Workpiece gradient nanocrystallization process parameter determination method

Abstract

The invention discloses a workpiece gradient nanocrystallization process parameter determination method, which is applied to the field of surface engineering, and is used for determining a workpiece gradient nanocrystallization process parameter through the dynamic yield strength of a material, the set gradient layer depth, a single-pass grain refinement parameter, a target grain size, a single-pass equivalent plastic strain, a machining head curvature radius, a work hardening coefficient, a cutter elastic modulus and a cutter Poisson ratio. According to the method, machining parameters such as tool bit pressure, working rotating speed, feeding amount and pass are calculated according to the material stacking fault energy of a workpiece, the elastic modulus of the workpiece, the Poisson ratio of the workpiece, roughness, the radius of the workpiece and the initial grain size, then machining verification is carried out, and the surface roughness, the surface hardness and the actual gradient layer depth of the workpiece are obtained as feedback. Each processing parameter is adjusted to an optimal value; according to the method, the process that the grain refinement effect of the workpiece is gradually verified through experience trial and error or complex orthogonal experiments by means of a scanning electron microscope and a transmission electron microscope can be avoided, the industrial implementation and efficiency of workpiece surface nanocrystallization are improved, and the cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of surface engineering, and in particular to a method for determining process parameters of gradient nano-processing of a workpiece. Background Art

[0002] Gradient nanocrystallization technology significantly improves the hardness, fatigue life, and wear resistance of metal parts by regulating the gradient distribution of grain size on the material's surface. It has important applications in high-precision industries such as aerospace and automotive manufacturing. Among related technologies, mainstream processes for achieving gradient nanocrystallization include surface mechanical attrition treatment (SMAT), surface mechanical rolling (SMRT), and traditional rolling techniques. However, these methods face significant bottlenecks in process parameter optimization and gradient control.

[0003] In related technologies, surface mechanical attrition treatment (SMAT) induces severe plastic deformation by impacting the material surface with high-speed projectiles, promoting dislocation proliferation and grain refinement, and forming a nano-submicron gradient structure from the surface to the inside; however, its surface roughness ( ) is high, and the gradient layer depth is shallow, making it difficult to meet the requirements of workpieces with high surface quality and gradient layer depth, such as precious metal rollers. Surface mechanical rolling (SMRT) uses a carbide ball to roll on the surface of a rotating workpiece, accumulating plastic strain through multiple passes to achieve a gradient distribution of nanograins. However, the selection of its process parameters (pressure, speed, feed rate) still relies on orthogonal experiments and lacks theoretical model guidance. Parameter optimization of traditional gradient nano-processes mainly relies on empirical trial and error or orthogonal experiments. To obtain a uniform gradient nano-layer, it is necessary to repeatedly adjust the tool head pressure, speed, feed rate and number of passes, and the experimental cycle can last up to several months.

[0004] The process parameter optimization methods used in related technologies lack theoretical models driven by material mechanical properties, resulting in low efficiency, high costs, and insufficient process stability. Specifically, they are manifested in the following aspects:

[0005] 1. Parameter isolation: Parameter screening relies on trial-and-error experiments, and no quantitative correlation model is established with the intrinsic parameters of the material (such as stacking fault energy, mechanical parameters, etc.);

[0006] 2. Redundant experiments: The grain refinement effect of the workpiece needs to be verified one by one through scanning electron microscopy (SEM) and transmission electron microscopy (TEM), which is inefficient;

[0007] 3. Difficulty in controlling gradient parameters: There is a lack of theoretical models to predict the quantitative relationship between gradient layer depth and process parameters; for example, gradient layer depth With the blade pressure The relationship is usually expressed as an empirical formula , but the influence of the material's dynamic yield strength and work hardening coefficient is not considered, resulting in poor process transplantability of different materials.

[0008] Therefore, how to effectively improve the optimization efficiency of the process parameters of gradient nano-processing of workpieces is a technical problem that those skilled in the art currently need to solve. Summary of the Invention

[0009] The purpose of the present invention is to provide a method for determining process parameters of gradient nano-processing of a workpiece, so as to improve the optimization efficiency of the parameters and reduce the cost.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] A method for determining process parameters of gradient nano-processing of a workpiece, comprising the following steps:

[0012] According to the dynamic yield strength of the material , single-pass equivalent plastic strain , set gradient layer depth , machining head curvature radius , work hardening coefficient and the stacking fault energy of the material , calculate the cutter head pressure ;

[0013] According to the dynamic yield strength of the material , the single-pass equivalent plastic strain , the work hardening coefficient and the stacking fault energy of the material , calculate the working speed ;

[0014] According to the cutter head pressure , the working speed , tool elastic modulus , Tool Poisson's ratio , workpiece elastic modulus , Poisson's ratio of workpiece , the curvature radius of the processing head and workpiece radius , calculate the feed rate ;

[0015] According to the initial grain size , target grain size and single-pass grain refinement parameters , calculate the number of passes ;

[0016] The calculated blade pressure , the working speed , the feed amount And the said pass performing nano-machining on the workpiece to obtain a workpiece test sample;

[0017] Detect the surface roughness, surface hardness and actual gradient layer depth of the workpiece test sample, and adjust the tool head pressure according to the detection results , the working speed , the feed amount and / or the said passes .

[0018] On the other hand, the calculation of the cutter head pressure Including: calculating the cutter head pressure according to formula 1 ; The formula (1) is:

[0019] (1)

[0020] in:

[0021] is the curvature radius of the machining head, in mm;

[0022] is the dynamic yield strength of the material, in MPa;

[0023] is the single-pass equivalent plastic strain, with a unit of 1;

[0024] is the work hardening coefficient, the unit is 1;

[0025] The stacking fault energy of the processed material, in mJ / m 2 ;

[0026] To set the gradient layer depth, the unit is mm.

[0027] On the other hand, the calculation of the working speed Including: calculating the working speed according to formula 2 ; The formula (2) is:

[0028] (2)

[0029] in:

[0030] is the dynamic yield strength of the material, in MPa;

[0031] is the single-pass equivalent plastic strain, with a unit of 1;

[0032] is the work hardening coefficient, the unit is 1;

[0033] is the stacking fault energy of the processed material, in units of .

[0034] On the other hand, the calculation feed amount Including: calculating the feed amount according to formula (3) ; The formula (3) is:

[0035] (3)

[0036] in:

[0037] is the working speed, in units of ;

[0038] is the processing head pressure, in N;

[0039] and are the tool elastic modulus and tool Poisson’s ratio, in MPa and 1 respectively;

[0040] and are the workpiece elastic modulus and the workpiece Poisson's ratio, in MPa and 1 respectively;

[0041] is the roughness of the workpiece, in units of ;

[0042] is the curvature radius of the machining head, in mm;

[0043] is the workpiece radius, in mm.

[0044] On the other hand, the calculation pass Including: calculating the pass according to formula (4) ; The formula (4) is:

[0045] (4)

[0046] in:

[0047] is the initial grain size, in nm;

[0048] is the target grain size, in nm;

[0049] is the single-pass grain refinement parameter, with a unit of 1.

[0050] On the other hand, the setting gradient layer depth ≤3mm.

[0051] On the other hand, when calculating the cutter head pressure Previously also included:

[0052] Determine the dynamic yield strength of the workpiece material , set gradient layer depth , single-pass grain refinement parameters and target grain size , according to the setting gradient layer depth Determine the single-pass equivalent plastic strain , and obtain the curvature radius of the machining head , work hardening coefficient , tool elastic modulus , Tool Poisson's ratio , the stacking fault energy of the workpiece material , workpiece elastic modulus , Poisson's ratio of workpiece , roughness , workpiece radius and initial grain size .

[0053] On the other hand, the cutter head pressure obtained by calculation , the working speed , the feed amount And the said pass Nano-processing the workpiece includes:

[0054] A nano-processing machine tool is used to perform nano-processing on the workpiece; the nano-processing machine tool includes a processing execution end, a temperature control system, a control system, and a sample stage. The processing execution end is used to perform nano-processing on the workpiece, the temperature control system is used to control the temperature and lubricate the surface of the workpiece, and the control system is used to control the processing execution end and the temperature control system; the sample stage is used to carry the workpiece;

[0055] The temperature control system is also used to adjust the temperature of the workpiece to -196°C to 300°C, and is also used to lubricate and cool the workpiece using a lubricant or a cooling medium; it is also used to add liquid nitrogen to cool and control the temperature of the workpiece when the temperature of the workpiece is lower than room temperature, and to turn on the heating device to heat and control the temperature of the workpiece and the cooling medium when the temperature of the workpiece is higher than room temperature.

[0056] On the other hand, the nano-processing of the workpiece using a nano-processing dedicated machine tool includes:

[0057] fixing the workpiece on the sample stage;

[0058] Applying tool head pressure to the workpiece using the machining execution end , pressing the tool head in the machining execution end into the surface of the workpiece to a certain depth;

[0059] The processing execution end performs a rotational motion with the center line of the workpiece as the axis, or the processing execution end controls the workpiece to perform a rotational motion around the center line of the workpiece, and the speed of the rotational motion is the working speed. The distance between the machining execution end and the axial end during each rotation is the single-turn feed amount. , until the length of the workpiece reaches the target length;

[0060] Machining passes on the surface of the workpiece Afterwards, a gradient nanostructured surface layer is obtained on the surface of the workpiece.

[0061] On the other hand, the surface roughness, surface hardness and actual gradient layer depth of the workpiece test sample are detected. , and adjust the cutter head pressure according to the test results , the working speed , the feed amount and / or the said passes include:

[0062] When the actual gradient layer depth is less than or equal to the target layer depth, the tool head pressure is adjusted to Adjusted to:

[0063] ;

[0064] When the surface roughness is less than or equal to the target roughness, the working speed is set to Adjusted to:

[0065] ;

[0066] Among them, θ=0.8-0.9;

[0067] When the surface hardness is less than or equal to the target hardness, the pass Adjusted to:

[0068] ;

[0069] Furthermore, when the layer depth fluctuation m of the adjacent areas of the surface of the workpiece test sample is greater than or equal to the preset fluctuation value, the feed amount is set to Adjusted to:

[0070] .

[0071] The method for determining process parameters of gradient nano-processing of a workpiece provided by the present invention calculates the tool head pressure by means of the dynamic yield strength of the material, the single-pass equivalent plastic strain, the set gradient layer depth, the curvature radius of the processing head, the work hardening coefficient, and the stacking fault energy of the material; calculates the working speed by means of the dynamic yield strength of the material, the single-pass equivalent plastic strain, the work hardening coefficient, and the stacking fault energy of the material; calculates the feed rate by means of the tool head pressure, the working speed, the tool elastic modulus, the tool Poisson's ratio, the workpiece elastic modulus, the workpiece Poisson's ratio, the curvature radius of the processing head, and the workpiece radius; calculates the pass by means of the initial grain size, the target grain size, and the single-pass grain refinement parameters; and calculates the pass according to the The mechanical properties, processing parameters and material properties of the workpiece are used to calculate the calculated values ​​of the tool head pressure, the working speed, the feed rate and the number of passes; then the workpiece is processed and verified according to the calculated values, and the tool head pressure, the working speed, the feed rate and the number of passes are adjusted to the optimal values ​​by obtaining the surface roughness, the surface hardness and the actual gradient layer depth of the workpiece as feedback; through this method, the process of verifying the grain refinement effect of the workpiece through empirical trial and error or complex orthogonal experiments with the help of scanning electron microscope and transmission electron microscope can be avoided. Only the surface roughness, surface hardness and actual gradient layer depth of the workpiece need to be detected, which can effectively improve the industrial feasibility of workpiece surface nano-processing, improve efficiency and reduce costs.

[0072] In one embodiment, detecting the surface roughness, surface hardness, and actual gradient layer depth of the workpiece test sample and adjusting the tool head pressure, operating speed, feed rate, and / or number of passes based on the detection results includes adjusting the tool head pressure, operating speed, feed rate, and number of passes when the measured surface index of the workpiece is less than or equal to a target index. This process, by determining the surface roughness, surface hardness, and actual gradient layer depth of the workpiece test sample and adjusting the feed rate, number of passes, and tool head pressure accordingly, eliminates the need to adjust the operating speed, thereby reducing workpiece processing time. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0074] Figure 1A flowchart of a specific implementation of the method for determining process parameters of gradient nano-processing of a workpiece provided by the present invention;

[0075] Figure 2 This is a flow chart of another specific implementation of the method for determining process parameters of gradient nano-processing of a workpiece provided by the present invention;

[0076] Figure 3 A schematic diagram of the actual gradient layer depth of a workpiece processed using the process parameters determined by the method provided by the present invention;

[0077] Figure 4-1 This is the bright field image of the TEM topography of the workpiece surface after nano-processing;

[0078] Figure 4-2 This is the dark field image of the TEM surface morphology of the workpiece after nano-processing. DETAILED DESCRIPTION

[0079] The core of the present invention is to provide a method for determining process parameters of gradient nano-processing of workpieces, which can significantly improve the efficiency of determining process parameters of gradient nano-processing of workpieces and reduce experimental costs.

[0080] In order to enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0081] Please refer to Figures 1 to 4-2 , Figure 1 A flowchart of a specific implementation of the method for determining process parameters of gradient nano-processing of a workpiece provided by the present invention; Figure 2 This is a flow chart of another specific implementation of the method for determining process parameters of gradient nano-processing of a workpiece provided by the present invention; Figure 3 A schematic diagram of the actual gradient layer depth of a workpiece processed using the process parameters determined by the method provided by the present invention; Figure 4-1 This is the bright field image of the TEM topography of the workpiece surface after nano-processing; Figure 4-2 This is the dark field image of the TEM surface morphology of the workpiece after nano-processing.

[0082] In this embodiment, the method for determining process parameters of gradient nano-sintering of a workpiece includes the following steps:

[0083] Step S1: According to the dynamic yield strength of the material , single-pass equivalent plastic strain , set gradient layer depth , machining head curvature radius , work hardening coefficient and the stacking fault energy of the material , calculate the cutter head pressure ;

[0084] Step S2: According to the dynamic yield strength of the material , the single-pass equivalent plastic strain , the work hardening coefficient and the stacking fault energy of the material , calculate the working speed ;

[0085] Step S3: According to the pressure of the cutting head , the working speed , tool elastic modulus , Tool Poisson's ratio , workpiece elastic modulus , Poisson's ratio of workpiece , the curvature radius of the processing head and workpiece radius , calculate the feed rate ;

[0086] Step S4: According to the initial grain size , target grain size and single-pass grain refinement parameters , calculate the number of passes ;

[0087] Step S5: Using the calculated blade pressure , the working speed , the feed amount And the said pass performing nano-machining on the workpiece to obtain a workpiece test sample;

[0088] Step S6: Detect the surface roughness, surface hardness and actual gradient layer depth of the workpiece test sample, and adjust the tool head pressure according to the detection results , the working speed , the feed amount and / or the said passes .

[0089] Specifically, the workpiece can be a metal rotating part. The surface roughness of the workpiece test sample can be detected by a contact profilometer or a microscope. The surface hardness of the workpiece test sample can be detected by tools such as a hardness tester or a nanoindenter. The actual gradient layer depth of the workpiece test sample can be detected by microhardness gradient testing or electron backscatter diffraction (EBSD). Compared with the process of successively verifying the grain refinement effect of the workpiece using scanning electron microscopy (SEM) and transmission electron microscopy (TEM) in related technologies, it can effectively shorten the cycle, simplify the process, and reduce costs.

[0090] This method for determining the process parameters of gradient nano-processing of workpieces adjusts the tool head pressure P, operating speed N, feed rate f, and pass number n to optimal values ​​by obtaining the surface roughness, surface hardness, and actual gradient layer depth of the workpiece as feedback. This method avoids the process of verifying the grain refinement effect of the workpiece through empirical trial and error or complex orthogonal experiments with the help of scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Instead, it only needs to detect the surface roughness, surface hardness, and actual gradient layer depth of the workpiece. This method can effectively improve the industrial feasibility of workpiece surface nano-processing, increase efficiency, and reduce costs.

[0091] In some embodiments, the tool tip pressure must meet the critical condition of dislocation proliferation on the material surface to ensure the plastic deformation depth and grain refinement efficiency; calculating the tool tip pressure P includes: calculating the tool tip pressure P according to formula (1); formula (1) is:

[0092] (1)

[0093] in:

[0094] is the curvature radius of the machining head, in mm;

[0095] is the dynamic yield strength of the material, in MPa;

[0096] is the single-pass equivalent plastic strain, with a unit of 1;

[0097] is the work hardening coefficient, the unit is 1;

[0098] The stacking fault energy of the processed material, in mJ / m 2 ;

[0099] To set the gradient layer depth, the unit is mm.

[0100] In some embodiments, the operating speed N is directly related to the strain rate, and calculating the operating speed N includes: calculating the operating speed N according to formula (2); formula (2) is:

[0101] (2)

[0102] in:

[0103] is the dynamic yield strength of the material, in MPa;

[0104] is the single-pass equivalent plastic strain, with a unit of 1;

[0105] is the work hardening coefficient, the unit is 1;

[0106] is the stacking fault energy of the processed material, in units of .

[0107] In some embodiments, the feed rate f determines the rolling track overlap rate and affects the deformation uniformity; calculating the feed rate f includes: calculating the feed rate f according to formula (3); formula (3) is:

[0108] (3)

[0109] in:

[0110] is the working speed, in units of ;

[0111] is the processing head pressure, in N;

[0112] and are the tool elastic modulus and tool Poisson’s ratio, in MPa and 1 respectively;

[0113] and are the workpiece elastic modulus and the workpiece Poisson's ratio, in MPa and 1 respectively;

[0114] is the roughness of the workpiece, in units of ;

[0115] is the curvature radius of the machining head, in mm;

[0116] is the workpiece radius, in mm.

[0117] In some embodiments, the pass number n is related to the accumulated plastic strain and the grain refinement depth. The value of the pass number affects the accumulated plastic strain and determines the nanograin size and gradient distribution of the workpiece. Calculating the pass number n includes: calculating the pass number n according to formula (4); formula (4) is:

[0118] (4)

[0119] in:

[0120] is the initial grain size, in nm;

[0121] is the target grain size, in nm;

[0122] is the single-pass grain refinement parameter, with a unit of 1.

[0123] The above process is based on the mechanical properties of the material, such as the dynamic yield strength of the material , work hardening coefficient k, etc., plastic deformation mechanism and grain parameters, such as initial grain size , target grain size , set the gradient layer depth h, etc., build a correlation model of tool head pressure P, working speed N, feed rate f and pass n, pre-select the calculated values ​​of tool head pressure P, working speed N, feed rate f and pass n as parameter combination through theoretical calculation, and then optimize the tool head pressure P, working speed N, feed rate f and pass n through processing verification to obtain the optimized value.

[0124] In some embodiments, the gradient layer depth h is set to ≤ 3 mm. This method is more suitable for scenarios where the gradient layer depth h is set to ≤ 3 mm. The selection of the gradient layer depth h can be determined according to different materials and process parameters.

[0125] In some embodiments, before calculating the cutter head pressure P, the method further includes:

[0126] Determine the dynamic yield strength of the workpiece material , set gradient layer depth , single-pass grain refinement parameters and target grain size , according to the setting gradient layer depth Determine the single-pass equivalent plastic strain , and obtain the curvature radius of the machining head , work hardening coefficient , tool elastic modulus , Tool Poisson's ratio , the stacking fault energy of the workpiece material , workpiece elastic modulus , Poisson's ratio of workpiece , roughness , workpiece radius and initial grain size Specifically, the single-pass equivalent plastic strain It is determined according to the set gradient layer depth h, and the single-pass equivalent plastic strain The target value is usually 2-5. When the gradient layer depth h is increased, the single-pass equivalent plastic strain Corresponding increase; material dynamic yield strength It is related to the strain rate and can be measured by the Hopkins on bar experiment. The strain rate is per second; the value of the work hardening coefficient k should be selected according to the different materials, for example, the work hardening coefficient k of 304 stainless steel is 0.25, and the work hardening coefficient k of titanium alloy is 0.15; the stacking fault energy of the material Determined by the material crystal structure; single-pass grain refinement parameters It should be based on different materials, mainly depending on the stacking fault energy and dominant deformation mechanism of the material. For example, the value of austenitic stainless steel is between 0.25-0.5, and the value of titanium alloy is between 0.36-075.

[0127] In some embodiments, nano-machining a workpiece using the calculated tool head pressure P, operating speed N, feed rate f, and pass number n includes:

[0128] A special nano-machine tool is used to perform nano-machining on the workpiece, so as to verify the tool head pressure P, working speed N, feed rate f and pass n; the special nano-machine tool includes a processing execution end, a temperature control system, a control system and a sample stage, the processing execution end is used to perform nano-machining on the workpiece, the temperature control system is used to control the temperature and lubrication of the surface of the workpiece, and the control system is used to control the processing execution end and the temperature control system; the sample stage is used to carry the workpiece; specifically, the special nano-machine tool includes but is not limited to equipment that is modified from CNC milling machines, CNC drilling machines, grinders and machining centers; the processing execution end includes a tool, a tool holder and a pressure device, the tool is made of cemented carbide, bearing steel or ceramic, and the tool head curvature diameter range is optional 4-10mm, that is, the curvature radius of the processing head. Under the same pressure, when the curvature radius of the processing head changes, the pressure on the workpiece surface is different; the number of tool ball heads can be single or multiple, and the appropriate amount is 1.

[0129] In some embodiments, the temperature control system is also used to adjust the temperature of the workpiece to between -196°C and 300°C. High temperatures can cause recrystallization of nanocrystals within the workpiece, while low temperatures can degrade the plasticity of the material within the workpiece. The specific requirements are determined based on the material. The temperature control system is also used to lubricate and cool the workpiece using a lubricant or cooling medium. When the workpiece temperature is below room temperature, liquid nitrogen is added to cool the workpiece. When the workpiece temperature is above room temperature, a heating device is activated to heat the workpiece and the cooling medium. The heating device can be a hot air blower, an induction heater, or other device. The lubricant can be lubricating oil, and the cooling medium can be water.

[0130] In some embodiments, as Figure 2 As shown, the nano-processing of workpieces using a special nano-machine tool includes:

[0131] Step S51: Fixing the workpiece on the sample stage;

[0132] Step S52: applying a tool head pressure P to the workpiece using the machining execution end, pressing the tool head in the machining execution end into the surface of the workpiece to a certain depth, for example, 0-800 μm;

[0133] Step S53: The machining execution end performs a rotational motion about the centerline of the workpiece, or the machining execution end controls the workpiece to perform a rotational motion about the centerline of the workpiece, the rotational speed being the working speed N, and the machining execution end translates toward one end of the axis by a single-turn feed amount f per revolution until the workpiece reaches the target length;

[0134] Step S54: After the surface of the workpiece is processed n times, a gradient nanostructured surface layer is obtained on the surface of the workpiece; the surface of the workpiece is composed of nano-sized grains, submicron-sized grains and original-sized grain structures from the outside to the inside, and the surface layer thickness can reach 800um-2500um.

[0135] In some embodiments, the surface roughness, surface hardness and actual gradient layer depth of the workpiece test sample are detected. , and adjust the cutter head pressure P, working speed N, feed rate f and / or pass number n according to the test results, including:

[0136] When the actual gradient layer depth is less than or equal to the target layer depth, the tool head pressure P is adjusted to:

[0137] ;

[0138] Also, it should be determined ≤1.2 , >1.2 In order to avoid material failure, you can set ;

[0139] When the surface roughness is less than or equal to the target roughness, the operating speed N is adjusted to:

[0140] ;

[0141] Among them, θ=0.8-0.9, for example, θ=0.85; the target roughness can be selected as 0.2.

[0142] When the surface hardness is less than or equal to the target hardness, the pass number n is adjusted to:

[0143] ;

[0144] Furthermore, when the layer depth fluctuation m of the adjacent areas on the surface of the workpiece test sample is ≥ the preset fluctuation value, the feed rate f is adjusted to:

[0145] .

[0146] The depth fluctuation m between adjacent areas on the surface of the workpiece test sample refers to the difference in actual gradient depth between two adjacent areas on the surface of the workpiece test sample, divided by the actual gradient depth of one of the areas. This depth fluctuation m is determined to determine the uniformity of the gradient depth on the surface of the workpiece test sample. Specifically, the preset fluctuation value can be 20%. That is, when the depth fluctuation m between adjacent areas on the surface of the workpiece test sample is ≥ 20%, the feed rate f is adjusted. Of course, the value of 10% in the formula can be set as needed, for example, it can be a value between 8% and 12%.

[0147] In the above process, the surface roughness, surface hardness and actual gradient layer depth of the workpiece test sample are judged respectively, and the values ​​of feed rate f, working speed N, pass number n and tool head pressure P are adjusted respectively.

[0148] Specifically, in a specific embodiment, taking a SUS304 stainless steel rod workpiece with a diameter of 30 mm as an example, the length of the workpiece is 400 mm, the curvature radius r of the processing head is 3 mm, and the dynamic yield strength of the material is 800Mpa, material stacking fault energy 45mJ / m 2 , single-pass equivalent plastic strain The value is 3, the work hardening coefficient k is 0.25, the ball material of the tool is steel, and the tool elastic modulus is =210000 MPa, Poisson's ratio of tool =0.3; the workpiece material is 304 stainless steel, the workpiece elastic modulus =200000 MPa, Poisson's ratio of the workpiece =0.29, roughness =0.8um, initial grain size =50um, target grain size =10nm, single-pass grain refinement parameters =0.3.

[0149] Specifically, the gradient layer depth h is set to 1.5 mm and the calculation is as follows:

[0150] Blade pressure , about 1.08kN;

[0151] Speed ;

[0152] Feed rate ;

[0153] path Second-rate;

[0154] The above parameters were verified using a nano-machine tool. The processing temperature was room temperature, and 0W40 engine oil was used for cooling and lubrication. First, the workpiece was fixed on the sample table. The processing execution end applied a pressure of 1080N to the workpiece, and the tool head was pressed into the metal surface of the workpiece to a certain depth. The nano-machine tool controlled the workpiece to rotate around the center line at a speed of 126 rpm. The processing execution end moved axially for each revolution with a single-turn feed of 0.11mm. This operation was performed until the rotating part length reached 400mm. Repeat the above process. After processing the surface of the workpiece 5 times, a gradient nanostructured surface layer was obtained on the surface of the workpiece. From the outside to the inside, there are nano-sized grains, submicron-sized grains, and original-sized grain structures. The layer thickness is 2mm. Please refer to the attached document. Figure 3 、 Figure 4-1 and Figure 4-2 , 304 stainless steel workpiece, after nano-processing, the gradient structure layer depth map and surface TEM morphology image show that the average grain size of the material surface is 23nm, and the actual gradient layer depth is about 2mm, which meets the processing requirements; if it does not meet the requirements, the surface roughness, surface hardness and actual gradient layer depth of the workpiece test sample are tested. , and adjust the corresponding tool head pressure P, working speed N, feed rate f and / or pass number n respectively.

[0155] This method for determining the process parameters of gradient nano-processing of workpieces unifies the tool head pressure P, working speed N, feed rate f and pass number n in a mathematical framework driven by mechanical properties for the first time. By pre-selecting parameter combinations through theoretical calculations, it upgrades from an "experience-driven" mode to a precise control mode of "theoretical guidance + data verification". This method can reduce the number of material processing experiments for workpieces by 90%. Related technologies often require 20 sets of orthogonal experiments, while this scheme only requires 2-3 sets. After preparing the nano-gradient material using this method, only the surface roughness, surface hardness, actual gradient layer depth and other properties need to be appropriately adjusted to adjust its process parameters. The grain refinement effect is no longer verified step by step by scanning electron microscopy (SEM) and transmission electron microscopy (TEM), which greatly improves the industrial feasibility of surface nano-processing. After optimizing the process parameters and processing the workpiece using this method, the mechanical properties of the workpiece material, including surface hardness, wear resistance, corrosion resistance, and fatigue resistance, are greatly improved, the material utilization rate is increased, and the overall cost can be significantly reduced.

[0156] The above is a detailed introduction to the method for determining the process parameters of the gradient nano-processing of the workpiece provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, the present invention can also be improved and modified in a number of ways, and these improvements and modifications also fall within the scope of protection of the present invention.

Claims

1. A method for determining process parameters of gradient nano-processing of a workpiece, characterized in that: The following steps are involved: According to the dynamic yield strength of the material ( ), single-pass equivalent plastic strain ( ), set gradient layer depth ( )、Machining head curvature radius( ), work hardening coefficient ( ) and the stacking fault energy of the material ( ), calculate the cutter head pressure ( ); According to the material dynamic yield strength ( ), the single-pass equivalent plastic strain ( ), the work hardening coefficient ( ) and the stacking fault energy of the material ( ), calculate the working speed ( ); According to the blade pressure ( ), the working speed ( )、Tool elastic modulus( ), Tool Poisson's ratio ( ), workpiece elastic modulus ( ), Poisson's ratio of workpiece ( ), the curvature radius of the machining head ( ) and workpiece radius ( ), calculate the feed rate ( ); According to the initial grain size ( ), target grain size ( ) and single-pass grain refinement parameters ( ), calculate the number of passes ( ); The calculated blade pressure ( ), the working speed ( ), the feed amount ( ) and the said passes ( ) performing nano-machining on the workpiece to obtain a workpiece test sample; Detect the surface roughness, surface hardness and actual gradient layer depth of the workpiece test sample, and adjust the tool head pressure according to the detection results ( ), the working speed ( ), the feed amount ( ) and / or the said passes ( ).

2. The method for determining process parameters of gradient nano-processing of a workpiece according to claim 1, characterized in that: The calculation of the cutter head pressure ( ) includes: calculating the cutter head pressure according to formula (1) ( ); the formula (1) is: （1） in: is the curvature radius of the machining head, in mm; is the dynamic yield strength of the material, in MPa; is the single-pass equivalent plastic strain, with a unit of 1; is the work hardening coefficient, the unit is 1; The stacking fault energy of the processed material, in mJ / m 2 ; To set the gradient layer depth, the unit is mm.

3. The method for determining process parameters of gradient nano-processing of a workpiece according to claim 1, characterized in that: The calculation of the working speed ( ) includes: calculating the working speed ( ); the formula (2) is: （2） in: is the dynamic yield strength of the material, in MPa; is the single-pass equivalent plastic strain, with a unit of 1; is the work hardening coefficient, the unit is 1; is the stacking fault energy of the processed material, in units of .

4. The method for determining process parameters of gradient nano-processing of a workpiece according to claim 1, wherein: The calculated feed amount ( ) includes: calculating the feed amount ( ); the formula (3) is: （3） in: is the working speed, in units of ; is the processing head pressure, in N; and are the tool elastic modulus and tool Poisson’s ratio, in MPa and 1 respectively; and are the workpiece elastic modulus and the workpiece Poisson's ratio, in MPa and 1 respectively; is the roughness of the workpiece, in units of ; is the curvature radius of the machining head, in mm; is the workpiece radius, in mm.

5. The method for determining process parameters of gradient nano-processing of a workpiece according to claim 1, characterized in that: The calculation pass ( ) includes: calculating the pass number ( ); the formula (4) is: （4） in: is the initial grain size, in nm; is the target grain size, in nm; is the single-pass grain refinement parameter, with a unit of 1.

6. The method for determining process parameters of gradient nano-processing of a workpiece according to any one of claims 1 to 5, characterized in that: The gradient layer depth is set ( )≤3mm.

7. The method for determining process parameters of gradient nano-processing of a workpiece according to any one of claims 1 to 5, characterized in that: In calculating the cutter head pressure ( ) also includes: Determine the dynamic yield strength of the workpiece material ( ), set gradient layer depth ( ), single-pass grain refinement parameters ( ) and target grain size ( ), according to the set gradient layer depth ( ) to determine the single-pass equivalent plastic strain ( ), and obtain the curvature radius of the machining head ( ), work hardening coefficient ( )、Tool elastic modulus( ), Tool Poisson's ratio ( ), the material stacking fault energy of the workpiece ( ), workpiece elastic modulus ( ), Poisson's ratio of workpiece ( ), roughness ( )、Workpiece radius( ) and the initial grain size ( ).

8. The method for determining process parameters of gradient nano-processing of a workpiece according to any one of claims 1 to 5, characterized in that: The cutter head pressure obtained by the calculation ( ), the working speed ( ), the feed amount ( ) and the said passes ( ) performing nano-machining on the workpiece includes: A nano-processing machine tool is used to perform nano-processing on the workpiece; the nano-processing machine tool includes a processing execution end, a temperature control system, a control system, and a sample stage. The processing execution end is used to perform nano-processing on the workpiece, the temperature control system is used to control the temperature and lubricate the surface of the workpiece, and the control system is used to control the processing execution end and the temperature control system; the sample stage is used to carry the workpiece; The temperature control system is also used to adjust the temperature of the workpiece to -196°C to 300°C, and is also used to lubricate and cool the workpiece using a lubricant or a cooling medium; it is also used to add liquid nitrogen to cool and control the temperature of the workpiece when the temperature of the workpiece is lower than room temperature, and to turn on the heating device to heat and control the temperature of the workpiece and the cooling medium when the temperature of the workpiece is higher than room temperature.

9. The method for determining process parameters of gradient nano-processing of a workpiece according to claim 8, characterized in that: The nano-processing of the workpiece using a nano-processing machine tool includes: fixing the workpiece on the sample stage; Applying tool head pressure to the workpiece by the machining execution end ( ), pressing the tool head in the processing execution end into the surface of the workpiece to a certain depth; The processing execution end performs a rotational motion with the center line of the workpiece as the axis, or the processing execution end controls the workpiece to perform a rotational motion around the center line of the workpiece, and the speed of the rotational motion is the working speed ( ), the distance between the machining execution end and the axial end during each rotation is the single-turn feed amount ( ), until the length of the workpiece reaches the target length; During the surface processing of the workpiece ( ), a gradient nanostructured surface layer is obtained on the surface of the workpiece.

10. The method for determining process parameters of gradient nano-processing of a workpiece according to claim 9, characterized in that: The surface roughness, surface hardness and actual gradient layer depth of the workpiece test sample are detected ( ), and adjust the cutter head pressure according to the test results ( ), the working speed ( ), the feed amount ( ) and / or the said passes ( )include: When the actual gradient layer depth is less than or equal to the target layer depth, the tool head pressure ( ) is adjusted to: ； When the surface roughness is less than or equal to the target roughness, the working speed ( ) is adjusted to: ； Among them, θ=0.8-0.9; When the surface hardness is less than or equal to the target hardness, the pass ( ) is adjusted to: ； Furthermore, when the layer depth fluctuation m of the adjacent areas of the surface of the workpiece test sample is greater than or equal to the preset fluctuation value, the feed amount ( ) is adjusted to: 。

Images