Disclosure of Invention
Therefore, the invention provides a method for manufacturing the additive of the high-nitrogen stainless steel pipe with thick walls and fine grain structures, which is used for solving the technical problem that the high-nitrogen stainless steel pipe with thick sections and large sizes cannot be obtained in the prior art, and simultaneously avoiding the metallurgical problems of coarse structures, element segregation, air holes, harmful intermetallic compounds and the like in the prior art.
In order to achieve the above object, the present invention provides a method for additive manufacturing of a high nitrogen stainless steel pipe having a thick wall and a fine grain structure, comprising:
step S1, placing a substrate on a workbench, clamping and fixing the substrate to ensure that the substrate cannot move in the deposition process, selecting high-nitrogen stainless steel bars meeting the requirements for screening, ensuring the quality of the high-nitrogen stainless steel bars, and establishing a model of the material-adding pipe fitting;
S2, determining an initial value of a process parameter of friction stir deposition additive manufacturing, setting the rotating speed and the moving speed of a hollow rotating tool according to the initial value of the process parameter, performing test operation according to the initial process parameter, and feeding back and adjusting the initial process parameter according to a test operation result;
Step S3, the high-nitrogen stainless steel bar is sent to the hollow rotary tool, the deposition feeding speed in the additive manufacturing process is adjusted by adjusting the pressure applied to the high-nitrogen stainless steel bar, and whether the heating capacity of the hollow rotary tool on the stainless steel bar can meet the additive manufacturing requirement of the current additive pipe fitting is judged;
S4, starting the hollow rotating tool to enable the hollow rotating tool to rotate and advance at a high speed on the substrate, simultaneously, conveying the high-nitrogen stainless steel bar to a joint between the hollow rotating tool and the substrate by a feeding system, and enabling the hollow rotating tool to generate friction heat and shearing plastic deformation in the rotating and advancing process so as to enable the high-nitrogen stainless steel bar to soften and form metallurgical connection with the substrate, thereby realizing the deposition of the high-nitrogen stainless steel bar;
S5, acquiring the deposition temperature of the stainless steel bar in the process of material addition according to an initial detection period, comparing and analyzing the deposition temperature with a target deposition temperature, and judging the change trend of the deposition temperature when the deposition temperature is in a target deposition temperature range;
step S6, repeating the step S4, wherein the high-nitrogen stainless steel bar material is deposited layer by layer, a multi-layer deposition structure is formed on the substrate, and after each layer of deposition is completed, the surface of the additive layer is subjected to cutting processing and polishing treatment so as to ensure that the surface of the additive layer is smooth and closely attached to the interface of the next layer;
the initial values of the process parameters are an initial rotational speed, an initial moving speed of the hollow rotary tool and an initial pressure applied to the stainless steel rod.
Further, the process of modeling the additive pipe comprises:
According to target parameters of the additive pipe fitting, a three-dimensional model of the pipe fitting is established, the three-dimensional model is cut into a plurality of two-dimensional graphs, and the width and thickness of each two-dimensional graph and the number of layers of the two-dimensional graphs cut by the three-dimensional model are determined;
Wherein the target parameters of the additive pipe are the height, width, outer diameter and inner diameter of the additive pipe.
Further, the process for determining the initial value of the process parameter of friction stir deposition additive manufacturing comprises the following steps:
Quantitatively analyzing the structural change of a friction stir deposition material adding process test piece under different parameters through a deep learning neural network, determining a process parameter initial value according to target parameter requirements of pipe fitting products, inputting the target requirements into the trained deep learning neural network to obtain initial process parameters, and performing deposition material adding on the high-nitrogen stainless steel rod under the action of initial pressure at an initial deposition feeding speed;
The deep neural network learning model is a process parameter window determining model, and the process parameter window determining model is obtained by collecting process parameters corresponding to a preset number of additive workpieces under different mechanical properties, inputting the corresponding process parameters into a preset neural network of the deep neural network learning model and performing deep learning training.
Further, the process of performing the commissioning with the initial process parameters includes:
Starting the hollow rotary tool to perform test running according to initial technological parameters, detecting the test piece height and the test piece thickness of the material-adding pipe fitting, and calculating the actual difference value between the test piece height and the test piece thickness of the material-adding pipe fitting and the target height and the target thickness of the material-adding pipe fitting;
if the actual difference value between the height and the thickness of the test piece and the target height and the target thickness of the additive pipe fitting is smaller than or equal to the evaluation value of the difference value of the test piece, judging that the test piece can meet the target parameter requirement of the additive pipe fitting required by additive manufacturing;
If the actual difference between the height and the thickness of the test piece and the target height and the target thickness of the additive pipe fitting are larger than the evaluation value of the difference of the test piece, the test piece is judged to be incapable of meeting the target parameter requirement of the additive pipe fitting required by additive manufacturing, and the initial process parameters are adjusted according to the actual difference between the height and the thickness of the test piece and the target height and the target thickness of the additive pipe fitting.
Further, the process of adjusting the initial process parameters according to the feedback of the test run result comprises the following steps:
the height of the material adding component is positively correlated with the deposition feeding rate, and if the height of the test piece is larger than the target height, the initial pressure deposition feeding speed is reduced by adjusting the initial pressure applied to the stainless steel bar material;
The effective width of the deposited layer is inversely related to the moving speed of the hollow rotary tool, if the thickness of the test piece is greater than the target thickness, the effective width of the deposited layer is reduced by increasing the initial moving speed of the hollow rotary tool, and if the thickness of the test piece is less than the target thickness, the effective width of the deposited layer is increased by decreasing the initial moving speed of the hollow rotary tool.
Further, the process for judging whether the heating capacity of the hollow rotary tool on the stainless steel bar can meet the additive manufacturing requirement of the current additive pipe fitting comprises the following steps:
Calculating heat input in the additive manufacturing process generated when the rotating speed of the hollow rotating tool is at the maximum value and the moving speed is at the minimum value, calculating the standard maximum temperature of the stainless steel bar according to the heat input in the additive manufacturing process, and comparing the standard maximum temperature of the stainless steel bar with a target deposition temperature range;
if the standard maximum temperature is greater than or equal to the maximum value of the target deposition temperature, judging that the maximum rotating speed of the hollow rotating tool meets the additive manufacturing requirement;
If the standard maximum temperature is smaller than the target deposition temperature minimum value, judging that the maximum rotating speed of the hollow rotating tool does not meet the additive manufacturing requirement, controlling the hollow rotating tool to rotate according to the maximum rotating speed, and increasing the initial pressure of the stainless steel rod.
Further, the process of comparing the deposition temperature with a target deposition temperature includes:
Detecting the deposition temperature of the stainless steel bar by measuring the temperature of the deposition layer according to the initial detection period, calculating the actual temperature difference value between any deposition temperature value and the minimum value of the target deposition temperature and the actual temperature difference value between any deposition temperature value and the maximum value of the target deposition temperature, comparing the actual temperature difference value with the maximum value and the minimum value of the target deposition temperature,
If the actual deposition temperature is less than or equal to the minimum value of the target deposition temperature, increasing the rotating speed of the hollow rotating tool according to the actual temperature difference value and the actual temperature difference evaluation value;
And if the actual deposition temperature is greater than the maximum value of the target deposition temperature, reducing the rotating speed of the hollow rotating tool according to the actual temperature difference value and the actual temperature difference evaluation value.
Further, the process of determining the 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 in the target deposition temperature range, drawing a deposition temperature change curve of the stainless steel bar according to the deposition temperature, obtaining a guide function of the temperature change curve, analyzing the temperature change curve of the stainless steel bar and the guide function thereof, and judging the change trend of the deposition temperature.
Further, if the derivative value of the temperature change curve is larger than zero, judging that the deposition temperature of the stainless steel bar is rising, and the deposition temperature is approaching to the maximum value of the target deposition temperature, and reducing the rotating speed of the hollow rotating tool according to the rising rate of the deposition temperature;
If the derivative value of the temperature change curve is equal to zero, judging that the deposition temperature of the stainless steel bar is kept unchanged, and the deposition temperature is stably in a target deposition temperature range, and rotating the hollow rotating tool at an initial rotating speed;
If the derivative value of the temperature change curve is smaller than zero, judging that the deposition temperature of the stainless steel bar is decreasing, and increasing the rotating speed of the hollow rotating tool according to the decreasing rate of the deposition temperature, wherein the deposition temperature is approaching to the minimum value of the target deposition temperature.
Further, the temperature expression parameter is a first preset value of the temperature expression parameter when the deposition temperature is in an ascending trend, and the temperature expression parameter is a second preset value of the temperature expression parameter when the deposition temperature is in a descending trend, wherein the second preset value of the temperature expression parameter is larger than the first preset value of the temperature expression parameter.
Compared with the prior art, the method has the beneficial effects that a large amount of data can be processed through the deep learning neural network, and useful information is extracted from the data, which is crucial to analyzing complex process parameters and material behaviors in the AFSD process, wherein the deep learning model can predict the influence of different process parameters on the material performance and the additive quality through the mode in learning historical data, so that the accuracy of process parameter selection is improved, the process parameters such as tool rotating speed, moving speed, deposition temperature and the like can be rapidly optimized based on the deep learning model, so that the optimal additive effect and the material performance can be obtained, meanwhile, the results of different process parameters can be simulated and predicted through the deep learning model under the condition that actual experiments are not carried out, so that trial-and-error cost and material waste are reduced, and the deep learning model has stronger adaptability and generalization capability and can rapidly adjust and optimize the process parameters under different materials and process conditions.
Further, if the heat input to the additive manufacturing process, which is generated when the rotational speed of the hollow rotary tool is at the maximum value and the moving speed is at the minimum value, is not calculated, whether the heating capacity of the hollow rotary tool to the stainless steel bar can meet the additive manufacturing requirement of the current additive pipe fitting cannot be accurately judged, so that the subsequent adjustment to the additive manufacturing process is affected, and the heating capacity of the hollow rotary tool to the stainless steel bar is quantized by calculating the standard maximum temperature, so that the accuracy of the additive manufacturing process is improved.
Further, if the deposition temperature of the stainless steel bar in the process of material addition is not detected and analyzed, the improper control of the deposition temperature can cause defects such as air holes and cracks on an material addition interface, the quality of the material addition is affected, the defects can be reduced by precisely controlling the deposition temperature, the reliability of material addition manufacturing is improved, the deposition temperature of the stainless steel bar is obtained in an initial detection period, the change trend of the deposition temperature is judged when the deposition temperature is in a target deposition temperature range, the rotating speed of the hollow rotary tool is regulated to different degrees by setting different temperature expression parameters for the change trend, so that the accuracy of temperature control regulation in the process of material addition manufacturing is improved, meanwhile, the deposition speed can be improved by proper temperature control, the production efficiency is improved, and the production period can be shortened on the premise of ensuring the quality by optimizing the temperature parameters.
Detailed Description
The invention will be further described with reference to examples for the purpose of making the objects and advantages of the invention more apparent, it being understood that the specific examples described herein are given by way of illustration only and are not intended to be limiting.
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 merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
In addition, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, integrally connected, mechanically connected, electrically connected, directly connected, indirectly connected through an intermediate medium, or in communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, fig. 1 is a flow chart illustrating steps of an additive manufacturing method of a thick-walled and fine-grained high-nitrogen stainless steel pipe according to an embodiment of the invention, fig. 2 is a schematic friction stir deposition diagram illustrating an additive manufacturing method of a thick-walled and fine-grained high-nitrogen stainless steel pipe according to an embodiment of the invention, and fig. 3 is a schematic diagram illustrating an additive manufacturing process of a thick-walled and fine-grained high-nitrogen stainless steel pipe according to an embodiment of the invention.
The invention provides a method for manufacturing a high-nitrogen stainless steel pipe with thick wall and fine grain structure by additive, which comprises the following steps:
step S1, placing a substrate on a workbench, clamping and fixing the substrate to ensure that the substrate cannot move in the deposition process, selecting high-nitrogen stainless steel bars meeting the requirements for screening, ensuring the quality of the high-nitrogen stainless steel bars, and establishing a model of the material-adding pipe fitting;
Establishing a model, and establishing a three-dimensional model of the pipe fitting according to design parameters of the additive pipe fitting;
slicing, namely cutting the three-dimensional model into a plurality of two-dimensional graphs, and determining the width and thickness of each two-dimensional graph and the number of layers of the three-dimensional model cut into the plurality of two-dimensional graphs.
S2, determining an initial value of a process parameter of friction stir deposition additive manufacturing, setting the rotating speed and the moving speed of a hollow rotating tool according to the initial value of the process parameter, performing test operation according to the initial process parameter, and feeding back and adjusting the initial process parameter according to a test operation result;
Quantitatively analyzing structural changes of an AFSD process test piece under different parameters through a deep learning neural network, determining a process parameter initial value according to target parameter requirements of a pipe fitting product, inputting the target requirements into the trained deep learning neural network to obtain initial process parameters, wherein the target parameter requirements comprise target height He and target thickness De of the pipe fitting product manufactured by additive manufacturing, the initial process parameters comprise initial rotating speed omega 0 of a hollow rotating tool, initial moving speed V0 and initial pressure F0 applied to a stainless steel rod, and depositing and adding the deposited material under the action of the initial pressure F0 at an initial deposition feeding speed Vf;
The deep neural network learning model is a process parameter window determining model, and the process parameter window determining model is obtained by collecting process parameters corresponding to a preset number of additive workpieces under different mechanical properties, inputting the corresponding process parameters into a preset neural network of the deep neural network learning model and performing deep learning training.
Performing trial run with the initial process parameters;
Detecting the test piece height H0 and the test piece thickness D0 of the pipe fitting product, calculating the actual difference value 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,
S3=|H0-He|,S4=|D0-De|;
If S3 is less than or equal to S20 and S4 is less than or equal to S20, judging that the test piece can meet the target parameter requirement of the pipe fitting product required by additive manufacturing;
If S3> S20, or S4> S20, judging that the test piece cannot meet the target parameter requirement of the pipe fitting product required by additive manufacturing, and adjusting initial process parameters according to the actual difference value between the height H0 and the thickness D0 of the test piece and the target height He and the target thickness De of the pipe fitting product;
If H0> He, reducing the initial pressure deposition feed rate Vf by adjusting the initial pressure F0 applied to the stainless steel rod, and setting the pressure to which the adjusted stainless steel rod is subjected to as f1, f1=he/h0×s20/s3×f0; if H0< He, increasing the initial pressure deposition feed rate Vf by adjusting the initial pressure F0 applied to the stainless steel rod, f1=he ++h0×s3 ++s20×f0;
If D0> De, decreasing the effective width D0 of the deposited layer by increasing the initial moving speed V0 of the hollow rotary tool, and setting the adjusted moving speed of the hollow rotary tool to be V1, V1=D0/De×S4/S20×V0;
s20 is a preset test piece difference evaluation value;
Specifically, the deep learning neural network can process a large amount of data and extract useful information from the data, which is important for analyzing complex process parameters and material behaviors in the AFSD process, wherein the deep learning model can predict the influence of different process parameters on the material performance and the material adding quality through learning modes in historical data, so that the accuracy of process parameter selection is improved, the process parameters such as tool rotating speed, moving speed, deposition temperature and the like can be quickly optimized based on the deep learning model, so that the best material adding effect and the best material performance are obtained, meanwhile, the results of different process parameters can be simulated and predicted under the condition that actual experiments are not carried out through the deep learning model, so that trial-and-error cost and material waste are reduced, and the deep learning model has stronger adaptability and generalization capability and can be quickly adjusted and optimized under different materials and process conditions.
Step S3, the high-nitrogen stainless steel bar is sent to the hollow rotary tool, the deposition feeding speed in the additive manufacturing process is adjusted by adjusting the pressure applied to the high-nitrogen stainless steel bar, and whether the heating capacity of the hollow rotary tool on the stainless steel bar can meet the additive manufacturing requirement of the current additive pipe fitting is judged;
In this embodiment, the heat input Q for the additive manufacturing process generated when the rotational speed of the hollow rotary tool is at a maximum ωm and the moving speed is at a minimum Vm is calculated,
Wherein Q is the heat input of unit deposition length, namely the total heat generation amount on the contact interface of the hollow rotary tool and the matrix, ts is the contact shear stress on the contact interface of the hollow rotary tool and the matrix, ωm is the maximum value of the rotating speed of the hollow rotary tool, vm is the minimum value of the moving speed of the hollow rotary tool, R0 and Ri are the radius of the shaft shoulder of the hollow rotary tool and the radius of the feeding hole respectively;
Calculating a standard maximum temperature Cep of the stainless steel bar according to the heat input Q in the additive manufacturing process, wherein Cep=kc×Q×Cr×alpha, kc is a conversion coefficient of heat and temperature, cr is a reference temperature value, alpha is a calculated compensation parameter of the standard maximum temperature, comparing the standard maximum temperature Cep of the stainless steel bar when the heat input Q in the additive manufacturing process with a target deposition temperature range [ Ce1, ce2],
If Cep is more than or equal to Ce2, judging that the maximum rotating speed omega of the hollow rotating tool meets the additive manufacturing requirement;
If Cep < Ce1, judging that the maximum rotation speed omega of the hollow rotating tool does not meet the additive manufacturing requirement, controlling the hollow rotating tool to rotate according to the maximum rotation speed, increasing the initial pressure F0 of the stainless steel bar, setting the pressure of the increased stainless steel bar to be F1, wherein F1 = Ce 1/(Cep x F0 x beta, wherein beta is the calculated compensation parameter of the initial pressure F0 of the stainless steel bar when the maximum rotation speed omega of the hollow rotating tool does not meet the additive manufacturing requirement.
Specifically, if the heat input to the additive manufacturing process, which is generated when the rotating speed of the hollow rotating tool is at the maximum value and the moving speed is at the minimum value, is not calculated, whether the heating capacity of the hollow rotating tool to the stainless steel bar can meet the additive manufacturing requirement of the current additive pipe fitting cannot be accurately judged, so that the subsequent adjustment to the additive manufacturing process is affected, the heating capacity of the hollow rotating tool to the stainless steel bar is quantized by calculating the standard maximum temperature, and the accuracy of the additive manufacturing process is improved.
S4, starting the hollow rotating tool to enable the hollow rotating tool to rotate and advance at a high speed on the substrate, simultaneously, conveying the high-nitrogen stainless steel bar to a joint between the hollow rotating tool and the substrate by a feeding system, and enabling the hollow rotating tool to generate friction heat and shearing plastic deformation in the rotating and advancing process so as to enable the high-nitrogen stainless steel bar to soften and form metallurgical connection with the substrate, thereby realizing the deposition of the high-nitrogen stainless steel bar;
S5, acquiring the deposition temperature of the stainless steel bar in the process of material addition according to an initial detection period, comparing and analyzing the deposition temperature with a target deposition temperature, and judging the change trend of the deposition temperature when the deposition temperature is in a target deposition temperature range;
in this embodiment, an initial detection period t0 is set, and according to the initial detection period t0, the deposition temperature of the stainless steel bar is directly detected by performing temperature measurement on the deposition layer through a temperature measuring device such as a thermal infrared imager, etc., where the deposition temperature includes an initial deposition temperature value C0, a first deposition temperature value C1, a second deposition temperature value C2, a third and fourth deposition temperature value Cn
For any deposition temperature value Ci, i=1, 2..the term "n, calculates the actual temperature difference S1 between the target deposition temperature and the minimum value Ce1 and the actual temperature difference S2 between the target deposition temperature and the maximum value Ce2, and compares the maximum value and the minimum value Ce1 and Ce2 of the target deposition temperature,
S1=|Ce1-Ci|,S2=|Ce2-Ci|;
If Ci is less than or equal to Ce1 and S1 is greater than S10, judging that the deposition temperature of the stainless steel bar is too low, increasing the rotating speed omega 0 of the hollow rotating tool, setting the rotating speed of the hollow rotating tool after adjustment to be omega 1, wherein omega 1 = Ce 1/(CixS1/(S10 x omega 0), and if S1 is less than or equal to S10, judging that the deposition temperature of the stainless steel bar is lower, increasing the rotating speed omega 0 of the hollow rotating tool, wherein omega 1 = Ce 1/(CixS10/(S1 x omega 0);
If Ci > Ce2 and S2> S10, the deposition temperature of the stainless steel bar is judged to be too high, and the rotating speed omega 0 of the hollow rotating tool is reduced, wherein omega 1 = Ce2/CixS10/S2 xomega 0, and if S2 is less than or equal to S10, the deposition temperature of the stainless steel bar is judged to be higher, and the rotating speed omega 0 of the hollow rotating tool is reduced, wherein omega 1 = Ce2/CixS2/S10 xomega 0;
S10 is a preset actual temperature difference evaluation value;
if Ce1< Ci < Ce2, determining that the deposition temperature of the stainless steel bar is in a target deposition temperature range, drawing a deposition temperature change curve F (x) of the stainless steel bar according to the deposition temperature, obtaining a guide function F (x) of the temperature change curve F (x), analyzing the temperature change curve F (x) of the stainless steel bar and the guide function F (x) thereof, and determining the change trend of the deposition temperature;
If f (ti) >0, judging that the deposition temperature of the stainless steel bar is rising, wherein the deposition temperature is approaching to the target deposition temperature maximum value Ce2, reducing the rotation speed omega 0 of the hollow rotary tool according to the rising rate of the deposition temperature, and setting the rotation speed of the hollow rotary tool after adjustment to be omega 1, wherein omega 1 = ki x omega 0, and alpha = alpha 1;
If f (ti) =0, judging that the deposition temperature of the stainless steel bar is kept unchanged, wherein the deposition temperature is stable in a target deposition temperature range, and rotating the hollow rotating tool at an initial rotating speed omega 0;
if f (ti) <0, judging that the deposition temperature of the stainless steel bar is decreasing, wherein the deposition temperature is approaching to the target deposition temperature minimum value Ce1, increasing the rotation speed omega 0 of the hollow rotary tool according to the decrease rate of the deposition temperature, and setting the rotation speed of the hollow rotary tool after adjustment to be omega 1, omega 1 = omega 0/ki, and alpha = alpha 2;
Wherein f (ti) is the curve 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;
For the expression parameter alpha of the temperature change curve, the value of the expression parameter alpha is related to the corresponding deposition temperature Ci, the expression parameters of different temperature change curves are set according to different deposition temperatures, alpha 1 is a first preset value of the temperature expression parameter when the deposition temperature is in an ascending trend, alpha 2 is a second preset value of the temperature expression parameter when the deposition temperature is in a descending trend, and alpha 2 is more than alpha 1;
The method comprises the steps of detecting and analyzing the deposition temperature of a stainless steel bar in an additive manufacturing process, judging the change trend of the deposition temperature when the deposition temperature is in a target deposition temperature range according to an initial detection period, and adjusting the rotating speed of a hollow rotating tool to different degrees by setting different temperature expression parameters for the change trend, so that the accuracy of temperature control adjustment in the additive manufacturing process is improved, meanwhile, the deposition speed can be improved by proper temperature control, the production efficiency is improved, and the production period can be shortened on the premise of guaranteeing the quality by optimizing the temperature parameters.
And S6, repeating the step S4, wherein the high-nitrogen stainless steel bar material is deposited layer by layer, a multi-layer deposition structure is formed on the substrate, and after each layer of deposition is completed, the surface of the additive layer is subjected to cutting processing and polishing treatment so as to ensure that the surface of the additive layer is smooth and closely attached to the interface of the next layer.
In the friction stir deposition additive manufacturing process, the temperature range of a deposition layer is usually 100-500 ℃, the peak temperature is generally 60-90% of the melting point of a base material, and the sum of friction heat and plastic heat dissipation increases the temperature at a deposition interface to 60-90% of the melting point of a raw material. The additive process is still in the solid state since the temperature of the interface and deposited material does not reach the melting point.
It can be seen that the setting of process parameters and tool head design affect the deposition process. The coupling change of parameters such as tool rotation speed, moving speed, deposition feeding speed and the like has important influence on heat input and strain rate in the deposition process. By adjusting AFSD process parameters, the microstructure characteristics formed in the deposition process can be controlled, thereby influencing the recrystallization degree of the subsequent deposition tissues, and thus the microstructure and mechanical properties of the additive component can be influenced.
The height of the material-increasing component is increased along with the increase of the deposition feeding rate, the material forming quality can be improved by properly reducing the single-layer thickness of the deposition layer, and finally the compact AFSD material-increasing component with less flash is formed, when the deposition thickness d is increased, the friction heating effect of the shaft shoulder on the deposition layer can be reduced, so that weak connection is generated between interfaces, the interface bonding strength is weakened, and otherwise, the interface bonding strength of the deposition layer is improved when d is reduced.
The migration amount of the interface of the deposition layer and the effective width of the deposition layer are reduced along with the increase of the moving speed, and the migration amount of the interface is increased along with the increase of the rotating speed. The excessive travelling speed reduces the heat input, the material is not softened sufficiently, and tunnel hole defects are easy to form in the material adding area. Increasing the rotational speed can increase the heat input, increase the interfacial migration volume and the effective width of the additive region, but excessive heat input easily causes softening of the material of the additive region, resulting in a decrease in the mechanical properties of the additive member.
The temperature difference between the advancing side and the retreating side is gradually reduced along with the increase of the deposition feeding speed, the proper increase of the deposition feeding speed is beneficial to obtaining a deposition layer with uniform tissue performance within a reasonable range, excessive flash can be generated by excessive deposition feeding speed, the utilization rate of materials is reduced, a deposition feeding speed threshold value for fully mixing materials exists in the AFSD process, uneven deposition of the materials is caused by the fact that the deposition feeding speed is obviously lower than the threshold value, and the waste of the materials caused by the flash is caused by the fact that the deposition feeding speed is obviously higher than the threshold value.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.