A method for determining process parameters for gradient nanocrystallization of a workpiece

Abstract

The application discloses a kind of workpiece gradient nanocrystallization process parameter determination methods, applied to surface engineering field, by material dynamic yield strength, set gradient layer depth, single pass grain refinement parameter, target grain size, single pass equivalent plastic strain, processing head curvature radius, work hardening coefficient, tool elastic modulus, tool poisson's ratio, workpiece's material dislocation energy, workpiece elastic modulus, workpiece poisson's ratio, roughness, workpiece radius and initial grain size, tool head pressure, work speed, feed and pass are calculated respectively Then process verification is carried out, and the surface roughness, surface hardness and actual gradient layer depth of the workpiece are obtained as feedback, and each processing parameter is adjusted to the optimal value;The method can avoid the process of verifying workpiece grain refinement effect by experience trial and error or complex orthogonal experiment and with scanning electron microscope and transmission electron microscope, improve the industrial implementability and efficiency of workpiece surface nanocrystallization, and reduce cost.

Description

Technical Field

[0001] This invention relates to the field of surface engineering, and in particular to a method for determining process parameters for gradient nano-sizing of workpieces. Background Technology

[0002] Gradient nanostructuring technology significantly improves the hardness, fatigue life, and wear resistance of metal parts by controlling the gradient distribution of grain size on the surface of materials, and has important applications in high-precision industrial fields such as aerospace and automotive manufacturing. Among related technologies, the mainstream processes for achieving gradient nanostructuring include surface mechanical abrasive treatment (SMAT), surface mechanical rolling (SMRT), and traditional rolling technology, but these methods have significant bottlenecks in terms of process parameter optimization and gradient control.

[0003] In related technologies, surface mechanical abrasive treatment (SMAT) induces severe plastic deformation on the material surface through high-speed projectile impact, promoting dislocation multiplication and grain refinement, forming a nano-to-submicron gradient structure from the surface inwards; however, its surface roughness ( The surface roughness (SMR) 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 rolls. Surface mechanical rolling (SMRT) uses a cemented carbide ball to roll on the surface of a rotating workpiece, accumulating plastic strain through multiple passes to achieve a gradient distribution of nanocrystals. However, the selection of its process parameters (pressure, speed, feed rate) still relies on orthogonal experiments and lacks theoretical model guidance. The parameter optimization of traditional gradient nanoforming processes mainly relies on empirical trial and error or orthogonal experiments. To obtain a uniform gradient nanolayer, it is necessary to repeatedly adjust the cutter pressure, speed, feed rate, and number of passes, with experimental cycles lasting for several months.

[0004] The optimization methods for process parameters in related technologies lack theoretical models driven by material mechanical properties, resulting in low efficiency, high cost, and insufficient process stability, specifically manifested in the following aspects:

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

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

[0007] 3. Difficulty in gradient control: 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 cutter head pressure The relationship is usually expressed as an empirical formula. However, the influence of the material's dynamic yield strength and work hardening coefficient was not considered, resulting in poor process transferability of different materials.

[0008] Therefore, how to effectively improve the optimization efficiency of the workpiece gradient nanocrystallization process parameters is a technical problem to be solved by those skilled in the art at present. SUMMARY

[0009] The purpose of the present application is to provide a workpiece gradient nanocrystallization process parameter determination method for improving the optimization efficiency of the parameters and reducing the cost.

[0010] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0011] A workpiece gradient nanocrystallization process parameter determination method, comprising the following steps:

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

[0013] According to the dynamic yield strength of the material , the single-pass equivalent plastic strain , the work hardening coefficient and the material dislocation energy , the working rotational speed is calculated ;

[0014] According to the tool head pressure , the working rotational speed , the tool elastic modulus , the tool Poisson's ratio , the workpiece elastic modulus , the workpiece Poisson's ratio , the machining head curvature radius and the workpiece radius , the feed amount is calculated ;

[0015] According to the initial grain size , the target grain size and the single-pass grain refinement parameter , the pass is calculated ;

[0016] The tool head pressure , the working rotational speed , the feed amount and the pass calculated are used The workpiece is processed into nanoscale materials to obtain a workpiece test sample;

[0017] The surface roughness, surface hardness, and actual gradient layer depth of the workpiece test sample are detected, and the cutting head pressure is adjusted based on the test results. The operating speed The feed rate and / or the aforementioned lanes .

[0018] On the other hand, the calculation of the cutting head pressure Includes: calculating the cutter head pressure according to Formula 1. The formula (1) is:

[0019] (1)

[0020] in:

[0021] The radius of curvature of the machining head is expressed in mm.

[0022] The dynamic yield strength of the material is expressed in MPa.

[0023] The value represents the equivalent plastic strain per pass, in units of 1.

[0024] This is the work hardening factor, in units of 1;

[0025] Stacking fault energy of processed materials, in mJ / m 2 ;

[0026] This is used to set the gradient layer depth, in mm.

[0027] On the other hand, the calculated operating speed This includes: calculating the operating speed according to Formula 2. The formula (2) is:

[0028] (2)

[0029] in:

[0030] The dynamic yield strength of the material is expressed in MPa.

[0031] The value represents the equivalent plastic strain per pass, in units of 1.

[0032] This is the work hardening factor, in units of 1;

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

[0034] On the other hand, the calculated feed rate Includes: calculating the feed rate according to formula (3) The formula (3) is:

[0035] (3)

[0036] in:

[0037] Operating speed, unit: ;

[0038] The pressure applied to the machining head is expressed in Newtons (N).

[0039] and These are the tool's elastic modulus and Poisson's ratio, respectively, in MPa and 1.

[0040] and These are the workpiece's elastic modulus and Poisson's ratio, respectively, with units of MPa and 1.

[0041] The roughness of the workpiece, in units of . ;

[0042] The radius of curvature of the machining head is expressed in mm.

[0043] The radius of the workpiece is in mm.

[0044] On the other hand, the calculated number of passes Includes: calculating the track number according to formula (4) The formula (4) is:

[0045] (4)

[0046] in:

[0047] The initial grain size is expressed in nm.

[0048] The target grain size is expressed in nm.

[0049] This is the single-pass grain refinement parameter, in units of 1.

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

[0051] On the other hand, in calculating the pressure of the cutting head Previously included:

[0052] Determine the dynamic yield strength of the workpiece material. Set gradient depth Single-pass grain refinement parameters and target grain size According to the set gradient layer depth The single-pass equivalent plastic strain was determined. And obtain the radius of curvature of the processing head. Work hardening coefficient Tool elastic modulus Poisson's ratio of cutting tools The stacking fault energy of the workpiece material Workpiece elastic modulus Poisson's ratio of the workpiece Roughness Workpiece radius and initial grain size .

[0053] On the other hand, the calculated cutting head pressure is used. The operating speed The feed rate and the aforementioned number of lanes The nano-processing of the workpiece includes:

[0054] A nanoscale-specific machine tool is used to perform nanoscale processing on the workpiece. The nanoscale-specific machine tool includes a processing execution end, a temperature control system, a control system, and a sample stage. The processing execution end is used for nanoscale processing of the workpiece. The temperature control system is used for temperature control and lubrication of the workpiece surface. The control system is used to control the processing execution end and the temperature control system. The sample stage is used to support the workpiece.

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

[0056] On the other hand, the process of using a nano-scale specialized machine tool to perform nano-scale machining on the workpiece includes:

[0057] The workpiece is fixed on the sample stage;

[0058] The machining actuator applies cutting head pressure to the workpiece. The cutting tool head in the machining execution end is pressed into the surface of the workpiece to a certain depth;

[0059] The machining actuator rotates around the centerline of the workpiece, or the machining actuator controls the workpiece to rotate around its centerline, the rotational speed being the operating speed. The interval of the machining execution end's translation towards one end of the axis for each revolution is the feed amount per revolution. This continues until the length of the workpiece reaches the target length.

[0060] Number of machining passes on the surface of the workpiece Subsequently, a gradient nanostructure surface layer is obtained on the surface of the workpiece.

[0061] On the other hand, the detection of the surface roughness, surface hardness, and actual gradient layer depth of the workpiece test sample is described. The pressure of the cutting head is adjusted according to the test results. The operating speed The feed rate and / or the aforementioned lanes include:

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

[0063] ;

[0064] When the surface roughness is less than or equal to the target roughness, the operating speed is adjusted. Adjusted to:

[0065] ;

[0066] Where θ = 0.8 - 0.9;

[0067] When the surface hardness is less than or equal to the target hardness, then the number of passes will be... Adjusted to:

[0068] ;

[0069] Furthermore, when the layer depth fluctuation m of the adjacent areas on the surface of the workpiece test sample is greater than or equal to a preset fluctuation value, the feed rate is adjusted accordingly. Adjusted to:

[0070] .

[0071] The method for determining workpiece gradient nano-sizing process parameters provided by this invention calculates the tool pressure using the material's dynamic yield strength, the single-pass equivalent plastic strain, the set gradient layer depth, the machining head radius of curvature, the work hardening coefficient, and the material stacking fault energy; calculates the operating speed using the material's dynamic yield strength, the single-pass equivalent plastic strain, the work hardening coefficient, and the material stacking fault energy; calculates the feed rate using the tool pressure, the operating speed, the tool's elastic modulus, the tool's Poisson's ratio, the workpiece's elastic modulus, the workpiece's Poisson's ratio, the machining head radius of curvature, and the workpiece radius; calculates the number of passes using the initial grain size, the target grain size, and the single-pass grain refinement parameters; and calculates the number of passes based on the... Based on the mechanical properties, processing parameters, and material properties of the workpiece, the calculated values ​​of the cutting head pressure, working speed, feed rate, and number of passes are obtained. Then, the workpiece is processed and verified according to the calculated values. By obtaining the surface roughness, surface hardness, and actual gradient layer depth of the workpiece as feedback, the cutting head pressure, working speed, feed rate, and number of passes are adjusted to optimal values. This method avoids the process of trial and error based on experience or complex orthogonal experiments and the need to verify the grain refinement effect of the workpiece using scanning electron microscopes and transmission electron microscopes. It only requires detecting the surface roughness, surface hardness, and actual gradient layer depth of the workpiece, which can effectively improve the industrial feasibility of workpiece surface nano-sizing, increase 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 cutting head pressure, operating speed, feed rate, and / or number of passes based on the detection results, includes: adjusting the values ​​of the cutting head pressure, operating speed, feed rate, and number of passes when the measured surface parameters of the workpiece are less than or equal to the target parameters. This process, by judging the surface roughness, surface hardness, and actual gradient layer depth of the workpiece test sample and adjusting the values ​​of the feed rate, number of passes, and cutting head pressure accordingly, eliminates the need to adjust the operating speed, thereby reducing workpiece machining requirements. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0074] Figure 1A flowchart illustrating a specific embodiment of the workpiece gradient nano-sizing process parameter determination method provided by the present invention;

[0075] Figure 2 A flowchart of another specific embodiment of the workpiece gradient nano-sizing process parameter determination method 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 in this invention;

[0077] Figure 4-1 Bright-field image of the TEM morphology of the workpiece surface after nano-sizing treatment;

[0078] Figure 4-2 This is a dark-field image of the TEM morphology of the workpiece surface after nano-sizing treatment. Detailed Implementation

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

[0080] 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 embodiments.

[0081] Please refer to Figures 1 to 4-2 , Figure 1 A flowchart illustrating a specific embodiment of the workpiece gradient nano-sizing process parameter determination method provided by the present invention; Figure 2 A flowchart of another specific embodiment of the workpiece gradient nano-sizing process parameter determination method 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 in this invention; Figure 4-1 Bright-field image of the TEM morphology of the workpiece surface after nano-sizing treatment; Figure 4-2 This is a dark-field image of the TEM morphology of the workpiece surface after nano-sizing treatment.

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

[0083] Step S1: Based on the material's dynamic yield strength Single-pass equivalent plastic strain Set gradient depth , radius of curvature of the machining head Work hardening coefficient and material stacking fault energy Calculate the cutting head pressure ;

[0084] Step S2: Based on 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 operating speed ;

[0085] Step S3: Based on the cutter head pressure The operating speed Tool elastic modulus Poisson's ratio of cutting tools Workpiece elastic modulus Poisson's ratio of the workpiece The radius of curvature of the processing head and workpiece radius Calculate the feed rate ;

[0086] Step S4: Based on the initial grain size Target grain size and single-pass grain refinement parameters Calculate the number of tracks ;

[0087] Step S5: Use the calculated cutter head pressure The operating speed The feed rate and the aforementioned number of lanes The workpiece is processed into nanoscale materials 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 cutting head pressure according to the detection results. The operating speed The feed rate and / or the aforementioned lanes .

[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 microscope. The surface hardness of the workpiece test sample can be detected by a hardness tester or 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 sequentially verifying the grain refinement effect of the workpiece by scanning electron microscopy (SEM) and transmission electron microscopy (TEM) in related technologies, this method can effectively shorten the cycle, simplify the process, and reduce costs.

[0090] This method for determining the process parameters of gradient nano-sizing of workpieces uses the surface roughness, surface hardness, and actual gradient layer depth of the workpiece as feedback to adjust the tool pressure P, working speed N, feed rate f, and number of passes n to optimal values. This method avoids the process of verifying the grain refinement effect of the workpiece through trial and error or complex orthogonal experiments using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). It only requires the detection of the surface roughness, surface hardness, and actual gradient layer depth of the workpiece, which can effectively improve the industrial feasibility of workpiece surface nano-sizing, increase efficiency, and reduce costs.

[0091] In some implementations, the cutting head pressure needs to meet the critical condition for dislocation multiplication on the material surface to ensure the plastic deformation depth and grain refinement efficiency; the calculation of the cutting head pressure P includes: calculating the cutting head pressure P according to formula (1); formula (1) is:

[0092] (1)

[0093] in:

[0094] The radius of curvature of the machining head is expressed in mm.

[0095] The dynamic yield strength of the material is expressed in MPa.

[0096] The value represents the equivalent plastic strain per pass, in units of 1.

[0097] This is the work hardening factor, in units of 1;

[0098] Stacking fault energy of processed materials, in mJ / m 2 ;

[0099] This is used to set the gradient layer depth, in mm.

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

[0101] (2)

[0102] in:

[0103] The dynamic yield strength of the material is expressed in MPa.

[0104] The value represents the equivalent plastic strain per pass, in units of 1.

[0105] This is the work hardening factor, in units of 1;

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

[0107] In some implementations, the feed rate f determines the overlap rate of the rolling trajectory, affecting the uniformity of deformation; calculating the feed rate f includes: calculating the feed rate f according to formula (3); formula (3) is:

[0108] (3)

[0109] in:

[0110] Operating speed, unit: ;

[0111] The pressure applied to the machining head is expressed in Newtons (N).

[0112] and These are the tool's elastic modulus and Poisson's ratio, respectively, in MPa and 1.

[0113] and These are the workpiece's elastic modulus and Poisson's ratio, respectively, with units of MPa and 1.

[0114] The roughness of the workpiece, in units of . ;

[0115] The radius of curvature of the machining head is expressed in mm.

[0116] The radius of the workpiece is in mm.

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

[0118] (4)

[0119] in:

[0120] The initial grain size is expressed in nm.

[0121] The target grain size is expressed in nm.

[0122] This is the single-pass grain refinement parameter, in units of 1.

[0123] The above process is based on material mechanical property parameters, such as the material's dynamic yield strength. Work hardening coefficient k, plastic deformation mechanism and grain parameters, such as initial grain size, etc. Target grain size The gradient layer depth h is set, and a correlation model is constructed for the tool head pressure P, working speed N, feed rate f, and number of passes n. The calculated values ​​of the tool head pressure P, working speed N, feed rate f, and number of passes n are pre-selected as parameter combinations through theoretical calculation. Then, through machining verification, the tool head pressure P, working speed N, feed rate f, and number of passes n are optimized to obtain optimized values.

[0124] In some implementations, 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 implementations, the following is included before calculating the cutting head pressure P:

[0126] Determine the dynamic yield strength of the workpiece material. Set gradient depth Single-pass grain refinement parameters and target grain size According to the set gradient layer depth The single-pass equivalent plastic strain was determined. And obtain the radius of curvature of the processing head. Work hardening coefficient Tool elastic modulus Poisson's ratio of cutting tools The stacking fault energy of the workpiece material Workpiece elastic modulus Poisson's ratio of the workpiece Roughness Workpiece radius and initial grain size Specifically, single-pass equivalent plastic strain It is determined based on 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 equivalent plastic strain per pass is... Corresponding increase; dynamic yield strength of the material It is related to the strain rate and can be measured by the Hopkins-on bar experiment. The strain rate is taken as... 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=0.25 for 304 stainless steel and k=0.15 for titanium alloys; material stacking fault energy. Determined by the material's crystal structure; single-pass grain refinement parameters The value should be determined based on the material, mainly depending on the stacking fault energy and dominant deformation mechanism. For example, the value is between 0.25 and 0.5 for austenitic stainless steel and between 0.36 and 0.75 for titanium alloys.

[0127] In some implementations, nano-machining of the workpiece using calculated tool pressure P, operating speed N, feed rate f, and number of passes n includes:

[0128] A nanoscale dedicated machine tool is used to perform nanoscale machining on the workpiece, thereby verifying the tool head pressure P, working speed N, feed rate f, and number of passes n. The nanoscale dedicated machine tool includes a machining execution end, a temperature control system, a control system, and a sample stage. The machining execution end is used for nanoscale machining of the workpiece, the temperature control system is used for temperature control and lubrication of the workpiece surface, and the control system is used to control the machining execution end and the temperature control system. The sample stage is used to hold the workpiece. Specifically, the nanoscale dedicated machine tool includes, but is not limited to, equipment modified from CNC milling machines, CNC drilling machines, grinding machines, and machining centers. The machining execution end includes a cutting tool, a tool holder, and a pressure device. The cutting tool is made of cemented carbide, bearing steel, or ceramic. The cutting tool's tip curvature diameter can be selected from 4-10mm, i.e., the radius of curvature of the machining head. Under the same pressure, the pressure on the workpiece surface varies when the radius of curvature of the machining head changes. The number of ball ends of the cutting tool can be single or multiple, with one being suitable.

[0129] In some implementations, the temperature control system is also used to adjust the workpiece temperature to between -196°C and 300°C. High temperatures cause the nanocrystals inside the workpiece to recrystallize, while low temperatures reduce the plasticity of the material inside the workpiece; the specific temperature control needs to be determined based on different materials. The temperature control system is also used to lubricate and cool the workpiece using lubricants or cooling media. Furthermore, it is used to add liquid nitrogen to cool and control the workpiece when its temperature is below room temperature, and to activate a heating device to heat and control the workpiece and cooling media when its temperature is above room temperature. The heating device can be a hot air blower, induction heating device, etc. The lubricant can be lubricating oil, and the cooling media can be water.

[0130] In some implementations, such as Figure 2 As shown, the nano-scale machining of workpieces using specialized nano-machine tools includes:

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

[0132] Step S52: Apply tool 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, 0-800um;

[0133] Step S53: The machining execution end rotates around the center line of the workpiece, or the machining execution end controls the workpiece to rotate around the center line of the workpiece. The rotation speed is the working speed N. The machining execution end moves to one end of the axis every one revolution, and the feed amount f is the single revolution until the length of the workpiece reaches the target length.

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

[0135] In some implementations, 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 number of passes 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 pressure P is adjusted as follows:

[0137] ;

[0138] Furthermore, it should be determined ≤1.2 , >1.2 In order to prevent material failure, a setting can be made ;

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

[0140] ;

[0141] Where 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 number of passes n is adjusted according to the cumulative plastic strain effect:

[0143] ;

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

[0145] .

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

[0147] The above process involves judging the surface roughness, surface hardness, and actual gradient layer depth of the workpiece test sample, and adjusting the values ​​of feed rate f, working speed N, number of passes n, and tool pressure P accordingly.

[0148] Specifically, in one embodiment, taking a 30mm diameter SUS304 stainless steel bar workpiece as an example, the workpiece length is 400mm, the machining head curvature radius r is 3mm, and the material dynamic yield strength is... The material stacking fault energy is 800 MPa. 45mJ / m 2 Single-pass equivalent plastic strain The value is 3, the work hardening coefficient k is 0.25, the material of the small ball of the tool is steel, and the elastic modulus of the tool is... =210000 MPa, tool Poisson's ratio =0.3; the workpiece material is 304 stainless steel, and the workpiece's elastic modulus is... =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 parameter =0.3.

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

[0150] Cutter head pressure Approximately 1.08 kN;

[0151] rotational speed ;

[0152] feed rate ;

[0153] path Second-rate;

[0154] The above parameters were verified using a specialized nanotechnology machine tool. The processing temperature was room temperature, and 0W40 machine oil was used for cooling and lubrication. First, the workpiece was fixed on the sample stage. The machining actuator applied a pressure of 1080N to the workpiece, pressing the tool tip into the metal surface of the workpiece to a certain depth. The specialized nanotechnology machine tool controlled the workpiece to rotate around its centerline at a speed of 126 rpm. For each rotation, the machining actuator moved axially by a distance of 0.11mm per rotation. This operation was continued until the length of the rotating part reached 400mm. The above process was repeated, and after five passes on the workpiece surface, a gradient nanostructure layer was obtained. From the surface inwards, the layer consisted of nano-sized grains, submicron-sized grains, and original-sized grains, with a layer thickness of 2mm. (See attached diagram.) Figure 3 , Figure 4-1 and Figure 4-2 Figure 2 Figure 3 Figure 4-1 Figure 4-2 For 304 stainless steel workpieces, the gradient structure layer depth image and surface TEM morphology image after nano-processing show that the average grain size on the material surface is 23 nm, and the actual gradient layer depth is approximately 2 mm, which meets the processing requirements. If the requirements are not met, the surface roughness, surface hardness, and actual gradient layer depth of the workpiece test sample will be tested. Simply adjust the corresponding tool head pressure P, working speed N, feed rate f and / or number of passes n respectively.

[0155] This method for determining the process parameters of gradient nano-sizing of workpieces is the first to unify the cutting head pressure P, working speed N, feed rate f, and number of passes n into a mathematical framework driven by mechanical properties. By theoretically calculating and pre-selecting parameter combinations, it upgrades from an "experience-driven" approach 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 method only requires 2-3 sets. After preparing nano-gradient materials, only surface roughness, surface hardness, and actual gradient layer depth need to be appropriately adjusted to its process parameters. It no longer requires the use of scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to verify the grain refinement effect step by step, which greatly improves the industrial feasibility of surface nano-sizing. 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 significantly improved, the material utilization rate is increased, and the overall cost can be significantly reduced.

[0156] The method for determining workpiece gradient nano-scale process parameters provided by this invention has been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are merely for the purpose of helping to understand the method and core ideas of this invention. It should be noted that those skilled in the art can make various improvements and modifications to this invention without departing from its principles, and these improvements and modifications also fall within the protection scope of this invention.

Claims

1. A method for determining process parameters for gradient nano-sizing of workpieces, characterized in that, Includes the following steps: According to the dynamic yield strength of the material ( ), single-pass equivalent plastic strain ( ), set gradient depth ( ), radius of curvature of the machining head ( ), work hardening coefficient ( ) and material stacking fault energy ( ), calculate the cutting head pressure ( ); Based on 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 operating speed ( ); According to the cutting head pressure ( The operating speed ( ), tool elastic modulus ( ), tool Poisson's ratio ( ), workpiece elastic modulus ( ), Poisson's ratio of the workpiece ( The radius of curvature of the processing head ( ) and workpiece radius ( ), calculate feed rate ( ); Based on the initial grain size ( ), target grain size ( ) and single-pass grain refinement parameters ( ), calculate track number ( ); The calculated cutter head pressure ( The operating speed ( The feed rate ( ) and the course ( The workpiece is then subjected to nano-processing to obtain a workpiece test sample; The surface roughness, surface hardness, and actual gradient layer depth of the workpiece test sample are detected, and the cutting head pressure is adjusted according to the detection results. The operating speed ( The feed rate ( ) and / or the aforementioned lanes ( ); In calculating the cutting head pressure ( This also includes: Determine the dynamic yield strength of the workpiece material ( ), set gradient depth ( Single-pass grain refinement parameters ( ) and target grain size ( According to the set gradient layer depth ( The single-pass equivalent plastic strain was determined. ), and obtain the radius of curvature of the processing 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 the workpiece ( ), roughness ( ), workpiece radius ( ) and initial grain size ( ); 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 operating speed ( The feed rate ( ) and / or the aforementioned lanes ( )include: When the actual gradient layer depth is less than or equal to the target layer depth, the cutting head pressure ( Adjusted to: ； When the surface roughness is less than or equal to the target roughness, the operating speed ( Adjusted to: ； Where θ = 0.8 - 0.9; When the surface hardness is less than or equal to the target hardness, then the number of passes ( Adjusted to: ； Furthermore, when the layer depth fluctuation m of the adjacent area on the surface of the workpiece test sample is greater than or equal to a preset fluctuation value, the feed rate ( Adjusted to: 。 2. The method for determining workpiece gradient nano-sizing process parameters according to claim 1, characterized in that, The calculation of the cutting head pressure ( This includes: calculating the cutter head pressure according to formula (1). The formula (1) is: （1） in: The radius of curvature of the machining head is expressed in mm. The dynamic yield strength of the material is expressed in MPa. The value represents the equivalent plastic strain per pass, in units of 1. This is the work hardening factor, in units of 1; Stacking fault energy of processed materials, in mJ / m 2 ; This is used to set the gradient layer depth, in mm.

3. The method for determining workpiece gradient nano-sizing process parameters according to claim 1, characterized in that, The calculation of the operating speed ( This includes: calculating the operating speed according to formula (2). Formula (2) is: （2） in: The dynamic yield strength of the material is expressed in MPa. The value represents the equivalent plastic strain per pass, in units of 1. This is the work hardening factor, in units of 1; The stacking fault energy of the processed material, in units of .

4. The method for determining workpiece gradient nano-sizing process parameters according to claim 1, characterized in that, The calculated feed rate ( This includes: calculating the feed rate according to formula (3). Formula (3) is: （3） in: Operating speed, unit: ; The pressure applied to the machining head is expressed in N (N). and These are the tool's elastic modulus and Poisson's ratio, respectively, in MPa and 1. and These are the workpiece's elastic modulus and Poisson's ratio, respectively, with units of MPa and 1. The roughness of the workpiece, in units of... ; The radius of curvature of the machining head is expressed in mm. The radius of the workpiece is in mm.

5. The method for determining workpiece gradient nano-sizing process parameters according to claim 1, characterized in that, The calculation of the number of passes ( This includes: calculating the number of passes according to formula (4). Formula (4) is: （4） in: The initial grain size is expressed in nm. The target grain size is expressed in nm. This is the single-pass grain refinement parameter, in units of 1.

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

7. The method for determining workpiece gradient nano-sizing process parameters according to any one of claims 1 to 5, characterized in that, The calculated cutting head pressure ( The operating speed ( The feed rate ( ) and the course ( The nano-processing of the workpiece includes: A nanoscale-specific machine tool is used to perform nanoscale processing on the workpiece. The nanoscale-specific machine tool includes a processing execution end, a temperature control system, a control system, and a sample stage. The processing execution end is used for nanoscale processing of the workpiece. The temperature control system is used for temperature control and lubrication of the workpiece surface. The control system is used to control the processing execution end and the temperature control system. The sample stage is used to support the workpiece. The temperature control system is also used to adjust the temperature of the workpiece to -196°C to 300°C, and to lubricate and cool the workpiece using a lubricant or cooling medium; it is also used to add liquid nitrogen to cool and control 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 workpiece and the cooling medium when the temperature of the workpiece is higher than room temperature.

8. The method for determining workpiece gradient nano-sizing process parameters according to claim 7, characterized in that, The process of using a nano-scale specialized machine tool to perform nano-scale machining on the workpiece includes: The workpiece is fixed on the sample stage; The machining actuator applies cutting head pressure to the workpiece. The cutting tool head in the machining execution end is pressed into the surface of the workpiece to a certain depth; The machining execution end rotates around the centerline of the workpiece, or the machining execution end controls the workpiece to rotate around the centerline of the workpiece, the rotational speed being the operating speed. The interval for the machining execution end to translate to one end along the axis for each revolution is the feed rate per revolution. (until the length of the workpiece reaches the target length); Number of machining passes on the surface of the workpiece ( After that, a gradient nanostructure surface layer is obtained on the surface of the workpiece.

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