parameters. This method can be executed by a medium-entropy steel manufacturing device, which can be implemented in the form of hardware and / or software, and the medium-entropy steel manufacturing device can be configured in an electronic device. As Figure 1 shown, the method includes:

[0029] S110. Control a laser metal deposition device to manufacture medium-entropy steel based on initial manufacturing parameters, and obtain the surface image and test results of the formed medium-entropy steel workpiece.

[0030] In this embodiment, the initial manufacturing parameters can be understood as the parameters used to control the laser metal deposition device for manufacturing, such as process parameters and iron element ratios. The laser metal deposition device can be understood as a device for manufacturing metal workpieces through laser. Medium-entropy steel can be understood as a new type of alloy material, and the content of different element characteristics will affect its hardness. For example, different iron element contents will result in different hardnesses of medium-entropy steel. The medium-entropy steel workpiece can be understood as a workpiece manufactured by a laser metal deposition device. The surface image can be understood as a high-frame-rate image of the surface of the medium-entropy steel workpiece. The test results can be understood as the results after testing the accuracy and performance of the medium-entropy steel workpiece.

[0031] Specifically, the processor can control the laser metal deposition device to manufacture medium-entropy steel based on the initial manufacturing parameters, and through the installed high-resolution industrial camera, monitor in real time in the LMD forming area to obtain the surface image of the formed medium-entropy steel workpiece. The processor can test the accuracy and performance of the medium-entropy steel workpiece through a testing device to obtain the test results.

[0032] Among them, the test results include the dimensional accuracy results and mechanical property test results of the medium-entropy steel workpiece.

[0033] In this embodiment, the dimensional accuracy results can be understood as the deviation results of the geometric dimensions of the medium-entropy steel workpiece. The mechanical property test results can be understood as the test results related to the material's own properties such as strength, toughness, and hardness.

[0034] Specifically, the combined dimensions of the medium-entropy steel workpiece can be measured by a coordinate measuring machine or a laser scanning device, and its deviation from the design model can be evaluated to test whether the dimensional tolerances of the key parts meet the requirements. The mechanical properties are tested by a material testing instrument (such as a tensile testing machine), for example, it can include strength, toughness, and hardness.

[0035] S120. When the test results do not meet the design requirements, determine the defect information of the medium-entropy steel workpiece according to the surface image and the test results.

[0036] In this embodiment, the design requirements can be understood as the accuracy and performance requirements of the workpiece. The defect information can be understood as the parts that do not meet the design requirements, such as the dimensions not meeting the requirements or there are defects in parts of the workpiece, etc.

[0037] Specifically, the processor can compare the test results with the design requirements. The test results should ensure that the medium-entropy steel parts meet the design requirements, and the performance test verifies whether the material has the set target performance indicators. By combining geometric measurement and microstructure analysis, the quality of the LMD formed parts can be comprehensively evaluated. When the test results do not meet the design requirements, the processor can analyze the surface image and the test results to locate the defect location and type, etc., constituting the defect information of the medium-entropy steel parts.

[0038] S130. Modify the initial manufacturing parameters based on the defect information, and control the laser metal deposition equipment to manufacture medium-entropy steel based on the modified manufacturing parameters. Continue to execute the steps of obtaining the surface image and the test results until the design requirements are met, and then obtain the final manufacturing parameters.

[0039] Specifically, the processor can determine the adjustment plan for the initial manufacturing parameters based on the defect information, and thus modify the initial manufacturing parameters. Then, control the laser metal deposition equipment to manufacture medium-entropy steel based on the modified manufacturing parameters, and obtain the surface image and the test results of the new medium-entropy steel parts to determine whether the medium-entropy steel parts manufactured this time meet the design requirements until the manufacturing parameters that meet the design requirements are obtained, so as to manufacture medium-entropy steel parts with better quality.

[0040] In the technical solution of the embodiment of the present invention, by controlling the laser metal deposition equipment to manufacture medium-entropy steel based on the initial manufacturing parameters, and obtaining the surface image and the test results of the formed medium-entropy steel parts; when the test results do not meet the design requirements, determine the defect information of the medium-entropy steel parts according to the surface image and the test results; modify the initial manufacturing parameters based on the defect information, and control the laser metal deposition equipment to manufacture medium-entropy steel based on the modified manufacturing parameters. Continue to execute the steps of obtaining the surface image and the test results until the design requirements are met, and then obtain the final manufacturing parameters. By establishing a closed-loop feedback control mechanism, real-time monitoring and adjustment are realized during the LMD processing, the manufacturing parameters are dynamically optimized, the dimensional accuracy and shape consistency of the medium-entropy steel parts are significantly improved, and the accuracy defect problem in traditional LMD manufacturing is solved.

[0041] Furthermore, on the basis of the above embodiment, the step of determining the defect information of the medium-entropy steel parts according to the surface image and the test results can be refined as:

[0042] Analyze the surface image through an image processing algorithm and combine it with the modeled defect database to determine the manufacturing defects of the medium-entropy steel parts; determine the accuracy defects based on the dimensional accuracy results and the medium-entropy steel design model; determine the mechanical defects of the medium-entropy steel parts according to the mechanical performance test results; determine the defect information of the medium-entropy steel parts according to the manufacturing defects, accuracy defects and mechanical defects.

[0043] In this embodiment, the image processing algorithm can be understood as an algorithm for image recognition, for example, it can include edge detection or pattern recognition, etc. The modeled defect database can be understood as a database including various types of defect data. For example, it includes real defect images (including different types of defects, such as cracks, holes, and lack of fusion, etc.); feature descriptions of defects (such as edge information, morphological features, and texture information); classification labels of defects (for pattern recognition and automated detection); it also includes defect data generated by simulation or manually labeled samples for training machine learning models. Manufacturing defects can be understood as defects in the manufactured parts that do not meet the design requirements due to processes, etc. during the manufacturing process. For example, it can include cracks, pores, discontinuous forming, and impurity inclusions, etc. Precision defects can be understood as defects in manufacturing precision. For example, it includes dimensional accuracy (the deviation of part dimensions from the tolerance range), shape accuracy (the deviation of geometric shapes (such as flatness, roundness) from the ideal form), and position accuracy (the error of the relative position of parts in space (such as coaxiality, parallelism), etc.). Mechanical defects can be understood as defects related to mechanical properties. For example, it can include insufficient yield, tensile strength, and hardness, or insufficient plasticity and unstable performance, etc. The design model can be understood as the most standard model and can be used as a dimensional reference. Defect information can be understood as the information formed by integrating all partial defects.

[0044] Specifically, the processor can analyze the surface image through the image processing algorithm and combine it with the modeled defect database to determine the type of defect to which the medium entropy steel part belongs and obtain manufacturing defects. The processor can compare based on the dimensional accuracy result and the medium entropy steel design model to determine precision defects. The processor can determine the mechanical defects of the medium entropy steel part according to the mechanical property test results and the set target performance indicators; and determine the defect information of the medium entropy steel part according to the manufacturing defects, precision defects, and mechanical defects.

[0045] Among them, the steps of determining the manufacturing defects of the medium entropy steel part by analyzing the surface image through the image processing algorithm and combining it with the modeled defect database can include:

[0046] Based on the image processing algorithm, defect detection is performed on the surface image to determine the defect area; based on the defect model, the defect area is matched and analyzed with the defect data included in the modeled defect database to determine the type of defect of the medium entropy steel part and obtain manufacturing defects.

[0047] In this embodiment, the defect area can be understood as the part of the surface image where there are defects. The defect model can be understood as the model used for retrieval in the modeled defect database. The defect type can be understood as the type to which the defect belongs.

[0048] Specifically, the processor can perform defect detection on the surface image based on an image processing algorithm, distinguish the background and defect areas included in the surface image. The processor can perform matching analysis on the defect area and the defect data included in the modeled defect database based on the defect model to determine the defect type of the medium-entropy steel workpiece and obtain manufacturing defects.

[0049] Exemplarily, a specific example is presented for illustration. Figure 2 FIG. is a defect example diagram in a medium-entropy steel manufacturing method provided in the first embodiment of the present invention, which is the result of recognizing a surface image through visual recognition technology (image processing algorithm), as Figure 2 shown. The upper left image is the result obtained by the image processing algorithm (including edge detection, morphological processing, or pattern recognition, etc.). Generally, this black-and-white contrast image is a binary image after enhanced contrast and grayscale conversion processing. Then, after denoising processing, threshold segmentation, and morphological processing, the defect contour is extracted, divided into black and white parts. The white part may represent the recognized crack or defect area, while the black part represents the background or non-defect area. This processing method can be used for automated defect detection and perform matching analysis through the defect model in the defect database to determine whether the defect type and determination accuracy meet the standards. The recognized result in the figure shows that the defect type is a crack.

[0050] Furthermore, based on the above embodiments, the step of correcting the initial manufacturing parameters based on the defect information and controlling the laser metal deposition equipment to manufacture medium-entropy steel based on the corrected manufacturing parameters, and continuing to execute the steps of obtaining the surface image and test results until the design requirements are met to obtain the final manufacturing parameters can be refined as follows:

[0051] Correct the process parameters in the initial manufacturing parameters according to the precision defects and manufacturing defects in the defect information; correct the iron element ratio in the initial manufacturing parameters according to the mechanical defects in the defect information; control the laser metal deposition equipment to manufacture medium-entropy steel based on the corrected manufacturing parameters, and continue to execute the steps of obtaining the surface image and test results until the design requirements are met to obtain the final manufacturing parameters.

[0052] In this embodiment, the process parameters can be understood as parameters related to the equipment manufacturing process, such as laser power: used to provide the energy required for melting the material; scanning speed: the speed at which the laser head moves, affecting the uniformity and cooling rate of the molten pool; powder feeding rate: the speed at which metal powder is transmitted through the nozzle. Among them, laser power is an important parameter that directly affects the temperature of the molten pool during the LMD process, and its change will affect the molding quality and the formation of defects. Too high laser power may cause the molten pool to overheat and melt too deeply, and high temperature may cause thermal cracks (such as liquefaction cracks); if the laser power is too low, the molten pool cannot fully melt the powder material, resulting in unfused defects and possible porosity. The scanning speed determines how fast the laser moves on the workpiece surface, which directly affects the shape, cooling rate and organizational structure of the molten pool. If the scanning speed is too fast, the shape of the molten pool becomes narrower and the molten pool cannot be fully expanded. If the cooling rate is too fast, it may cause residual stress to increase and cracks to occur. If the scanning speed is too slow, the molten pool becomes too wide, the material melts too much, and the cooling time is long, which may form a coarse grain structure and reduce material performance. The powder feeding rate determines the supply of metal powder in the molten pool. Its mismatch will lead to material waste or unstable molten pool. If the powder feeding rate is too high, too much powder will accumulate around the molten pool, causing the unmelted powder to aggregate, forming pores or inclusion defects, increasing the heat load of the molten pool, which may lead to prolonged cooling time, and then cause molten pool instability and defect expansion; if the powder feeding rate is too low, the metal powder supply in the molten pool is insufficient, which may lead to discontinuous forming, increased molten pool gaps and unfused defects.

[0053] In this embodiment, the iron element ratio can be understood as adjusting the iron content in the powder according to the alloy design requirements. The mechanical properties (such as strength, plasticity, and hardness) of medium entropy steel are significantly affected by the proportion of iron elements. A high proportion of iron elements will dilute the concentration of other alloying elements, resulting in a weakened solid solution strengthening effect, an increased tendency of grain coarsening, a reduction in the yield strength and tensile strength of the material, and an increase in plasticity, as well as a decrease in the hardness of the material; a low proportion of iron elements enhances the solid solution strengthening and precipitation strengthening effects, improves the strength and hardness of the material, but causes a decrease in the plasticity of the material; if the iron element is insufficient, it may affect the uniformity of the matrix structure, resulting in instability of performance.

[0054] Specifically, the processor can correct the process parameters in the initial manufacturing parameters according to the precision defects and manufacturing defects in the defect information. The processor can correct the iron element ratio in the initial manufacturing parameters according to the mechanical defects in the defect information. For example, when the mechanical defect is insufficient yield, tensile strength or hardness, the iron element ratio can be reduced; when the mechanical defect is insufficient plasticity or unstable performance, the iron element ratio can be increased. The processor can control the laser metal deposition equipment to manufacture medium-entropy steel based on the corrected manufacturing parameters, and continue to execute the steps of obtaining the surface image and test results until the design requirements are met, and finally obtain the final manufacturing parameters.

[0055] Among them, the step of correcting the process parameters in the initial manufacturing parameters according to the precision defects and manufacturing defects in the defect information can be refined as follows:

[0056] Correct the laser scanning range in the process parameters based on the precision defect; when the defect type in the manufacturing defect is a crack defect or a pore defect, correct the laser power in the process parameters;

[0057] When the defect type is discontinuous forming or impurity inclusion, correct the powder feeding rate in the process parameters.

[0058] Specifically, for precision defects, the processor can adjust the LMD process parameters, and the adjustment rules are as follows: if the dimensional accuracy is too large, reduce the laser scanning range; if the dimensional accuracy is too small, expand the laser scanning range; if the positional accuracy is insufficient, adjust the relative position of the substrate. For manufacturing defects, the processor can reduce the laser power and lower the scanning speed when the defect type is a crack; increase the laser power and increase the scanning speed when the defect type is a pore; increase the powder feeding rate when the defect type is discontinuous forming; when the defect type is impurity inclusion: reduce the powder feeding rate.

[0059] The technical solution of the embodiment of the present invention realizes real-time monitoring by acquiring the surface image and test results of the formed medium-entropy steel parts, and conducts defect analysis on the surface image and test results from multiple aspects, achieving a comprehensive evaluation of the size, material microstructure, and mechanical properties of the formed parts. When the parts do not meet the standards, the LMD process parameters are dynamically adjusted, and the adjusted laser power, scanning speed, powder feeding rate, and iron element ratio are input into the LMD equipment to continue generating medium-entropy steel parts, effectively optimizing the stability of the molten pool and the deposition accuracy, greatly improving the forming accuracy of the parts, ensuring the stability and consistency of the part quality, and using the closed-loop control method to feedback the adjusted process parameters to the manufacturing process in real time to achieve dynamic optimization. Finally, all forming parameters and iron element ratios are recorded to provide accurate reference data and experience support for subsequent manufacturing. It solves the accuracy and defect problems existing in the traditional LMD process, and also realizes continuous improvement through the feedback mechanism, providing a reliable and efficient technical route for the high-performance and high-precision manufacturing of medium-entropy steel parts, with broad industrial application prospects.

[0060] Exemplarily, on the basis of the above embodiments, a specific example can be used to elaborate on the overall steps of the method. Figure 3 FIG. is a schematic flow chart of an example of a method for manufacturing medium-entropy steel provided in Embodiment 1 of the present invention. As Figure 3 shown, the steps may include:

[0061] S301. Control the laser metal deposition equipment to manufacture medium-entropy steel based on manufacturing parameters;

[0062] S302. Acquire the surface image and test results of the formed medium-entropy steel parts;

[0063] S303. Whether the test results meet the design requirements. If so, jump to step S306; if not, jump to step S304;

[0064] S304. Determine the defect information based on the surface image and test results;

[0065] S305. Correct the laser power, scanning speed, powder feeding rate, and iron element ratio based on the defect information to obtain the corrected manufacturing parameters, and return to step S301;

[0066] S306. Obtain medium-entropy steel parts that meet the requirements;

[0067] S307. Record the final forming parameters and iron element ratio.

[0068] Embodiment 2

[0069] Figure 4 FIG. is a schematic structural diagram of a medium-entropy steel manufacturing device provided in Embodiment 2 of the present invention. As Figure 4As shown, the device includes: a result acquisition module 41, a defect determination module 42, and a parameter determination module 43.

[0070] The result acquisition module 41 is configured to control a laser metal deposition device to manufacture medium-entropy steel based on initial manufacturing parameters, and acquire a surface image and test results of the formed medium-entropy steel workpiece.

[0071] The defect determination module 42 is configured to determine defect information of the medium-entropy steel workpiece according to the surface image and the test results when the test results do not meet the design requirements.

[0072] The parameter determination module 43 is configured to correct the initial manufacturing parameters based on the defect information, and control the laser metal deposition device to manufacture medium-entropy steel based on the corrected manufacturing parameters, and continue to execute the steps of acquiring the surface image and the test results until the design requirements are met, and then obtain the final manufacturing parameters.

[0073] The technical solution of the embodiment of the present invention controls a laser metal deposition device to manufacture medium-entropy steel based on initial manufacturing parameters, and acquires a surface image and test results of the formed medium-entropy steel workpiece; when the test results do not meet the design requirements, defect information of the medium-entropy steel workpiece is determined according to the surface image and the test results; the initial manufacturing parameters are corrected based on the defect information, and the laser metal deposition device is controlled to manufacture medium-entropy steel based on the corrected manufacturing parameters, and the steps of acquiring the surface image and the test results are continued until the design requirements are met, and then the final manufacturing parameters are obtained. By establishing a closed-loop feedback control mechanism, real-time monitoring and adjustment are realized during the LMD processing, the manufacturing parameters are dynamically optimized, the dimensional accuracy and shape consistency of the medium-entropy steel workpiece are significantly improved, and the accuracy defect problem in traditional LMD manufacturing is solved.

[0074] Wherein, the test results include the dimensional accuracy results and mechanical property test results of the medium-entropy steel workpiece.

[0075] Further, the defect determination module 42 includes:

[0076] The first determination unit is configured to analyze the surface image through an image processing algorithm and combine a modeled defect database to determine the manufacturing defects of the medium-entropy steel workpiece when the test results do not meet the design requirements.

[0077] The second determination unit is configured to determine accuracy defects based on the dimensional accuracy results and the medium-entropy steel design model.

[0078] The third determination unit is configured to determine the mechanical defects of the medium-entropy steel workpiece according to the mechanical property test results.

[0079] A fourth determination unit, configured to determine defect information of the medium-entropy steel workpiece according to the manufacturing defect, the precision defect, and the mechanical defect.

[0080] Further, the first determination unit is specifically configured to:

[0081] Perform defect detection on the surface image based on the image processing algorithm to determine the defect area;

[0082] Match and analyze the defect area with the defect data included in the modeled defect database based on the defect model to determine the defect type of the medium-entropy steel workpiece and obtain the manufacturing defect.

[0083] Further, the parameter determination module 43 includes:

[0084] A first correction unit, configured to correct the process parameters in the initial manufacturing parameters according to the precision defect and the manufacturing defect in the defect information;

[0085] A second correction unit, configured to correct the iron element ratio in the initial manufacturing parameters according to the mechanical defect in the defect information;

[0086] A parameter determination unit, configured to control the laser metal deposition equipment to manufacture medium-entropy steel based on the corrected manufacturing parameters, and continue to execute the steps of obtaining the surface image and the test result until the design requirements are met, and then obtain the final manufacturing parameters.

[0087] Wherein, the first correction unit is specifically configured to:

[0088] Correct the laser scanning range in the process parameters based on the precision defect;

[0089] When the defect type in the manufacturing defect is a crack defect or a pore defect, correct the laser power in the process parameters;

[0090] When the defect type is discontinuous forming or impurity inclusion, correct the powder feeding rate in the process parameters.

[0091] The video tracking device provided by the embodiments of the present invention can execute the medium-entropy steel manufacturing method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0092] Embodiment III

[0093] Figure 5FIG. 0 shows a schematic structural diagram of an electronic device 50 that can be used to implement an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0094] As Figure 5 shown, the electronic device 50 includes at least one processor 51 and a memory communicatively connected to the at least one processor 51, such as a read-only memory (ROM) 52, a random access memory (RAM) 53, etc. The memory stores a computer program executable by the at least one processor. The processor 51 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 52 or the computer program loaded from the storage unit 58 into the random access memory (RAM) 53. In the RAM 53, various programs and data required for the operation of the electronic device 50 can also be stored. The processor 51, the ROM 52, and the RAM 53 are connected to each other via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0095] A plurality of components in the electronic device 50 are connected to the I / O interface 55, including: an input unit 56, such as a keyboard, a mouse, etc.; an output unit 57, such as various types of displays, speakers, etc.; a storage unit 58, such as a magnetic disk, an optical disk, etc.; and a communication unit 59, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 59 allows the electronic device 50 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0096] The processor 51 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 51 executes the various methods and processes described above, such as the medium entropy steel manufacturing method.

[0097] In some embodiments, the method for manufacturing medium entropy steel can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 58. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 50 via the ROM 52 and / or the communication unit 59. When the computer program is loaded into the RAM 53 and executed by the processor 51, one or more steps of the medium entropy steel manufacturing method described above can be performed. Alternatively, in other embodiments, the processor 51 can be configured to execute the medium entropy steel manufacturing method by any other suitable means (e.g., by means of firmware).

[0098] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0099] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0100] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0101] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0102] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0103] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is created by computer programs that run on respective computers and have a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0104] In one embodiment, the embodiment of the present invention further includes a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the medium-entropy steel manufacturing method of any embodiment of the present invention.

[0105] In the process of implementation, the computer program product can write computer program code for performing the operations of the present invention in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0106] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0107] The above embodiments are only the preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantive changes and substitutions made by those skilled in the art based on the present invention fall within the scope of protection required by the present invention.

Claims

1. A method for manufacturing medium-entropy steel, characterized in that, Including: Controlling a laser metal deposition device to manufacture medium-entropy steel based on initial manufacturing parameters, and obtaining a surface image and test results of the formed medium-entropy steel workpiece; When the test results do not meet the design requirements, determining defect information of the medium-entropy steel workpiece according to the surface image and the test results; Based on the defect information, correcting the initial manufacturing parameters, and controlling the laser metal deposition device to manufacture medium-entropy steel based on the corrected manufacturing parameters, and continuing to execute the steps of obtaining the surface image and the test results until the design requirements are met, and obtaining the final manufacturing parameters.

2. The method according to claim 1, characterized in that, The test results include the dimensional accuracy results and mechanical property test results of the medium-entropy steel workpiece.

3. The method according to claim 2, wherein The determining the defect information of the medium-entropy steel workpiece according to the surface image and the test results includes: Analyzing the surface image through an image processing algorithm and combining with a modeled defect database to determine the manufacturing defects of the medium-entropy steel workpiece; Based on the dimensional accuracy results and the medium-entropy steel design model, determining accuracy defects; According to the mechanical property test results, determining the mechanical defects of the medium-entropy steel workpiece; According to the manufacturing defects, the accuracy defects and the mechanical defects, determining the defect information of the medium-entropy steel workpiece.

4. The method according to claim 3, characterized in that, The analyzing the surface image through an image processing algorithm and combining with a modeled defect database to determine the manufacturing defects of the medium-entropy steel workpiece includes: Based on the image processing algorithm, detecting defects in the surface image to determine the defect area; Based on the defect model, matching and analyzing the defect area with the defect data included in the modeled defect database to determine the defect type of the medium-entropy steel workpiece to obtain manufacturing defects.

5. The method according to claim 1, characterized in that, The correcting the initial manufacturing parameters based on the defect information, and controlling the laser metal deposition device to manufacture medium-entropy steel based on the corrected manufacturing parameters, and continuing to execute the steps of obtaining the surface image and the test results until the design requirements are met, and obtaining the final manufacturing parameters includes: Correcting the process parameters in the initial manufacturing parameters according to the accuracy defects and manufacturing defects in the defect information; According to the mechanical defects in the defect information, correcting the iron element ratio in the initial manufacturing parameters; Based on the corrected manufacturing parameters, controlling the laser metal deposition device to manufacture medium-entropy steel, and continuing to execute the steps of obtaining the surface image and the test results until the design requirements are met, and obtaining the final manufacturing parameters.

6. The method according to claim 5, characterized in that The correcting the process parameters in the initial manufacturing parameters according to the accuracy defects and manufacturing defects in the defect information includes: Based on the accuracy defects, correcting the laser scanning range in the process parameters; When the defect type in the manufacturing defects is a crack defect or a pore defect, correcting the laser power in the process parameters; When the defect type is discontinuous forming or impurity inclusion, correcting the powder feeding rate in the process parameters.

7. A medium-entropy steel manufacturing device, characterized in that The device includes: A result acquisition module, configured to control a laser metal deposition device to manufacture medium-entropy steel based on initial manufacturing parameters, and acquire a surface image and test results of the formed medium-entropy steel workpiece; A defect determination module, configured to determine defect information of the medium-entropy steel workpiece according to the surface image and the test results when the test results do not meet the design requirements; A parameter determination module, configured to correct the initial manufacturing parameters based on the defect information, and control the laser metal deposition device to manufacture medium-entropy steel based on the corrected manufacturing parameters, and continue to execute the steps of acquiring the surface image and the test results until the design requirements are met, and obtain the final manufacturing parameters.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the medium-entropy steel manufacturing method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a processor to execute the medium-entropy steel manufacturing method according to any one of claims 1-6 when executed.

10. A computer program product, characterized in that, The computer program product includes a computer program which, when executed by a processor, implements the medium-entropy steel manufacturing method according to any one of claims 1-6.

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