Titanium alloy artificial bone abrasive flow machining grinding amount prediction method and device and medium

Abstract

The invention discloses a titanium alloy artificial bone abrasive flow machining grinding amount prediction method and device and a medium, and relates to the technical field of ultra-precision surface machining. The method comprises the following steps: constructing a grinding amount prediction model; the method comprises the following steps: constructing a titanium alloy right-angle elbow model; based on the titanium alloy right-angle bent pipe model, the right-angle bent pipe is machined multiple times through a one-way abrasive particle flow machine tool, a change curve of the grinding amount is obtained, numerical simulation is conducted on the change curve of the grinding amount, and a grinding amount correction coefficient function is obtained; and the grinding amount correction coefficient function is substituted into a grinding amount formula, a grinding amount removal prediction model is obtained, and based on the grinding amount removal prediction model, titanium alloy artificial bone abrasive flow machining grinding amount prediction is achieved. According to the method, the grinding amount removal prediction model is established on the basis of the grinding amount correction coefficient function, the grinding amount of titanium alloy artificial bone abrasive flow machining is effectively predicted, and the method can be suitable for abrasive flow polishing of complex curved surfaces.

Description

Technical Field

[0001] The present invention relates to the technical field of ultra-precision surface machining, and more specifically, to a method, device and medium for predicting the grinding amount in abrasive flow machining of titanium alloy artificial bone. Background Art

[0002] In modern manufacturing processes, accurate calculation of grinding amount is crucial to improving production efficiency, reducing costs and ensuring processing quality. At present, the grinding calculator is a practical tool that supports engineers and technicians to accurately calculate key parameters in mechanical grinding. The grinding calculator predicts the grinding amount through parameters such as grinding speed, rotation speed, grinding force, grinding power, and grinding time to help operators optimize processing accuracy, production efficiency, and tool life. However, when faced with the prediction of the grinding amount of abrasive flow machining of complex surfaces, the prediction accuracy of the grinding calculator is not high. Therefore, when faced with abrasive flow polishing of complex surfaces, how to perform accurate simulation predictions to optimize process parameters and predict removal amounts is a technical problem that urgently needs to be solved. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a method, device and medium for predicting the grinding amount of titanium alloy artificial bone by abrasive flow machining, so as to improve the prediction accuracy of the grinding amount of complex curved surface by abrasive flow machining.

[0004] In a first aspect, the present invention provides a method for predicting the grinding amount during abrasive flow machining of titanium alloy artificial bone, the method comprising: Construct a grinding amount prediction model; wherein the grinding amount prediction model is expressed as: ; In the formula, MR is the material removal amount, v is the abrasive speed, t is the processing time, is the diameter of the abrasive particle, is the volume fraction of abrasive particles, is the percentage of time that the abrasive wears the surface, is the normal pressure on the abrasive particles, M(x) is the grinding amount correction coefficient, is the workpiece hardness; Construct a titanium alloy right-angle elbow model; Based on the titanium alloy right-angle bend model, the right-angle bend is processed multiple times by a unidirectional abrasive flow machine tool to obtain a grinding amount variation curve, and the grinding amount variation curve is numerically simulated to obtain a grinding amount correction coefficient function; The grinding amount correction coefficient is substituted into the grinding amount formula to obtain a grinding amount removal prediction model, and based on the grinding amount removal prediction model, the grinding amount prediction of titanium alloy artificial bone abrasive flow machining is realized.

[0005] Furthermore, the grinding amount prediction model is constructed by the following method: The first grinding amount formula describing abrasive flow polishing is determined and expressed as: ; In the formula, is the embedding depth of abrasive particles before correction; Introducing the grinding amount correction coefficient M(x) into the first grinding amount formula, the second grinding amount formula is obtained, which is expressed as: ; In the formula, is the corrected wear particle embedding depth; The number of abrasive particles interacting with the workpiece surface is determined by the following formula: ; Where N is the number of abrasive particles interacting with the workpiece surface; The number of abrasive particles interacting with the workpiece surface is The grinding amount prediction model is obtained by multiplying them.

[0006] Furthermore, the titanium alloy right-angle bend pipe model includes a right-angle bend pipe, a measuring bar and a printing support; wherein the measuring bar is arranged on the inner and outer side walls of the right-angle bend pipe, and the printing support is used to support the right-angle bend pipe.

[0007] Furthermore, the right-angle elbow is processed multiple times using a unidirectional abrasive flow machine tool to obtain a grinding amount variation curve, including: Under fixed pressure, the right-angle elbow is processed n times by a unidirectional abrasive flow machine, where n≥105. The grinding amount is measured each time during the first 30 times of processing, and is measured every 5 times after 30 times of processing. After the 95th processing, the grinding amount is measured every 10 times of processing until n times of processing are completed, and the grinding amount change curve is obtained.

[0008] Further, the grinding amount correction coefficient is expressed as: ; In the formula, x represents the number of processing times.

[0009] Furthermore, the grinding removal prediction model is expressed as: .

[0010] In a second aspect, the present invention provides a device for predicting the grinding amount during abrasive flow machining of titanium alloy artificial bone, the device comprising: The prediction model building module is configured to build a grinding amount prediction model; wherein the grinding amount prediction model is expressed as: ; In the formula, MR is the material removal amount, v is the abrasive speed, t is the processing time, is the diameter of the abrasive particle, is the volume fraction of abrasive particles, is the percentage of time that the abrasive wears the surface, is the normal pressure on the abrasive particles, M(x) is the grinding amount correction coefficient, is the workpiece hardness; A bending tube model building module is configured to build a titanium alloy right-angle bending tube model; The correction coefficient calculation module is configured to perform multiple processing on the right-angle bend pipe using a unidirectional abrasive flow machine tool based on the titanium alloy right-angle bend pipe model to obtain a grinding amount variation curve, perform numerical simulation on the grinding amount variation curve, and obtain a grinding amount correction coefficient function; The grinding amount prediction module is configured to substitute the grinding amount correction coefficient into the grinding amount prediction model to obtain a grinding amount removal prediction model, and realize the grinding amount prediction of titanium alloy artificial bone abrasive flow machining based on the grinding amount removal prediction model.

[0011] Furthermore, the prediction model building module is further configured as follows: The first grinding amount formula describing abrasive flow polishing is determined and expressed as: ; In the formula, is the embedding depth of abrasive particles before correction; Introducing the grinding amount correction coefficient M(x) into the first grinding amount formula, the second grinding amount formula is obtained, which is expressed as: ; In the formula, is the corrected wear particle embedding depth; The number of abrasive particles interacting with the workpiece surface is determined by the following formula: ; Where N is the number of abrasive particles interacting with the workpiece surface; The number of abrasive particles interacting with the workpiece surface is The grinding amount prediction model is obtained by multiplying them.

[0012] Furthermore, the correction coefficient calculation module is further configured as follows: Under fixed pressure, the right-angle elbow is processed n times by a unidirectional abrasive flow machine, where n≥105. The grinding amount is measured each time during the first 30 times of processing, and is measured every 5 times after 30 times of processing. After the 95th processing, the grinding amount is measured every 10 times of processing until n times of processing are completed, and the grinding amount change curve is obtained.

[0013] In a third aspect, the present invention provides a readable storage medium, wherein the readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method as described above.

[0014] The present invention has at least the following beneficial effects: The present invention sets the process parameters such as abrasive flow processing pressure and number of cycles, conducts pipe bending processing experiments, fits the grinding amount correction coefficient, and establishes a grinding amount removal prediction model. This model is applied to abrasive flow processing 3D printing titanium alloy artificial bones, and the surface morphology information of the artificial bones is measured before processing to obtain surface feature data. The pressure and velocity values ​​of the flow channel near the wall are calculated using fluid simulation software, and substituted into the prediction model for calculation. After processing, the processed workpiece is scanned by a three-dimensional scanner, the workpiece before and after processing is fitted, and the deviation result graph is derived. The prediction result has a high degree of consistency with the fitting deviation. The present invention establishes a grinding amount removal prediction model based on the indentation depth formula, which effectively predicts the grinding amount of titanium alloy artificial bones in abrasive flow processing, and can be applied to abrasive flow polishing of complex curved surfaces, providing a simulation prediction solution for how to optimize process parameters, how to predict removal, and how to solve uneven processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of a method for predicting grinding amount during abrasive flow machining of titanium alloy artificial bone according to an embodiment of the present invention is shown.

[0016] Figure 2 A schematic diagram of the design of a titanium alloy right-angle bend tube model according to an embodiment of the present invention is shown; wherein, (a) is a two-dimensional diagram of the bend tube size; (b) is the structure of the titanium alloy right-angle bend tube model; in the figure, L1 is the inlet section, L2 is the outlet section, D2 is the inner diameter of the tube, D1 is the outer diameter of the tube, 201 is the right-angle bend tube, 202 is the measuring bar, and 203 is the printing support.

[0017] Figure 3 An abrasive flow device according to an embodiment of the present invention and a schematic diagram of its operation are shown; wherein, (a) an abrasive flow device; (b) an abrasive flow principle diagram; in the figure, 301 is a hydraulic cylinder, 302 is a piston, 303 is a material cylinder, 304 is abrasive, and 305 is a bent pipe.

[0018] Figure 4 The graph shows the variation curve of the grinding amount obtained after 135 times of machining according to the embodiment of the present invention and the result of numerical simulation in MATLAB software. In the graph, the abscissa represents the number of abrasive particles, the ordinate represents the material removal rate, R 2 It indicates the degree of fit between the fitted curve and the actual data.

[0019] Figure 5 The pressure cloud diagram of the knee joint under 4Mpa according to an embodiment of the present invention is shown; wherein, a) the pressure cloud diagram of the forward processing; b) the pressure cloud diagram of the reverse processing.

[0020] Figure 6 The knee joint processing effect diagram according to an embodiment of the present invention is shown; wherein, a) the processing effect of the front section of the knee joint A and B surfaces; b) the processing effect of the rear section of the knee joint A and B surfaces; c) the processing effect of the knee joint C surface; d) the processing effect of the knee joint D surface.

[0021] Figure 7 The deviation results of the scanning results and the printed model of the processed knee joint workpiece according to an embodiment of the present invention are shown. In the figure, A018 represents the 18th sampling point code, A016 represents the 16th sampling point code, A024 represents the 24th sampling point code, A013 represents the 13th sampling point code, A008 represents the 08th sampling point code, A007 represents the 07th sampling point code, A006 represents the 06th sampling point code, A005 represents the 05th sampling point code, and D represents the sampling point deviation.

[0022] Figure 8 A structural diagram of a grinding amount prediction device for abrasive flow machining of titanium alloy artificial bone according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and specific embodiments, but are not intended to limit the present invention. For the various steps described herein, if there is no necessity for a causal relationship between each other, the order in which they are described as examples herein should not be regarded as a limitation, and those skilled in the art should know that they can be adjusted in order, as long as the logic between them is not destroyed, resulting in the inability to implement the entire process.

[0024] The embodiment of the present invention provides a method for predicting the grinding amount of titanium alloy artificial bone in abrasive flow machining. Figure 1 As shown, the method includes steps S10 to S40, which are described in detail as follows.

[0025] S10, constructing a grinding amount prediction model.

[0026] In some embodiments, step S1 constructs a grinding amount prediction model through the following steps S11 to S14.

[0027] S11, determine the first grinding amount formula that can describe abrasive flow polishing under certain conditions: ; In the formula, is the abrasive embedding depth before correction, is the diameter of the abrasive particle, is the normal pressure on the abrasive particles, is the hardness of the workpiece.

[0028] S12, taking titanium alloy right-angle elbow as the research object, the grinding amount correction coefficient M(x) was introduced into the first grinding amount formula to obtain the second grinding amount formula: ; In the formula, is the corrected abrasive embedding depth, and M(x) is the grinding amount correction coefficient, which is related to the number of processing times. As the number of processing times increases, as the rough surface is removed, the smooth surface makes the indentation depth caused by the abrasive grains shallower and shallower, resulting in a reduction in the grinding amount of a single processing. Therefore, under the condition that the hardness of the workpiece remains unchanged and the pressure on the abrasive grains remains unchanged, the pressure on the abrasive grains is corrected by M(x) to simulate the phenomenon that the grinding amount decreases as the number of processing times increases. M(x) is obtained by fitting the results of the subsequent processing test.

[0029] S13, the number of abrasive particles interacting with the workpiece surface can be expressed as: ; Where v is the abrasive velocity and t is the processing time; is the volume fraction of abrasive particles, is the percentage of time that the abrasive wears the surface. For the three-body case, ≈0.1.

[0030] S14, the final grinding amount prediction model can be expressed as: ; Where MR is the material removal amount.

[0031] S20, build a titanium alloy right-angle elbow model.

[0032] In some embodiments, Figure 2 As shown in (b), the titanium alloy right-angle bend pipe model includes a right-angle bend pipe 201, a measuring bar 202 and a printing support 203; wherein the measuring bar 202 is arranged on the inner and outer side walls of the right-angle bend pipe 201, and the printing support 203 is used for the right-angle bend pipe 201.

[0033] For example, SOLIDWORKS software is used to create Figure 2In the 90° elbow shown in (a), L1 is the inlet section, 80 mm long, L2 is the outlet section, 55 mm long, R1 is a 30 mm arc, the inner diameter D2 is 19 mm, and the outer diameter D1 is 30 mm. In order to ensure that the elbow has good shape accuracy, the design is as follows Figure 2 In (b), a printing support 203 is provided, and measuring strips 202 are designed on the inner and outer side walls of the right-angle bend tube 201 to facilitate subsequent measurements.

[0034] S30, based on the titanium alloy right-angle bend model, the right-angle bend was processed multiple times using a unidirectional abrasive flow machine tool to obtain the grinding amount change curve, and the grinding amount correction coefficient function was obtained by numerical simulation of the grinding amount change curve.

[0035] Exemplarily, this embodiment uses Farsoon High-Tech (model FS421) equipment to print the above model, and the printing powder is AlSi10Mg. Figure 3 (a) is the SMK unidirectional abrasive flow machine used in the abrasive flow test. The abrasives used are provided by Suzhou Smank Abrasive Flow Equipment Co., Ltd. The working principle of the SMK unidirectional abrasive flow machine is as follows: Figure 3 As shown in (b), the SMK unidirectional abrasive flow machine tool includes a hydraulic cylinder 301, a piston 302, a material cylinder 303 and abrasive 304. When working, the hydraulic cylinder 301 pushes the piston 302, so that the piston 302 can generate thrust on the abrasive 304 in the material cylinder 303, so that the abrasive 304 is squeezed out of the elbow 305 and then discharged back into the material cylinder 303, forming a cycle. The main parameters that can be set for the SMK unidirectional abrasive flow machine tool are: processing pressure, number of cycles, etc.

[0036] In this embodiment, a fixed pressure is used to change the number of processing times to conduct a processing test study on a right-angle elbow. In the test of correcting parameter fitting, as the number of processing times increases, the grinding amount will gradually decrease, and eventually the ultrasonic measuring instrument cannot measure the single grinding amount. Therefore, the measurement method is to measure each time for the first 30 processing times, measure every 5 times after 30 times, and change to every 10 times for the 95th time. After 135 times of processing, the change curve of the grinding amount is obtained and numerical simulation is performed in MATLAB software as shown below. Figure 4 As shown, the correction coefficient M(x) of the grinding amount is obtained as follows: ; In the formula, x represents the number of processing times.

[0037] according to Figure 4 It can be found that the overall increasing trend of the grinding amount is fast at first and then slow. When removing the rough surface, the grinding amount is large, and as the rough surface gradually becomes smooth, the grinding amount also decreases.

[0038] S40, substituting the grinding amount correction coefficient function into the grinding amount formula to obtain a grinding amount removal prediction model, and realizing the grinding amount prediction of titanium alloy artificial bone abrasive flow machining based on the grinding amount removal prediction model.

[0039] In this embodiment, the correction coefficient M(x) of the grinding amount obtained in step S30 is substituted into the grinding amount formula to obtain a corrected grinding amount prediction model, that is, the corrected grinding amount prediction model is used as the grinding amount removal prediction model; the grinding amount removal prediction model can be expressed as: .

[0040] The following embodiment of the present invention will further illustrate the feasibility and advancement of the present invention in conjunction with a specific case.

[0041] Take the processing of knee joints as an example. Considering that the abrasive flows continuously in the right-angle elbow during processing, it is very difficult to measure the wall pressure and velocity, so the simulation method is used to obtain the wall pressure in the flow channel.

[0042] In this specific case, the processing parameters are 4MPa, 20 forward processing times, and 20 reverse processing times. The surface pressure and speed are obtained through simulation, and the pressure, speed, and processing times are brought into the grinding removal prediction model. The pressure cloud diagram of the knee joint under 4Mpa is shown in the figure. Figure 5 The parameter values ​​of the grinding removal prediction model are shown in Table 1. The results of multi-layer roughness measurement and average value of the four surfaces of the knee joint after processing are shown in Table 2.

[0043] Table 1 Parameter values ​​of grinding removal prediction model

[0044] Table 2 Surface roughness of knee joint

[0045] Combination Figure 5, Table 1 and Table 2, it can be found that under this process parameter, the overall surface quality is good, the average surface roughness is 0.67mm, the main surface A, B has a high finish, 0.38um and 0.46um, respectively, C, D surface roughness is 0.76um and 1.07um. According to the known TC4 biomaterial surface roughness on the osteoblast adhesion mechanism of the study, based on the cell counting method to compare the biocompatibility of 0.5um-1.0mm titanium sheet, the surface roughness of 0.9um-1.0um titanium sheet, the cell has good adhesion and proliferation; 0.5um-0.6um titanium sheet cell adhesion rate and proliferation rate are low, while the surface of 0.6um-0.7um and 0.7um-0.8mm, the biological test results have no statistical difference, the average surface roughness of the knee joint workpiece under this process parameter is 0.67um, bone cells on the rough surface adhesion rate and proliferation rate is good.

[0046] Figure 6 This is the processing effect diagram of the knee joint surface. Figure 6 As shown in (a) and (b), the surfaces of the knee joint workpieces A and B have a certain mirror effect, such as Figure 6 As shown in (c) and (d), the mirror effect of the two sides C and D is slightly weaker than that of the A and B surfaces. The main reason for this difference is the design and position of the tooling inlet. The inlet is facing the A and B surfaces, allowing the abrasive to act on these surfaces with greater pressure for polishing, thereby achieving a higher finish. The processing of the C and D surfaces involves the extrusion process of the abrasive from the inlet downward. During this process, the grinding force of the material on these surfaces is relatively low, resulting in relatively high roughness values.

[0047] The processed knee joint workpiece was scanned using a portable 3D scanner (DUUMM V700+). The scan results and the printed model were optimally fitted in Geomagic Control software, and the deviation results were exported. Figure 7 ,Due to the inevitable machine error, redundant parts appeared in the fitting process, that is, the part of the processed knee joint workpiece that was more than the printed model. The overall deviation of the processed knee joint workpiece was between -0.0672 and +0.0159 mm, the average deviation range was -0.0076 and +0.0014 mm, and the overall contour deviation was low. The test results show that the process parameters can meet the processing requirements of the knee joint, and obtain good surface quality and contour shape with high processing efficiency.

[0048] The embodiment of the present invention also provides a device for predicting the grinding amount of titanium alloy artificial bone abrasive flow machining, such as Figure 8 As shown, the device comprises: The prediction model building module 801 is configured to build a grinding amount prediction model; wherein the grinding amount prediction model is expressed as: ; In the formula, MR is the material removal amount, v is the abrasive speed, t is the processing time, is the diameter of the abrasive particle, is the volume fraction of abrasive particles, is the percentage of time that the abrasive wears the surface, is the normal pressure on the abrasive particles, M(x) is the grinding amount correction coefficient, is the workpiece hardness; The elbow model building module 802 is configured to build a titanium alloy right-angle elbow model; The correction coefficient calculation module 803 is configured to perform multiple processing on the right-angle bend pipe based on the titanium alloy right-angle bend pipe model using a unidirectional abrasive flow machine tool to obtain a grinding amount variation curve, perform numerical simulation on the grinding amount variation curve, and obtain a grinding amount correction coefficient function; The grinding amount prediction module 804 is configured to substitute the grinding amount correction coefficient function into the grinding amount formula to obtain a grinding amount removal prediction model, and realize the grinding amount prediction of titanium alloy artificial bone abrasive flow machining based on the grinding amount removal prediction model.

[0049] In some embodiments, the prediction model building module 801 is further configured to: The first grinding amount formula describing abrasive flow polishing is determined and expressed as: ; In the formula, is the embedding depth of abrasive particles before correction; Introducing the grinding amount correction coefficient M(x) into the first grinding amount formula, the second grinding amount formula is obtained, which is expressed as: ; In the formula, is the corrected wear particle embedding depth; The number of abrasive particles interacting with the workpiece surface is determined by the following formula: ; Where N is the number of abrasive particles interacting with the workpiece surface; The number of abrasive particles interacting with the workpiece surface is The grinding amount prediction model is obtained by multiplying them.

[0050] In some embodiments, the correction coefficient calculation module 803 is further configured as follows: Under fixed pressure, the right-angle elbow is processed n times by a unidirectional abrasive flow machine, where n≥105. The grinding amount is measured each time during the first 30 times of processing, and is measured every 5 times after 30 times of processing. After the 95th processing, the grinding amount is measured every 10 times of processing until n times of processing are completed, and the grinding amount change curve is obtained.

[0051] In some embodiments, the titanium alloy right-angle bend pipe model includes a right-angle bend pipe, a measuring bar and a printing support; wherein the measuring bar is arranged on the inner and outer side walls of the right-angle bend pipe, and the printing support is used to support the right-angle bend pipe.

[0052] In some embodiments, the grinding amount correction coefficient is expressed as: ; In the formula, x represents the number of processing times.

[0053] In some embodiments, the grinding removal prediction model is expressed as: .

[0054] It should be noted that the various device structures described in this embodiment belong to the same technical concept as the previously described method, and achieve the same technical effect through the same principle, which will not be repeated here.

[0055] An embodiment of the present invention further provides a readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the method described in any of the above embodiments.

[0056] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present invention with equivalent elements, modifications, omissions, combinations (e.g., various embodiments intersecting schemes), adaptations or changes. The elements in the claims are to be interpreted broadly based on the language adopted in the claims, and are not limited to the examples described in this specification or during the prosecution of this application, which examples are to be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered as examples only, and the true scope and spirit are indicated by the following claims and the full scope of their equivalents.

[0057] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of them) may be used in combination with each other. For example, a person of ordinary skill in the art may use other embodiments when reading the above description. In addition, in the above-mentioned specific embodiments, various features may be grouped together to simplify the present invention. This should not be interpreted as an intention that a feature of an invention that is not claimed for protection is necessary for any claim. On the contrary, the subject matter of the present invention may be less than all the features of an embodiment of a particular invention. Thus, the following claims are incorporated herein as examples or embodiments in the specific embodiments, wherein each claim is independently a separate embodiment, and it is considered that these embodiments may be combined with each other in various combinations or arrangements. The scope of the present invention should be determined with reference to the appended claims and the full scope of equivalent forms granted by these claims.

Claims

1. A method for predicting the grinding amount of titanium alloy artificial bone by abrasive flow machining, characterized in that: The method comprises: Construct a grinding amount prediction model; wherein the grinding amount prediction model is expressed as: ； In the formula, MR is the material removal amount, v is the abrasive speed, t is the processing time, is the diameter of the abrasive particle, is the volume fraction of abrasive particles, is the percentage of time that the abrasive wears the surface, is the normal pressure on the abrasive particles, M(x) is the grinding amount correction coefficient, is the workpiece hardness; Construct a titanium alloy right-angle elbow model; Based on the titanium alloy right-angle bend model, the right-angle bend is processed multiple times by a unidirectional abrasive flow machine tool to obtain a grinding amount variation curve, and the grinding amount variation curve is numerically simulated to obtain a grinding amount correction coefficient function; The grinding amount correction coefficient function is substituted into the grinding amount formula to obtain a grinding amount removal prediction model. Based on the grinding amount removal prediction model, the grinding amount prediction of titanium alloy artificial bone abrasive flow machining is realized.

2. The method according to claim 1, characterized in that The grinding amount prediction model is constructed by the following method: The first grinding amount formula describing abrasive flow polishing is determined and expressed as: ； In the formula, is the embedding depth of abrasive particles before correction; Introducing the grinding amount correction coefficient M(x) into the first grinding amount formula, the second grinding amount formula is obtained, which is expressed as: ； In the formula, is the corrected wear particle embedding depth; The number of abrasive particles interacting with the workpiece surface is determined by the following formula: ； Where N is the number of abrasive particles interacting with the workpiece surface; The number of abrasive particles interacting with the workpiece surface is The grinding amount prediction model is obtained by multiplying them.

3. The method according to claim 1, characterized in that The titanium alloy right-angle bend pipe model comprises a right-angle bend pipe, a measuring bar and a printing support; wherein the measuring bar is arranged on the inner and outer side walls of the right-angle bend pipe, and the printing support is used to support the right-angle bend pipe.

4. The method according to claim 1, characterized in that: The right-angle elbow was processed multiple times using a unidirectional abrasive flow machine to obtain the grinding amount change curve, including: Under fixed pressure, the right-angle elbow is processed n times by a unidirectional abrasive flow machine, where n≥105. The grinding amount is measured each time during the first 30 times of processing, and is measured every 5 times after 30 times of processing. After the 95th processing, the grinding amount is measured every 10 times of processing until n times of processing are completed, and the grinding amount change curve is obtained.

5. The method according to claim 1, characterized in that The grinding amount correction coefficient is expressed as: ； In the formula, x represents the number of processing times.

6. The method according to claim 5, characterized in that The grinding removal prediction model is expressed as: 。 7. A grinding amount prediction device for titanium alloy artificial bone abrasive flow machining, characterized in that: The device comprises: The prediction model building module is configured to build a grinding amount prediction model; wherein the grinding amount prediction model is expressed as: ； In the formula, MR is the material removal amount, v is the abrasive speed, t is the processing time, is the diameter of the abrasive particle, is the volume fraction of abrasive particles, is the percentage of time that the abrasive wears the surface, is the normal pressure on the abrasive particles, M(x) is the grinding amount correction coefficient, is the workpiece hardness; A bending tube model building module is configured to build a titanium alloy right-angle bending tube model; The correction coefficient calculation module is configured to perform multiple processing on the right-angle bend pipe based on the titanium alloy right-angle bend pipe model using a unidirectional abrasive flow machine tool to obtain a grinding amount variation curve, perform numerical simulation on the grinding amount variation curve, and obtain a grinding amount correction coefficient function; The grinding amount prediction module is configured to substitute the grinding amount correction coefficient into the grinding amount prediction model to obtain a grinding amount removal prediction model, and realize the grinding amount prediction of titanium alloy artificial bone abrasive flow machining based on the grinding amount removal prediction model.

8. The device according to claim 7, characterized in that The prediction model building module is further configured to: The first grinding amount formula describing abrasive flow polishing is determined and expressed as: ； In the formula, is the embedding depth of abrasive particles before correction; Introducing the grinding amount correction coefficient M(x) into the first grinding amount formula, the second grinding amount formula is obtained, which is expressed as: ； In the formula, is the corrected wear particle embedding depth; The number of abrasive particles interacting with the workpiece surface is determined by the following formula: ； Where N is the number of abrasive particles interacting with the workpiece surface; The number of abrasive particles interacting with the workpiece surface is The grinding amount prediction model is obtained by multiplying them.

9. The device according to claim 7, characterized in that The correction coefficient calculation module is further configured as follows: Under fixed pressure, the right-angle elbow is processed n times by a unidirectional abrasive flow machine, where n≥105. The grinding amount is measured each time during the first 30 times of processing, and is measured every 5 times after 30 times of processing. After the 95th processing, the grinding amount is measured every 10 times of processing until n times of processing are completed, and the grinding amount change curve is obtained. 10 . A non-transitory computer-readable storage medium storing instructions, which, when executed by a processor, executes the method according to claim 1 .

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