An arc additive manufacturing process optimization method and system based on deposition rate

Abstract

The present invention relates to the technical field of welding parameter control, and an optimization method and system for an arc additive manufacturing process based on deposition rate, comprising: performing melting deposition according to an original welding parameter set to obtain an intermediate metal component, evaluating welding influencing factors for the melting deposition step to obtain a welding correction coefficient, calculating the original deposition rate, using a photographing unit to perform surface photographing on the intermediate metal component to obtain a component surface image, obtaining a component defect score based on the component surface image, obtaining an adjusted deposition rate based on the component defect score and a standard defect score, obtaining an adjusted welding parameter set according to the adjusted deposition rate, taking the adjusted welding parameter set as the original welding parameter set, and repeating the melting deposition until the intermediate metal component is completed. The present invention can improve the component quality and component production efficiency in the arc additive manufacturing process.

Description

Technical Field

[0001] The present invention relates to the technical field of welding parameter control, and particularly to an optimization method and system for arc additive manufacturing process based on deposition rate. Background Art

[0002] Arc additive manufacturing is an advanced manufacturing technology that constructs components layer by layer by melting metal wires through an arc. It has the advantages of high material utilization rate, fast prototype manufacturing, and high production efficiency. Therefore, it is widely applicable to various fields, especially high-value-added fields such as aerospace, automotive, and medical, and is an important development direction for future manufacturing, which is of great significance for promoting industrial upgrading and sustainable development.

[0003] At present, arc additive mainly relies on manual experience for parameter setting, and the parameters determined according to experience often lead to an uncontrolled welding process, thus reducing the quality of components. In addition, this method mainly relies on post-detection in quality control and lacks real-time feedback and optimization mechanisms, which makes it difficult to detect and correct component defects in a timely manner, thereby affecting the forming quality of components and reducing production efficiency. Summary of the Invention

[0004] The present invention provides an optimization method and system for arc additive manufacturing process based on deposition rate, and its main purpose is to improve the quality and production efficiency of components in the arc additive process.

[0005] To achieve the above object, an optimization method for arc additive manufacturing process based on deposition rate provided by the present invention includes:

[0006] Receiving an arc additive instruction, and determining an original welding parameter set based on the arc additive instruction, wherein the original welding parameter set includes: original voltage, original current, and original wire feeding speed;

[0007] Under the control of the original welding parameter set, using a pre-constructed arc additive device to melt and deposit a pre-obtained metal wire to obtain an intermediate metal component, wherein the duration of the melting and deposition is a preset welding cycle, and the arc additive device includes: a sensor monitoring unit and a photographing unit;

[0008] Evaluating welding influencing factors for the melting and deposition step to obtain a welding correction coefficient, and calculating an original deposition rate according to the welding correction coefficient and the original welding parameter set;

[0009] Using the photographing unit to photograph the surface of the intermediate metal component to obtain a component surface image, and based on the component surface image, analyzing the surface quality of the intermediate metal component to obtain a component defect score;

[0010] Adjust the original deposition rate based on the component defect score and the preset standard defect score to obtain the adjusted deposition rate;

[0011] Adjust the original welding parameter set according to the adjusted deposition rate to obtain the adjusted welding parameter set;

[0012] Take the adjusted welding parameter set as the original welding parameter set, and return the step of melting and depositing the pre-obtained metal wire using the pre-built arc additive manufacturing equipment under the control of the original welding parameter set until the intermediate metal component is completed.

[0013] Optionally, the evaluation of welding influencing factors for the melting deposition step to obtain the welding correction coefficient includes:

[0014] Obtain the welding influencing factor group, where the welding influencing factor group includes: wire diameter, wire extension length, welding angle, and shielding gas flow rate;

[0015] Use the sensor monitoring unit to detect the environmental influencing factor group, where the sensor monitoring unit includes: temperature sensor, humidity sensor, and wind speed sensor, and the environmental influencing factor group includes: environmental temperature, environmental humidity, and environmental wind speed;

[0016] Calculate the welding correction coefficient according to the welding influencing factor group and the environmental influencing factor group, where the welding correction coefficient is expressed as:

[0017] ,

[0018] Where, represents the welding correction coefficient, represents the preset empirical fitting coefficient, represents the environmental temperature, represents the wire diameter, represents the shielding gas flow rate, represents the wire extension length, represents the environmental humidity, represents the environmental wind speed, represents the sine function, represents the welding angle.

[0019] Optionally, the calculation of the original deposition rate according to the welding correction coefficient and the original welding parameter set includes:

[0020] Calculate the original deposition rate using the preset deposition rate calculation formula, where the deposition rate calculation formula is expressed as:

[0021] ,

[0022] Where, represents the original deposition rate, represents the original voltage, represents the original current, represents the preset arc efficiency, represents the preset density of the metal welding wire, represents the original wire feeding speed.

[0023] Optionally, based on the image of the component surface, perform component surface quality analysis on the intermediate metal component to obtain a component defect score, including:

[0024] Perform image detection on the component surface image to obtain a quality evaluation parameter group, where the quality evaluation parameter group includes: crack growth rate and surface roughness;

[0025] Based on the quality evaluation parameter group, use the following formula to calculate the component defect score:

[0026] ,

[0027] where, represents the component defect score, represents the surface roughness, represents the preset standard roughness, represents the crack growth rate.

[0028] Optionally, the performing image detection on the component surface image to obtain a quality evaluation parameter group includes:

[0029] Perform graying on the component surface image to obtain a gray surface image, and perform roughness detection on the gray surface image to obtain the surface roughness;

[0030] Use a preset edge detection algorithm to perform crack contour detection on the gray surface image to obtain a crack surface image, and determine whether the crack surface image contains cracks;

[0031] If the crack surface image does not contain cracks, record the crack growth rate as 0;

[0032] If the crack surface image contains cracks, identify the surface crack group in the crack surface image, where the surface crack group includes one or more surface cracks;

[0033] Identify the surface crack length group of the surface crack group, where the surface crack length in the surface crack length group corresponds to the surface crack in the surface crack group one by one, and the surface crack length is the length of the corresponding surface crack;

[0034] Calculate the crack growth rate according to the surface crack length group, and confirm the crack growth rate and the surface roughness as the quality evaluation parameter group.

[0035] Optionally, calculating the crack growth rate according to the surface crack length group includes:

[0036] Constructing a surface crack vector based on the surface crack length group, where the dimension of the surface crack vector is the same as the number of surface crack lengths in the surface crack length group;

[0037] Constructing a historical crack vector according to the surface crack group, where the dimension of the historical crack vector is the same as the dimension of the surface crack vector;

[0038] Calculating the crack growth rate according to the surface crack vector and the historical crack vector, where the crack growth rate is expressed as:

[0039] ,

[0040] where, represents the dimension of the surface crack vector or the dimension of the historical crack vector, represents the surface crack vector, represents the historical crack vector, represents taking the modulus length.

[0041] Optionally, constructing the historical crack vector according to the surface crack group includes:

[0042] Confirming the start time of the melting deposition step and querying the previous cycle crack group based on the start time;

[0043] Respectively obtaining the number of previous cycle cracks in the previous cycle crack group and the number of surface cracks in the surface crack group, and judging whether the number of previous cycle cracks is equal to the number of surface cracks;

[0044] If the number of previous cycle cracks is equal to the number of surface cracks, obtaining the previous cycle crack length group of the previous cycle crack group and constructing a historical crack vector according to the previous cycle crack length group, where the dimension of the historical crack vector is the same as the number of previous cycle crack lengths in the previous cycle crack length group;

[0045] If the number of previous cycle cracks is not equal to the number of surface cracks, identifying the newly added crack group and the homologous crack group in the surface crack group;

[0046] Respectively obtaining the newly added serial number group and the homologous serial number group of the newly added crack group and the homologous crack group in the surface crack group, where the newly added crack group corresponds one-to-one with the newly added serial number group, and the homologous crack group corresponds one-to-one with the homologous serial number group;

[0047] Based on the newly added crack group and the newly added serial number group, expand the previous cycle crack group to obtain a historical crack group. Among them, the historical crack group includes all the previous cycle cracks in the previous cycle crack group and multiple empty cracks. The arrangement serial number of the previous cycle crack in the historical crack group is the same as the homologous serial number of the corresponding homologous crack. The empty crack refers to a crack with a length of 0, and the arrangement serial number of the empty crack in the historical crack group is the same as the newly added serial number of the corresponding newly added crack;

[0048] Obtain the historical crack length group of the historical crack group, and construct a historical crack vector based on the historical crack length group.

[0049] Optionally, the roughness detection of the grayscale surface image to obtain the surface roughness includes:

[0050] Extract the grayscale directions in the preset grayscale direction group in sequence, and calculate the grayscale co-occurrence matrix of the grayscale surface image in the grayscale direction;

[0051] Calculate the contrast of the grayscale co-occurrence matrix;

[0052] Summarize the contrasts to obtain a contrast group, calculate the average contrast of the contrast group, and record the average contrast as the roughness.

[0053] Optionally, the adjustment of the original deposition rate based on the component defect score and the preset standard defect score to obtain the adjusted deposition rate includes:

[0054] Calculate the score data difference between the component defect score and the standard defect score, and determine whether the score data difference is greater than the preset controllable data difference;

[0055] If the score data difference is not greater than the controllable data difference, record the original deposition rate as the adjusted deposition rate;

[0056] If the score data difference is greater than the controllable data difference, calculate the adjusted deposition rate using the following formula:

[0057] ,

[0058] Among them, represents the adjusted deposition rate, represents the standard defect score.

[0059] To achieve the above object, the present invention also provides an arc additive manufacturing process optimization system based on the deposition rate, including:

[0060] A metal component deposition module for receiving an arc additive manufacturing instruction, determining an original welding parameter set based on the arc additive manufacturing instruction, where the original welding parameter set includes: an original voltage, an original current, and an original wire feeding speed. Under the control of the original welding parameter set, a pre-acquired metal wire is melted and deposited using a pre-built arc additive manufacturing device to obtain an intermediate metal component, where the duration of the melting and deposition is a preset welding cycle, and the arc additive manufacturing device includes: a sensor monitoring unit and a photographing unit;

[0061] A component defect evaluation module for evaluating welding influencing factors in the melting and deposition step to obtain a welding correction coefficient, and calculating an original deposition rate based on the welding correction coefficient and the original welding parameter set. The photographing unit is used to photograph the surface of the intermediate metal component to obtain a component surface image, and based on the component surface image, a component surface quality analysis is performed on the intermediate metal component to obtain a component defect score;

[0062] A welding parameter adjustment module for adjusting the original deposition rate based on the component defect score and a preset standard defect score to obtain an adjusted deposition rate, and adjusting the original welding parameter set according to the adjusted deposition rate to obtain an adjusted welding parameter set;

[0063] A melting and deposition cycle module for using the adjusted welding parameter set as the original welding parameter set and returning the step of melting and depositing a pre-acquired metal wire using a pre-built arc additive manufacturing device under the control of the original welding parameter set until the intermediate metal component is completed.

[0064] To solve the above problems, the present invention also provides an electronic device, which includes:

[0065] A memory storing at least one instruction;

[0066] A processor for executing the instructions stored in the memory to implement the above-mentioned arc additive manufacturing process optimization method based on the deposition rate.

[0067] To solve the above problems, the present invention also provides a computer-readable storage medium storing at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned arc additive manufacturing process optimization method based on the deposition rate.

[0068] To solve the problems described in the background art, the present invention first determines an original welding parameter set based on an arc additive manufacturing instruction, which provides initial conditions for the subsequent additive manufacturing process. Then, under the control of the original welding parameter set, melting deposition is carried out to obtain an intermediate metal component. Next, an evaluation of welding influencing factors for the melting deposition step is performed to obtain a welding correction coefficient, which combines factors that may affect the deposition rate during the welding process, making the subsequently calculated original deposition rate more accurate. Further, a surface image of the component is obtained through an imaging unit, and a component defect score shown by the surface image of the component is calculated. This component defect score can intuitively reflect the defect conditions on the surface of the component, such as cracks, pores, roughness, etc. Therefore, surface defects can be detected in a timely manner through the component defect score, avoiding the further expansion of defects during the subsequent manufacturing process, thereby improving the overall quality of the component. Then, based on the component defect score and a standard defect score, the original deposition rate is adjusted to obtain an adjusted deposition rate. This step can dynamically adjust the deposition rate through the comparison between the component defect score and the standard defect score, thereby realizing the real-time optimization of the additive manufacturing process. And by adjusting the deposition rate, the production efficiency can be improved on the premise of ensuring the quality of the component. In addition, the present invention also adjusts the original welding parameter set according to the adjusted deposition rate to obtain an adjusted welding parameter set. This step demonstrates the process of precisely controlling melting deposition, further improving the quality of the component, and can dynamically adjust the welding parameters according to different manufacturing stages and component states, so that the present invention can adapt to complex manufacturing environments and requirements. Finally, by using the adjusted welding parameter set as the new original welding parameter set, the melting deposition process is cyclically executed. This step reduces the influence of random factors on the manufacturing process through multiple cyclic optimizations, improving the stability of component manufacturing. Therefore, the present invention can improve the quality of components and the production efficiency of components during the arc additive manufacturing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 FIG. is a schematic flow chart of an arc additive manufacturing process optimization method based on deposition rate provided by an embodiment of the present invention;

[0070] Figure 2 FIG. is a functional module diagram of an arc additive manufacturing process optimization system based on deposition rate provided by an embodiment of the present invention;

[0071] Figure 3 FIG. is a schematic structural diagram of an electronic device for implementing the arc additive manufacturing process optimization method based on deposition rate provided by an embodiment of the present invention.

[0072] DESCRIPTION OF THE REFERENCE NUMERALS:

[0073] 1, electronic device; 10, processor; 11, memory; 12, bus.

[0074] The realization, functional features and advantages of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Specific Embodiments

[0075] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0076] An embodiment of the present application provides an optimization method for an arc additive manufacturing process based on deposition rate. The execution subject of the optimization method for the arc additive manufacturing process based on deposition rate includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the optimization method for the arc additive manufacturing process based on deposition rate can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0077] Refer to Figure 1 As shown, it is a schematic flow chart of an optimization method for an arc additive manufacturing process based on deposition rate provided by an embodiment of the present invention. In this embodiment, the optimization method for the arc additive manufacturing process based on deposition rate includes:

[0078] S1. Receive an arc additive manufacturing instruction, and determine an original welding parameter set based on the arc additive manufacturing instruction. Among them, the original welding parameter set includes: an original voltage, an original current, and an original wire feeding speed.

[0079] It can be understood that the arc additive manufacturing instruction refers to a signal instruction initiated by a human for arc additive manufacturing. This instruction contains the parameters that need to be set during the arc additive manufacturing process, that is, the original welding parameter set. In the original welding parameter set, it includes: an original voltage, an original current, and an original wire feeding speed. Among them, the original voltage refers to the output voltage of the welding power supply preset during the arc additive manufacturing process, the original current refers to the output current of the welding power supply preset during the arc additive manufacturing process, and the original wire feeding speed refers to the wire feeding speed preset during the arc additive manufacturing process.

[0080] S2. Under the control of the original welding parameter set, use a pre-constructed arc additive manufacturing device to melt and deposit a pre-obtained metal wire to obtain an intermediate metal component. Among them, the duration of the melting and deposition is a preset welding cycle. The arc additive manufacturing device includes: a sensor monitoring unit and a photographing unit.

[0081] It should be explained that the arc additive manufacturing equipment refers to the equipment for melting and depositing metal welding wires. Among them, the sensor monitoring unit refers to the set of temperature sensors, wind speed sensors, and humidity sensors, which are respectively used to detect the temperature, wind speed, and humidity during the melting and deposition process. The shooting unit refers to a high-definition camera used to shoot the components after melting and deposition. In addition to the above-mentioned sensor monitoring unit and shooting unit, the arc additive manufacturing equipment also includes: a power source, a wire feeding system, a welding robot, a welding torch, a shielding gas system, a cooling system, and a fixture workbench. Among them, the power source is used to provide stable arc energy and control the current and voltage during the welding process. The wire feeding system refers to the conveying equipment that transports the metal welding wire from the wire outlet to the arc molten pool, and the speed at which the wire feeding system operates the metal welding wire is the subsequent original wire feeding speed. The welding robot is used to control the movement trajectory of the welding torch during the arc welding process, so that the welding process can proceed along the pre-set path. The shielding gas system refers to the equipment that provides inert gas, which is used to protect the arc molten pool from oxidation and pollution. The cooling system refers to the equipment used to cool the welding torch, and by cooling the welding torch, the problem of overheating of the welding torch is avoided. The fixture workbench refers to a flat table used to fix the workpiece to be processed, ensuring the stability during the processing process.

[0082] It can be understood that the welding cycle refers to the duration of melting and deposition between every two adjustments of the original parameter set. It should be noted that the welding cycle here does not refer to the duration of completing the entire melting and deposition, but the original parameter set in the current melting and deposition process is adjusted every welding cycle.

[0083] S3. Evaluate the welding influencing factors of the melting and deposition step to obtain a welding correction coefficient, and calculate the original deposition rate based on the welding correction coefficient and the original welding parameter set.

[0084] It can be understood that the evaluation of welding influencing factors refers to the process of comprehensively analyzing the influence degree of various factors (including process parameters, environmental conditions, etc.) during the welding process on the melting and deposition effect, so as to obtain the welding correction coefficient. The welding correction coefficient refers to the quantitative value of the influence degree of welding influencing factors and environmental influencing factors on the deposition rate during the melting and deposition process. The greater the influence degree of welding influencing factors and environmental influencing factors on the deposition rate, the greater the welding correction factor. The original deposition rate refers to the volume of the metal welding wire deposited per unit time in the melting and deposition step.

[0085] Specifically, the evaluation of the welding influencing factors of the melting and deposition step to obtain the welding correction coefficient includes:

[0086] Obtain a set of welding influencing factors, where the set of welding influencing factors includes: wire diameter, wire extension length, welding angle, and shielding gas flow rate;

[0087] Detect an environmental impact factor group by using the sensor monitoring unit, where the sensor monitoring unit includes: a temperature sensor, a humidity sensor, and a wind speed sensor, and the environmental impact factor group includes: environmental temperature, environmental humidity, and environmental wind speed;

[0088] Calculate a welding correction coefficient according to the welding impact factor group and the environmental impact factor group, where the welding correction coefficient is expressed as:

[0089] ,

[0090] where, represents the welding correction coefficient, represents a preset empirical fitting coefficient, represents the environmental temperature, represents the wire diameter, represents the shielding gas flow rate, represents the wire extension length, represents the environmental humidity, represents the environmental wind speed, represents the sine function, represents the welding angle.

[0091] It is understandable that the wire diameter refers to the diameter of the metal wire used for welding, and the wire extension length refers to the distance from the nozzle of the welding torch to the end of the arc. The welding angle refers to the angle between the welding torch and the surface of the component to be welded. The temperature sensor, humidity sensor, and wind speed sensor respectively refer to the sensors used to detect temperature, humidity, and wind speed, and the environmental temperature, environmental humidity, and environmental wind speed respectively refer to the temperature, humidity, and wind speed of the environment during the melt deposition process. The shielding gas flow rate refers to the volume of shielding gas delivered to the molten pool through the nozzle of the welding torch per unit time. The main function of the shielding gas is to isolate the molten pool from the outside air and prevent the metal in the molten pool from undergoing chemical reactions with oxygen, nitrogen, etc. at high temperatures, thereby avoiding the generation of defects such as oxidation and nitridation. The shielding gas flow rate can be measured by a gas flow meter. The empirical fitting coefficient refers to a constant set artificially based on historical experience, and it can be fitted through multiple previous experiments. Optionally, the empirical fitting coefficient is set to 0.9.

[0092] Specifically, calculating the original deposition rate according to the welding correction coefficient and the original welding parameter group includes:

[0093] Calculate the original deposition rate by using a preset deposition rate calculation formula, where the deposition rate calculation formula is expressed as:

[0094] ,

[0095] where, represents the original deposition rate, represents the original voltage, represents the original current, represents the preset arc efficiency, represents the preset density of the metal welding wire, represents the original wire feeding speed.

[0096] It can be understood that the arc efficiency refers to the energy required to melt a unit mass of the metal welding wire, and the density of the metal welding wire refers to the density of the metal welding wire. Both the arc efficiency and the density of the metal welding wire are related to the metal welding wire, and can be obtained by querying the specification description of the metal welding wire.

[0097] S4. Use the photographing unit to photograph the surface of the intermediate metal member to obtain a member surface image, and based on the member surface image, perform member surface quality analysis on the intermediate metal member to obtain a member defect score.

[0098] It can be understood that the "performing surface photographing" refers to the photographing unit photographing the surface of the intermediate metal member. Among them, the position of the photographing unit is fixed. This solution does not limit the specific position, but it should be noted that the fixed position of the photographing unit should not interfere with the normal operation of the welding torch, and the photographing unit should be able to photograph the surface of the intermediate metal member to the greatest extent. For example: in the pre-determined three-dimensional drawing of the member, if the left side of the intermediate metal member to be welded is a smooth and complete plane, then in order to photograph the surface of the intermediate metal member to the greatest extent, the photographing unit can be fixed on the left side of the intermediate metal member. The "member surface image" refers to the surface image of the intermediate metal member. The "member surface quality analysis" refers to the process of obtaining a member defect score by performing image analysis on the member surface image. The "member defect score" refers to the quantitative value of the surface defect degree of the intermediate metal member. The higher the surface defect degree of the intermediate metal member, the greater the member defect score.

[0099] Specifically, the "performing member surface quality analysis on the intermediate metal member based on the member surface image to obtain a member defect score" includes:

[0100] Perform image detection on the member surface image to obtain a quality evaluation parameter group, where the quality evaluation parameter group includes: crack growth rate and surface roughness;

[0101] Based on the quality evaluation parameter group, use the following formula to calculate the member defect score:

[0102] ,

[0103] where, represents the member defect score, represents the surface roughness, represents the preset standard roughness, Indicates the crack growth rate.

[0104] It should be explained that the crack growth rate refers to the quantitative value of the growth degree of the surface crack of the intermediate metal component compared with the previous melting deposition cycle. The higher the growth degree of the surface crack of the intermediate metal component compared with the previous melting deposition cycle, the greater the crack growth rate. The surface roughness refers to the quantitative value of the roughness degree of the surface of the intermediate metal component. The rougher the surface of the intermediate metal component, the greater the surface roughness. The standard roughness rate refers to the constant of the roughness set artificially.

[0105] Furthermore, the crack growth rate reflects the expansion of the surface crack of the intermediate metal component between adjacent melting deposition cycles. By detecting the crack growth rate, potential problems can be discovered at the initial stage of crack expansion. At the same time, the crack growth rate is closely related to welding process parameters (such as current, voltage, wire feeding speed, etc.). By detecting the crack growth rate, it also helps the staff to judge whether the current process parameters are reasonable. The surface roughness reflects the microscopic unevenness of the component surface. Higher surface roughness may lead to problems such as stress concentration and accelerated corrosion. Therefore, by detecting the surface roughness, the quality of the component surface can be intuitively evaluated.

[0106] Specifically, performing image detection on the surface image of the component to obtain a quality evaluation parameter group, including:

[0107] Gray-scale the surface image of the component to obtain a gray-scale surface image, and perform roughness detection on the gray-scale surface image to obtain the surface roughness;

[0108] Use a preset edge detection algorithm to detect the crack contour of the gray-scale surface image to obtain a crack surface image, and judge whether the crack surface image contains cracks;

[0109] If the crack surface image does not contain cracks, record the crack growth rate as 0;

[0110] If the crack surface image contains cracks, identify the surface crack group in the crack surface image, where the surface crack group includes one or more surface cracks;

[0111] Identify the surface crack length group of the surface crack group, where the surface crack lengths in the surface crack length group correspond one by one to the surface cracks in the surface crack group, and the surface crack length is the length of the corresponding surface crack;

[0112] Calculate the crack growth rate according to the surface crack length group, and confirm the crack growth rate and the surface roughness as the quality evaluation parameter group.

[0113] It is understandable that the grayscale surface image refers to the surface image of the component after grayscale conversion, and the edge detection algorithm refers to an image processing technology that can identify areas with significant changes in grayscale values in a grayscale image. Among them, the areas with significant changes can be regarded as cracks on the surface of the component in the grayscale surface image. Some existing edge detection algorithms can achieve this purpose, such as: Canny edge detection, Sobel edge detection, etc. The cracked surface image refers to the grayscale surface image that highlights the crack contour after edge detection processing. The surface crack group refers to the set of cracks that appear in the cracked surface image, and the surface crack length group refers to the set of the lengths of all surface cracks in the surface crack group.

[0114] Specifically, calculating the crack growth rate according to the surface crack length group includes:

[0115] Constructing a surface crack vector based on the surface crack length group, where the dimension of the surface crack vector is the same as the number of surface crack lengths in the surface crack length group;

[0116] Constructing a historical crack vector according to the surface crack group, where the dimension of the historical crack vector is the same as the dimension of the surface crack vector;

[0117] Calculating the crack growth rate according to the surface crack vector and the historical crack vector, where the crack growth rate is expressed as:

[0118] ,

[0119] Where, represents the dimension of the surface crack vector or the dimension of the historical crack vector, represents the surface crack vector, represents the historical crack vector, represents taking the modulus length.

[0120] It should be explained that the surface crack vector refers to a vector representing the surface crack length information in the surface crack length group, and this surface crack vector is composed of the surface crack lengths in the surface crack length group.

[0121] Exemplarily, a certain surface crack group is: ( , , ), where, , , respectively represent three different cracks, and the lengths of these three cracks are: 2.5 mm, 1.2 mm, 0.9 mm. Then the surface crack length group corresponding to the surface crack group is (2.5 mm, 1.2 mm, 0.9 mm), and the corresponding surface crack vector is: , It should be noted that the order of the surface crack lengths in the surface crack vector should be the same as the order of the surface cracks in the surface crack group.

[0122] It is understandable that the historical crack vector refers to the surface crack vector in the previous cycle (i.e., the previous welding cycle of the current welding cycle). It should be noted that since new cracks may be generated in the current welding cycle, in this case, the number of vector elements in the historical crack vector will be different from the number of vector elements in the surface crack vector. Therefore, when actually constructing the historical crack vector, 0 elements need to be inserted according to the actual situation, and the detailed steps will be given later.

[0123] Specifically, constructing the historical crack vector according to the surface crack group includes:

[0124] Determine the start time of the melting deposition step, and query the previous cycle crack group based on the start time;

[0125] Obtain the number of previous cycle cracks in the previous cycle crack group and the number of surface cracks in the surface crack group respectively, and judge whether the number of previous cycle cracks is equal to the number of surface cracks;

[0126] If the number of previous cycle cracks is equal to the number of surface cracks, obtain the previous cycle crack length group of the previous cycle crack group, and construct the historical crack vector according to the previous cycle crack length group, where the dimension of the historical crack vector is the same as the number of previous cycle crack lengths in the previous cycle crack length group;

[0127] If the number of previous cycle cracks is not equal to the number of surface cracks, identify the newly added crack group and the homologous crack group in the surface crack group;

[0128] Obtain the newly added serial number group and the homologous serial number group of the newly added crack group and the homologous crack group in the surface crack group respectively, where the newly added crack group corresponds to the newly added serial number group one by one, and the homologous crack group corresponds to the homologous serial number group one by one;

[0129] Based on the newly added crack group and the newly added serial number group, expand the previous cycle crack group to obtain the historical crack group, where the historical crack group includes all the previous cycle cracks in the previous cycle crack group and multiple empty cracks, and the arrangement serial number of the previous cycle cracks in the historical crack group is the same as the homologous serial number of the corresponding homologous cracks. The empty crack refers to a crack with a length of 0, and the arrangement serial number of the empty crack in the historical crack group is the same as the newly added serial number of the corresponding newly added crack;

[0130] Obtain the historical crack length group of the historical crack group, and construct the historical crack vector based on the historical crack length group.

[0131] It is understandable that the start time refers to the start time of the melting deposition in the current welding cycle. The previous cycle crack group refers to the surface crack group in the surface component image captured in the previous welding cycle at the start time. It should be noted that when this welding cycle is the first welding cycle, the previous cycle crack group will be an empty set. The previous cycle crack number refers to the number of previous cycle cracks in the previous cycle crack group, and the surface crack number refers to the number of surface cracks in the surface crack group. The previous cycle crack length group refers to the length combination of all previous cycle cracks in the previous cycle crack group.

[0132] It should be explained that the surface crack number should be greater than or equal to the previous cycle crack number, because during the melting deposition process, the cracks will gradually increase or remain unchanged over time, but will not decrease. At the same time, the increase in cracks not only means an increase in the number of cracks, and the newly added cracks at this time are the so-called newly added cracks, but also means that for the same crack, its length becomes longer compared to the previous cycle. Here, the same crack refers to the homologous crack. It is understandable that the newly added serial number refers to the sorting serial number of the newly added cracks in the newly added crack group in the surface crack group, and the homologous serial number refers to the sorting serial number of the homologous cracks in the homologous crack group in the surface crack group. Expanding the previous cycle crack group means using 0 values to supplement the previous cycle crack group with fewer crack numbers to make the crack number consistent with that of the surface crack group.

[0133] Exemplarily, a certain surface crack group is: ( , , ), and its corresponding surface crack length group is (2.5mm, 1.2mm, 0.9mm). The previous cycle crack group in the previous welding cycle is: ( , ), and its corresponding previous cycle crack length group is (2.2mm, 1.0mm). Here, the number of cracks in the previous cycle crack group is not equal to the number of cracks in the surface crack group. At this time, the cracks with the same origin in the surface crack group and the previous cycle crack group are and , and the newly added crack is . Then the newly added crack group and the homologous crack group are respectively ( ) and ( , ), and their corresponding newly added serial number groups and homologous serial number groups are respectively (3) and (1, 2). It should be noted here that although The arrangement serial number is 1 (i.e., it is arranged in the first place), but its arrangement serial number in the surface crack group is 3, so its new serial number is recorded as 3 instead of 1. Since the new cracks in the new crack group do not appear in the previous cycle crack group, it is necessary to increase the positions corresponding to the new serial number group in the previous cycle crack group. Then the previous cycle crack group after the increase is ( , , ). At this time, is a crack with a length of 0. Since the previous cycle crack serial numbers in the previous cycle crack group are the same as the homologous serial number group, no adjustment is required. At this time, the historical crack length group corresponding to the previous cycle crack group after the increase is (2.2 mm, 1.0 mm, 0 mm).

[0134] Specifically, the roughness detection of the grayscale surface image to obtain the surface roughness includes:

[0135] Successively extract the grayscale directions in the preset grayscale direction group, and calculate the gray-level co-occurrence matrix of the grayscale surface image in the grayscale direction;

[0136] Calculate the contrast of the gray-level co-occurrence matrix;

[0137] Summarize the contrasts to obtain a contrast group, and calculate the average contrast of the contrast group, and record the average contrast as the roughness.

[0138] It can be understood that the grayscale direction group refers to a direction combination set artificially. In the calculation of the gray-level co-occurrence matrix, 0 degrees, 45 degrees, 90 degrees, and 135 degrees can be selected as this grayscale direction group. The gray-level co-occurrence matrix refers to a two-dimensional matrix describing the distribution of pixel gray values in the grayscale surface image. Among them, calculating the gray-level co-occurrence matrix of the grayscale surface image in the grayscale direction is a prior art and will not be elaborated here. The contrast refers to a numerical value measuring the change range of pixel gray values in the surface grayscale image, and the average contrast refers to the average value of all contrasts in the contrast group. Calculating the contrast of the gray-level co-occurrence matrix can be achieved through the prior art. For example, the contrast can be calculated through the contrast calculation formula, and the contrast calculation formula is expressed as:

[0139] ,

[0140] Among them, represents the contrast, and respectively represent the gray values of two pixel points in the grayscale surface image, and their value ranges are both from 1 to , represents the number of gray levels of the surface grayscale image, represents the gray value of The frequency of simultaneous occurrence of pixels with a certain value and pixels with a gray value of j in the gray scale direction.

[0141] S5. Adjust the original deposition rate based on the component defect score and a preset standard defect score to obtain an adjusted deposition rate.

[0142] It can be understood that the standard defect score refers to a constant set artificially, which is used to represent the maximum value that the component defect score can be accepted. The adjusted deposition rate refers to the original deposition rate after adjustment.

[0143] Specifically, adjusting the original deposition rate based on the component defect score and the preset standard defect score to obtain an adjusted deposition rate includes:

[0144] Calculate the score data difference between the component defect score and the standard defect score, and determine whether the score data difference is greater than a preset controllable data difference;

[0145] If the score data difference is not greater than the controllable data difference, record the original deposition rate as the adjusted deposition rate;

[0146] If the score data difference is greater than the controllable data difference, calculate the adjusted deposition rate using the following formula:

[0147] ,

[0148] where, represents the adjusted deposition rate, represents the standard defect score.

[0149] It should be explained that the score data difference refers to the difference between the component defect score and the standard defect score, and the controllable data difference refers to a constant set artificially. When the score data difference is not greater than the controllable data difference, it means that the component defect score is within the controllable range and does not need to be adjusted. When the score data difference is greater than the controllable data difference, it means that the deviation between the component defect score and the standard defect score is relatively large.

[0150] It should be noted that when the score data difference is greater than the controllable data difference, if the component defect score is higher than the standard defect score, it means that the number or severity of defects in the current component exceeds the preset acceptable range. In this case, in order to reduce the possible defects in the subsequent fused deposition process, the deposition rate should be appropriately reduced, which can more precisely control the melting process and reduce the generation of defects. On the contrary, when the component defect score is lower than or equal to the standard defect score, it means that the number or severity of defects in the current component is within the acceptable range, or even better than expected. At this time, in order to improve production efficiency, the deposition rate can be appropriately increased to speed up the fused deposition process, thereby improving the manufacturing efficiency while ensuring the quality of the component.

[0151] S6. Adjust the original welding parameter set according to the adjusted deposition rate to obtain an adjusted welding parameter set.

[0152] Understandably, the adjusted welding parameter set includes: adjusted voltage, adjusted current, and adjusted wire feeding speed. The adjusted voltage, adjusted current, and adjusted wire feeding speed respectively refer to the adjusted original voltage, original current, and original wire feeding speed. The detailed steps for adjusting the original welding parameter set are as follows: Determine the adjustment ratio, and the adjustment ratio is: , where represents the adjustment ratio. Since the deposition rate increases as the voltage and current increase, and the deposition rate decreases as the wire feeding speed increases, the original welding parameter set can be adjusted according to the following formula: , , , where , and respectively represent the adjusted voltage, adjusted current, and adjusted wire feeding speed.

[0153] S7. Use the adjusted welding parameter set as the original welding parameter set, and return to the step of melting and depositing the pre-obtained metal wire using the pre-constructed arc additive manufacturing equipment under the control of the original welding parameter set until the intermediate metal component is completed.

[0154] It should be explained that the standard for completing the construction of the intermediate metal component is that the size, shape, and overall structure of the intermediate metal component are consistent with the design model.

[0155] To solve the problems described in the background art, the present invention first determines an original welding parameter set based on an arc additive manufacturing instruction, which provides initial conditions for the subsequent additive manufacturing process. Then, under the control of the original welding parameter set, melting deposition is carried out to obtain an intermediate metal component. Next, an evaluation of the welding influencing factors for the melting deposition step is performed to obtain a welding correction coefficient. This welding correction coefficient combines the factors that may affect the deposition rate during the welding process, making the subsequently calculated original deposition rate more accurate. Further, a component surface image is obtained through an imaging unit, and a component defect score shown by the component surface image is calculated. This component defect score can intuitively reflect the defect conditions on the component surface, such as cracks, pores, roughness, etc. Therefore, surface defects can be detected in a timely manner through the component defect score, avoiding the further expansion of defects during the subsequent manufacturing process, thereby improving the overall quality of the component. Then, based on the component defect score and a standard defect score, the original deposition rate is adjusted to obtain an adjusted deposition rate. This step can dynamically adjust the deposition rate through the comparison between the component defect score and the standard defect score, thereby realizing the real-time optimization of the additive manufacturing process. And by adjusting the deposition rate, the production efficiency can be improved on the premise of ensuring the component quality. In addition, the present invention also adjusts the original welding parameter set according to the adjusted deposition rate to obtain an adjusted welding parameter set. This step demonstrates the process of precisely controlling the melting deposition, further improving the quality of the component, and enabling dynamic adjustment of the welding parameters according to different manufacturing stages and component states, so that the present invention can adapt to complex manufacturing environments and requirements. Finally, by using the adjusted welding parameter set as the new original welding parameter set, the melting deposition process is cyclically executed. This step reduces the influence of random factors on the manufacturing process through multiple cycles of optimization, improving the stability of component manufacturing. Therefore, the present invention can improve the component quality and component production efficiency in the arc additive manufacturing process.

[0156] As Figure 2 shown, it is a functional module diagram of an arc additive manufacturing process optimization system based on deposition rate provided by an embodiment of the present invention.

[0157] The arc additive manufacturing process optimization system 100 based on deposition rate according to the present invention can be installed in an electronic device. According to the functions achieved, the arc additive manufacturing process optimization system 100 based on deposition rate can include a metal component deposition module 101, a component defect evaluation module 102, a welding parameter adjustment module 103, and a melting deposition cycle module 104. The modules of the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0158] The metal component deposition module 101 is configured to receive an arc additive manufacturing instruction, and determine an original welding parameter set based on the arc additive manufacturing instruction. The original welding parameter set includes: an original voltage, an original current, and an original wire feeding speed. Under the control of the original welding parameter set, a pre-acquired metal wire is melted and deposited by using a pre-constructed arc additive manufacturing device to obtain an intermediate metal component. The duration of the melting and deposition is a preset welding cycle. The arc additive manufacturing device includes: a sensor monitoring unit and a photographing unit;

[0159] The component defect evaluation module 102 is configured to evaluate welding influencing factors in the melting and deposition step to obtain a welding correction coefficient, calculate an original deposition rate according to the welding correction coefficient and the original welding parameter set, use the photographing unit to perform a surface photograph of the intermediate metal component to obtain a component surface image, and perform component surface quality analysis on the intermediate metal component based on the component surface image to obtain a component defect score;

[0160] The welding parameter adjustment module 103 is configured to adjust the original deposition rate based on the component defect score and a preset standard defect score to obtain an adjusted deposition rate, and adjust the original welding parameter set according to the adjusted deposition rate to obtain an adjusted welding parameter set;

[0161] The melting and deposition cycle module 104 is configured to use the adjusted welding parameter set as the original welding parameter set, and return to the step of melting and depositing the pre-acquired metal wire by using the pre-constructed arc additive manufacturing device under the control of the original welding parameter set until the intermediate metal component is completed.

[0162] Specifically, each module in the arc additive manufacturing process optimization system 100 based on deposition rate in the embodiments of the present invention adopts the same technical means as those in the Figure 1 arc additive manufacturing process optimization method based on deposition rate described above, and can produce the same technical effects, which will not be elaborated here.

[0163] As Figure 3 shown, it is a schematic structural diagram of an electronic device for implementing an arc additive manufacturing process optimization method based on deposition rate provided by an embodiment of the present invention.

[0164] The electronic device 1 may include a processor 10, a memory 11, and a bus 12, and may further include a computer program stored in the memory 11 and executable on the processor 10, such as an arc additive manufacturing process optimization method program based on deposition rate.

[0165] Among them, the memory 11 at least includes one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disc, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as the mobile hard disk of the electronic device 1. In some other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 1. Further, the memory 11 also includes the internal storage unit of the electronic device 1 and also includes an external storage device. The memory 11 can not only be used to store application software installed in the electronic device 1 and various types of data, such as the code of the arc additive manufacturing process optimization method based on deposition rate, etc., but also can be used to temporarily store data that has been output or will be output.

[0166] In some embodiments, the processor 10 may be composed of integrated circuits. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same or different functions packaged, including a combination of one or more Central Processing Units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, connecting various components of the entire electronic device through various interfaces and lines, and by running or executing programs or modules stored in the memory 11 (such as the arc additive manufacturing process optimization method program based on deposition rate, etc.), and calling data stored in the memory 11, to execute various functions of the electronic device 1 and process data.

[0167] The bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is set to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0168] Figure 3 Only the electronic device with components is shown. Those skilled in the art can understand that, Figure 3The structures shown do not constitute a limitation on the electronic device 1, and it may include fewer or more components than those shown, or combine certain components, or have different component arrangements.

[0169] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for powering each component. Preferably, the power source can be logically connected to the at least one processor 10 through a power management system, so as to implement functions such as charge management, discharge management, and power consumption management through the power management system. The power source may also include any components such as one or more DC or AC power sources, a recharge system, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may also include a variety of sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0170] Furthermore, the electronic device 1 may further include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0171] Optionally, the electronic device 1 may further include a user interface. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visual user interface.

[0172] The program of the arc additive manufacturing process optimization method based on the deposition rate stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can implement:

[0173] Receive an arc additive manufacturing instruction, and determine an original welding parameter set based on the arc additive manufacturing instruction. Among them, the original welding parameter set includes: an original voltage, an original current, and an original wire feeding speed;

[0174] Under the control of the original welding parameter set, use a pre-built arc additive manufacturing device to melt and deposit a pre-acquired metal wire to obtain an intermediate metal component. Among them, the duration of the melting and deposition is a preset welding cycle, and the arc additive manufacturing device includes: a sensor monitoring unit and a shooting unit;

[0175] Evaluate the welding influencing factors for the melting deposition step to obtain a welding correction coefficient, and calculate the original deposition rate based on the welding correction coefficient and the original welding parameter set;

[0176] Use the shooting unit to perform surface shooting on the intermediate metal component to obtain a component surface image. Based on the component surface image, perform component surface quality analysis on the intermediate metal component to obtain a component defect score;

[0177] Adjust the original deposition rate based on the component defect score and the preset standard defect score to obtain an adjusted deposition rate;

[0178] Adjust the original welding parameter set according to the adjusted deposition rate to obtain an adjusted welding parameter set;

[0179] Use the adjusted welding parameter set as the original welding parameter set, and return to the step of performing melting deposition on the pre-obtained metal wire using the pre-constructed arc additive manufacturing device under the control of the original welding parameter set until the intermediate metal component is completed.

[0180] Specifically, the specific implementation method of the instructions by the processor 10 can refer to Figures 1 to 3 the description of the relevant steps in the corresponding embodiment, which will not be elaborated here.

[0181] Furthermore, if the modules / units integrated in the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory).

[0182] The present invention also provides a computer-readable storage medium, where the readable storage medium stores a computer program, and when the computer program is executed by the processor of the electronic device, it can implement:

[0183] Receive an arc additive manufacturing instruction, and determine an original welding parameter set based on the arc additive manufacturing instruction, where the original welding parameter set includes: original voltage, original current, and original wire feeding speed;

[0184] Under the control of the original welding parameter set, use the pre-constructed arc additive manufacturing device to perform melting deposition on the pre-obtained metal wire to obtain an intermediate metal component, where the duration of the melting deposition is a preset welding cycle, and the arc additive manufacturing device includes: a sensor monitoring unit and a shooting unit;

[0185] Evaluate the welding influencing factors for the melting deposition step to obtain a welding correction coefficient, and calculate the original deposition rate based on the welding correction coefficient and the original welding parameter set;

[0186] Use the photographing unit to photograph the surface of the intermediate metal component to obtain a component surface image, and based on the component surface image, perform component surface quality analysis on the intermediate metal component to obtain a component defect score;

[0187] Adjust the original deposition rate based on the component defect score and the preset standard defect score to obtain an adjusted deposition rate;

[0188] Adjust the original welding parameter set according to the adjusted deposition rate to obtain an adjusted welding parameter set;

[0189] Take the adjusted welding parameter set as the original welding parameter set, and return to the step of melting and depositing the pre-acquired metal wire using a pre-built arc additive manufacturing device under the control of the original welding parameter set until the intermediate metal component is completed.

[0190] In several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and there can be other division methods in actual implementation.

[0191] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0192] In addition, the functional modules in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware, or in the form of hardware plus software functional modules.

[0193] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0194] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing arc additive process based on deposition rate, characterized in that: The method comprises: Receiving an arc additive instruction, and determining an original welding parameter group based on the arc additive instruction, wherein the original welding parameter group includes: an original voltage, an original current, and an original wire feeding speed; Under the control of the original welding parameter group, a pre-constructed arc additive device is used to melt and deposit the pre-acquired metal welding wire to obtain an intermediate metal component, wherein the duration of the melting and deposition is a preset welding cycle, and the arc additive device includes: a sensor monitoring unit and a shooting unit; The welding influencing factors of the melting deposition step are evaluated to obtain a welding correction coefficient, and an original deposition rate is calculated according to the welding correction coefficient and an original welding parameter group; The step of evaluating the welding influencing factors on the melting deposition step to obtain the welding correction coefficient includes: Obtaining a welding influencing factor group, wherein the welding influencing factor group includes: welding wire diameter, welding wire extension length, welding angle and shielding gas flow rate; The sensor monitoring unit is used to detect the environmental influencing factor group, wherein the sensor monitoring unit includes: a temperature sensor, a humidity sensor and a wind speed sensor, and the environmental influencing factor group includes: ambient temperature, ambient humidity and ambient wind speed; According to the welding influencing factor group and the environmental influencing factor group, the welding correction coefficient is calculated, wherein the welding correction coefficient is expressed as: ， in, Indicates the welding correction factor, represents the preset empirical fitting coefficient, Indicates the ambient temperature, Indicates the wire diameter, Indicates the protective gas flow rate, Indicates the wire extension length. Indicates the ambient humidity. Indicates the ambient wind speed, represents the sine function, Indicates welding angle; Using a photographing unit to photograph the surface of the intermediate metal component to obtain a component surface image, and based on the component surface image, performing a component surface quality analysis on the intermediate metal component to obtain a component defect score; Based on the component defect score and the preset standard defect score, the original deposition rate is adjusted to obtain an adjusted deposition rate; According to the adjusted deposition rate, the original welding parameter group is adjusted to obtain an adjusted welding parameter group; The adjusted welding parameter group is used as the original welding parameter group, and the step of melting and depositing the pre-acquired metal welding wire using the pre-built arc additive equipment under the control of the original welding parameter group is returned until the intermediate metal component is completed.

2. The arc additive process optimization method based on deposition rate according to claim 1, characterized in that: The calculating of the original deposition rate according to the welding correction coefficient and the original welding parameter group includes: The original deposition rate is calculated using a preset deposition rate calculation formula, wherein the deposition rate calculation formula is expressed as: ， in, represents the original deposition rate, represents the original voltage, represents the original current, Indicates the preset arc efficiency, Indicates the preset metal wire density, Indicates the original wire feed speed.

3. The arc additive process optimization method based on deposition rate according to claim 2, characterized in that: The component surface quality analysis of the intermediate metal component is performed based on the component surface image to obtain the component defect score, including: Performing image detection on the component surface image to obtain a quality assessment parameter group, wherein the quality assessment parameter group includes: crack growth rate and surface roughness; Based on the quality assessment parameter group, the component defect score is calculated using the following formula: ， in, represents the component defect score, Indicates the surface roughness, Indicates the preset standard roughness, represents the crack growth rate.

4. The arc additive process optimization method based on deposition rate according to claim 3, characterized in that: The step of performing image detection on the component surface image to obtain a quality assessment parameter group includes: Graying the surface image of the component to obtain a gray surface image, and performing roughness detection on the gray surface image to obtain surface roughness; Using a preset edge detection algorithm to perform crack contour detection on the grayscale surface image to obtain a crack surface image, and determining whether the crack surface image contains a crack; If the crack surface image does not contain cracks, the crack growth rate is recorded as 0; If the crack surface image includes cracks, a surface crack group in the crack surface image is identified, wherein the surface crack group includes one or more surface cracks; Identifying a surface crack length group of the surface crack group, wherein the surface crack lengths in the surface crack length group correspond one-to-one to the surface cracks in the surface crack group, and the surface crack lengths are the lengths of the corresponding surface cracks; The crack growth rate is calculated according to the surface crack length group, and the crack growth rate and surface roughness are confirmed as a quality evaluation parameter group.

5. The arc additive process optimization method based on deposition rate according to claim 4, characterized in that: Calculating the crack growth rate according to the surface crack length group includes: constructing a surface crack vector based on the surface crack length group, wherein the dimension of the surface crack vector is the same as the number of surface crack lengths in the surface crack length group; Constructing a historical crack vector according to the surface crack group, wherein the dimension of the historical crack vector is the same as the dimension of the surface crack vector; The crack growth rate is calculated based on the surface crack vector and the historical crack vector, where the crack growth rate is expressed as: ， in, represents the dimension of the surface crack vector or the dimension of the historical crack vector, represents the surface crack vector, represents the historical crack vector, Indicates the modulus length.

6. The arc additive process optimization method based on deposition rate according to claim 5, characterized in that: The constructing of the historical crack vector according to the surface crack group includes: Determining the start time of the melt deposition step, and querying the previous cycle crack group based on the start time; Respectively obtain the number of previous cycle cracks of the previous cycle crack group and the number of surface cracks of the surface crack group, and determine whether the number of previous cycle cracks is equal to the number of surface cracks; If the number of previous cycle cracks is equal to the number of surface cracks, a previous cycle crack length group of the previous cycle crack group is obtained, and a historical crack vector is constructed according to the previous cycle crack length group, wherein the dimension of the historical crack vector is the same as the number of previous cycle crack lengths in the previous cycle crack length group; If the number of cracks in the previous cycle is not equal to the number of surface cracks, then the newly added crack group and the homologous crack group are identified in the surface crack group; Respectively obtain the newly added sequence number group and the homologous sequence number group of the newly added crack group and the homologous crack group in the surface crack group, wherein the newly added crack group corresponds to the newly added sequence number group one by one, and the homologous crack group corresponds to the homologous sequence number group one by one; Based on the newly added crack group and the newly added sequence number group, the previous cycle crack group is expanded to obtain a historical crack group, wherein the historical crack group includes all previous cycle cracks and multiple empty cracks in the previous cycle crack group, and the arrangement sequence number of the previous cycle crack in the historical crack group is the same as the homologous sequence number of the corresponding homologous crack, the empty crack refers to a crack with a length of 0, and the arrangement sequence number of the empty crack in the historical crack group is the same as the newly added sequence number of the corresponding newly added crack; A historical crack length group of the historical crack group is obtained, and a historical crack vector is constructed based on the historical crack length group.

7. The arc additive process optimization method based on deposition rate according to claim 6, characterized in that: The step of performing roughness detection on the grayscale surface image to obtain the surface roughness comprises: Extracting grayscale directions in a preset grayscale direction group in sequence, and calculating a grayscale co-occurrence matrix of the grayscale surface image in the grayscale direction; Calculating the contrast of the gray level co-occurrence matrix; The contrasts are summarized to obtain a contrast group, and the average contrast of the contrast group is calculated, and the average contrast is recorded as the roughness.

8. The arc additive process optimization method based on deposition rate according to claim 7, characterized in that: The adjusting the original deposition rate based on the component defect score and the preset standard defect score to obtain the adjusted deposition rate includes: Calculate the score data difference between the component defect score and the standard defect score, and determine whether the score data difference is greater than a preset controllable data difference; If the score data difference is not greater than the controllable data difference, the original deposition rate is recorded as the adjusted deposition rate; If the scoring data difference is greater than the controllable data difference, the adjusted deposition rate is calculated using the following formula: ， in, represents the adjustment of the deposition rate, Indicates the standard defect score.

9. An arc additive process optimization system based on deposition rate, characterized in that: The system comprises: A metal component deposition module, used to receive an arc additive instruction, determine an original welding parameter group based on the arc additive instruction, wherein the original welding parameter group includes: an original voltage, an original current and an original wire feeding speed, and under the control of the original welding parameter group, use a pre-built arc additive device to melt and deposit the pre-acquired metal welding wire to obtain an intermediate metal component, wherein the duration of the melting and deposition is a preset welding cycle, and the arc additive device includes: a sensor monitoring unit and a shooting unit; A component defect assessment module is used to assess the welding influencing factors of the melting and deposition step to obtain a welding correction coefficient, and calculate the original deposition rate according to the welding correction coefficient and the original welding parameter group. The assessment of the welding influencing factors of the melting and deposition step to obtain the welding correction coefficient includes: Obtaining a welding influencing factor group, wherein the welding influencing factor group includes: welding wire diameter, welding wire extension length, welding angle and shielding gas flow rate; The sensor monitoring unit is used to detect the environmental influencing factor group, wherein the sensor monitoring unit includes: a temperature sensor, a humidity sensor and a wind speed sensor, and the environmental influencing factor group includes: ambient temperature, ambient humidity and ambient wind speed; According to the welding influencing factor group and the environmental influencing factor group, the welding correction coefficient is calculated, wherein the welding correction coefficient is expressed as: ， in, Indicates the welding correction factor, represents the preset empirical fitting coefficient, Indicates the ambient temperature, Indicates the wire diameter, Indicates the protective gas flow rate, Indicates the wire extension length. Indicates the ambient humidity. Indicates the ambient wind speed, represents the sine function, Indicates the welding angle, Using a photographing unit to photograph the surface of the intermediate metal component to obtain a component surface image, and based on the component surface image, performing a component surface quality analysis on the intermediate metal component to obtain a component defect score; A welding parameter adjustment module, used to adjust the original deposition rate based on the component defect score and the preset standard defect score to obtain an adjusted deposition rate, and to adjust the original welding parameter group according to the adjusted deposition rate to obtain an adjusted welding parameter group; The melting deposition cycle module is used to use the adjusted welding parameter group as the original welding parameter group and return to the step of melting and depositing the pre-acquired metal welding wire using the pre-built arc additive equipment under the control of the original welding parameter group until the intermediate metal component is completed.

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