An arc length control method, system, apparatus, and medium for electric arc additive manufacturing

Abstract

The application discloses an arc length control method, system, device and medium for electric arc additive manufacturing, and relates to the field of additive manufacturing; the control method comprises the following steps: acquiring image data of a target part; determining arc length data by using an image edge data detection method according to the image data; obtaining a fuzzy error by using fuzzy mapping according to the arc length data and set reference arc length data; determining a control quantity according to the fuzzy error; the control quantity comprises a pulse load ratio; and the deviation of the arc length data of the target part in the manufacturing process is compensated according to the control quantity, and the target part is manufactured by using a non-gas shielded welding method; and the application can realize automatic control of the arc length.

Description

Technical Field

[0001] This invention relates to the field of additive manufacturing, and in particular to a method, system, equipment and medium for controlling the arc length in electric arc additive manufacturing. Background Technology

[0002] Additive manufacturing technology can be broadly categorized into metal additive manufacturing and non-metal additive manufacturing. Metal additive manufacturing can be further classified based on the type of raw material and the energy source. According to the type of raw material, it can be divided into powder-lay additive manufacturing, powder-feed additive manufacturing, and wire-feed additive manufacturing. According to the energy source, it can be divided into three types: laser, electron beam, and electric arc. Electric arc additive manufacturing (WAAM) uses a layer-by-layer welding method to create dense metal solid components. Because it uses an electric arc as the energy beam, it has high heat input and fast forming speed, making it suitable for low-cost, high-efficiency, and rapid near-net-shape forming of large-sized complex components.

[0003] Related research on metal arc additive manufacturing (WAAM) methods also includes arc length-related topics. Most of these are based on metal arc shielded welding (MIG) + robot + PID control. First, the MIG method is a relatively stable method with smooth changes and easy control. The PID method is an error-based controller design method that includes proportional, integral, and derivative components. The proportional component is used to improve the controller's response speed, the integral component is used to eliminate steady-state errors, and the derivative component plays a proactive role, compensating for errors before they occur and improving the controller's dynamic characteristics.

[0004] However, the above method has the following problems:

[0005] PID control is a zero-error control method with the ultimate goal of zero error. It is a mature algorithm that does not rely on human experience. Theoretically, it is not suitable for the WAAM process. The WAAM process is complex and the PID algorithm has difficulty eliminating errors while maintaining the stability of the manufacturing process. Often, the system stability will decrease because the control quantity calculated by the PID algorithm changes too drastically, which will eventually lead to the failure of the experiment.

[0006] Therefore, it is crucial to achieve automatic arc length control in additive manufacturing. Summary of the Invention

[0007] The purpose of this invention is to provide a method, system, equipment, and medium for controlling the arc length in electric arc additive manufacturing, so as to achieve automatic control of the arc length.

[0008] To achieve the above objectives, the present invention provides the following solution:

[0009] A method for controlling the arc length in electric arc additive manufacturing, the control method comprising:

[0010] Acquire image data of the target part during the manufacturing process;

[0011] The arc length data is determined using an image edge detection method based on the image data.

[0012] Based on the arc length data and the set reference arc length data, fuzzification error is obtained by fuzzification mapping.

[0013] The control quantity is determined based on the fuzzification error; the control quantity includes the pulse load ratio.

[0014] Based on the control quantity, the deviation of the arc length data of the target part during the manufacturing process is compensated, and the target part is manufactured by non-consumable electrode argon gas shielded welding.

[0015] Optionally, an image edge detection method is used to determine the arc length data based on the image data, specifically including:

[0016] The image data is subjected to a set matrix operation to obtain image processing data; the set matrix operation includes: grayscale conversion matrix;

[0017] The image processing data is filtered to obtain filtered image data;

[0018] The image filtering data is processed by pixel edge localization and edge connection to obtain the arc length data.

[0019] Optionally, based on the arc length data and the set reference arc length data, fuzzification mapping is used to obtain the fuzzification error, specifically including:

[0020] The arc length error is determined based on the arc length data and the set reference arc length data;

[0021] The arc length error is fuzzified using a set fuzzy rule and mapped to a set fuzzy universe to obtain the fuzzified error.

[0022] Optionally, the control quantity is determined based on the fuzzification error, specifically including:

[0023] The parameters of the set adaptive law are updated based on the fuzzification error to obtain the adaptive law update parameters; the set adaptive law is a convergence matrix equation determined based on the stability analysis method.

[0024] The control quantity is calculated based on the adaptive law update parameters and the fuzzification error.

[0025] Optionally, the stability analysis method used is the Lyapunov energy analysis method.

[0026] An arc length control system for electric arc additive manufacturing, the control system comprising:

[0027] The image data acquisition module is used to acquire image data of the target part during the manufacturing process;

[0028] The arc length detection module is used to determine the arc length data based on the image data using an image edge data detection method;

[0029] The fuzzification control module is used to obtain the fuzzification error by using fuzzification mapping based on the arc length data and the set reference arc length data;

[0030] The determination module determines a control quantity based on the fuzzification error; the control quantity includes the pulse load ratio.

[0031] The execution module is used to compensate for the deviation of the arc length data of the target part during the manufacturing process according to the control quantity, and to manufacture the target part by means of non-consumable electrode argon gas shielded welding.

[0032] An electronic device includes a memory and a processor, the memory storing a computer program, and the processor running the computer program to cause the electronic device to perform the arc length control method for arc additive manufacturing described above.

[0033] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the arc length control method for arc additive manufacturing described above.

[0034] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0035] This invention provides a method, system, device, and medium for arc length control in electric arc additive manufacturing. The method involves determining the arc length data based on image data of the target part during the manufacturing process using an image edge detection method. Then, based on the arc length data and a set reference arc length data, fuzzification mapping is used to obtain the fuzzification error, thereby determining the control quantity. Based on the control quantity, the deviation of the arc length data of the target part during the manufacturing process is compensated, and the target part is manufactured using non-consumable electrode argon gas shielded welding, thus achieving automatic control of the arc length. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A flowchart of an arc length control method for electric arc additive manufacturing provided in an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of the membership function provided in an embodiment of the present invention;

[0039] Figure 3 This is a structural diagram of the arc length control system for electric arc additive manufacturing provided in an embodiment of the present invention;

[0040] Figure 4 This is a flowchart illustrating the overall process structure of the solution provided in the embodiments of the present invention.

[0041] Figure 5 This is an error curve diagram showing the difference between the actual arc length data and the set reference arc length data during the manufacturing process.

[0042] Figure 6 This is a schematic diagram of the manufactured target part provided in an embodiment of the present invention.

[0043] Symbol explanation:

[0044] Image data acquisition module-1, arc length detection module-2, fuzzing control module-3, determination module-4, execution module-5. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] The purpose of this invention is to provide a method, system, equipment, and medium for controlling the arc length in electric arc additive manufacturing, so as to achieve automatic control of the arc length.

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Example 1

[0049] like Figure 1 As shown, this embodiment of the invention provides a method for controlling the arc length in electric arc additive manufacturing. The control method includes:

[0050] Step 100: Acquire image data of the target part during the manufacturing process.

[0051] Step 200: Determine the arc length data using the image edge detection method based on the image data.

[0052] Specifically, the arc length data is determined using an image edge detection method based on the image data, including:

[0053] Image data is processed by performing matrix operations on the image data; the matrix operations include grayscale conversion matrix.

[0054] The image processing data is filtered to obtain filtered image data.

[0055] Pixel edge localization and edge connection processing are performed on the filtered image data to obtain arc length data.

[0056] Step 300: Based on the arc length data and the set reference arc length data, fuzzification error is obtained by fuzzification mapping.

[0057] Specifically, based on the arc length data and the set reference arc length data, fuzzification error is obtained using fuzzification mapping, which includes:

[0058] The arc length error is determined based on the arc length data and the set reference arc length data; the arc length error is fuzzified using the set fuzzy rules and mapped to the set fuzzy universe of discourse to obtain the fuzzified error.

[0059] Step 400: Determine the control quantity based on the fuzzification error; the control quantity includes the pulse load ratio.

[0060] Specifically, the control quantity is determined based on the fuzzification error, including:

[0061] The parameters of the adaptive law are updated based on the fuzzification error to obtain the adaptive law update parameters; the adaptive law is set as a convergence matrix equation determined based on stability analysis; the control quantity is calculated based on the adaptive law update parameters and the fuzzification error.

[0062] The stability analysis method employed is Lyapunov energy analysis. The mapping between the fundamental universe and the fuzzy universe is a triangular membership function.

[0063] Step 500: Based on the control quantity, compensate for the deviation of the arc length data of the target part during the manufacturing process, and use non-consumable electrode argon gas shielded welding to manufacture the target part.

[0064] Specifically, the process design for fuzzification is as follows:

[0065] Based on experience, the length of the arc in the obtained arc length data generally fluctuates between 2mm and 8mm. Taking 5mm as the center, the actual range of change of the arc length error is the difference between the arc length (target) of the reference arc length data and the length of the arc in the arc length data, that is: error = target - length.

[0066] Therefore, error lies in the closed interval [-3, 3].

[0067] Then, a one-dimensional fuzzy controller is selected, and there are a total of N = 7 fuzzy sets.

[0068] At this point, the variables are defined according to fuzzy theory:

[0069] Z: 0; NS: negative small; NM: negative medium; NB: negative large; PS: positive small; PM: positive medium; PB: positive large; These are the terms used in fuzzy theory to describe the magnitude of variables.

[0070] Next, we define the fuzzy universe of discourse for error as {NB, NM, NS, Z, PS, PM, PB}; the fuzzy universe of discourse corresponds to the numbers {-3, -2, -1, 0, 1, 2, 3}.

[0071] The error is converted into a fuzzy error (mf_error) through fuzzification, which is used for membership calculation. The conversion formula is as follows, where the whole part before error is the fuzziness factor, and error_max_real and error_min_real are the actual maximum and minimum error values, respectively, which are taken as ±4 to prevent the error from exceeding the upper and lower limits of ±3 in case of unexpected situations.

[0072]

[0073] Figure 2 The definition of membership functions is shown, all of which are triangular membership functions. In addition to the two marginal universes of discourse, NB and PB, the other five fuzzy sets can be activated under any circumstances, ensuring the continuous updating of all parameters. Figure 2 The membership degree ranges from 0 to 1, and the membership degree is a function value of mf_error.

[0074] Regarding the design of fuzzy control output, i.e., the output control quantity:

[0075] Using a single-valued fuzzy logic generator, a product inference engine, and a center-average defuzzy logic generator, there are a total of M=7 control rules, which can be obtained as follows, where u D For the output, μ is the membership degree, y is the intermediate parameter, e is the arc length error, i is the control rule number, θ is the parameter, and T is the transpose matrix. μ is the intermediate parameter corresponding to the membership degree under the i-th control rule. i (e) represents the membership degree of the arc length error under the i-th control rule.

[0076]

[0077] By rearranging y into a vector, the above expression can be written in matrix form as follows:

[0078] u D (e|θ)=θ T ζ(e)

[0079] Where θ T It is a 1×7 parameter matrix:

[0080]

[0081] The second matrix ζ(e) is represented as follows:

[0082]

[0083] For the parameter matrix θ T The update is as follows:

[0084] First, regarding the fuzzy adaptive law:

[0085] The dynamic equation of a closed-loop system can be expressed by the following equation, where u * For the control law, u D For the output, Λ is the parameter matrix and b is the parameter vector. This represents the rate of change of the arc length error.

[0086]

[0087] Since there is only one dimension, we take Λ and b as numbers that satisfy the following equation (Lyapunov equation), where P is a positive definite matrix and Q is a positive definite square matrix. Q and P can take any values, and we take Q = 4 and Λ = 1.

[0088] Λ T P+PΛ=-Q

[0089] The final calculated value is P = 0.25. Based on Lyapunov stability analysis, the θ update method is as follows:

[0090]

[0091] in, This is an adaptive law.

[0092] Taking the learning rate γ = 2, the final result simplifies to:

[0093]

[0094] Initial values ​​for the parameters of the adaptive law:

[0095] The parameter θ needs an initial value, which should be selected based on actual experimental experience.

[0096] θ T ={5,6,7,8,9,10,11}.

[0097] Example 2

[0098] like Figure 3 As shown, this embodiment of the invention provides an arc length control system for electric arc additive manufacturing. The control system includes: an image data acquisition module 1, an arc length detection module 2, a fuzzification control module 3, a determination module 4, and an execution module 5.

[0099] Image data acquisition module 1 is used to acquire image data of the target part during the manufacturing process.

[0100] Arc length detection module 2 is used to determine arc length data based on image edge data detection methods.

[0101] The fuzzification control module 3 is used to obtain the fuzzification error by using fuzzification mapping based on the arc length data and the set reference arc length data.

[0102] Module 4 is used to determine the control quantity based on the fuzziness error; the control quantity includes the pulse load ratio.

[0103] Execution module 5 is used to compensate for the deviation of arc length data of the target part during the manufacturing process according to the control quantity, and to manufacture the target part by means of non-consumable electrode argon gas shielded welding.

[0104] In practical applications, this invention underwent extensive experimentation in the early stages. For example... Figure 4 As shown, after the experiment begins, starting from the controlled object, which is a physical process, referred to as execution module 5, a welding machine or machine tool can be selected. Execution module 5 has one input and one output. The product of this physical process is a straight rod. During the manufacturing process of the straight rod, image data acquisition module 1, such as a camera, monitors the changes in the image in real time and transmits the data to the industrial control computer (via network cable). The computer then sends the image data to the control software through resource allocation. The control software contains an image processing module, namely arc length detection module 2, which is responsible for analyzing the image to obtain the arc length data. Specifically, a CCD camera captures image data and then transmits it to the main control computer via the GigE network protocol. The main control computer then transmits the image data to the control software.

[0105] The arc length data enters the second module in the software. This module has two inputs: the first is a reference input, the ideal given arc length, i.e., the set reference arc length data. The second is the calculated arc length data, which enters the controller within the red dotted box, i.e., the calculation begins in fuzzy control module 3. After a series of calculations, a control quantity is finally given. In actual practice, the pulse load ratio is selected as the control quantity, which is a parameter in the manufacturing process. This control quantity is input to the controlled object, i.e., execution module 5, thus forming a closed-loop process to achieve automatic control of the arc length.

[0106] Arc length detection module 2 performs image processing through certain matrix operations, detects edge information (black and white image) in the image, and then detects arc length data through the edge information map.

[0107] The fuzzification control module 3 receives two data sets: a set reference arc length and the actual arc length. First, it performs an error calculation: the error is calculated by subtracting the actual arc length from the set reference arc length (target), resulting in the error (error = target - length). This error is then fuzzified using knowledge-driven rules based on human experience. The fuzzified error is mapped to the fuzzy universe to obtain the fuzzy error (mf_error). mf_error has two paths: the first is used for adaptive law calculation to update parameters. The adaptive law update part has an initial value, which is a column vector, written based on experimental experience; it can also be all zeros. The initial value only needs to contain numbers and only affects the convergence speed of the controller. The adaptive law is a matrix equation used to update the parameter vector. The convergence of this equation is determined by Lyapunov stability analysis. After the adaptive law update, the new parameters are fed into the fuzzy inference engine. The second path of `mf_error` is the fuzzy inference engine. After joint calculation with the updated parameters, `mf_error` yields a pulse load ratio, which is the controller's output. This output is then fed into the controlled object and executed in module 5. The rules governing the fuzzification and fuzzy inference processes are driven by human experience, therefore... Figure 4 The two arrows in the knowledge base point to them.

[0108] The controlled object, i.e., execution module 5, receives the control input, so the output of execution module 5 changes, i.e., the arc length changes. Although the arc length is calculated later, the change in arc length and control input is equivalent, so it will affect the image data acquired in the next iteration. Then, in the next loop, the control input will be calculated again based on the new error, until the control is stable and the error is eliminated.

[0109] Example 3

[0110] This invention provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to perform the arc length control method for electric arc additive manufacturing in Embodiment 1.

[0111] In one embodiment, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the arc length control method for arc additive manufacturing in Embodiment 1.

[0112] This invention relates to an arc length control method for arc additive manufacturing based on fuzzy control. Arc additive manufacturing will be referred to as WAAM (Wastewater Additive Manufacturing). This invention encompasses the non-consumable electrode argon shielded welding (TIG) category of WAAM, CNC machine tools, and adaptive fuzzy control. In the actual WAAM process, for a straight rod made of 304 grade steel, during 3D printing, the embedded fuzzy control algorithm continuously adjusts the equipment control parameters based on the arc length until the arc length stabilizes, achieving automatic arc length control. Overall, the process falls under the category of a constant value problem in controller design, similar to the constant temperature control of an air conditioner.

[0113] By using an embedded fuzzy control algorithm, the control parameters are adjusted to keep the arc length stable within a certain range, thus achieving the purpose of automatic control. At the same time, the step curve of the arc length with respect to the control parameters achieves the goals of fast response, small overshoot, and small error.

[0114] Figure 5 This is the error curve between the arc length data and the set reference arc length data during the actual manufacturing process of this invention. Figure 5 The horizontal axis represents time in seconds, and the vertical axis represents arc length in millimeters.

[0115] from Figure 5 It can be seen that after t=400, the actual arc length data changes rapidly and has good stability during the falling and rising step. The instability in the stage from t=200 to t=300 is because the parameter update has not yet reached a stable stage. After t=400, it shows good stability, and at this time the arc length can be automatically controlled. Therefore, the present invention has the characteristics of strong adaptability, good speed, small overshoot, and small error.

[0116] The aforementioned low error rate stems from the selection of positive definite coefficients in the Lyapunov equations of the adaptive law. A larger coefficient selection leads to faster convergence. Good adaptability arises from the type selection; the adaptive fuzzy controller itself is an intelligent controller designed based on human experience, exhibiting characteristics similar to human experience during the control process. Fastness and low overshoot are due to the well-defined adaptive law, an error-driven parameter compensation algorithm. Combined with a reasonable selection of the learning rate γ, this results in better and more stable dynamic characteristics.

[0117] Figure 6 In actual manufacturing, a 300mm long straight rod was produced without any human intervention, and the entire process was controlled and adjusted automatically by the machine.

[0118] This invention aims to solve the problem of automatic arc length control during the straight rod forming process. Since arc length changes frequently occur during straight rod forming, and the errors caused by these changes can accumulate, it becomes very difficult to correct errors once they are detected. Therefore, a computer-based automatic arc length control method is needed. This method corrects for any changes in arc length, preventing error accumulation, ensuring the orderly conduct of the experiment, and freeing up manual labor. Workers only need to start the program and wait for the straight rod to complete forming, eliminating the need for real-time manual observation and judgment. After investigating the WAAM intelligent control method, it was found that the PID control method suffers from significant fluctuations. Furthermore, the WAAM process is a complex process with multiple phase transitions, and currently there is no effective physical model to mathematically represent it. Therefore, general model-based control methods are not feasible. Thus, a knowledge-based intelligent control method was considered. However, considering that this controller needs to be based on human experience while the process can be understood by humans, fuzzy control was chosen. Since parameter corrections in neural network control are irregular, merely pure numerical changes that cannot be given physical meaning, they were not selected. Fuzzy control does not require a physical process model; it only requires human experience. However, experience can be inaccurate, and one person's experience cannot be perfect or comprehensive. Therefore, a parameter-adaptive fuzzy controller was ultimately chosen—an adaptive fuzzy control method that automatically learns parameters during the process based on known human experience.

[0119] This invention can not only automatically control the arc length of a straight rod in actual experiments without human intervention, but it has also been put into use.

[0120] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0121] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for controlling the arc length in electric arc additive manufacturing, characterized in that, The control method includes: Acquire image data of the target part during the manufacturing process; The arc length data is determined using an image edge detection method based on the image data. Based on the arc length data and the set reference arc length data, fuzzification error is obtained by fuzzification mapping. The control quantity is determined based on the fuzzification error; the control quantity includes the pulse load ratio. Based on the control quantity, the deviation of the arc length data of the target part during the manufacturing process is compensated, and the target part is manufactured by non-consumable electrode argon gas shielded welding. Based on the image data, an image edge detection method is used to determine the arc length data, specifically including: The image data is subjected to a set matrix operation to obtain image processing data; the set matrix operation includes: grayscale conversion matrix; The image processing data is filtered to obtain filtered image data; The image filtering data is processed by pixel edge localization and edge connection to obtain the arc length data; Determining the control quantity based on the fuzzification error specifically includes: The parameters of the set adaptive law are updated based on the fuzzification error to obtain the adaptive law update parameters; the set adaptive law is a convergence matrix equation determined based on the stability analysis method. The control quantity is calculated based on the adaptive law update parameters and the fuzzification error; Arc length error The actual range of variation is calculated by subtracting the arc length from the arc length in the reference arc length data. Will Converting fuzzification into fuzzy error Used for membership degree calculation; membership degree is The function value; ； This represents the actual maximum error. This represents the actual minimum error. The number of fuzzy sets; The fuzzy universe of discourse for error is {NB, NM, NS, Z, PS, PM, PB}. Z: 0; NS: Negative Small; NM: Negative Medium; NB: Negative Large; PS: Positive Small; PM: Positive Medium; PB: Positive Large; The membership functions are defined as triangular membership functions, and except for the two marginal universes NB and PB, the other five fuzzy sets can be activated under any circumstances, ensuring the continuous updating of all parameters.

2. The arc length control method for electric arc additive manufacturing according to claim 1, characterized in that, Based on the arc length data and the set reference arc length data, fuzzification error is obtained using fuzzification mapping, specifically including: The arc length error is determined based on the arc length data and the set reference arc length data; The arc length error is fuzzified using a set fuzzy rule and mapped to a set fuzzy universe to obtain the fuzzified error.

3. The arc length control method for electric arc additive manufacturing according to claim 1, characterized in that, The stability analysis method used is Lyapunov energy analysis.

4. An arc length control system for electric arc additive manufacturing, characterized in that, The control system includes: The image data acquisition module is used to acquire image data of the target part during the manufacturing process; The arc length detection module is used to determine the arc length data based on the image data using an image edge data detection method; The fuzzification control module is used to obtain the fuzzification error by using fuzzification mapping based on the arc length data and the set reference arc length data; The determination module determines a control quantity based on the fuzzification error; the control quantity includes the pulse load ratio. The execution module is used to compensate for the deviation of the arc length data of the target part during the manufacturing process according to the control quantity, and to manufacture the target part by means of non-consumable electrode argon gas shielded welding. Based on the image data, an image edge detection method is used to determine the arc length data, specifically including: The image data is subjected to a set matrix operation to obtain image processing data; the set matrix operation includes: grayscale conversion matrix; The image processing data is filtered to obtain filtered image data; The image filtering data is processed by pixel edge localization and edge connection to obtain the arc length data; Determining the control quantity based on the fuzzification error specifically includes: The parameters of the set adaptive law are updated based on the fuzzification error to obtain the adaptive law update parameters; the set adaptive law is a convergence matrix equation determined based on the stability analysis method. The control quantity is calculated based on the adaptive law update parameters and the fuzzification error; Arc length error The actual range of variation is calculated by subtracting the arc length from the arc length in the reference arc length data. Will Converting fuzzification into fuzzification error Used for membership degree calculation; membership degree is The function value; ； This represents the actual maximum error. This represents the actual minimum error. The number of fuzzy sets; The fuzzy universe of discourse for error is {NB, NM, NS, Z, PS, PM, PB}. Z: 0; NS: Negative Small; NM: Negative Medium; NB: Negative Large; PS: Positive Small; PM: Positive Medium; PB: Positive Large; The membership functions are defined as triangular membership functions, and except for the two marginal universes NB and PB, the other five fuzzy sets can be activated under any circumstances, ensuring the continuous updating of all parameters.

5. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the arc length control method for electric arc additive manufacturing as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the arc length control method for electric arc additive manufacturing as described in any one of claims 1 to 3.

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