A welding control method and device based on dynamic nitrogen regulation

Abstract

The application discloses a welding control method and device based on dynamic nitrogen regulation. The method comprises the following steps: obtaining process parameters and material parameters; obtaining a dynamic nitrogen retention coefficient based on the process parameters and the material parameters; obtaining a ferrite content prediction value based on the process parameters, the material parameters and the dynamic nitrogen retention coefficient; judging whether the ferrite content prediction value is greater than a content threshold value; if the ferrite content prediction value is greater than the content threshold value, adjusting the process parameters until the ferrite content prediction value is less than or equal to the content threshold value; and if the ferrite content prediction value is less than or equal to the content threshold value, maintaining the process parameters unchanged. The method can realize real-time monitoring and accurate control of the ferrite content in a stainless steel weld, realize intelligentization and precision of a welding process, greatly reduce process debugging time, and improve welding quality and production efficiency.

Description

Technical Field

[0001] This application relates to the field of advanced welding manufacturing technology, specifically to a welding control method and apparatus based on dynamic nitrogen regulation. The welding control system provided by this invention achieves precise control of ferrite content without introducing nitrogen, solely through dynamic adjustment of welding parameters and intelligent control of the fusion ratio. It is suitable for high-purity environments and welding scenarios where nitrogen cannot be added. Background Technology

[0002] In the welding process of austenitic stainless steel, traditional methods for controlling ferrite content (FN) mostly rely on the initial design of the material composition or on adjusting the nitrogen content in the shielding gas to achieve compositional balance. However, under high-purity process requirements, the shielding gas must be strictly limited to high-purity argon, which poses a certain challenge to achieving ultra-low ferrite content welding (FN≤0.5%). In addition, due to environmental fluctuations, power drift, uneven heat input and other factors during the welding process, the actual FN content is prone to deviation. Conventional methods lack a response mechanism, so an intelligent welding control system that does not require nitrogen adjustment and relies on real-time feedback and optimization of internal process parameters is needed to meet the requirements of high consistency and high cleanliness welding.

[0003] Traditional WRC models fail to account for the dynamic thermal and chemical environment present in pulsed arc welding. They are static and empirical, derived from steady-state conditions, and often lead to ferrite number (FN) prediction errors exceeding ±2% FN under transient thermal cycling, local nitrogen activity, or inherent non-equilibrium solidification conditions. During pulsed welding, the rapid fluctuations in heat input and the interaction between fusion ratio adjustment and time-dependent cooling rates introduce complex nonlinear effects on the phase transformation. These mechanisms are not adequately characterized or modeled, limiting the reliability of process simulations and rendering real-time control infeasible.

[0004] In specific ultra-low ferrite content welding (FN ≤ 0.5%), which is crucial for applications requiring high corrosion resistance and non-magnetic properties, current methods heavily rely on empirical approaches and extensive trial-and-error testing. This leads to lengthy development cycles, inconsistent repeatability, and high material waste, particularly in critical areas such as nuclear fusion. Therefore, an advanced control system is needed to dynamically adjust the nitrogen content level in real time, based on pulsed thermal cycling, to achieve precise and stable control of ferrite content during the weld process.

[0005] Existing technology CN115389733B discloses a method for controlling the ferrite content of austenitic stainless steel weld overlay, including the following steps: a server generates a weld overlay state analysis signal and sends it to a weld overlay state analysis unit, which performs real-time state analysis of the weld overlay; the server generates a surrounding environment detection signal and sends it to a surrounding environment detection unit, which, upon receiving the signal, detects the surrounding environment of the corresponding analysis object; the server generates a process influence analysis signal and sends it to a process influence analysis unit, which receives the signal. This method cannot dynamically adjust the nitrogen content level and cannot accurately and stably control the ferrite content during the weld process.

[0006] Existing technology CN103336102B discloses a method for determining the δ-ferrite content in welds of 9-12%Cr martensitic heat-resistant steel, including the following steps: analyzing and determining the mass percentages of twelve elements—carbon, nitrogen, nickel, cobalt, copper, manganese, chromium, molybdenum, tungsten, vanadium, niobium, and silicon—in the weld metal of 9-12%Cr martensitic heat-resistant steel; substituting the obtained mass percentages of each element into a formula to calculate the volume percentage of δ-ferrite in the weld metal. This method cannot dynamically adjust the nitrogen content level and cannot accurately and stably control the ferrite content during the welding process.

[0007] In summary, neither of the two existing technologies can dynamically adjust the nitrogen content level, nor can they accurately and stably control the ferrite content during the welding process. Summary of the Invention

[0008] The purpose of this application is to solve the aforementioned technical problems.

[0009] To achieve the above objectives, the first aspect of this application proposes a welding control method based on dynamic nitrogen regulation, comprising:

[0010] Obtain process parameters and material parameters;

[0011] The dynamic nitrogen retention coefficient is obtained based on process parameters and material parameters;

[0012] The predicted ferrite content is obtained based on process parameters, material parameters, and dynamic nitrogen retention coefficient;

[0013] Determine whether the predicted ferrite content value is greater than the content threshold;

[0014] If the predicted ferrite content is greater than the content threshold, adjust the process parameters until the predicted ferrite content is less than or equal to the content threshold; if the predicted ferrite content is less than or equal to the content threshold, keep the process parameters unchanged.

[0015] Furthermore, the dynamic nitrogen retention coefficient obtained based on process parameters and material parameters includes one or more of the following: process parameters include pulse frequency, peak time, welding speed, and wall thickness; and material parameters include one or more of the following: initial nitrogen content of the base material, nitrogen content of the welding wire, and fusion ratio.

[0016] Furthermore, the dynamic nitrogen retention coefficient obtained based on process parameters and material parameters includes: obtaining the equivalent initial nitrogen content based on the initial nitrogen content of the base material, the nitrogen content of the welding wire, and the fusion ratio; and obtaining the dynamic nitrogen retention coefficient based on the equivalent initial nitrogen content, pulse frequency, peak time, welding speed, and wall thickness.

[0017] Furthermore, the predicted ferrite content based on process parameters, material parameters, and dynamic nitrogen retention coefficient includes pulse frequency and pulse current as process parameters, and material property coefficients as material property coefficients. The predicted ferrite content is obtained based on pulse current, pulse frequency, material property coefficients, and dynamic nitrogen retention coefficient.

[0018] Furthermore, the predicted ferrite content values ​​were obtained as follows:

[0019]

[0020] in, This is the predicted value for ferrite content; , , , , and The material property coefficients are calibrated through orthogonal experiments and machine learning; I is the pulse current; f is the pulse frequency. This represents the dynamic nitrogen retention coefficient.

[0021] Furthermore, the equivalent initial nitrogen content and dynamic nitrogen retention coefficient obtained are as follows:

[0022]

[0023]

[0024] in, This represents the equivalent initial nitrogen content; The dynamic nitrogen retention coefficient; It is the melting ratio; The initial nitrogen content of the parent material; Nitrogen content of the welding wire; This is the process coupling coefficient; Peak time; The pulse frequency; For welding speed; The wall thickness is [not specified].

[0025] Furthermore, adjusting process parameters includes adjusting one or more of the following: adjusting the pulse waveform, correcting the fusion ratio, and optimizing the welding speed.

[0026] Furthermore, the content threshold is 0.5.

[0027] To achieve the above objectives, a second aspect of this application proposes a welding control device based on dynamic nitrogen regulation, comprising:

[0028] The acquisition module is used to acquire process parameters and material parameters;

[0029] The dynamic nitrogen retention coefficient calculation module is used to obtain the dynamic nitrogen retention coefficient based on process parameters and material parameters.

[0030] The ferrite content prediction calculation module is used to obtain the predicted ferrite content based on process parameters, material parameters, and dynamic nitrogen retention coefficient.

[0031] The judgment module is used to determine whether the predicted ferrite content value is greater than the content threshold.

[0032] The adjustment module is used to adjust the process parameters if the predicted ferrite content is greater than the content threshold, until the predicted ferrite content is less than or equal to the content threshold; if the predicted ferrite content is less than or equal to the content threshold, the process parameters are kept unchanged.

[0033] To achieve the above objectives, a third aspect of this application proposes a welding control system based on dynamic nitrogen regulation. The welding control device based on dynamic nitrogen regulation described above includes:

[0034] The sensor module is used to acquire data during the welding process. The sensor module includes one or more of a multispectral sensor array, a probe, and a high-speed thermal imager.

[0035] The process database module communicates with the sensor module and is used to construct a knowledge graph of the mapping relationship between welding parameters and ferrite content.

[0036] The welding control device communicates with the sensor module and the process database module to generate control commands;

[0037] The execution module is connected in communication with the welding control device to receive control commands and execute corresponding processing actions. The execution module includes an adaptive pulse power supply and a fusion ratio control module.

[0038] The human-machine interface module communicates with the sensor module and includes one or more of the following: a touch screen HMI, an alarm system, and a log system.

[0039] To achieve the above objectives, a fourth aspect of this application provides a computer-readable storage medium comprising a stored computer program, wherein the computer program can be executed by an electronic device to perform the welding control method based on dynamic nitrogen regulation.

[0040] To achieve the above objectives, the fifth aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the welding control method based on dynamic nitrogen regulation.

[0041] To achieve the above objectives, a sixth aspect of this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to execute the welding control method based on dynamic nitrogen regulation through the computer program.

[0042] By applying the above-described technical solution of the present invention, at least the following technical effects are achieved:

[0043] 1. This method discloses an advanced ferrite prediction model, which can monitor and precisely control the ferrite content in stainless steel welds in real time, realize the intelligent and precise welding process, significantly reduce process debugging time, and improve welding quality and production efficiency.

[0044] 2. This method significantly improves the accuracy of the prediction model by introducing a dynamic nitrogen retention coefficient and considering the inverse relationship between welding speed and wall thickness, thus greatly enhancing the accuracy of the prediction.

[0045] 3. This method constructs a knowledge graph of the mapping relationship between welding parameters and ferrite content, and further optimizes the prediction model through machine learning algorithms. This enables the system to quickly learn and adjust prediction and control strategies when faced with different materials and process parameters, thereby improving the adaptability and flexibility of the process.

[0046] 4. When the predicted ferrite content exceeds the target threshold, the system will automatically trigger parameter adjustment logic. Through a series of adaptive control strategies, the ferrite content can be stabilized at an extremely low level during the welding process.

[0047] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0048] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0049] Figure 1A flowchart of a welding control method based on dynamic nitrogen regulation is presented as an embodiment;

[0050] Figure 2 A detailed flowchart of a welding control method based on dynamic nitrogen regulation according to one embodiment is presented;

[0051] Figure 3 A schematic diagram of a welding control device based on dynamic nitrogen regulation according to one embodiment is shown;

[0052] Figure 4 A schematic diagram of a welding control system based on dynamic nitrogen regulation according to an embodiment is shown;

[0053] Figure 5 A schematic diagram of the structure of a welding control product based on dynamic nitrogen regulation according to one embodiment is shown;

[0054] Figure 6 A schematic diagram of the structure of an electronic device according to an embodiment is shown. Detailed Implementation

[0055] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0056] The present invention will be further described in detail below with reference to specific embodiments, which should not be construed as limiting the scope of protection claimed by the present invention.

[0057] Example 1

[0058] According to one aspect of the present invention, a welding control method based on dynamic nitrogen regulation is proposed.

[0059] like Figure 1 The image illustrates a welding control method based on dynamic nitrogen regulation according to an embodiment of the present invention. The process mainly includes the following steps:

[0060] S1. Obtain process parameters and material parameters.

[0061] Specifically, in this embodiment, the process parameters include pulse current (I), frequency (f), speed (v), and wall thickness (δ); the material parameters include the nitrogen content of the base material (…). The data includes spectral signals and material property coefficients (α-η); real-time data includes spectral signals, thermal imaging data, and electric arc signals.

[0062] S2. The dynamic nitrogen retention coefficient is obtained based on the process parameters and material parameters.

[0063] Furthermore, the process parameters include one or more of the following: pulse frequency, peak time, welding speed, and wall thickness; and the material parameters include one or more of the following: initial nitrogen content of the base material, nitrogen content of the welding wire, and fusion ratio.

[0064] Furthermore, the equivalent initial nitrogen content is obtained based on the initial nitrogen content of the base material, the nitrogen content of the welding wire, and the fusion ratio. The dynamic nitrogen retention coefficient is obtained based on the equivalent initial nitrogen content, pulse frequency, peak time, welding speed, and wall thickness.

[0065] Furthermore, the equivalent initial nitrogen content and dynamic nitrogen retention coefficient obtained are as follows:

[0066]

[0067]

[0068] in, This represents the equivalent initial nitrogen content; The dynamic nitrogen retention coefficient; It is the melting ratio; The initial nitrogen content of the parent material; Nitrogen content of the welding wire; This is the process coupling coefficient; Peak time; The pulse frequency; For welding speed; The wall thickness is [not specified].

[0069] S3. Based on the process parameters, material parameters, and dynamic nitrogen retention coefficient, the predicted ferrite content is obtained.

[0070] Furthermore, the process parameters include pulse frequency and pulse current, and the material parameters include material property coefficients. The predicted ferrite content is obtained based on the pulse current, pulse frequency, material property coefficients, and dynamic nitrogen retention coefficient.

[0071] Furthermore, the predicted ferrite content values ​​were obtained as follows:

[0072]

[0073] in, This is the predicted value for ferrite content; , , , , and The material property coefficients are calibrated through orthogonal experiments and machine learning; I is the pulse current; f is the pulse frequency. This represents the dynamic nitrogen retention coefficient.

[0074] When FN≤0, it means FN=0.

[0075] Through dynamic nitrogen retention coefficient With pulse parameters (I, f, The coupled equations achieve accurate predictions with FN ≤ ​​0.5%, where The calculation includes the reciprocal relationship between welding speed v and wall thickness δ.

[0076] S4. Determine whether the predicted ferrite content value is greater than the content threshold.

[0077] Furthermore, the content threshold is 0.5.

[0078] S5. If the predicted ferrite content is greater than the content threshold, adjust the process parameters until the predicted ferrite content is less than or equal to the content threshold; if the predicted ferrite content is less than or equal to the content threshold, keep the process parameters unchanged.

[0079] Furthermore, adjusting process parameters includes adjusting one or more of the following: adjusting the pulse waveform, correcting the fusion ratio, and optimizing the welding speed.

[0080] Specifically, in this embodiment, when the predicted FN > 0.5%, a three-level parameter adjustment is triggered, including:

[0081] —Adjust the pulse waveform;

[0082] — Correcting the fusion ratio: Generally, when the nitrogen content of the welding material and the base material differs significantly and the heat input boundary is close to the limit, the fusion ratio is adjusted, including adjusting the wire feed speed and adjusting the heat input center offset.

[0083] —Optimize welding speed.

[0084] Based on the objective function optimization strategy of "minimizing ferrite content (FN)," combined with a preset parameter boundary range and a dynamic feedback prediction model, real-time control is achieved. When FN is greater than a threshold, I, f, and t are reduced. p Reduce v to comprehensively reduce heat input. When FN fluctuates greatly, stabilize f and appropriately reduce v to improve process stability.

[0085] like Figure 2 The figure shows a detailed flowchart of a welding control method based on dynamic nitrogen regulation in this embodiment.

[0086] Example 2

[0087] In this embodiment, FN prediction is performed under normal parameters.

[0088] Base material: 316LN stainless steel plate, butt welded, wall thickness (δ) is 10 mm, initial nitrogen content of base material ( The content was 0.16 wt%.

[0089] Welding wire: nitrogen content ( The content was 0.12 wt%.

[0090] Welding process parameters: Welding current (I) is 100 A, pulse frequency (f) is 90 Hz, welding speed (v) is 3 mm / s, peak time ( The melting time was 5 ms, and the melting ratio (θ) was 0.6.

[0091] The prediction steps are as follows:

[0092] 1. Calculate the equivalent initial nitrogen content ( ):

[0093]

[0094] 2. Calculate the dynamic nitrogen retention coefficient ( ):

[0095] Use the formula: .

[0096] Substituting the process coupling coefficient k=0.04, we get .

[0097] 3. Predicting ferrite content (FN):

[0098] Use the formula: .

[0099] Substitute the calibrated coefficients (α=4.5, β=0.015, γ=0.01, ε=4) into the equation. 10 -5 ζ=5 10 -6 (η=6.6), resulting in FN=1.3.

[0100] Example 3

[0101] In this embodiment, precise control of FN is achieved by adjusting parameters. The current process requires that the ferrite content be strictly controlled at FN≤0.5.

[0102] The adjustment steps are as follows:

[0103] 1. Initial prediction: The calculation shows that FN=1.3>0.5, which does not meet the requirements.

[0104] 2. Parameter adjustment:

[0105] For this implementation case, the current boundary range is 80-110 A to ensure fusion between the base material and the welding material; the frequency is 50-130 Hz to avoid excessive heat input due to high frequency; the peak time is 2-6 ms to control the duty cycle; and the welding speed is 1.5-4 mm / s to ensure penetration depth.

[0106] The specific adjustments include: increasing the pulse frequency from 90 Hz to 80 Hz; decreasing the welding current from 100 A to 92 A; and increasing the pulse duration t. p The welding time was reduced from 5 ms to 3.5 ms; the welding speed v was slightly reduced from 3 mm / s to 2.3 mm / s.

[0107] The melting of the base material decreases, and the melting ratio is 0.5.

[0108] 3. Recalculate :

[0109] It is approximately 0.145%.

[0110] 4. Re-predict FN:

[0111] Using the same coefficients and substituting the new parameters, we get FN=0.32.

[0112] After adjusting the welding parameters, the ferrite content (FN) can be reduced to approximately 0.32, meeting the requirements for ultra-low ferrite welding.

[0113] Example 4

[0114] Voltage fluctuations caused the actual current to surge to 96 A and the frequency to rise to 85 Hz. The system calculated FN to be approximately 0.58 in real time. At this point, the system immediately activated the secondary dynamic control mechanism: first, it checked the current status of all parameters; second, it assessed the parameter sensitivity, prioritized adjusting the current, and simultaneously evaluated the combined effect.

[0115] The welding current was restored to 90 A, the frequency was reduced to 75 Hz, and the pulse time was reduced to 3.0 ms. The welding speed was slightly reduced to 2.2 mm / s. After adjustment, FN dropped to 0.34, returning to a stable state, and adjustment was stopped.

[0116] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0117] Example 5

[0118] According to another aspect of the embodiments of this application, the present invention also provides a welding control device based on dynamic nitrogen regulation. For example... Figure 3 As shown, the device includes:

[0119] Module 301 is used to acquire process parameters and material parameters;

[0120] The dynamic nitrogen retention coefficient calculation module 302 is used to obtain the dynamic nitrogen retention coefficient based on process parameters and material parameters;

[0121] The ferrite content prediction calculation module 303 is used to obtain the ferrite content prediction value based on process parameters, material parameters and dynamic nitrogen retention coefficient.

[0122] The judgment module 304 is used to determine whether the predicted value of ferrite content is greater than the content threshold.

[0123] The adjustment module 305 is used to adjust the process parameters if the predicted ferrite content is greater than the content threshold, until the predicted ferrite content is less than or equal to the content threshold; if the predicted ferrite content is less than or equal to the content threshold, the process parameters are kept unchanged.

[0124] As an alternative approach, the dynamic nitrogen retention coefficient is obtained based on process parameters and material parameters. The process parameters include one or more of the following: pulse frequency, peak time, welding speed, and wall thickness. The material parameters include one or more of the following: initial nitrogen content of the base material, nitrogen content of the welding wire, and fusion ratio.

[0125] As an optional approach, the dynamic nitrogen retention coefficient is obtained based on process parameters and material parameters, including obtaining the equivalent initial nitrogen content based on the initial nitrogen content of the base material, the nitrogen content of the welding wire, and the fusion ratio, and obtaining the dynamic nitrogen retention coefficient based on the equivalent initial nitrogen content, pulse frequency, peak time, welding speed, and wall thickness.

[0126] As an optional approach, the predicted ferrite content is obtained based on process parameters, material parameters, and dynamic nitrogen retention coefficient. The process parameters include pulse frequency and pulse current, and the material parameters include material property coefficients. The predicted ferrite content is obtained based on pulse current, pulse frequency, material property coefficients, and dynamic nitrogen retention coefficient.

[0127] As an alternative approach, the predicted ferrite content is:

[0128]

[0129] in, This is the predicted value for ferrite content; , , , , and The material property coefficients are calibrated through orthogonal experiments and machine learning; I is the pulse current; f is the pulse frequency. This represents the dynamic nitrogen retention coefficient.

[0130] As an alternative approach, the equivalent initial nitrogen content and dynamic nitrogen retention coefficient obtained are as follows:

[0131]

[0132]

[0133] in, This represents the equivalent initial nitrogen content; The dynamic nitrogen retention coefficient; It is the melting ratio; The initial nitrogen content of the parent material; Nitrogen content of the welding wire; This is the process coupling coefficient; Peak time; The pulse frequency; For welding speed; The wall thickness is [not specified].

[0134] As an optional approach, adjusting process parameters may include adjusting one or more of the following: adjusting the pulse waveform, correcting the fusion ratio, and optimizing the welding speed.

[0135] As an alternative, the content threshold is 0.5.

[0136] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0137] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0138] Example 6

[0139] According to another aspect of the embodiments of this application, the present invention also provides a welding control system based on dynamic nitrogen regulation. For example... Figure 4 As shown, the system includes:

[0140] The sensor module is used to acquire data during the welding process. The sensor module includes one or more of a multispectral sensor array, a probe, and a high-speed thermal imager.

[0141] The process database module communicates with the sensor module and is used to construct a knowledge graph of the mapping relationship between welding parameters and ferrite content.

[0142] The welding control device communicates with the sensor module and the process database module to generate control commands;

[0143] The actuator is connected in communication with the welding control device to receive control commands and execute corresponding processing actions. The actuator includes an adaptive pulse power supply and a fusion ratio control module.

[0144] The human-machine interface module communicates with the sensor module and includes one or more of the following: a touch screen HMI, an alarm system, and a log system.

[0145] Furthermore, the multispectral sensor array acquires plasma emission spectra in real time, monitors the Cr / N atomic ratio, and supports composition feedback during the process.

[0146] Furthermore, the high-speed thermal imager integrates a thermal imaging camera based on a micro-thermometer, which has ultra-fine temperature resolution (≤10μm / ℃) for molten pool temperature gradient feedback.

[0147] Furthermore, the adaptive pulse power supply has a response time of <50μs and actively adjusts the pulse frequency, amplitude, and duration based on feedback from the spectral and thermal sensors. This ensures optimal energy transfer, promotes consistent arc stability, minimizes spatter, and controls penetration.

[0148] Furthermore, the process database module constructs a knowledge graph of extensive welding parameter-ferrite content mapping relationships to support process optimization. The process database module uses a data-driven correlation model refined through regression analysis and machine learning algorithms to predict and control the ferrite content in stainless steel welds.

[0149] Furthermore, the welding control device also includes a dual closed-loop PID controller.

[0150] Furthermore, the response time of the welding control system is ≤100μs.

[0151] Example 7

[0152] According to one aspect of this application, a computer program product is provided, the computer program product comprising a computer program.

[0153] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0154] Figure 5 A schematic block diagram of a computer system architecture for implementing an electronic device according to embodiments of the present application is shown.

[0155] It should be noted that, Figure 5 The computer system 500 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0156] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 502 or programs loaded from storage section 508 into random access memory (RAM). The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output interface 505 (I / O interface) is also connected to the bus 504.

[0157] The following components are connected to the input / output interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a local area network card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.

[0158] Specifically, according to embodiments of this application, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit 501, it performs various functions defined in the system of this application.

[0159] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable media 511. When the computer program is executed by central processing unit 501, it performs various functions provided in the embodiments of this application.

[0160] Example 8

[0161] According to another aspect of the embodiments of this application, an electronic device for a welding control method based on dynamic nitrogen regulation is also provided. This embodiment uses this electronic device as an example of a terminal device for illustration. Figure 6 As shown, the electronic device includes a memory 602 and a processor 604. The memory 602 stores a computer program, and the processor 604 is configured to execute the steps in any of the above method embodiments via the computer program.

[0162] Optionally, in this embodiment, the aforementioned electronic device may be located in at least one of a plurality of network devices in a computer network.

[0163] Optionally, in this embodiment, the processor may be configured to execute the methods in the embodiments of this application via a computer program.

[0164] Alternatively, as those skilled in the art will understand, Figure 6 The structure shown is for illustrative purposes only. Figure 6 This does not limit the structure of the aforementioned electronic devices. For example, the electronic device may also include components that are more... Figure 6 The more or fewer components shown (such as network interfaces, etc.), or having the same Figure 6 The different configurations shown.

[0165] The memory 602 can be used to store software programs and modules, such as the program instructions / modules corresponding to a welding control method and device based on dynamic nitrogen regulation in this embodiment of the application. The processor 604 executes various functional applications and data processing by running the software programs and modules stored in the memory 602, thereby realizing the aforementioned welding control method based on dynamic nitrogen regulation. The memory 602 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 602 may further include memory remotely located relative to the processor 604, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, enterprise intranets, local area networks, mobile communication networks, and combinations thereof. Specifically, the memory 602 may be used, but is not limited to, for storing process parameters and material parameters. As an example, such as... Figure 6 As shown, the memory 602 may include, but is not limited to, the acquisition module 301, the dynamic nitrogen retention coefficient calculation module 302, the ferrite content prediction value calculation module 303, the judgment module 304, and the adjustment module 305 from the aforementioned device. Furthermore, it may include, but is not limited to, other module units from the aforementioned device, which will not be elaborated upon in this example.

[0166] Optionally, the transmission device 606 described above is used to receive or send data via a network. Specific examples of the network described above may include wired networks and wireless networks. In one example, the transmission device 606 includes a Network Interface Controller (NIC), which can be connected to other network devices and routers via a network cable to communicate with the Internet or a local area network. In another example, the transmission device 606 is a radio frequency (RF) module, used for wireless communication with the Internet.

[0167] In addition, the above-mentioned electronic device also includes: a display 608 for displaying the predicted ferrite content value; and a connection bus 610 for connecting the various module components in the above-mentioned electronic device.

[0168] In other embodiments, the aforementioned terminal device or server can be a node in a distributed system, wherein the distributed system can be a blockchain system, which is a distributed system formed by connecting multiple nodes through network communication. The nodes can form a peer-to-peer network, and any form of computing device, such as a server, terminal, or other electronic device, can become a node in the blockchain system by joining this peer-to-peer network.

[0169] According to one aspect of this application, a computer-readable storage medium is provided, wherein a processor of an electronic device reads computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the electronic device to perform a welding control method based on dynamic nitrogen regulation provided in various alternative implementations of the above-described welding control method based on dynamic nitrogen regulation.

[0170] Optionally, in this embodiment, the computer-readable storage medium described above may be configured to store methods for performing the embodiments of this application.

[0171] Optionally, in this embodiment, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0172] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0173] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more electronic devices to execute all or part of the steps of the methods described in the various embodiments of this application.

[0174] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0175] In the several embodiments provided in this application, it should be understood that the disclosed application can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0176] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0177] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0178] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0179] By applying the above-described technical solution of the present invention, at least the following technical effects are achieved:

[0180] 1. This method discloses an advanced ferrite prediction model, which can monitor and precisely control the ferrite content in stainless steel welds in real time, realize the intelligent and precise welding process, significantly reduce process debugging time, and improve welding quality and production efficiency.

[0181] 2. This method significantly improves the accuracy of the prediction model by introducing a dynamic nitrogen retention coefficient and considering the inverse relationship between welding speed and wall thickness, thus greatly enhancing the accuracy of the prediction.

[0182] 3. This method constructs a knowledge graph of the mapping relationship between welding parameters and ferrite content, and further optimizes the prediction model through machine learning algorithms. This enables the system to quickly learn and adjust prediction and control strategies when faced with different materials and process parameters, thereby improving the adaptability and flexibility of the process.

[0183] 4. When the predicted ferrite content exceeds the target threshold, the system will automatically trigger parameter adjustment logic. Through a series of adaptive control strategies, the ferrite content can be stabilized at an extremely low level during the welding process.

[0184] The above are merely several specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

[0185] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0186] It should be noted that, in the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

Claims

1. A welding control method based on dynamic nitrogen regulation, characterized in that, include: Obtain process parameters and material parameters, wherein the process parameters include pulse frequency, peak time, welding speed and wall thickness, and the material parameters include initial nitrogen content of base material, nitrogen content of welding wire and fusion ratio; The dynamic nitrogen retention coefficient is obtained based on the process parameters and the material parameters, including: obtaining the equivalent initial nitrogen content based on the initial nitrogen content of the base material, the nitrogen content of the welding wire and the fusion ratio, and obtaining the dynamic nitrogen retention coefficient based on the equivalent initial nitrogen content, the pulse frequency, the peak time, the welding speed and the wall thickness; The equivalent initial nitrogen content and dynamic nitrogen retention coefficient obtained are as follows: in, This represents the equivalent initial nitrogen content; The dynamic nitrogen retention coefficient; It is the melting ratio; The initial nitrogen content of the parent material; Nitrogen content of the welding wire; This is the process coupling coefficient; Peak time; The pulse frequency; For welding speed; For wall thickness; The predicted ferrite content is obtained based on the process parameters, the material parameters, and the dynamic nitrogen retention coefficient. The process parameters also include pulse current, and the material parameters also include material characteristic coefficients. The predicted ferrite content is obtained based on the pulse current, the pulse frequency, the material characteristic coefficients, and the dynamic nitrogen retention coefficient. The predicted ferrite content obtained is: in, This is the predicted value for ferrite content; , , , , and The material property coefficients are calibrated through orthogonal experiments and machine learning; I is the pulse current; f is the pulse frequency. The dynamic nitrogen retention coefficient; Determine whether the predicted ferrite content is greater than the content threshold; If the predicted ferrite content is greater than the content threshold, the process parameters are adjusted until the predicted ferrite content is less than or equal to the content threshold; if the predicted ferrite content is less than or equal to the content threshold, the process parameters are kept unchanged.

2. The control method according to claim 1, characterized in that, The adjustment of the process parameters includes adjusting one or more of the following: adjusting the pulse waveform, correcting the fusion ratio, and optimizing the welding speed.

3. The control method according to claim 1, characterized in that, The content threshold is 0.

5.

4. A welding control device based on dynamic nitrogen regulation, employing a welding control method based on dynamic nitrogen regulation as described in any one of claims 1 to 3, characterized in that, include: The acquisition module is used to acquire process parameters and material parameters; A dynamic nitrogen retention coefficient calculation module is used to obtain the dynamic nitrogen retention coefficient based on the process parameters and the material parameters; The ferrite content prediction calculation module is used to obtain the ferrite content prediction value based on the process parameters, the material parameters and the dynamic nitrogen retention coefficient. The judgment module is used to determine whether the predicted ferrite content value is greater than the content threshold. The adjustment module is used to adjust the process parameters if the predicted ferrite content is greater than the content threshold, until the predicted ferrite content is less than or equal to the content threshold; if the predicted ferrite content is less than or equal to the content threshold, the process parameters are kept unchanged.

5. A welding control system based on dynamic nitrogen regulation, using the welding control device based on dynamic nitrogen regulation as described in claim 4, characterized in that, include: A sensor module for acquiring data during the welding process, the sensor module comprising one or more of a multispectral sensor array, a probe, and a high-speed thermal imager; The process database module is communicatively connected to the sensor module and is used to construct a knowledge graph of the mapping relationship between welding parameters and ferrite content. A welding control device, which is communicatively connected to the sensor module and the process database module, is used to generate control commands; An execution module, which is communicatively connected to the welding control device, is used to receive the control commands and execute corresponding processing actions. The execution module includes an adaptive pulse power supply and a fusion ratio control module. The human-machine interaction module is communicatively connected to the sensor module, and the human-machine interaction module includes one or more of a touch screen HMI, an alarm system, and a log system.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein the computer program can be executed by an electronic device to perform the method according to any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1 to 3.

8. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method described in any one of claims 1 to 3 through the computer program.

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