Electric arc additive manufacturing process for isomeric low-alloy high-strength steel structure

Abstract

The invention discloses an isomeric low-alloy high-strength steel structure electric arc additive manufacturing process, and relates to the technical field of electric arc additive manufacturing, the isomeric low-alloy high-strength steel structure electric arc additive manufacturing process comprises the following steps: carrying out heat treatment and tensile test on a low-alloy high-strength steel wire to obtain test data, and establishing a process parameter-performance prediction model according to the test data; obtaining a performance demand of a target structural member, and drawing up a process parameter by using a process parameter-performance prediction model according to the performance demand; an electric arc additive manufacturing system is used for conducting electric arc additive manufacturing on the low-alloy high-strength steel wire, and a low-alloy high-strength steel structural part is obtained; acquiring temperature information of the low-alloy high-strength steel structural member by using an infrared thermal imager, and adjusting the process parameters in real time according to the temperature information to obtain a target structural member; gradient distribution of precipitation strengthening phases is achieved, isomerism of alloy element in-situ strengthening low-alloy high-strength steel materials is achieved, and the diversified requirements of target structural parts for different strengths and plasticity are met.

Description

Technical Field

[0001] The present invention relates to the technical field of arc additive manufacturing, and in particular to an arc additive manufacturing process for a homogeneous and heterogeneous low-alloy high-strength steel structure. Background Art

[0002] In recent years, high-strength steel has been widely used in many fields such as weapons, ships, and energy due to its high strength characteristics, and has become one of the new engineering materials that are booming. However, traditional ultra-high-strength steel usually adds a large amount of expensive alloying elements such as Co and Ni. Taking 18Ni maraging steel and Ni-Co system secondary hardening ultra-high-strength steel as examples, the widespread use of such expensive materials is severely restricted by cost and cannot meet the needs of commercial and mass production. Therefore, the development of low-cost, high-performance high-strength steel has become a research hotspot in this field. Arc additive manufacturing technology, as an emerging manufacturing process, can well solve the difficulties of traditional manufacturing methods in manufacturing complex structural parts. Compared with other additive manufacturing technologies, arc additive manufacturing technology has significant characteristics such as high deposition efficiency, low cost, and simple process, so it has been widely used in related fields.

[0003] At present, a Chinese invention with publication number CN112287489B discloses a method for determining process parameters of arc fuse additive manufacturing based on multi-physical field simulation. Although it can determine the target process parameters corresponding to the target formed part and improve the manufacturing accuracy, it cannot achieve the gradient distribution of the precipitation strengthening phase, nor can it meet the diverse needs of structural parts for different strengths and shapes. Summary of the invention

[0004] The technical problem solved by the present invention is that the prior art cannot achieve the gradient distribution of the precipitation strengthening phase, and cannot meet the diverse demands of structural parts for different strengths and plasticity.

[0005] In order to solve the above technical problems, in a first aspect, the present invention provides an arc additive process for isomorphous low-alloy high-strength steel structure, comprising the following steps:

[0006] Step S1, performing heat treatment and tensile test on a low alloy high strength steel wire to obtain test data of the low alloy high strength steel wire, and establishing a process parameter-performance prediction model according to the test data;

[0007] Step S2, obtaining the performance requirements of the target structural part, and formulating the process parameters using the process parameter-performance prediction model according to the performance requirements;

[0008] Step S3, performing arc additive manufacturing on the low-alloy high-strength steel wire by using an arc additive manufacturing system to obtain a low-alloy high-strength steel structural part;

[0009] Step S4, using an infrared thermal imager to obtain temperature information of the low-alloy high-strength steel structural part, and adjusting the process parameters in real time according to the temperature information to obtain a target structural part;

[0010] As a preferred solution of the arc additive manufacturing process for isomorphous low-alloy high-strength steel structure described in the present invention, wherein:

[0011] The step S1 specifically includes the following steps:

[0012] Step S101, normalizing three times and air cooling m times on a low alloy high strength steel wire material in-situ strengthened by alloying elements, wherein m is a natural number greater than 1, obtaining a first low alloy high strength steel, setting gradient tempering parameters for the first low alloy high strength steel, and tempering the first low alloy high strength steel according to the gradient tempering parameters to obtain a second low alloy high strength steel, wherein the gradient tempering parameters include gradient tempering temperature and gradient tempering time;

[0013] Step S102, using a transmission electron microscope to perform TEM analysis on the second low-alloy high-strength steel to obtain precipitation phase information, performing a tensile test on the second low-alloy high-strength steel to obtain tensile property information of the second low-alloy high-strength steel, and performing a one-to-one correspondence between the precipitation phase information, the tensile property information, and the gradient tempering parameter to obtain a first mapping relationship, wherein the precipitation phase information includes precipitation phase density, precipitation phase size, and precipitation phase mismatch, and the tensile property information includes elastic limit, elongation, elastic modulus, proportional limit, area reduction, tensile strength, yield point, and yield strength;

[0014] Step S103, adjusting the gradient tempering parameters, and repeating steps S101 to S102, obtaining n groups of test data, wherein n is a natural number, storing the test data in a database, and setting corresponding process parameters according to the gradient tempering parameters to establish a process library for low-alloy high-strength steel, and extracting tensile property information in the database as input, extracting gradient tempering parameters in the database as output, and training a machine learning model using a support vector machine algorithm and a genetic algorithm until the model fit is greater than or equal to a first expected threshold, thereby obtaining a process parameter-performance prediction model;

[0015] As a preferred solution of the arc additive manufacturing process for isomorphous low-alloy high-strength steel structure described in the present invention, wherein:

[0016] The step S2 specifically includes the following steps:

[0017] Acquire performance requirements of a target structural component, input the performance requirements into a process parameter-performance prediction model, acquire target gradient tempering parameters of the structural component, input the target gradient tempering parameters into a process library to acquire first process parameters, wherein the process parameters include an interlayer cooling rate, a printing strategy, and an external preheating substrate temperature;

[0018] As a preferred solution of the arc additive manufacturing process for isomorphous low-alloy high-strength steel structure described in the present invention, wherein:

[0019] The step S4 specifically comprises the following steps:

[0020] Performing arc additive manufacturing on a low-alloy high-strength steel wire according to a first process parameter to obtain a target low-alloy high-strength steel structure, and using an infrared thermal imager to obtain an infrared radiation energy distribution diagram of the target low-alloy high-strength steel structure in real time, obtaining temperature information of the target low-alloy high-strength steel structure through the infrared radiation energy distribution diagram, inputting the temperature information into a process library to obtain a second process parameter, and performing arc additive manufacturing through the second process parameter;

[0021] As a preferred solution of the arc additive manufacturing process for isomorphous low-alloy high-strength steel structure described in the present invention, wherein:

[0022] The process parameter-performance prediction model obtained by using support vector machine algorithm and genetic algorithm includes:

[0023] Extract the gradient tempering parameters and the corresponding tensile performance information in the test data and merge them into a sample training set, divide the sample training set according to a certain ratio to obtain a training set and a test set, encode the kernel function type, kernel parameters and penalty factor of the support vector machine by binary coding, randomly generate an initial population according to the training set, use the support vector machine algorithm to perform regression prediction according to the training sample, use the tensile performance information as the prediction input, and use the gradient tempering parameters as the prediction output, use the mean square error formula to obtain the fitness value of each initial population, perform genetic operator operations of selection, crossover and mutation on the initial population, generate the next generation population, until the fitness value of the next generation population is greater than or equal to the second expected threshold, then output the optimal kernel parameters, and use the optimal parameters to establish a primary process parameter-performance prediction model, input the test set into the primary process parameter-performance prediction model for testing, and obtain the model fit, if the model fit is greater than or equal to the first expected threshold, then obtain the process parameter-performance prediction model, if the model fit is less than the first expected threshold, then adjust the model parameters;

[0024] As a preferred solution of the arc additive manufacturing process for isomorphous low-alloy high-strength steel structure described in the present invention, wherein:

[0025] The mathematical expression of the mean square error formula is as follows:

[0026]

[0027] Among them, E is the fitness value, n is the population size, y i is the actual value, y i is the predicted value;

[0028] As a preferred solution of the arc additive manufacturing process for isomorphous low-alloy high-strength steel structure described in the present invention, wherein:

[0029] The test data includes gradient tempering parameters, precipitation phase information and tensile property information;

[0030] The temperature information includes the temperature of each position of the target low-alloy high-strength steel structural part;

[0031] As a preferred solution of the arc additive manufacturing process for isomorphous low-alloy high-strength steel structure described in the present invention, wherein:

[0032] An arc additive manufacturing system for isomorphous and heterogeneous low-alloy high-strength steel structural parts comprises a digital control system, a wire feeder, a welding robot, an arc welding power source, a welding gun, a workbench, a fixture, an infrared thermal imager and a computer.

[0033] In a second aspect, the present invention provides an electronic device, comprising: a processor and a memory, wherein:

[0034] The memory stores a computer program that can be called by the processor;

[0035] The processor executes any one of the above-mentioned arc additive processes for isomorphous low-alloy high-strength steel structures in the background by calling the computer program stored in the memory.

[0036] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the arc additive manufacturing process for isomorphous low-alloy high-strength steel structures described in any one of the above items is implemented.

[0037] Beneficial effects of the present invention: The present invention obtains the gradient tempering parameters, tensile performance information and precipitation phase information of the low-alloy high-strength steel wire by performing heat treatment and tensile testing on the low-alloy high-strength steel additive, establishes a first mapping relationship according to the gradient tempering parameters, tensile performance information and precipitation phase information, establishes a process parameter-performance prediction model according to the tensile performance information and the gradient tempering parameters using a vector machine algorithm and a genetic algorithm, and obtains the target gradient tempering parameters according to the performance requirements of the target structural parts using the process parameter-performance prediction model for arc additive manufacturing, thereby improving the accuracy of arc additive manufacturing, adjusting the process parameters in real time during the arc additive manufacturing process, utilizing the forming characteristics of arc additive manufacturing, regulating the thermal history of different positions of the structural parts, realizing the gradient distribution of the precipitation strengthening phase, and realizing the in-situ strengthening of the isomorphism of the low-alloy high-strength steel materials by alloy elements, thereby meeting the diversified requirements of the target structural parts for different strengths and plasticities. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of the basic process of arc additive manufacturing of a homogeneous low-alloy high-strength steel structure provided by one embodiment of the present invention;

[0039] Figure 2 A variation trend of the tensile strength of a homogeneous low-alloy high-strength steel structure provided by an embodiment of the present invention;

[0040] Figure 3 A variation trend of the elongation of a homogeneous low-alloy high-strength steel structure provided by one embodiment of the present invention;

[0041] Figure 4 The present invention provides a performance test result of a target structural component of isomorphous low-alloy high-strength steel according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0043] Example 1, reference Figure 1-Figure 4 , as an embodiment of the present invention, provides an arc additive manufacturing process for isomorphous low-alloy high-strength steel structure, comprising the following steps:

[0044] Step S1, performing heat treatment and tensile test on low alloy high strength steel wire to obtain test data of the low alloy high strength steel wire, and establishing a process parameter-performance prediction model according to the test data;

[0045] Step S2, obtaining the performance requirements of the target structural part, and formulating the process parameters using the process parameter-performance prediction model according to the performance requirements;

[0046] Step S3, using an arc additive manufacturing system to perform arc additive manufacturing on the low-alloy high-strength steel wire to obtain a low-alloy high-strength steel structure;

[0047] Step S4, using an infrared thermal imager to obtain temperature information of the low-alloy high-strength steel structural part, and adjusting the process parameters in real time according to the temperature information to obtain the target structural part.

[0048] In this embodiment, the gradient tempering parameters, tensile performance information and precipitation phase information of the low alloy high strength steel wire are obtained by performing heat treatment and tensile test on the low alloy high strength steel additive, a first mapping relationship is established according to the gradient tempering parameters, tensile performance information and precipitation phase information, a process parameter-performance prediction model is established according to the tensile performance information and the gradient tempering parameters using a vector machine algorithm and a genetic algorithm, and the target gradient tempering parameters are obtained according to the performance requirements of the target structural parts using the process parameter-performance prediction model for arc additive manufacturing, thereby improving the accuracy of arc additive manufacturing, adjusting the process parameters in real time during the arc additive manufacturing process, utilizing the forming characteristics of arc additive manufacturing, regulating the thermal history of different positions of the structural parts, realizing the gradient distribution of the precipitation strengthening phase, and realizing the in-situ strengthening of the isomorphism of the low alloy high strength steel materials by alloy elements, thereby meeting the diversified requirements of the target structural parts for different strengths and plasticities.

[0049] In one embodiment, the temperature is divided into gradients, and the process parameters at different temperatures are recorded, and the temperature information is mapped one by one with the process parameters. The following table is a mapping relationship table of arc additive manufacturing process parameters;

[0050]

[0051]

[0052] Step S1 specifically includes the following steps:

[0053] Step S101, normalizing three times and air cooling m times for a low-alloy high-strength steel wire material in-situ strengthened by alloying elements, wherein m is a natural number greater than 1, obtaining a first low-alloy high-strength steel, setting gradient tempering parameters for the first low-alloy high-strength steel, and tempering the first low-alloy high-strength steel according to the gradient tempering parameters to obtain a second low-alloy high-strength steel, wherein the gradient tempering parameters include gradient tempering temperature and gradient tempering time;

[0054] Step S102, using a transmission electron microscope to perform TEM analysis on the second low alloy high strength steel to obtain precipitation phase information, performing a tensile test on the second low alloy high strength steel to obtain tensile property information of the second low alloy high strength steel, and performing one-to-one correspondence between the precipitation phase information, the tensile property information and the gradient tempering parameter to obtain a first mapping relationship, wherein the precipitation phase information includes precipitation phase density, precipitation phase size and precipitation phase mismatch, and the tensile property information includes elastic limit, elongation, elastic modulus, proportional limit, area reduction, tensile strength, yield point and yield strength;

[0055] Step S103, adjust the gradient tempering parameters, and repeat steps S101 to S102 to obtain n groups of test data, where n is a natural number, store the test data in a database, and set corresponding process parameters according to the gradient tempering parameters to establish a process library for low-alloy high-strength steel, extract tensile performance information in the database as input, extract the gradient tempering parameters in the database as output, and use the support vector machine algorithm and genetic algorithm to train the machine learning model until the model fit is greater than or equal to the first expected threshold, then obtain the process parameter-performance prediction model.

[0056] In this embodiment, strong carbide-forming elements such as V, Ti, and Nb are selected as in-situ strengthening phases, and the mismatch of the precipitated phase is controlled at 0.03%-0.5%, wherein the composition range of C is 0.1-0.3%, the composition range of V is 0.2-1.5%, the composition range of Ti is 0.05-0.3%, and the remaining content is Fe, trace Al, and trace Mo.

[0057] In this embodiment, the gradient tempering temperature is set as a gradient tempering temperature every 5 degrees Celsius starting from 100°C until the gradient tempering temperature reaches 300°C, and the gradient tempering time is set as a gradient tempering time every 0.5 hour starting from 0.5 hour until it reaches 6 hours.

[0058] In this embodiment, the precipitated phase density is (10 23 -10 24 )m -3 , the size of the precipitated phase is 2-20nm, and the mismatch of the precipitated phase is 0.03%-0.5%.

[0059] In this embodiment, TEM analysis is a high-resolution electron microscope technique, which is mainly used to observe the microstructure and composition of materials.

[0060] In this embodiment, the support vector machine algorithm is a type of machine learning algorithm that performs binary classification on data in a supervised learning manner.

[0061] In this embodiment, the genetic algorithm is a computational model of biological evolution process that simulates the natural selection and genetic mechanism of Darwin's theory of biological evolution, and is a method for searching for the optimal solution by simulating the natural evolution process.

[0062] In this embodiment, setting the process parameters according to the gradient tempering parameters includes:

[0063] The heat input is calculated by the heat formula, and the welding current (150-300A) and the travel speed (5-15mm / s) are adjusted in real time. The mathematical expression of the heat formula is as follows:

[0064] Q = ηUI / v (η = 0.8-0.85)

[0065] Among them, Q is the heat input, U is the welding voltage, I is the welding current, v is the travel speed, and η ranges from 0.8 to 0.85.

[0066] A double pulse waveform is used, with a base current of 80-120A to maintain a stable molten pool and a peak current of 200-350A to control the melting depth.

[0067] After each layer was deposited, there was a delay of 5-10 seconds, the temperature field was scanned by a FLIR A65 infrared camera (accuracy ±2°C), and the lowest point temperature was extracted as the control benchmark.

[0068] Adaptive cooling strategy:

[0069] When the actual temperature is higher than the set temperature, start compressed air cooling (flow rate 10-20m 3 / h)

[0070] When the actual temperature is lower than the set temperature, local induction heating (frequency 50kHz, power 3-5kW) is enabled.

[0071] In this embodiment, the first expected threshold is 95%.

[0072] In this embodiment, heat treatment and tensile tests are carried out on low-alloy high-strength steel wire to obtain gradient tempering parameters, tensile performance information and precipitation phase information, and a process parameter-performance prediction model is established using a vector machine algorithm and a genetic algorithm based on the tensile performance information and gradient tempering parameters, so as to provide accurate and reliable model support and process library support for obtaining target gradient tempering parameters and process parameters according to the performance requirements of target structural parts and realizing the gradient distribution of precipitation strengthening phases.

[0073] Step S2 specifically includes the following steps:

[0074] The performance requirements of the target structural part are obtained, the performance requirements are input into a process parameter-performance prediction model, the target gradient tempering parameters of the structural part are obtained, the target gradient tempering parameters are input into a process library to obtain first process parameters, and the process parameters include an interlayer cooling rate, a printing strategy, and an external preheating substrate temperature.

[0075] In this embodiment, the first process parameters are obtained by using a process parameter-performance prediction model and a process library according to the performance requirements of the target structural parts, providing detailed and accurate process parameters for achieving the isomorphism of low-alloy high-strength steel materials by in-situ strengthening of alloy elements and meeting the diverse requirements of the target structural parts for different strengths and plasticities.

[0076] Step S4 specifically includes the following steps:

[0077] According to the first process parameters, arc additive is performed on the low-alloy high-strength steel wire to obtain a target low-alloy high-strength steel structure, and an infrared thermal imager is used to obtain an infrared radiation energy distribution map of the target low-alloy high-strength steel structure in real time. The temperature information of the target low-alloy high-strength steel structure is obtained through the infrared radiation energy distribution map, and the temperature information is input into the process library to obtain the second process parameters, and arc additive is performed according to the second process parameters.

[0078] In this embodiment, an infrared thermal imager is used to detect the temperature information of the target low-alloy high-strength steel structural part in real time, and the second process parameter is adjusted in real time using the process library according to the temperature information, thereby ensuring the accuracy of the process of manufacturing the target structural part and ensuring the quality of the target structural part on the basis of meeting the multi-strength and multi-plasticity requirements of the target structural part.

[0079] The process parameter-performance prediction model obtained by using support vector machine algorithm and genetic algorithm includes:

[0080] The gradient tempering parameters and the corresponding tensile performance information in the test data are extracted and merged into a sample training set, and the sample training set is divided according to a certain ratio to obtain a training set and a test set. The kernel function type, kernel parameters and penalty factor of the support vector machine are encoded by binary coding. An initial population is randomly generated according to the training set, and regression prediction is performed according to the training sample using a support vector machine algorithm. The tensile performance information is used as a prediction input, and the gradient tempering parameters are used as a prediction output. The fitness value of each initial population is obtained by using a mean square error formula, and the genetic operator operations of selection, crossover and mutation are performed on the initial population to generate the next generation population. When the fitness value of the next generation population is greater than or equal to the second expected threshold, the optimal kernel parameters are output, and a primary process parameter-performance prediction model is established using the optimal parameters. The test set is input into the primary process parameter-performance prediction model for testing to obtain the model fit. If the model fit is greater than or equal to the first expected threshold, the process parameter-performance prediction model is obtained. If the model fit is less than the first expected threshold, the model parameters are adjusted.

[0081] In this embodiment, a support vector machine algorithm and a genetic algorithm are used to obtain a process parameter-performance prediction model, and accurate and reliable model support is provided to obtain target gradient tempering parameters and process parameters according to the performance requirements of the target structural parts and to achieve the gradient distribution of the precipitation strengthening phase.

[0082] The mathematical expression of the mean square error formula is as follows:

[0083]

[0084] Among them, E is the fitness value, n is the population size, y i is the actual value, y i is the predicted value.

[0085] In this embodiment, the mean square error formula is used to obtain the fitness value of each initial population, provide optimal kernel parameters for establishing a process parameter performance prediction model, and ensure the accuracy of the model parameters.

[0086] The test data include gradient tempering parameters, precipitation phase information and tensile properties information;

[0087] The temperature information includes the temperature of each position of the target low alloy high strength steel structural part.

[0088] Embodiment 2, an arc additive manufacturing system for isomorphous low-alloy high-strength steel structural parts, includes: a digital control system, a wire feeder, a welding robot, an arc welding power supply, a welding gun, a workbench, a fixture, an infrared thermal imager and a computer.

[0089] In this embodiment, a performance test is performed on the target structural part obtained after arc additive manufacturing, and the test results show that both the bottom of the target structural part and the top of the target structural part meet the performance requirements.

[0090] In this embodiment, an arc additive manufacturing system is used to perform additive manufacturing according to process parameters, and the wire feeding speed of the wire feeder, the movement path and speed of the welding robot, and the output parameters of the arc welding power supply are precisely controlled to ensure stable burning of the arc and uniform melting and deposition of the wire. At the same time, the welding robot performs layer-by-layer accumulation according to a preset path to ensure the accurate generation of the target structural parts.

[0091] In this embodiment, the gradient tempering parameters, tensile performance information and precipitation phase information of the low alloy high strength steel wire are obtained by performing heat treatment and tensile test on the low alloy high strength steel additive, and a first mapping relationship is established according to the tensile performance information and the precipitation phase information, a process parameter-performance prediction model is established according to the tensile performance information and the gradient tempering parameters using a vector machine algorithm and a genetic algorithm, and the target gradient tempering parameters are obtained according to the performance requirements of the target structural parts using the first mapping relationship and the process parameter-performance prediction model for arc additive manufacturing, thereby improving the accuracy of arc additive manufacturing, adjusting the process parameters in real time during the arc additive manufacturing process, utilizing the forming characteristics of arc additive manufacturing, regulating the thermal history of different positions of the structural parts, realizing the gradient distribution of the precipitation strengthening phase, and realizing the in-situ strengthening of the isomorphism of the low alloy high strength steel materials by alloy elements, thereby meeting the diversified requirements of the target structural parts for different strengths and plasticities.

[0092] It should be understood by those skilled in the art that the embodiments of the present invention can be provided as methods, systems or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An arc additive process for isomorphous low-alloy high-strength steel structure, characterized in that: The following steps are involved: Step S1, performing heat treatment and tensile test on a low alloy high strength steel wire to obtain test data of the low alloy high strength steel wire, and establishing a process parameter-performance prediction model according to the test data; Step S2, obtaining the performance requirements of the target structural part, and formulating the process parameters using the process parameter-performance prediction model according to the performance requirements; Step S3, performing arc additive manufacturing on the low-alloy high-strength steel wire by using an arc additive manufacturing system to obtain a low-alloy high-strength steel structural part; Step S4, using an infrared thermal imager to obtain temperature information of the low-alloy high-strength steel structural part, and adjusting the process parameters in real time according to the temperature information to obtain a target structural part.

2. The arc additive process for isomorphous low-alloy high-strength steel structure according to claim 1, characterized in that: The step S1 specifically includes the following steps: Step S101, normalizing three times and air cooling m times on a low alloy high strength steel wire material in-situ strengthened by alloying elements, wherein m is a natural number greater than 1, obtaining a first low alloy high strength steel, setting gradient tempering parameters for the first low alloy high strength steel, and tempering the first low alloy high strength steel according to the gradient tempering parameters to obtain a second low alloy high strength steel, wherein the gradient tempering parameters include gradient tempering temperature and gradient tempering time; Step S102, using a transmission electron microscope to perform TEM analysis on the second low-alloy high-strength steel to obtain precipitation phase information, performing a tensile test on the second low-alloy high-strength steel to obtain tensile property information of the second low-alloy high-strength steel, and performing a one-to-one correspondence between the precipitation phase information, the tensile property information, and the gradient tempering parameter to obtain a first mapping relationship, wherein the precipitation phase information includes precipitation phase density, precipitation phase size, and precipitation phase mismatch, and the tensile property information includes elastic limit, elongation, elastic modulus, proportional limit, area reduction, tensile strength, yield point, and yield strength; Step S103, adjust the gradient tempering parameters, and repeat steps S101 to S102 to obtain n groups of test data, where n is a natural number, store the test data in a database, set corresponding process parameters according to the gradient tempering parameters to establish a process library for low-alloy high-strength steel, extract tensile performance information in the database as input, extract gradient tempering parameters in the database as output, train the machine learning model using a support vector machine algorithm and a genetic algorithm until the model fit is greater than or equal to a first expected threshold, and then obtain a process parameter-performance prediction model.

3. The arc additive process for isomorphous low alloy high strength steel structure according to claim 1, characterized in that: The step S2 specifically includes the following steps: The performance requirements of the target structural part are obtained, the performance requirements are input into a process parameter-performance prediction model, the target gradient tempering parameters of the structural part are obtained, the target gradient tempering parameters are input into a process library to obtain first process parameters, and the process parameters include an interlayer cooling rate, a printing strategy, and an external preheating substrate temperature.

4. The arc additive process for isomorphous low alloy high strength steel structure according to claim 1, characterized in that: The step S4 specifically comprises the following steps: According to the first process parameters, arc additive is performed on the low-alloy high-strength steel wire to obtain a target low-alloy high-strength steel structure, and an infrared radiation energy distribution diagram of the target low-alloy high-strength steel structure is obtained in real time using an infrared thermal imager, and temperature information of the target low-alloy high-strength steel structure is obtained through the infrared radiation energy distribution diagram, and the temperature information is input into a process library to obtain a second process parameter, and arc additive is performed using the second process parameter.

5. The arc additive process for isomorphous low-alloy high-strength steel structure according to claim 1, characterized in that: The process parameter-performance prediction model obtained by using support vector machine algorithm and genetic algorithm includes: The gradient tempering parameters and the corresponding tensile performance information in the test data are extracted and merged into a sample training set, and the sample training set is divided according to a certain ratio to obtain a training set and a test set. The kernel function type, kernel parameters and penalty factor of the support vector machine are encoded by binary coding. An initial population is randomly generated according to the training set, and regression prediction is performed according to the training sample using a support vector machine algorithm. The tensile performance information is used as a prediction input, and the gradient tempering parameters are used as a prediction output. The fitness value of each initial population is obtained by using a mean square error formula, and the genetic operator operations of selection, crossover and mutation are performed on the initial population to generate a next generation population. When the fitness value of the next generation population is greater than or equal to a second expected threshold, the optimal kernel parameters are output, and a primary process parameter-performance prediction model is established using the optimal parameters. The test set is input into the primary process parameter-performance prediction model for testing to obtain the model fit. If the model fit is greater than or equal to the first expected threshold, the process parameter-performance prediction model is obtained. If the model fit is less than the first expected threshold, the model parameters are adjusted.

6. The arc additive process for isomorphous low alloy high strength steel structure according to claim 1, characterized in that: The mathematical expression of the mean square error formula is as follows: Among them, E is the fitness value, n is the population size, y i is the actual value, is the predicted value.

7. The arc additive process for isomorphous low alloy high strength steel structure according to claim 1, characterized in that: The test data includes gradient tempering parameters, precipitation phase information and tensile property information; The temperature information includes the temperature of each position of the target low-alloy high-strength steel structural part.

8. The arc additive manufacturing process for isomeric low alloy high strength steel structure according to claim 1 comprises an arc additive manufacturing system for isomeric low alloy high strength steel structure, characterized in that: include: It includes digital control system, wire feeder, welding robot, arc welding power supply, welding gun, workbench, fixture, infrared thermal imager and computer.

9. An electronic device, characterized in that: include: A processor and a memory, wherein: The memory stores a computer program that can be called by the processor; The processor executes the arc additive process for isomorphous low-alloy high-strength steel structure described in any one of claims 1 to 8 in the background by calling the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, an arc additive manufacturing process for isomorphous low-alloy high-strength steel structures as described in any one of claims 1 to 8 is implemented.

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