Surface microstructure optimization method for improving bonding strength of titanium alloy surface coating

Abstract

The invention discloses a surface microstructure optimization method for improving the bonding strength of a titanium alloy surface coating, which comprises the following steps of: based on finite element simulation, firstly selecting titanium alloy surface microstructures of different types or size parameters to be evaluated, and then constructing a two-dimensional model of the bonding section of the coating and a matrix in finite element software; through the steps of grid division, parameter setting, solution analysis and the like, a tensile test method of a coating strength test is simulated, and optimization is performed by comparing peak stress under different microstructure size parameters; the method can efficiently and economically realize the evaluation of the bonding strength of the titanium alloy microstructure surface and the coating, realizes the optimization of the surface microstructure, provides guidance for the design and manufacturing of the titanium alloy surface microstructure, and reduces the peeling failure risk of the coating; the bonding strength is evaluated through finite element simulation stress distribution, physical tests are not needed, the test period is remarkably shortened, and the cost is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of surface coatings, and in particular to a surface microstructure optimization method for improving the bonding strength of titanium alloy surface coatings. Background Art

[0002] Titanium alloys are widely used in fields such as medicine and aerospace due to their high strength, low density, and excellent corrosion resistance. In practical industrial applications, microstructuring is often required to enhance the surface properties of titanium alloys. Coating technology, as an important surface modification method, can enhance titanium alloys' wear resistance, corrosion resistance, heat resistance, electrical conductivity, biocompatibility, and hydrophilicity and hydrophobicity, and is often used in conjunction with metal surface microstructuring.

[0003] Titanium alloy surface microstructures significantly enhance the bonding strength of coatings (e.g., tensile and shear strength). Creating specific microstructures on the substrate surface increases the contact area, improving the mechanical interlock between the coating and substrate to a certain extent, thereby enhancing the bonding strength between the coating and substrate. Different microstructures have varying degrees of influence on the coating-substrate bond strength. Therefore, optimizing the microstructure is crucial for enhancing the bonding strength between the surface microstructure and the coating.

[0004] For the surface microstructure optimization of titanium alloy surface coating bonding strength, the core is how to compare the evaluation of surface coating bonding strength under different microstructure size parameters and different types of microstructures, that is, to quickly evaluate the size of surface coating bonding strength for different surface microstructures, so as to determine the better microstructure type or size parameters.

[0005] Current methods for evaluating the bond strength of surface coatings primarily rely on experimental testing, including physical testing, optical and electron microscopy analysis, and chemical analysis using nondestructive testing techniques. Commonly used physical bond strength testing methods include peel testing, scratch testing, and tensile testing. These methods suffer from limitations in sample preparation, complexity of test conditions, and equipment and operating costs. They also often suffer from long test cycles and test results that are susceptible to multiple factors. Furthermore, physical testing can cause irreversible damage to samples. Optical and electron microscopy analysis is difficult to directly measure the bond strength of metal surface coatings and lacks sensitivity for bond strength assessment.

[0006] However, the above surface coating bond strength assessment methods are difficult to use for microstructure optimization to improve the bond strength of titanium alloy surface coatings. In addition to the above-mentioned shortcomings, more importantly, there are limitations in sample preparation. Processing microstructures on titanium alloy surfaces has high costs, technical requirements, and processing cycles. More importantly, it is difficult to efficiently and accurately select the microstructure type and size parameters that theoretically correspond to the highest coating bond strength. In addition, there are few studies on obtaining bond strength from a purely theoretical analysis perspective. Existing theoretical analysis methods are mainly aimed at analyzing the strength of coatings on smooth flat substrates and are not suitable for analyzing the bond strength of surfaces with microstructures.

[0007] Therefore, in view of the above-mentioned shortcomings, the present invention provides a microstructure optimization method for improving the bonding strength of titanium alloy surface coatings. Summary of the Invention

[0008] In order to overcome the shortcomings of current technology, the present invention provides a microstructure optimization method for improving the bonding strength of titanium alloy surface coatings, which can conveniently and efficiently realize the evaluation of the bonding strength between the titanium alloy microstructure surface and the coating, thereby efficiently and economically selecting microstructures with higher coating bonding strength, thereby providing guidance for the design and manufacture of microstructures, and at the same time minimizing the possibility of coating peeling failure in practical applications.

[0009] A microstructure optimization method for improving the bonding strength of a titanium alloy surface coating comprises the following steps:

[0010] Step 1) selecting titanium alloy surface microstructures of different types or size parameters for which the bonding strength of the surface coating needs to be evaluated;

[0011] Step 2) constructing a two-dimensional model of the cross-section of the coating and the substrate with the microstructured surface in finite element simulation software (taking ABAQUS as an example);

[0012] Step 3) Divide the grid, set the grid type and material parameters;

[0013] Step 4) Define cross-sectional behavior and boundary conditions;

[0014] Step 5) Select the solver algorithm, matrix storage method, equation solving technique, and how the load changes over time to analyze and solve the problem;

[0015] Step 6) performing calculations to obtain a time-stress curve and peak stress;

[0016] Step 7) comparing the advantages and disadvantages of surface microstructures of different types or size parameters based on the magnitude of the peak stress, and selecting the surface microstructure with the larger peak stress as the preferred surface microstructure;

[0017] Preferably, the combined portion of the coating and substrate described in step 2) is defined as a cohesive unit. There is a corresponding relationship between the virtual opening displacement and the cohesive force, which is called the cohesive force relationship. For the cohesive unit modeling the bonding interface, the elastic behavior can be defined based on the traction-separation constitutive response of the cohesive force unit, which can be directly expressed by nominal traction and nominal strain, supporting coupled and uncoupled behaviors. For uncoupled behavior, each traction component depends only on its conjugate nominal strain. In the local unit direction, the stress-strain relationship of the uncoupled behavior is as follows:

[0018]

[0019] where t n , t s and t t denote the nominal traction forces in the normal and two local shear directions, respectively. For coupled traction-separation behavior, the stress-strain relationship is as follows:

[0020]

[0021] The present invention uses a cohesive model based on displacement damage, and the failure state of the material is determined according to the Benzeggagh-Kenane (BK) mixed-mode fracture criterion. This criterion is a failure criterion for mixed damage based on the energy release rate, as shown below:

[0022]

[0023] Where G c is the total fracture energy of the crack; is the fracture energy of mode I crack; is the fracture energy of mode II crack; G s is the shear deformation energy; G n is the tensile deformation energy; η is the material constant.

[0024] At the same time, the degradation index SDEG is used to measure the damage degree of the cohesive unit, and the equation is:

[0025]

[0026] Where, is the displacement when the cohesive unit breaks completely; δ m is the actual relative displacement of the cohesive element; is the critical displacement at which the element begins to be damaged.

[0027] When the stress is low and does not meet the damage initiation criterion, SDEG is 0. After exceeding the critical displacement, SDEG begins to increase. When the critical displacement of failure is reached, SDEG is 1, at which point the cohesive unit fails.

[0028] Preferably, in step 3), the cohesive layer needs to be de-associated when dividing the grid types.

[0029] Preferably, the material parameters in step 3) include the material property parameter settings of the matrix and cohesive unit, and the mass density, Young's modulus and Poisson's ratio of the matrix material TC4, the normal nominal stress, the first direction nominal stress (Nominal Stress First Direction), the second direction nominal stress (Nominal Stress Second Direction), the normal elastic modulus E nn , tangential elastic modulus E ss 、E tt and fracture energy.

[0030] Preferably, the tangential elastic modulus E of the Cohesive unit material in the material parameters of step 3) is ss =E tt =64%E nn .

[0031] Preferably, in step 4), the boundary conditions are set such that the bottom substrate is set to be completely fixed and a displacement of 0.002 mm in the Y direction is applied to the top.

[0032] Preferably, in step 6), the microstructure of the prepared coating is analyzed and calculated based on ABAQUS finite element simulation software, which can quickly perform qualitative comparison on multiple microstructures, thereby achieving optimal design of the microstructure.

[0033] Preferably, in step 7), microstructure parameters are established through Abaqus simulation, and after the above simulation process, the peak stresses of the microstructures to be compared are evaluated and compared, and the one with the largest peak stress is selected as the optimal one.

[0034] The design ideas of the present invention are as follows:

[0035] Based on finite element simulation, the method first selects titanium alloy surface microstructures of different types or size parameters to be evaluated, then constructs a two-dimensional cross-sectional model of the coating and substrate in the finite element software. After meshing, setting parameters, and solving and analyzing, the tensile test method for coating strength testing is simulated, and the peak stress under different microstructure size parameters is compared for optimization. The present invention can efficiently and economically evaluate the bonding strength between titanium alloy microstructure surfaces and coatings, achieve surface microstructure optimization, provide guidance for the design and manufacture of titanium alloy surface microstructures, and reduce the risk of coating peeling failure. By simulating stress distribution through finite element simulation to evaluate bonding strength, physical testing is not required, significantly shortening the testing cycle and reducing costs.

[0036] The beneficial effects of the present invention are:

[0037] 1) This invention uses finite element simulation technology to simulate the stress distribution of titanium alloy surface coatings under specific working conditions, thereby indirectly evaluating the bonding strength of titanium alloy surface coatings;

[0038] 2) Compared with other traditional testing methods, this invention does not require physical testing, significantly shortens the test evaluation cycle, and reduces testing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of the method of the present invention;

[0040] Figure 2 Schematic diagram of two-dimensional modeling of the coating-substrate bonding interface in the present invention, part a is a schematic diagram of two-dimensional modeling of structure A, and part b is a schematic diagram of two-dimensional modeling of structure B;

[0041] Figure 3 1 is a diagram showing the stress distribution simulation results of the coating-substrate interface in the present invention, wherein part a is a diagram showing the simulation results of structure A, and part b is a diagram showing the simulation results of structure B;

[0042] Figure 4 This is a stress-time diagram of the coating-substrate bonding interface in the y direction under tension in the present invention. Part a is the stress-time diagram of structure A, and part b is the stress-time diagram of structure B. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] Referring to the accompanying drawings, the present invention provides a microstructure optimization method for high bonding strength of titanium alloy surface coating based on ABAQUS, the flow chart of which is as follows: Figure 1 shown.

[0045] Two titanium alloy surface microstructures of different types or size parameters that need to be evaluated for the bonding strength of the surface coating are selected. The actual geometric dimensions of the coating and the substrate are used as a reference in ABAQUS. At the same time, a local model of the interface is selected to facilitate the calculation. A two-dimensional physical model of the bonding interface between the coating and the substrate with the microstructure surface is constructed, including the coating, the substrate, and the cohesive zone interface layer between them. The unit type of the modeling is the shell unit, and the overall size is selected as 0.3mm×0.7mm. The microstructure cross-sections are modeled and compared according to trapezoids and rectangles, respectively, which are called structure A and structure B for convenience of explanation. The modeling is as follows Figure 2As shown in parts a and b.

[0046] The matrix is meshed using quadrilateral elements, with the middle interface layer set as cohesive elements to improve the solution accuracy. The mass density of TC4 is set to 4.43×10 -9 t / mm 3 , Poisson's ratio 0.34, Young's modulus 110000MPa; Cohesive unit normal nominal stress 150MPa, total fracture energy 1.345×10 -7 MJ / mm 3 A completely fixed constraint is applied to the bottom of the substrate, and according to the damage starting displacement obtained by theoretical calculation, a displacement load of 0.002mm in the Y direction is applied to the top of the coating. The full Newton technique is selected for solution. A linear ramp is used in the entire analysis step. Finally, the field output method is selected for analysis and solution. In addition to the default settings, the output variables also need to obtain the SDEG stiffness reduction rate. The stress distribution simulation results in the Y direction are shown as follows: Figure 3 As shown in parts a and b.

[0047] To qualitatively compare the bonding strength of the coating, according to the "Guidelines for the Registration and Review of the Antibacterial Performance Evaluation of Orthopedic Implants", tensile strength can be used as one of the evaluation methods for the bonding strength of the coating, and tensile strength and tensile stress are positively correlated. Now, according to the embodiment, the tensile stress in the Y direction is output, and the same node in the complete damage state is selected for analysis, and the results are as follows: Figure 4 As shown in parts a and b. From the comparison, it can be concluded that during the entire process of coating peeling failure, the tensile stress in the Y direction of structures A and B is significantly different, and the tensile stress is positively correlated with the bonding strength, so the better structure can be selected. Figure 4 From parts a and b, it can be seen that the peak stress of structure A is larger, so structure A is better.

[0048] The above embodiments are only preferred embodiments of the present invention and are not limitations on the technical solutions of the present invention. Any technical solution that can be implemented on the basis of the above embodiments without creative work should be deemed to fall within the scope of protection of the patent of the present invention.

Claims

1. A surface microstructure optimization method for improving the bonding strength of titanium alloy surface coatings, comprising the following steps: Step 1) selecting titanium alloy surface microstructures of different types or size parameters for which the bonding strength of the surface coating needs to be evaluated; Step 2) constructing a two-dimensional model of the cross-section of the coating and the substrate with the microstructured surface in finite element simulation software; Step 3) Divide the grid, set the grid type and material parameters; Step 4) Define cross-sectional behavior and boundary conditions; Step 5) Selecting a solver algorithm, a matrix storage method, an iterative algorithm, and a time-varying load pattern to analyze and solve the physical response of the structure, including the displacement field, stress field, strain field, and the spatial and temporal distribution of contact stress and stiffness reduction rate of the structure; Step 6) performing calculations to obtain a time-stress curve and peak stress; Step 7) Compare the advantages and disadvantages of surface microstructures of different types or size parameters based on the magnitude of the peak stress, wherein the surface microstructure with the largest peak stress is the preferred surface microstructure.

2. A surface microstructure optimization method for improving the bonding strength of titanium alloy surface coating according to claim 1, characterized in that: In step 2): The two-dimensional model includes the coating, substrate, and the cohesive interface layer between them. The modeling unit type is shell element, and the combined part of the coating and substrate is defined as cohesive element. For cohesive elements modeled in conjunction with interfaces, the elastic behavior is defined based on the traction-separation constitutive response of the cohesive elements, directly expressed in terms of nominal traction and nominal strain, supporting both coupled and uncoupled behaviors. For uncoupled behavior, each traction component depends only on its conjugate nominal strain. In the local element direction, the stress-strain relationship for uncoupled behavior is as follows: where t n , t s and t t denote the nominal traction forces in the normal and two local shear directions, respectively; For coupled pull-separation behavior, the stress-strain relationship is as follows: Using the cohesive model based on displacement damage, the material failure state is determined according to the Benzeggagh-Kenane mixed-mode fracture criterion; this criterion is a failure criterion that determines mixed damage based on the energy release rate, as shown below: Where G c is the total fracture energy of the crack; is the fracture energy of mode I crack; is the fracture energy of mode II crack; G s is the shear deformation energy; G n is the tensile deformation energy; η is the material constant; At the same time, the degradation index SDEG is used to measure the damage degree of the cohesive unit, and the equation is: Where, is the displacement when the cohesive unit breaks completely; δ m is the actual relative displacement of the cohesive element; is the critical displacement at which the unit begins to be damaged; When the stress is low and does not meet the damage initiation criterion, SDEG is 0. After exceeding the critical displacement, SDEG begins to increase. When the critical displacement of failure is reached, SDEG is 1, at which point the cohesive unit fails.

3. The surface microstructure optimization method for improving the bonding strength of titanium alloy surface coating according to claim 1, characterized in that: When dividing the grid type described in step 3), the cohesive layer needs to be de-associated.

4. The surface microstructure optimization method for improving the bonding strength of titanium alloy surface coating according to claim 1, characterized in that: The material parameters in step 3) include the material property parameter settings of the matrix and cohesive elements. It is necessary to set the mass density, Young's modulus and Poisson's ratio of the matrix material TC4, the normal nominal stress, the first direction nominal stress, the second direction nominal stress, the normal elastic modulus E of the cohesive element material. nn , tangential elastic modulus E ss 、E tt and fracture energy.

5. The surface microstructure optimization method for improving the bonding strength of titanium alloy surface coating according to claim 1, characterized in that: The boundary condition setting in step 4) includes setting the bottom substrate and applying a Y-direction displacement on the top.

6. The surface microstructure optimization method for improving the bonding strength of titanium alloy surface coating according to claim 1, characterized in that: Step 6) The microstructure of the prepared coating is analyzed and calculated based on the Abaqus finite element simulation software, and a qualitative comparison of multiple microstructures can be performed to achieve an optimal design of the microstructure.

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