Additive manufacturing method for thick-walled high-nitrogen stainless steel tube with fine grain structure

Abstract

The present invention relates to the field of friction stir deposition technology, and in particular to an additive manufacturing method for a thick-walled high-nitrogen stainless steel tube with a fine-grained structure, comprising placing a substrate on a workbench and clamping and fixing it to ensure that it will not move during the deposition process, selecting high-nitrogen stainless steel bars that meet the requirements, screening and pre-treating them, and ensuring the quality of the high-nitrogen stainless steel bars; setting the rotation speed and feed speed of the stirring head, and adjusting them according to material properties and process requirements; sending the high-nitrogen stainless steel bars to a hollow rotating tool, and adjusting the deposition feed speed during the additive manufacturing process by adjusting the pressure applied to the high-nitrogen stainless steel bars; starting the stirring head to rotate and advance at high speed on the substrate, and at the same time, the feeding system sends the material to the joint between the stirring head and the substrate. The present invention avoids the problems of thermal cracks and porosity by additively manufacturing high-nitrogen stainless steel tubes, and the additive components have high density, low residual stress, and higher quality.

Description

Technical Field

[0001] The invention relates to the technical field of rapid prototyping and manufacturing of advanced steel materials, and in particular to an additive manufacturing method for a high-nitrogen stainless steel pipe with a thick wall and a fine-grained structure. Background Art

[0002] High nitrogen stainless steel (HNS) is a stainless steel that improves its strength and corrosion resistance by adding a high content of nitrogen. The manufacturing methods of high nitrogen stainless steel include traditional methods such as pressurized induction melting (PIM) and pressurized electroslag remelting (PESR). For example, Chinese Patent Publication No.: CN115896601A discloses a method for manufacturing high-purity high nitrogen austenitic stainless steel, which includes heating the main raw material for preparing the high nitrogen austenitic stainless steel in a protective gas atmosphere of 50Pa to 0.2MPa in a closed chamber, and melting and pouring in a protective gas atmosphere of 0.1MPa to 0.2MPa, wherein the main raw material for preparing the high nitrogen austenitic stainless steel is heated in a smelting furnace in a closed chamber at one time. This method has certain limitations. Since the solidification of the metal needs to be completed under high pressure, the product size is limited by the space of the smelting device, and it is difficult to obtain large-size products. At the same time, the coarse structure, element segregation and metallurgical defects in this casting structure cannot be eliminated by subsequent heat treatment. Traditional steel pipe production processes mainly include hot extrusion and curling welding. In particular, thick-walled steel pipes also require large-size steel ingots and large-scale special equipment. The above problems have resulted in the inability to obtain thick-walled, large-size high-nitrogen steel pipe products.

[0003] The use of additive manufacturing technology is a feasible way to achieve large-thickness additive products. At present, the high-nitrogen steel additive technologies reported at home and abroad are mainly based on melting additive methods, such as laser additive using high-nitrogen steel powder, arc additive using high-nitrogen steel welding wire, etc. The common problems of these methods are low production efficiency, low nitrogen content in the prepared additive structure, and poor metallurgical quality and mechanical properties. Stir friction deposition (AFSD) technology is developed based on the principle of stir friction welding (FSW). Stir friction deposition (AFSD) uses a high-speed rotating stirring head to cause plastic deformation of the material to be processed in the solid state, thereby realizing layer-by-layer deposition and forming of the material. This technology has the advantages of fast forming, high additive efficiency, and green process, and is suitable for additive manufacturing of a variety of metal materials. AFSD technology can effectively avoid common defects such as shrinkage and porosity in the melting additive method. At the same time, due to its solid phase processing characteristics, it is suitable for the forming and manufacturing of high-nitrogen stainless steel.

[0004] In summary, in the prior art, the high nitrogen stainless steel manufacturing method has the following problems:

[0005] Since metal solidification needs to be completed under high pressure, the product size is limited by the space of the smelting equipment, and it is difficult to obtain large-size products. At the same time, the coarse structure, element segregation and metallurgical defects existing in this casting structure cannot be eliminated by subsequent heat treatment. Summary of the invention

[0006] To this end, the present invention provides an additive manufacturing method for a thick-walled high-nitrogen stainless steel tube with a fine-grained structure, which is used to overcome the technical difficulty that thick-section and large-size high-nitrogen stainless steel products cannot be obtained in the prior art, and at the same time can avoid the metallurgical problems such as coarse structure, element segregation, pores and harmful intermetallic compounds existing in the prior art.

[0007] To achieve the above object, the present invention provides an additive manufacturing method for a thick-walled high-nitrogen stainless steel tube with a fine-grained structure, comprising:

[0008] Step S1, placing the substrate on a workbench and clamping it to ensure that it does not move during the deposition process; selecting high-nitrogen stainless steel bars that meet the requirements for screening to ensure the quality of the high-nitrogen stainless steel bars, and establishing a model of the additive pipe fitting;

[0009] Step S2, determining initial values ​​of process parameters for friction stir deposition additive manufacturing, setting the rotation speed and moving speed of the hollow core rotary tool according to the initial values ​​of the process parameters, performing a trial run with the initial process parameters, and adjusting the initial process parameters according to feedback from the trial run results;

[0010] Step S3, delivering the high nitrogen stainless steel bar to the hollow rotating tool, adjusting the deposition feed speed in the additive manufacturing process by adjusting the pressure applied to the high nitrogen stainless steel bar, and determining whether the heating capacity of the hollow rotating tool for the stainless steel bar can meet the additive manufacturing requirements of the current additive pipe fitting;

[0011] Step S4, starting the hollow rotating tool to rotate and advance at high speed on the substrate, while the feeding system delivers the high nitrogen stainless steel bar to the joint between the hollow rotating tool and the substrate, the hollow rotating tool generates friction heat and shear plastic deformation during the rotation and advancement process, which causes the high nitrogen stainless steel bar to soften and form a metallurgical connection with the substrate, thereby achieving the deposition of the high nitrogen stainless steel bar;

[0012] Step S5, obtaining the deposition temperature of the stainless steel bar during the additive process in an initial detection cycle, comparing and analyzing the deposition temperature with a target deposition temperature, and determining a change trend of the deposition temperature when the deposition temperature is within the target deposition temperature range;

[0013] Step S6, repeating step S4, the high nitrogen stainless steel rod is deposited layer by layer to form a multi-layer deposition structure on the substrate, and after each layer is deposited, the surface of the additive layer is cut and polished to ensure that the surface of the additive layer is flat and closely fits with the interface of the next layer;

[0014] The initial values ​​of the process parameters are the initial rotation speed, the initial movement speed of the hollow rotating tool and the initial pressure applied to the stainless steel bar.

[0015] Furthermore, the process of establishing the model of the additive tube includes:

[0016] According to the target parameters of the additive tube, a three-dimensional model of the tube is established, and the three-dimensional model is cut into a plurality of two-dimensional figures, and the width and thickness of each two-dimensional figure and the number of layers of the three-dimensional model cut into the plurality of two-dimensional figures are determined;

[0017] The target parameters of the additive tube are the height, width, outer diameter and inner diameter of the additive tube.

[0018] Furthermore, the process of determining the initial value of the process parameter of the friction stir deposition additive manufacturing includes:

[0019] The structural changes of the friction stir deposition additive process specimens under different parameters are quantitatively analyzed by a deep learning neural network, the initial values ​​of the process parameters are determined according to the target parameter requirements of the pipe fittings, the target requirements are input into the trained deep learning neural network, and the initial process parameters are obtained. The high nitrogen stainless steel bar is deposited and added at an initial deposition feed rate under the action of an initial pressure;

[0020] Among them, the deep neural network learning model is a process parameter window determination model. By collecting the process parameters corresponding to a preset number of additive workpieces under different mechanical properties, the corresponding process parameters are input into the preset neural network of the deep neural network learning model for deep learning training to obtain the process parameter window determination model.

[0021] Furthermore, the process of conducting a trial run with initial process parameters includes:

[0022] Starting the hollow rotating tool to perform a test run with initial process parameters, detecting a test piece height and a test piece thickness of the additive tube, and calculating an actual difference between the test piece height and the test piece thickness of the additive tube and a target height and a target thickness of the additive tube;

[0023] If the actual difference between the test piece height and the test piece thickness and the target height and the target thickness of the additive tube is less than or equal to the test piece difference evaluation value, it is determined that the test piece can meet the target parameter requirements of the additive tube required for additive manufacturing;

[0024] If the actual difference between the specimen height and specimen thickness and the target height and target thickness of the additive tube is greater than the specimen difference evaluation value, it is determined that the specimen cannot meet the target parameter requirements of the additive tube required for additive manufacturing, and the initial process parameters are adjusted according to the actual difference between the specimen height and specimen thickness and the target height and target thickness of the additive tube.

[0025] Furthermore, the process of adjusting the initial process parameters according to the feedback of the trial operation results includes:

[0026] The height of the additive component is positively correlated with the deposition feed rate. If the specimen height is greater than the target height, the initial pressure deposition feed rate is reduced by adjusting the initial pressure applied to the stainless steel bar; if the specimen height is less than the target height, the initial pressure deposition feed rate is increased by adjusting the initial pressure applied to the stainless steel bar.

[0027] The effective width of the deposited layer is negatively correlated with the moving speed of the hollow rotating tool. If the specimen thickness is greater than the target thickness, the effective width of the deposited layer is reduced by increasing the initial moving speed of the hollow rotating tool; if the specimen thickness is less than the target thickness, the effective width of the deposited layer is increased by reducing the initial moving speed of the hollow rotating tool.

[0028] Furthermore, the process of determining whether the heating capacity of the hollow rotating tool for the stainless steel bar can meet the additive manufacturing requirements of the current additive pipe fittings includes:

[0029] Calculate the heat input for the additive manufacturing process when the hollow core rotary tool speed is at the maximum value and the moving speed is at the minimum value, calculate the standard maximum temperature of the stainless steel bar according to the heat input in the additive manufacturing process, and compare the standard maximum temperature of the stainless steel bar with the target deposition temperature range;

[0030] If the standard maximum temperature is greater than or equal to the target deposition temperature maximum value, it is determined that the maximum rotation speed of the hollow core rotary tool meets the additive manufacturing requirements;

[0031] If the standard maximum temperature is less than the target minimum deposition temperature, it is determined that the maximum rotation speed of the hollow core rotary tool does not meet the additive manufacturing requirements, and the hollow core rotary tool is controlled to rotate at the maximum rotation speed, and the initial pressure on the stainless steel bar is increased.

[0032] Furthermore, the process of comparing and analyzing the deposition temperature with the target deposition temperature includes:

[0033] The temperature of the deposited layer is measured according to the initial detection cycle to detect the deposition temperature of the stainless steel bar, and the actual temperature difference between any deposition temperature value and the minimum target deposition temperature and the actual temperature difference with the maximum target deposition temperature are calculated, and compared with the maximum and minimum target deposition temperatures,

[0034] If the actual deposition temperature is less than or equal to the minimum target deposition temperature, the rotation speed of the hollow core rotating tool is increased according to the actual temperature difference value and the actual temperature difference evaluation value;

[0035] If the actual deposition temperature is greater than the maximum target deposition temperature, the rotation speed of the hollow core rotating tool is reduced according to the magnitude of the actual temperature difference and the actual temperature difference evaluation value.

[0036] Furthermore, the process of determining the change trend of the deposition temperature when the deposition temperature is within the target deposition temperature range includes:

[0037] If the deposition temperature of the stainless steel bar is within the target deposition temperature range, a deposition temperature change curve of the stainless steel bar is drawn according to the deposition temperature, and a derivative of the temperature change curve is obtained. The temperature change curve of the stainless steel bar and its derivative are analyzed to determine the change trend of the deposition temperature.

[0038] Furthermore, if the derivative value of the temperature change curve is greater than zero, it is determined that the deposition temperature of the stainless steel bar is rising and the deposition temperature is approaching the target maximum deposition temperature, and the rotation speed of the hollow rotating tool is reduced according to the rising rate of the deposition temperature;

[0039] If the derivative value of the temperature change curve is equal to zero, it is judged that the deposition temperature of the stainless steel bar remains unchanged, the deposition temperature is stably within the target deposition temperature range, and the hollow rotary tool rotates at the initial speed;

[0040] If the derivative value of the temperature change curve is less than zero, it is judged that the deposition temperature of the stainless steel bar is decreasing and the deposition temperature is approaching the target minimum deposition temperature. The rotation speed of the hollow rotating tool is increased according to the decreasing rate of the deposition temperature.

[0041] Furthermore, when the deposition temperature is in an upward trend, the temperature expression parameter is a first preset value of the temperature expression parameter; when the deposition temperature is in a downward trend, the temperature expression parameter is a second preset value of the temperature expression parameter; and the second preset value of the temperature expression parameter is greater than the first preset value of the temperature expression parameter.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows: a large amount of data can be processed through a deep learning neural network, and useful information can be extracted from it, which is crucial for analyzing the complex process parameters and material behaviors in the AFSD process. The deep learning model can predict the effects of different process parameters on material properties and additive quality by learning patterns in historical data, thereby improving the accuracy of process parameter selection. The model based on deep learning can quickly optimize process parameters, such as tool speed, movement speed, deposition temperature, etc., to obtain the best additive effect and material properties. At the same time, through the deep learning model, the results of different process parameters can be simulated and predicted without actual experiments, thereby reducing trial and error costs and material waste. Moreover, the deep learning model has strong adaptability and generalization ability, and can quickly adjust and optimize process parameters under different material and process conditions.

[0043] Furthermore, if the heat input in the additive manufacturing process generated when the hollow rotary tool speed is at its maximum value and the moving speed is at its minimum value is not calculated, it is impossible to accurately judge whether the heating capacity of the hollow rotary tool for stainless steel bars can meet the current additive manufacturing needs of additive pipes, thereby affecting the subsequent adjustment of the additive process. By calculating the standard maximum temperature, the heating capacity of the hollow rotary tool for stainless steel bars is quantified, thereby improving the accuracy of the additive manufacturing process.

[0044] Furthermore, if the deposition temperature of the stainless steel bar during the additive process is not detected and analyzed, improper control of the deposition temperature may lead to defects such as pores and cracks at the additive interface, affecting the quality of the additive. By accurately controlling the deposition temperature, the occurrence of these defects can be reduced and the reliability of additive manufacturing can be improved. The deposition temperature of the stainless steel bar is obtained in an initial detection cycle, and the change trend of the deposition temperature is determined when the deposition temperature is within the target deposition temperature range. By setting different temperature expression parameters for the change trend, the rotation speed of the hollow rotating tool is adjusted to different degrees, thereby increasing the accuracy of temperature control in the additive manufacturing process. At the same time, proper temperature control can increase the deposition speed, thereby improving production efficiency. By optimizing the temperature parameters, the production cycle can be shortened while ensuring quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flowchart of the steps of an additive manufacturing method for a thick-walled high-nitrogen stainless steel tube with a fine-grained structure according to an embodiment of the present invention;

[0046] Figure 2 A schematic diagram of friction stir deposition of an additive manufacturing method for a high nitrogen stainless steel tube with a thick wall and a fine grain structure according to an embodiment of the present invention;

[0047] Figure 3Schematic diagram of an additive manufacturing process of a high nitrogen stainless steel tube with a thick wall and fine grain structure in an embodiment of the present invention;

[0048] In the figure: 1-substrate, 2-tool shoulder, 3-hollow rotary tool, 4-high nitrogen stainless steel rod, 5-Nth additive layer, 6-additively manufactured pipe fittings DETAILED DESCRIPTION

[0049] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; 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.

[0050] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0051] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0052] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0053] See also Figure 1 As shown, Figure 1 This is a flowchart of the steps of an additive manufacturing method for a thick-walled high-nitrogen stainless steel tube with a fine-grained structure according to an embodiment of the present invention; Figure 2 A schematic diagram of friction stir deposition of an additive manufacturing method for a high nitrogen stainless steel tube with a thick wall and a fine grain structure according to an embodiment of the present invention; Figure 3 Schematic diagram of an additive manufacturing process for a thick-walled, fine-grained, high-nitrogen stainless steel tube according to an embodiment of the present invention.

[0054] The present invention provides an additive manufacturing method for a thick-walled high-nitrogen stainless steel tube with a fine-grained structure, comprising:

[0055] Step S1, placing the substrate on a workbench and clamping it to ensure that it does not move during the deposition process; selecting high-nitrogen stainless steel bars that meet the requirements for screening to ensure the quality of the high-nitrogen stainless steel bars, and establishing a model of the additive pipe fitting;

[0056] Establish a model, and establish a three-dimensional model of the pipe fitting according to the design parameters of the additive pipe fitting;

[0057] Slicing is to cut the three-dimensional model into several two-dimensional graphics, determine the width and thickness of each two-dimensional graphic, and the number of layers of the three-dimensional model into several two-dimensional graphics.

[0058] Step S2, determining initial values ​​of process parameters for friction stir deposition additive manufacturing, setting the rotation speed and moving speed of the hollow core rotary tool according to the initial values ​​of the process parameters, performing a trial run with the initial process parameters, and adjusting the initial process parameters according to feedback from the trial run results;

[0059] The structural changes of the AFSD process specimens under different parameters are quantitatively analyzed by a deep learning neural network, and the initial values ​​of the process parameters are determined according to the target parameter requirements of the pipe fittings. The target requirements are input into the trained deep learning neural network to obtain the initial process parameters, and the target parameter requirements include the target height He and the target thickness De of the pipe fittings manufactured by additive manufacturing. The initial process parameters include the initial rotation speed ω0 of the hollow rotating tool, the initial moving speed V0, and the initial pressure F0 applied to the stainless steel bar. The deposited material is deposited at the initial deposition feed speed Vf under the action of the initial pressure F0 for additive manufacturing;

[0060] Among them, the deep neural network learning model is a process parameter window determination model. By collecting the process parameters corresponding to a preset number of additive workpieces under different mechanical properties, the corresponding process parameters are input into the preset neural network of the deep neural network learning model for deep learning training to obtain the process parameter window determination model.

[0061] Conduct a trial run with initial process parameters;

[0062] The target height He and the target thickness De of the additively manufactured pipe fitting product are detected, and the test piece height H0 and the test piece thickness D0 of the pipe fitting product are detected, and the actual difference between the test piece height H0 and the test piece thickness D0 of the pipe fitting product and the target height He and the target thickness De of the pipe fitting product is calculated.

[0063] S3=|H0-He|, S4=|D0-De|;

[0064] If S3≤S20, and S4≤S20, it is determined that the test piece can meet the target parameter requirements of the pipe fitting product required for additive manufacturing;

[0065] If S3>S20, or S4>S20, it is determined that the specimen cannot meet the target parameter requirements of the pipe fitting product required for additive manufacturing, and the initial process parameters are adjusted according to the actual difference between the specimen height H0 and the specimen thickness D0 and the target height He and the target thickness De of the pipe fitting product;

[0066] Wherein, if H0>He, the initial pressure deposition feed speed Vf is reduced by adjusting the initial pressure F0 applied to the stainless steel bar, and the pressure on the stainless steel bar after adjustment is assumed to be F1, F1=He÷H0×S20÷S3×F0; if H0<He, the initial pressure deposition feed speed Vf is increased by adjusting the initial pressure F0 applied to the stainless steel bar, F1=He÷H0×S3÷S20×F0;

[0067] If D0>De, the effective width D0 of the deposited layer is reduced by increasing the initial moving speed V0 of the hollow rotating tool, and the adjusted moving speed of the hollow rotating tool is set to V1, V1=D0÷De×S4÷S20×V0; if D0<De, the effective width D0 of the deposited layer is increased by reducing the initial moving speed V0 of the hollow rotating tool, V1=D0÷De×S20÷S4×V0;

[0068] Among them, S20 is the preset specimen difference evaluation value;

[0069] Specifically, deep learning neural networks are able to process large amounts of data and extract useful information from them, which is crucial for analyzing complex process parameters and material behaviors in the AFSD process. The deep learning model can predict the impact of different process parameters on material properties and additive quality by learning patterns in historical data, thereby improving the accuracy of process parameter selection. The deep learning-based model can quickly optimize process parameters such as tool speed, movement speed, deposition temperature, etc. to obtain the best additive effect and material properties. At the same time, through the deep learning model, the results of different process parameters can be simulated and predicted without actual experiments, thereby reducing trial and error costs and material waste. In addition, the deep learning model has strong adaptability and generalization capabilities, and can quickly adjust and optimize process parameters under different material and process conditions.

[0070] Step S3, delivering the high nitrogen stainless steel bar to the hollow rotating tool, adjusting the deposition feed speed in the additive manufacturing process by adjusting the pressure applied to the high nitrogen stainless steel bar, and determining whether the heating capacity of the hollow rotating tool for the stainless steel bar can meet the additive manufacturing requirements of the current additive pipe fitting;

[0071] In this embodiment, the heat input Q for the additive manufacturing process generated when the hollow core rotating tool speed is at the maximum value ωm and the moving speed is at the minimum value Vm is calculated.

[0072]

[0073] Where: Q is the heat input per unit deposition length, that is, the total heat generated on the contact interface between the hollow rotating tool and the substrate; Ts is the contact shear stress on the contact interface between the hollow rotating tool and the substrate; ωm is the maximum rotation speed of the hollow rotating tool; Vm is the minimum moving speed of the hollow rotating tool; R0 and Ri are the radius of the hollow rotating tool shoulder and the radius of the feed port, respectively;

[0074] The standard maximum temperature Cep of the stainless steel bar is calculated according to the heat input Q in the additive manufacturing process. Cep=kc×Q×Cr×α, where kc is the conversion coefficient of heat and temperature, Cr is the reference temperature value, and α is the calculation compensation parameter of the standard maximum temperature. The standard maximum temperature Cep of the stainless steel bar when the heat input is Q in the additive manufacturing process is compared with the target deposition temperature range [Ce1, Ce2].

[0075] If Cep≥Ce2, it is judged that the maximum rotation speed ω of the hollow core rotary tool meets the additive manufacturing requirements;

[0076] If Cep<Ce1, it is judged that the maximum rotation speed ω of the hollow rotating tool does not meet the additive manufacturing requirements, the hollow rotating tool is controlled to rotate at the maximum rotation speed, and the initial pressure F0 of the stainless steel bar is increased. The increased pressure on the stainless steel bar is set to F1, F1=Ce1÷Cep×F0×β, where β is the calculation compensation parameter for increasing the initial pressure F0 on the stainless steel bar when the maximum rotation speed ω of the hollow rotating tool does not meet the additive manufacturing requirements.

[0077] Specifically, if the heat input in the additive manufacturing process generated when the hollow rotary tool speed is at its maximum value and the moving speed is at its minimum value is not calculated, it is impossible to accurately judge whether the heating capacity of the hollow rotary tool for stainless steel bars can meet the current additive manufacturing needs of additive pipes, thereby affecting the subsequent adjustment of the additive process. By calculating the standard maximum temperature, the heating capacity of the hollow rotary tool for stainless steel bars can be quantified, thereby improving the accuracy of the additive manufacturing process.

[0078] Step S4, starting the hollow rotating tool to rotate and advance at high speed on the substrate, while the feeding system delivers the high nitrogen stainless steel bar to the joint between the hollow rotating tool and the substrate, the hollow rotating tool generates friction heat and shear plastic deformation during the rotation and advancement process, which causes the high nitrogen stainless steel bar to soften and form a metallurgical connection with the substrate, thereby achieving the deposition of the high nitrogen stainless steel bar;

[0079] Step S5, obtaining the deposition temperature of the stainless steel bar during the additive process in an initial detection cycle, comparing and analyzing the deposition temperature with a target deposition temperature, and determining a change trend of the deposition temperature when the deposition temperature is within the target deposition temperature range;

[0080] In this embodiment, an initial detection period t0 is provided, and the temperature of the deposited layer is directly measured by a temperature measuring device such as an infrared thermal imager according to the initial detection period t0 to detect the deposition temperature of the stainless steel bar, including: an initial deposition temperature value C0, a first deposition temperature value C1, a second deposition temperature value C2, ..., an nth deposition temperature value Cn

[0081] For any deposition temperature value Ci, i=1, 2, ..., n, calculate its actual temperature difference S1 with the target deposition temperature minimum value Ce1 and its actual temperature difference S2 with the target deposition temperature maximum value Ce2, and compare it with the target deposition temperature maximum and minimum values ​​Ce1 and Ce2.

[0082] S1=|Ce1-Ci|, S2=|Ce2-Ci|;

[0083] If Ci≤Ce1, and S1>S10, it is determined that the deposition temperature of the stainless steel bar is too low, and the rotation speed ω0 of the hollow rotating tool is increased. The rotation speed of the adjusted hollow rotating tool is set to ω1, ω1=Ce1÷Ci×S1÷S10×ω0; if S1≤S10, it is determined that the deposition temperature of the stainless steel bar is low, and the rotation speed ω0 of the hollow rotating tool is increased, ω1=Ce1÷Ci×S10÷S1×ω0;

[0084] If Ci>Ce2, and S2>S10, it is determined that the deposition temperature of the stainless steel bar is too high, and the rotation speed of the hollow rotating tool ω0 is reduced, ω1=Ce2÷Ci×S10÷S2×ω0; if S2≤S10, it is determined that the deposition temperature of the stainless steel bar is high, and the rotation speed of the hollow rotating tool ω0 is reduced, ω1=Ce2÷Ci×S2÷S10×ω0;

[0085] Wherein, S10 is the preset actual temperature difference evaluation value;

[0086] If Ce1<Ci<Ce2, it is determined that the deposition temperature of the stainless steel bar is within the target deposition temperature range, and a deposition temperature change curve F(x) of the stainless steel bar is drawn according to the deposition temperature, and the derivative function of the temperature change curve F(x) is obtained as f(x), and the temperature change curve F(x) of the stainless steel bar and its derivative function f(x) are analyzed to determine the change trend of the deposition temperature;

[0087] If f(ti)>0, it is judged that the deposition temperature of the stainless steel bar is rising, and the deposition temperature is approaching the target maximum deposition temperature Ce2. The rotation speed ω0 of the hollow rotating tool is reduced according to the rising rate of the deposition temperature. The rotation speed of the adjusted hollow rotating tool is ω1, ω1=ki×ω0, α=α1;

[0088] If f(ti)=0, it is judged that the deposition temperature of the stainless steel bar remains unchanged, the deposition temperature is stable in the target deposition temperature range, and the hollow rotary tool rotates at the initial rotation speed ω0;

[0089] If f(ti) < 0, it is judged that the deposition temperature of the stainless steel bar is decreasing, and the deposition temperature is approaching the target minimum deposition temperature Ce1. The rotation speed ω0 of the hollow rotating tool is increased according to the decreasing rate of the deposition temperature. The rotation speed of the adjusted hollow rotating tool is ω1, ω1=ω0÷ki, α=α2;

[0090] Where f(ti) is the slope of the temperature change curve at any time point ti, f(ti)=ki, and α is the temperature expression parameter of the temperature change curve;

[0091] For the expression parameter α of the temperature change curve, its value is related to the corresponding deposition temperature Ci. The expression parameters of different temperature change curves are set according to different deposition temperatures. α1 is the first preset value of the temperature expression parameter when the deposition temperature is in an upward trend, and α2 is the second preset value of the temperature expression parameter when the deposition temperature is in a downward trend, α2>α1;

[0092] Specifically, if the deposition temperature of stainless steel bars during the additive process is not detected and analyzed, improper control of the deposition temperature may lead to defects such as pores and cracks at the additive interface, affecting the quality of the additive. By accurately controlling the deposition temperature, the occurrence of these defects can be reduced and the reliability of additive manufacturing can be improved. The deposition temperature of the stainless steel bar is obtained in the initial detection cycle, and the change trend of the deposition temperature is determined when the deposition temperature is in the target deposition temperature range. By setting different temperature expression parameters for the change trend, the rotation speed of the hollow rotating tool is adjusted to different degrees, thereby increasing the accuracy of temperature control in the additive manufacturing process. At the same time, proper temperature control can increase the deposition speed, thereby improving production efficiency. By optimizing the temperature parameters, the production cycle can be shortened while ensuring quality.

[0093] Step S6, repeating step S4, the high nitrogen stainless steel rod is deposited layer by layer to form a multi-layer deposition structure on the substrate, and after each layer is deposited, the surface of the additive layer is cut and polished to ensure that the surface of the additive layer is flat and closely fits with the interface of the next layer.

[0094] In the process of friction stir deposition additive manufacturing, the temperature range of the deposited layer is usually 100~500°C, the peak temperature is generally 60%~90% of the melting point of the parent material, and the sum of friction heat and plastic dissipation heat raises the temperature at the deposition interface to 60%~90% of the melting point of the raw material. Since the temperature of the interface and the deposited material will not reach the melting point, the additive process is still in the solid state.

[0095] It can be seen that the setting of process parameters and tool head design will affect the deposition process. The coupled changes of parameters such as tool speed, moving speed and deposition feed rate have an important influence on the heat input and strain rate in the deposition process. By adjusting the AFSD process parameters, the microstructure characteristics formed by the deposition process can be controlled, thereby affecting the degree of recrystallization of the subsequent deposition structure. As a result, the microstructure and mechanical properties of the additive component will also be affected.

[0096] The height of the additive component increases with the increase of the deposition feed rate. Appropriately reducing the thickness of the single layer of the deposited layer can improve the material forming quality and ultimately form a dense AFSD additive component with less flash. When the deposition thickness d increases, the friction heating effect of the shoulder on the deposited layer will decrease, resulting in a weak connection between the interfaces and weakening the interface bonding strength. On the contrary, when d decreases, it helps to improve the interface bonding strength of the deposited layer.

[0097] The migration amount of the deposition layer interface and the effective width of the deposition layer both decrease with the increase of the moving speed, and the interface migration amount increases with the increase of the rotation speed. Excessive travel speed will reduce the heat input, insufficient material softening, and easily form tunnel-type hole defects in the additive area. Increasing the rotation speed can increase the heat input, increase the interface migration amount and the effective width of the additive area, but excessive heat input can easily cause the material in the additive area to soften, reducing the mechanical properties of the additive component.

[0098] As the deposition feed rate increases, the temperature difference between the advancing side and the retreating side gradually decreases; within a reasonable range, appropriately increasing the deposition feed rate is conducive to obtaining a deposited layer with uniform microstructure and properties, but an excessively large deposition feed rate will produce excessive flash and reduce material utilization; in the AFSD process, there is a deposition feed rate threshold for sufficient material mixing. A deposition feed rate significantly lower than this threshold will lead to uneven deposition of the material, and a deposition feed rate significantly higher than this threshold will lead to material waste caused by flash.

[0099] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0100] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An additive manufacturing method for a thick-walled high-nitrogen stainless steel tube with a fine-grained structure, characterized in that: include: Step S1, placing the substrate on a workbench and clamping it to ensure that it does not move during the deposition process; Select high nitrogen stainless steel bars that meet the requirements for screening, ensure the quality of the high nitrogen stainless steel bars, and establish a model of additive pipe fittings; Step S2, determining initial values ​​of process parameters for friction stir deposition additive manufacturing, setting the rotation speed and moving speed of the hollow core rotary tool according to the initial values ​​of the process parameters, performing a trial run with the initial process parameters, and adjusting the initial process parameters according to feedback from the trial run results; Step S3, delivering the high nitrogen stainless steel bar to the hollow rotating tool, adjusting the deposition feed speed in the additive manufacturing process by adjusting the pressure applied to the high nitrogen stainless steel bar, and determining whether the heating capacity of the hollow rotating tool for the stainless steel bar can meet the additive manufacturing requirements of the current additive pipe fitting; Step S4, starting the hollow rotating tool to rotate and advance at high speed on the substrate, while the feeding system delivers the high nitrogen stainless steel bar to the joint between the hollow rotating tool and the substrate, the hollow rotating tool generates friction heat and shear plastic deformation during the rotation and advancement process, which causes the high nitrogen stainless steel bar to soften and form a metallurgical connection with the substrate, thereby achieving the deposition of the high nitrogen stainless steel bar; Step S5, obtaining the deposition temperature of the stainless steel bar during the additive process in an initial detection cycle, comparing and analyzing the deposition temperature with a target deposition temperature, and determining a change trend of the deposition temperature when the deposition temperature is within the target deposition temperature range; The process of comparing and analyzing the deposition temperature with the target deposition temperature includes: Measuring the temperature of the deposited layer according to the initial detection cycle to detect the deposition temperature of the stainless steel bar, calculating the actual temperature difference between any deposition temperature value and the minimum target deposition temperature and the actual temperature difference with the maximum target deposition temperature, and comparing it with the maximum and minimum target deposition temperature; If the actual deposition temperature is less than or equal to the minimum target deposition temperature, the rotation speed of the hollow core rotating tool is increased according to the magnitude of the actual temperature difference and the actual temperature difference evaluation value; If the actual deposition temperature is greater than the maximum target deposition temperature, the rotation speed of the hollow core rotating tool is reduced according to the magnitude of the actual temperature difference value and the actual temperature difference evaluation value; The process of determining the change trend of the deposition temperature when the deposition temperature is within the target deposition temperature range includes: If the deposition temperature of the stainless steel bar is within the target deposition temperature range, a deposition temperature change curve of the stainless steel bar is drawn according to the deposition temperature, and a derivative function of the temperature change curve is obtained, and the temperature change curve of the stainless steel bar and its derivative function are analyzed to determine the change trend of the deposition temperature; The process of determining the variation trend of the deposition temperature includes: If the derivative value of the temperature change curve is greater than zero, it is judged that the deposition temperature of the stainless steel bar is rising and the deposition temperature is approaching the maximum target deposition temperature, and the rotation speed of the hollow rotating tool is reduced according to the rising rate of the deposition temperature; If the derivative value of the temperature change curve is equal to zero, it is judged that the deposition temperature of the stainless steel bar remains unchanged, the deposition temperature is stably within the target deposition temperature range, and the hollow rotary tool rotates at the initial speed; If the derivative value of the temperature change curve is less than zero, it is judged that the deposition temperature of the stainless steel bar is decreasing and the deposition temperature is approaching the minimum value of the target deposition temperature, and the rotation speed of the hollow rotating tool is increased according to the decreasing rate of the deposition temperature; Step S6, repeating step S4, the high nitrogen stainless steel rod is deposited layer by layer to form a multi-layer deposition structure on the substrate, and after each layer is deposited, the surface of the additive layer is cut and polished to ensure that the surface of the additive layer is flat and closely fits with the interface of the next layer; The initial values ​​of the process parameters are the initial rotation speed, the initial movement speed of the hollow rotating tool and the initial pressure applied to the stainless steel bar.

2. The additive manufacturing method of a thick-walled high-nitrogen stainless steel tube with a fine-grained structure according to claim 1, characterized in that: The process of establishing a model of an additive tube comprises: According to the target parameters of the additive tube, a three-dimensional model of the tube is established, and the three-dimensional model is cut into a plurality of two-dimensional figures, and the width and thickness of each two-dimensional figure and the number of layers of the three-dimensional model cut into the plurality of two-dimensional figures are determined; The target parameters of the additive tube are the height, width, outer diameter and inner diameter of the additive tube.

3. The additive manufacturing method of a thick-walled high-nitrogen stainless steel tube with a fine-grained structure according to claim 2, characterized in that: The process of determining the initial value of the process parameter of the friction stir deposition additive manufacturing comprises: The structural changes of the friction stir deposition additive process specimens under different parameters are quantitatively analyzed by a deep learning neural network, the initial values ​​of the process parameters are determined according to the target parameter requirements of the pipe fittings, the target requirements are input into the trained deep learning neural network, and the initial process parameters are obtained. The high nitrogen stainless steel bar is deposited and added at an initial deposition feed rate under the action of an initial pressure; Among them, the deep neural network learning model is a process parameter window determination model. By collecting the process parameters corresponding to a preset number of additive workpieces under different mechanical properties, the corresponding process parameters are input into the preset neural network of the deep neural network learning model for deep learning training to obtain the process parameter window determination model.

4. The additive manufacturing method of a thick-walled high-nitrogen stainless steel tube with a fine-grained structure according to claim 3, characterized in that: The process of conducting a trial run with initial process parameters includes: Starting the hollow rotating tool to perform a test run with initial process parameters, detecting a test piece height and a test piece thickness of the additive tube, and calculating an actual difference between the test piece height and the test piece thickness of the additive tube and a target height and a target thickness of the additive tube; If the actual difference between the test piece height and the test piece thickness and the target height and the target thickness of the additive tube is less than or equal to the test piece difference evaluation value, it is determined that the test piece can meet the target parameter requirements of the additive tube required for additive manufacturing; If the actual difference between the specimen height and specimen thickness and the target height and target thickness of the additive tube is greater than the specimen difference evaluation value, it is determined that the specimen cannot meet the target parameter requirements of the additive tube required for additive manufacturing, and the initial process parameters are adjusted according to the actual difference between the specimen height and specimen thickness and the target height and target thickness of the additive tube.

5. The additive manufacturing method of a thick-walled high-nitrogen stainless steel tube with a fine-grained structure according to claim 4, characterized in that: The process of adjusting the initial process parameters according to the feedback of the trial operation results includes: The height of the additive component is positively correlated with the deposition feed rate. If the specimen height is greater than the target height, the initial pressure deposition feed rate is reduced by adjusting the initial pressure applied to the stainless steel bar; if the specimen height is less than the target height, the initial pressure deposition feed rate is increased by adjusting the initial pressure applied to the stainless steel bar. The effective width of the deposited layer is negatively correlated with the moving speed of the hollow rotating tool. If the specimen thickness is greater than the target thickness, the effective width of the deposited layer is reduced by increasing the initial moving speed of the hollow rotating tool; if the specimen thickness is less than the target thickness, the effective width of the deposited layer is increased by reducing the initial moving speed of the hollow rotating tool.

6. The additive manufacturing method of a thick-walled high-nitrogen stainless steel tube with a fine-grained structure according to claim 5, characterized in that: The process of determining whether the heating capacity of the hollow rotary tool for the stainless steel bar can meet the current additive manufacturing requirements of the additive pipe fittings includes: Calculate the heat input for the additive manufacturing process when the hollow core rotary tool speed is at the maximum value and the moving speed is at the minimum value, calculate the standard maximum temperature of the stainless steel bar according to the heat input in the additive manufacturing process, and compare the standard maximum temperature of the stainless steel bar with the target deposition temperature range; If the standard maximum temperature is greater than or equal to the target deposition temperature maximum value, it is determined that the maximum rotation speed of the hollow core rotary tool meets the additive manufacturing requirements; If the standard maximum temperature is less than the target minimum deposition temperature, it is determined that the maximum rotation speed of the hollow core rotary tool does not meet the additive manufacturing requirements, and the hollow core rotary tool is controlled to rotate at the maximum rotation speed, and the initial pressure on the stainless steel bar is increased.

7. The additive manufacturing method of a thick-walled high-nitrogen stainless steel tube with a fine-grained structure according to claim 6, characterized in that: When the deposition temperature is in an upward trend, the temperature expression parameter is a first preset value of the temperature expression parameter; when the deposition temperature is in a downward trend, the temperature expression parameter is a second preset value of the temperature expression parameter, and the second preset value of the temperature expression parameter is greater than the first preset value of the temperature expression parameter.

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