Cross-scale bionic gradient coating design method based on multi-principal-element high-entropy system

Abstract

The invention belongs to the technical field of high-performance cutter coatings, and relates to a cross-scale bionic gradient coating design method based on a multi-principal-element high-entropy system, which comprises the following steps: sequentially forming a high-entropy alloy plastic layer, a high-entropy alloy / high-entropy ceramic tough layer and a nanocrystalline high-entropy ceramic superhard layer on the surface of a cutter matrix, and constructing a gradient transition structure; the method comprises the following steps: matching a high-entropy alloy plastic layer with a tool matrix binding phase, optimizing a high-entropy alloy / high-entropy ceramic tough layer material, improving interface binding force, optimizing gradient transition design by adopting thermal-mechanical coupling calculation, constructing an atomic model, simulating and optimizing a microstructure based on molecular dynamics, and establishing a finite element model to simulate thermal cycle and mechanical load. The interface performance is optimized, a mathematical model is constructed through a BP neural network and a particle swarm optimization algorithm, and gradual transition design of the tool base body and the coating is achieved. The problems that the tool coating interface is high in brittleness, insufficient in adhesiveness, limited in thermal stability and the like are solved, and the bonding performance of the coating interface and the stability and durability of the coated tool are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-performance tool coatings. Specifically, it relates to a cross-scale bionic gradient coating design method based on a multi-principal element high-entropy system. Background Art

[0002] In the field of modern manufacturing, tool coating technology is widely used to improve the machining performance of tools. It mainly enhances the wear resistance, oxidation resistance, and high-temperature stability of tools by depositing single or multiple layers of high-performance coatings on the surface of the tool substrate. Traditional tool coating materials, such as TiC, TiN, and AlTiN, show good service life and stability in conventional machining environments. However, under high-temperature, high-pressure, and extreme working conditions in aerospace, mold manufacturing, etc., these coatings are prone to failure due to problems such as thermal stress concentration, crack propagation, and brittle spalling, resulting in a shortened service life of the tool and affecting the machining quality.

[0003] To improve the bonding performance between the tool substrate and the coating, existing technologies mainly adopt physical and chemical treatment methods. Physical treatment methods include plasma etching and oxidation treatment, etc. These methods can remove the bonding phase components on the surface layer of the blade substrate that are unfavorable for coating growth, thereby improving the bonding force between the coating and the substrate. However, due to the possible generation of pores or surface defects during the treatment process, the overall fracture strength of the tool may decrease. Chemical treatment methods mainly use penetration technologies of elements such as carbon, nitrogen, and titanium to increase the nucleation density, quality, and bonding strength of superhard coatings (such as diamond coatings) on the surface layer of the blade substrate. However, due to the low diffusion rate of elements, the penetration rate of this method is limited, and it is difficult to accurately control the morphology of the products after penetration, thus affecting the final performance of the tool.

[0004] In addition, setting a transition layer is one of the important methods to improve the bonding force between the tool substrate and the coating. Common transition layers include single-layer metal transition layers such as Ti, Cr, W, etc., and multi-component composite transition layers such as TiZrNbTa, CrNbTaN, TiCNCr, etc. Among them, the single-layer metal transition layer can promote the rapid nucleation and growth of superhard phases on the surface of the transition layer, and the process is relatively mature. However, for the diverse requirements of difficult-to-machine materials, the single-layer transition layer may not provide sufficient versatility in some application scenarios, so the research has gradually turned to multi-component composite transition layers. In recent years, high-entropy alloy (HEA) materials, such as TiZrNbTaMo, TiZrHfNb, and TiZrHfNbTa, due to their excellent adhesion, ductility, and toughness, have been applied to some cemented carbide tool coatings and shown good bonding force and anti-spalling ability.

[0005] Although certain progress has been made in existing tool coating technologies, they still face problems such as relatively large interfacial brittleness, insufficient adhesiveness, and limited thermal stability. Therefore, how to further optimize the tool coating structure, improve the bonding strength between the substrate and the coating, reduce interfacial stress concentration, and enhance the wear resistance and oxidation resistance of the coating remains a key issue to be solved in the current technical field. Summary of the Invention

[0006] In view of the above problems, the present invention combines the theory of gradient coating structure design, and comprehensively considers the surface components of the tool substrate and the machining integrity, the excellent adhesiveness and toughness of high-entropy alloy materials, and the wear resistance of high-entropy ceramic (HEC) materials, and proposes a cross-scale bionic gradient coating design method based on a multi-principal-element high-entropy system, which solves the problems that the existing tool coating interfaces are prone to stress concentration, cracks, and brittle spalling under high-temperature and high-pressure conditions during the cutting process, improves the interfacial bonding performance of the tool coating, and enhances its stability and durability under extreme working conditions.

[0007] A cross-scale bionic gradient coating design method based on a multi-principal-element high-entropy system provided by the present invention, the method comprising:

[0008] Step 1: Sequentially form a high-entropy alloy plastic layer, a high-entropy alloy / high-entropy ceramic tough layer, and a nanocrystalline high-entropy ceramic superhard layer on the surface of the tool substrate to construct a bionic gradient coating with a gradient transition structure;

[0009] Step 2: Determine the material compositions of the tool substrate and each layer of the gradient coating, make the material of the high-entropy alloy plastic layer in the gradient coating match the high-entropy alloy bonding phase in the tool substrate, select the material of the nanocrystalline high-entropy ceramic superhard layer according to the target machining object of the tool, and determine the material of the high-entropy alloy / high-entropy ceramic composite tough layer based on the material of the high-entropy alloy plastic layer and the material of the nanocrystalline high-entropy ceramic superhard layer;

[0010] Step 3: Analyze the interfacial properties of the bionic gradient coating by using a thermo-mechanical coupling calculation method, and optimize the gradient transition design of the interfacial components according to the calculation and analysis results to determine the gradual transition scheme of the interfacial material components;

[0011] Step 4: Construct an atomic model reflecting the interfacial structures of the high-entropy alloy plastic layer, the high-entropy alloy / high-entropy ceramic composite tough layer, and the nanocrystalline high-entropy ceramic superhard layer, and perform molecular dynamics simulation on the atomic system under the set temperature, pressure, and boundary conditions to obtain the data of the lattice and grain parameters, grain boundary width, and phase ratio changing with position, and determine a gradual transition scheme of a microscopic structure based on the obtained data, so that the coating layers are smoothly connected in terms of lattice, grain, grain boundary, and phase structure;

[0012] Step 5: According to the actual process parameters and the temperature-dependent mechanical properties of each layer of material, establish a finite element model including the tool substrate and each layer of the gradient coating. Apply thermo-mechanical coupling load conditions in the model to simulate the thermal cycle and mechanical load during the working process, obtain the distributions of stress, strain and residual stress at the interface, analyze the stress concentration and deformation in the interface region, and determine an adaptively optimized interface mechanical property transition design scheme;

[0013] Step 6: Preprocess and extract features from the obtained data of the composition, microstructure and mechanical properties of each layer of material to form a multi-dimensional data set. Use the BP neural network and particle swarm optimization algorithm to train and optimize the data, construct a mathematical model reflecting the interrelationship of composition, structure and performance between each layer of the gradient coating, and embed the mathematical model into the MATLAB mathematical modeling platform to realize the gradient transition design between the tool substrate and the gradient coating and dynamically regulate the interface parameters.

[0014] In the preferred implementation manner, further, in Step 2, the material of the tool substrate includes, but is not limited to, any one of TiC-CrFeCoNi, Ti(C,N)-CoCrCuFeNi, and TiC-CrMnFeCoNi.

[0015] In the preferred implementation manner, further, in Step 2, the material of the high-entropy alloy plastic layer includes, but is not limited to, any one of CrFeCoNi, CoCrCuFeNi, and CrMnFeCoNi; the material of the nanocrystalline high-entropy ceramic superhard layer includes, but is not limited to, any one of AlTiSiN, (TiZrNbTaMo)C / N, and (TiAlTaCrZr)N; the material of the high-entropy alloy / high-entropy ceramic composite toughening layer includes, but is not limited to, any one of (CrFeCoNi)-AlTiSiN and (CoCrCuFeNi)-(TiZrNbTaMo)C.

[0016] In the preferred implementation manner, further, Step 3 includes:

[0017] Step 3.1: Select a database applicable to the bionic gradient coating in the Thermo-Calc software, establish a bionic gradient coating structure model, and set the required simulation environment parameters;

[0018] Step 3.2: Calculate the interface binding energy of the substrate, the high-entropy alloy plastic layer, the high-entropy alloy / high-entropy ceramic toughening layer, and the nanocrystalline high-entropy ceramic superhard layer, and adjust the components of the interface material according to the calculation results;

[0019] Step 3.3: Calculate the interface residual stress of each layer of the bionic gradient coating under thermal cycle and mechanical load conditions respectively;

[0020] Step 3.4: Calculate the differences in thermal expansion coefficients between the substrate and the high-entropy alloy plastic layer in the bionic gradient coating, as well as between adjacent layers in the bionic gradient coating, and optimize the interfacial thermal matching accordingly;

[0021] Step 3.5: Calculate the phase stability between the substrate and the bionic gradient coating in a high-temperature environment.

[0022] In a preferred implementation, further, in Step 2, the material of the tool substrate includes a high-entropy alloy binder phase and a ceramic hard phase that are compositely formed, wherein the high-entropy alloy binder phase in the tool substrate material is the same as the high-entropy alloy binder phase in the material of the high-entropy alloy plastic layer.

[0023] In a preferred implementation, further, Step 5 includes:

[0024] Step 5.1: Establish a three-dimensional geometric model including the tool substrate and each layer of the gradient coating, specifically divide the interface region to form independent interface elements or use bonded contact to clarify the transition region between different layers;

[0025] Step 5.2: Assign temperature-dependent elastic modulus, Poisson's ratio, thermal expansion coefficient, yield strength, and other plastic parameters to each layer respectively;

[0026] Step 5.3: Reasonably mesh the entire model, use locally refined meshes in the interface and regions with high stress gradients, and determine the optimal mesh size through mesh independence verification;

[0027] Step 5.4: Apply thermal loads according to the actual process flow, simulate the thermal cycle process to form a temperature gradient;

[0028] Step 5.5: Apply fixed boundary conditions or appropriate constraints to the tool substrate, apply external forces or displacement loads under working conditions in the coating and interface regions to simulate the mechanical responses generated during machining or use, and set the interface contact properties at the same time;

[0029] Step 5.6: Use a non-linear thermal-mechanical coupling solver to jointly solve the temperature field and stress field, and ensure convergence through step-by-step solution;

[0030] Step 5.7: Extract stress, strain, residual stress, and local deformation data in the key regions, use post-processing tools to generate distribution maps and local enlarged maps of the temperature field and stress field, and clarify the stress concentration positions and amplitudes;

[0031] Step 5.8: Compare the maximum stress, strain distributions, and residual stress levels in the interface region under different design schemes, establish a stress concentration index, use the constructed objective function as the optimization basis, and analyze the effects of different interface contact parameters, layer thicknesses, and material transition zone designs on the objective function;

[0032] Step 5.9: Conduct a sensitivity analysis on the key parameters to determine their influence on the mechanical response. Based on the results of the sensitivity analysis, propose a design improvement plan to optimize the interface transition design;

[0033] Step 5.10: Conduct multi-condition simulation verification on the optimized design plan, feedback the verification results to the model, and perform multiple iterative optimizations until an interface mechanical property transition plan that meets the requirements of the actual working conditions is obtained.

[0034] In the preferred implementation manner, further, Step 6 includes:

[0035] Step 6.1: Collect the multi-scale data obtained in Steps 3 - 5, remove missing values, process outliers, and perform normalization transformation on the original data. Use principal component analysis or feature extraction techniques to screen the key indicators related to interface bonding strength, toughness, and overall performance, and construct an input data matrix as the basic data for the subsequent model;

[0036] Step 6.2: Define component entropy, structure entropy, and performance entropy, and form a unified index system with the entropy values of component entropy, structure entropy, and performance entropy. Construct a multi-objective optimization model and set the collaborative regulation target.

[0037] Step 6.3: Select a multi-layer feedforward neural network as the basic model structure. The input layer receives the preprocessed component, structure, and performance data, and the output layer outputs the predicted values of interface bonding strength and toughness. Set the number of hidden layers and nodes according to the data complexity, and use activation functions to improve the nonlinear mapping ability of the model;

[0038] Step 6.4: Train the network using the error backpropagation algorithm, set the loss function as the mean square error or other appropriate indicators, use cross-validation to evaluate the generalization ability of the model, adjust the network parameters until both the training error and the validation error reach the expected requirements, and use the MATLAB mathematical modeling platform to implement the training process and generate training reports and performance evaluation charts;

[0039] Step 6.5: Use the PSO algorithm to globally search for the weights, biases, and hyperparameters of the BP neural network to further reduce the training error;

[0040] Step 6.6: Integrate the "component entropy - structure entropy - performance entropy" collaborative regulation model after data preprocessing, BP neural network training, and PSO optimization into the MATLAB platform, establish a systematic simulation calculation framework, verify the model using historical data and newly generated simulation data, make the model prediction results coincide with the actual performance indicators, analyze the prediction error and sensitivity, and feedback to adjust the feature engineering, network structure, and optimization parameters to form a closed-loop improvement process.

[0041] In a preferred implementation, further, in step 4, the process of generating the optimal microstructure gradual transition scheme includes: First, according to the crystal structures, lattice parameters, and defect information of the selected high-entropy alloy plastic layer, high-entropy alloy / high-entropy ceramic composite toughening layer, and nanocrystalline high-entropy ceramic superhard layer, an atomic model that meets the requirements of periodic and aperiodic boundary conditions is constructed, and the atomic arrangement and transition zone in the interface region are particularly designed; Subsequently, an applicable interatomic interaction potential is selected according to the characteristics of each material system, the initial atomic positions and velocities are set, and the system equilibrium configuration is obtained by using the energy minimization method. Then, the Nosé-Hoover temperature controller is used to perform constant temperature or temperature gradient control under simulated real heat treatment conditions. By refining the time step and multi-step simulation, the interface atom rearrangement, grain boundary diffusion, and local phase change phenomena are captured; Finally, methods such as radial distribution function, coordination number statistics, and local strain analysis are used to collect and quantitatively evaluate the simulation data, calculate the interface energy, bonding strength, and lattice matching degree, and determine the optimal microstructure gradual transition scheme.

[0042] In a preferred implementation, further, in step 6.5, the process of globally searching for the weights, biases, and hyperparameters of the BP neural network by the PSO algorithm includes: Initializing a certain number of particles, defining the fitness function as the prediction error of the network on the validation set, and updating the velocities and positions of each particle. Iteratively search until convergence, finally obtain the optimal parameter group, and apply it to the BP neural network model to form a comprehensively optimized collaborative regulation model.

[0043] In a preferred implementation, further, in step 6.2, the compositional entropy is used to reflect the uniformity and diversity of the material composition and element distribution of each layer, and the calculation formula can refer to the information entropy model; the structural entropy is used to describe the order degree of lattice matching, grain boundary width, and defect distribution in the interface microstructure; the performance entropy is used to quantify the balance and robustness of the interface mechanical properties.

[0044] The beneficial effects of the present invention are:

[0045] First, by making full use of the unique advantages of the multi-principal-component high-entropy system, the method of the present invention constructs a bionic gradient coating composed of a high-entropy alloy plastic layer, a high-entropy alloy / high-entropy ceramic toughening layer, and a nanocrystalline high-entropy ceramic superhard layer, achieving precise cross-scale design from the atomic scale to the engineering scale. The construction of this gradient structure effectively solves problems such as thermal stress concentration, crack propagation, and brittle spalling that often occur in traditional tool coatings under high temperature, high pressure, and extreme working conditions. Compared with single materials or simple multi-layer structures (such as TiC, TiN, AlTiN coatings) in traditional technologies that are prone to tool failure due to large interfacial brittleness and insufficient adhesion, although existing physical and chemical treatments and single or composite transition layers have improved the bonding strength between the substrate and the coating to a certain extent, they still have difficulty fully meeting the performance requirements under high-load conditions. The present invention is optimized and designed specifically for these defects. Specifically, by using various numerical simulation technologies such as thermal-mechanical coupling calculation, molecular dynamics simulation, and finite element analysis, the smooth transition of each layer of the coating in terms of lattice, grain boundary, and phase structure can be precisely controlled, effectively alleviating the problems of interfacial stress concentration and local deformation. At the same time, by introducing the BP neural network and particle swarm optimization algorithm for intelligent analysis and dynamic regulation of multi-dimensional data, real-time optimization of interfacial parameters is achieved, improving the bonding strength, interfacial toughness, thermal matching, and fatigue resistance between the coating and the tool substrate. This not only extends the service life of the coated tool but also improves the processing efficiency, meeting the requirements for high-performance tools in the modern manufacturing field.

[0046] Second, in the preferred implementation mode, the material combination of the tool substrate, high-entropy alloy plastic layer, nanocrystalline high-entropy ceramic superhard layer, and high-entropy alloy / high-entropy ceramic composite toughening layer of the present invention realizes the balance of high hardness, high wear resistance, and good toughness by optimizing the synergistic effect between the tool substrate and the multi-layer composite structure.

[0047] Third, in the preferred implementation mode, step 3 of the present invention precisely simulates and optimizes the interfacial binding energy, residual stress, thermal expansion matching, and phase stability at high temperature between each layer of the coating, realizing the optimal configuration of material components and interfacial structures, thereby improving the overall heat resistance, wear resistance, and fatigue resistance of the coating, extending the service life of the substrate and the coating, and ensuring stable and reliable operation of the system under complex thermal cycling and mechanical load conditions.

[0048] Fourth, in the preferred implementation mode, the high-entropy alloy bonding phase used in the tool substrate and the plastic layer of the present invention is exactly the same, thus achieving the best match of chemical and mechanical properties at the material interface. This match not only enhances the interfacial bonding strength but also reduces the internal stress caused by thermal expansion mismatch.

[0049] Fifth, in the preferred implementation mode, step 5 of the present invention realizes the accurate simulation of the temperature gradient and stress distribution at the interface between the tool substrate and the gradient coating by finely establishing a three-dimensional geometric model, assigning temperature-dependent material parameters, and locally densifying the mesh. It can accurately capture the stress concentration area and the residual stress level. By using a non-linear thermal-mechanical coupling solver and multi-condition iterative verification, it not only establishes an objective evaluation index for different design schemes, but also optimizes the interface transition design through sensitivity analysis, thereby improving the heat resistance, wear resistance, and fatigue resistance of the tool.

[0050] Sixth, in the preferred implementation mode, step 6 of the present invention realizes the effective integration of multi-scale data and the fine extraction of key features through this model, constructs a unified index system of "composition entropy - structure entropy - performance entropy", and successfully maps the complex relationship between the interface bonding strength, toughness, and overall performance by means of multi-objective optimization, BP neural network, and PSO global search. Finally, the closed-loop simulation and feedback improvement mechanism not only significantly improves the prediction accuracy and the generalization ability of the model, but also enhances the robustness to abnormal data.

[0051] Seventh, in the preferred implementation mode, step 4 of the present invention can accurately capture the atomic rearrangement, grain boundary diffusion, and local phase transformation in the interface region during the heat treatment process, realize the optimal matching of the crystal structure and interface characteristics, thereby improving the interface bonding strength and the overall lattice matching degree, and reducing the internal stress and the risk of defect diffusion of the coating. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flowchart of the cross-scale bionic gradient coating design method based on a multi-principal element high-entropy system in an embodiment of the present invention;

[0053] Figure 2 is a schematic diagram of the structure of the high-entropy alloy / ceramic tool substrate material in an embodiment of the present invention;

[0054] Figure 3 is a schematic diagram of the high-entropy multi-principal element gradient coating architecture in an embodiment of the present invention;

[0055] Figure 4 is a schematic diagram of the grain and grain boundary microstructure in an embodiment of the present invention.

[0056] Among them, 1 - high-entropy alloy bonding phase; 2 - ceramic hard phase; 3 - high-entropy alloy plastic phase; 4 - high-entropy ceramic superhard phase; 5 - tool substrate; 6 - high-entropy alloy plastic layer; 7 - high-entropy alloy / high-entropy ceramic tough layer; 8 - nanocrystalline high-entropy ceramic superhard layer; 9 - grain; 10 - grain boundary. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] To enable those skilled in the art to better understand the technical solution of this application, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0058] The orientation terms such as up, down, left, right, front and back in this application document are established based on the positional relationship shown in the accompanying drawings. If the accompanying drawings are different, the corresponding positional relationship may also change accordingly, so it should not be understood as a limitation of the protection scope.

[0059] In this application, the terms "installation", "connection", "engagement", "connection", "fixation", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, an integral connection, a mechanical connection, an electrical connection or a connection that can communicate with each other, a direct connection, an indirect connection through an intermediate medium, a connection inside two components, or an interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0060] Inspired by the material composition, microstructure scale and gradient change of mechanical properties in the biological shell-like structure, and combined with the excellent adhesion of high-entropy alloys (HEAs), the excellent wear resistance of high-entropy ceramics (HECs) and the gradient coating design theory, a cross-scale bionic gradient coating design method based on a multi-principal-element high-entropy system is proposed, aiming to optimize the interfacial bonding performance of the tool coating and improve its stability and durability under extreme working conditions. This method constructs a gradient coating structure composed of an HEA plastic layer, an HEA / HEC tough layer and a nanocrystalline HEC superhard layer. By selecting an HEA plastic phase material that matches the tool substrate, combined with thermo-mechanical coupling calculation, molecular dynamics simulation and finite element analysis to optimize the interfacial material transition, a "composition entropy - structure entropy - performance entropy" collaborative optimization model is constructed using the BP neural network, particle swarm optimization algorithm (PSO) and MATLAB mathematical modeling to comprehensively improve the coating interfacial performance. At the same time, through bionic gradient design, the composition and microstructure are adjusted layer by layer to achieve the best match between the toughness of the coating interior and the superhard characteristics of the surface layer, thereby reducing interfacial brittleness, improving adhesiveness, enhancing wear resistance and impact resistance, and finally overcoming the bottleneck problems of large interfacial brittleness and insufficient adhesiveness in the existing tool coating technology, and improving the performance stability and service life of the tool under extreme working conditions such as high temperature, high speed and high load cutting.

[0061] Refer to the attached drawings of the specification Figure 1 , a cross-scale bionic gradient coating design method based on a multi-principal-element high-entropy system, the method comprising:

[0062] Step 1: Sequentially form a high-entropy alloy plastic layer, a high-entropy alloy / high-entropy ceramic toughening layer, and a nanocrystalline high-entropy ceramic superhard layer on the surface of the tool substrate to construct a bionic gradient coating with a gradient transition structure.

[0063] The purpose of Step 1 is to construct the overall architecture of the bionic gradient coating. By reasonably designing the material composition and structure layout, optimizing the interfacial bonding force, and improving the wear resistance and impact resistance of the tool. This design takes into account high strength, high toughness, and high wear resistance, enabling the tool to have better performance and a longer service life in high-speed, high-temperature, and high-load cutting environments.

[0064] Refer to the attached Figures 2 - 3 of the specification. The present invention provides a high-performance gradient coating tool, which includes a tool substrate 5 composed of a high-entropy alloy binder phase 1 and a ceramic hard phase 2. This substrate combines the good ductility of the high-entropy alloy and the high hardness characteristics of the ceramic hard phase, ensuring that the tool has excellent strength-toughness synergy optimization characteristics in high-load cutting environments, and improving the overall service life and cutting performance.

[0065] In the interfacial region between the tool substrate 5 and the coating, a high-entropy alloy plastic layer 6 is provided. The high-entropy alloy plastic layer 6 is composed of a high-entropy alloy plastic phase 3 (abbreviated as HEA plastic phase, High-Entropy Alloy). The high-entropy alloy material in the plastic phase has excellent bonding properties and high ductility, which can effectively improve the bonding strength between the substrate and the coating, and at the same time relieve the impact stress caused by the cutting force to reduce interfacial spalling and microcrack propagation, and improve the overall durability and stability of the coating.

[0066] On the outermost surface of the coating, a nanocrystalline high-entropy ceramic superhard layer 8 is deposited. The nanocrystalline high-entropy ceramic superhard layer 8 is composed of a high-entropy ceramic superhard phase 4 (abbreviated as HEC superhard phase, High-Entropy Ceramic). The nanocrystalline high-entropy ceramic material therein has ultra-high hardness, excellent wear resistance, and excellent high-temperature oxidation resistance, ensuring that the tool can still maintain stable cutting performance in high-speed cutting and high-temperature environments, and significantly improving the tool's anti-wear ability.

[0067] Between the high-entropy alloy plastic layer 6 and the nanocrystalline high-entropy ceramic superhard layer 8, a high-entropy alloy / high-entropy ceramic toughening layer 7 is provided. The high-entropy alloy / high-entropy ceramic toughening layer 7 is jointly composed of an HEA toughening phase and an HEC toughening phase, forming a composite gradient structure of high-entropy alloy and high-entropy ceramic. This toughening layer not only ensures a smooth gradual change in the composition of the coating from the substrate to the surface layer, effectively reducing the interfacial stress, but also enhances the interlayer bonding strength, realizing the overall enhancement of strength and toughness. In addition, the HEA toughening phase provides good impact resistance, while the HEC toughening phase improves the wear resistance and high-temperature stability, enabling the coating to have excellent anti-fatigue characteristics and long-term stable mechanical properties.

[0068] It should be noted that the differences between the plastic phase, the strong and tough phase, and the superhard phase in step 1 in materials science are mainly reflected in hardness, toughness, and structural functions, and they play different roles in the gradient coating design. The plastic phase has good plastic deformation ability, can enhance the bonding force between the coating and the substrate, adapt to the changes in thermal stress and mechanical stress during the cutting process, and can improve the bonding force of the coating and buffer stress. The strong and tough phase has both a certain hardness and toughness, connects the plastic phase and the superhard phase, forms a smooth gradient transition, reduces the interfacial stress between different phases, and improves the overall mechanical properties of the coating. The superhard phase is composed of materials with high hardness and wear resistance, can reduce the loss of the tool during high-speed cutting, and improve the wear resistance and corrosion resistance of the tool surface layer.

[0069] Step 2: Determine the material compositions of the tool substrate and each layer of the gradient coating, make the material of the high-entropy alloy plastic layer in the gradient coating match the high-entropy alloy bonding phase in the tool substrate, select the material of the nanocrystalline high-entropy ceramic superhard layer according to the target machining object of the tool, and determine the material of the high-entropy alloy / high-entropy ceramic composite strong and tough layer based on the materials of the high-entropy alloy plastic layer and the nanocrystalline high-entropy ceramic superhard layer.

[0070] The purpose of step 2 is to reasonably select and optimize the material components of the gradient coating to ensure the bonding strength between the tool substrate and the coating, and improve the wear resistance, impact resistance, and high-temperature stability of the tool.

[0071] Specifically, the tool substrate material needs to have high strength, high toughness, and good wear resistance, and at the same time be able to provide good bonding ability with the coating. In this application, the material of the tool substrate includes, but is not limited to, any one of TiC-CrFeCoNi (titanium carbide-chromium iron cobalt nickel high-entropy alloy), Ti(C,N)-CoCrCuFeNi (titanium carbonitride-cobalt chromium copper iron nickel high-entropy alloy), and TiC-CrMnFeCoNi (titanium carbide-chromium manganese iron cobalt nickel high-entropy alloy). If the machining object is difficult-to-machine materials such as titanium alloy and stainless steel, TiC-CrFeCoNi can be selected, which has high thermal crack resistance and toughness. If the machining object is high-hardness steel (above HRC 50) or heat-resistant alloy, Ti(C,N)-CoCrCuFeNi can be selected, which has good wear resistance and oxidation resistance. If the machining object is high-strength composite materials, carbon fiber reinforced composite materials (CFRP), etc., TiC-CrMnFeCoNi can be selected, which can provide better thermal stability and fracture toughness.

[0072] The selection of the HEA (high-entropy alloy) plastic phase material needs to be consistent with the high-entropy alloy bonding phase material of the tool substrate to improve the interfacial bonding strength, reduce interfacial stress concentration, and enhance the impact resistance. In this application, the materials of the high-entropy alloy plastic layer include, but are not limited to, any one of CrFeCoNi (chromium-iron-cobalt-nickel high-entropy alloy), CoCrCuFeNi (cobalt-chromium-copper-iron-nickel high-entropy alloy), and CrMnFeCoNi (chromium-manganese-iron-cobalt-nickel high-entropy alloy). The matching principles of the high-entropy alloy plastic layer material in the gradient coating and the high-entropy alloy bonding phase in the tool substrate are as follows: 1. Ensure that the bonding phase elements of the plastic phase material and the tool substrate material are the same to reduce interfacial stress and compatibility problems. 2. The plastic phase should have good ductility and impact resistance to avoid coating spalling and improve the overall durability. 3. Optimize the layer thickness of the plastic phase (usually between 1 - 5 μm) to ensure that it can absorb impacts without being overly softened to affect the wear resistance. For example: If the substrate material is TiC-CrFeCoNi, then the HEA plastic phase material is selected as CrFeCoNi. If the substrate material is Ti(C,N)-CoCrCuFeNi, then the HEA plastic phase material is selected as CoCrCuFeNi. If the substrate material is TiC-CrMnFeCoNi, then the HEA plastic phase material is selected as CrMnFeCoNi.

[0073] The HEC (high-entropy ceramic) superhard phase material determines the wear resistance, high-temperature stability, and oxidation resistance of the tool. Its selection is based on the machining object of the coated tool. In this application, the materials of the nanocrystalline high-entropy ceramic superhard layer include, but are not limited to, any one of AlTiSiN (aluminum-titanium-silicon-nitride), (TiZrNbTaMo)C / N (titanium-zirconium-niobium-tantalum-molybdenum carbon / nitride), and (TiAlTaCrZr)N (titanium-aluminum-tantalum-chromium-zirconium nitride). For example: If the machining object is superalloys, heat-resistant steels, stainless steels (such as Inconel, Hastelloy), then the AlTiSiN material is selected because AlTiSiN has excellent oxidation resistance, hot hardness, and high-temperature wear resistance and can remain stable at high temperatures of 900 - 1000 °C. If the machining object is high-strength alloy steels (above HRC 50), hardened steels, then the (TiZrNbTaMo)C / N material is selected because this material belongs to high-entropy carbon / nitrides, has ultra-high hardness (≥3500 HV), excellent heat resistance, and thermal crack resistance, and is suitable for high-speed cutting of high-hardness materials. If machining aerospace composite materials (such as CFRP, glass fiber-reinforced plastic GFRP), then the (TiAlTaCrZr)N material is selected because TiAlTaCrZrN has a low friction coefficient, high hardness, and high oxidation resistance, and can reduce frictional heat during cutting of composite materials to prevent tool adhesion and failure.

[0074] To ensure the high toughness and bonding strength of the coating, the interlayer transition needs to be reasonably designed. The HEA / HEC composite layer needs to balance the compatibility of both to avoid spalling or crack propagation caused by mismatched interface materials. It is necessary to not only maintain a high hardness (close to HEC) but also maintain a certain toughness (close to HEA), and improve the overall fatigue resistance through microstructure optimization (such as nanostructure, gradient transition). The materials of the composite layer must include two types of components: high-entropy alloy (HEA) and high-entropy ceramic (HEC). High-entropy ceramic (HEC) nanoparticles are introduced into the high-entropy alloy (HEA) to form a metal-ceramic composite phase. Therefore, in this application, the materials of the high-entropy alloy / high-entropy ceramic composite tough layer include, but are not limited to, any one of (CrFeCoNi)-AlTiSiN (chromium-iron-cobalt-nickel high-entropy alloy matrix-aluminum-titanium-silicon-nitride coating) and (CoCrCuFeNi)-(TiZrNbTaMo)C (cobalt-chromium-copper-iron-nickel high-entropy alloy matrix-titanium-zirconium-niobium-tantalum-molybdenum carbide coating). The (CrFeCoNi)-AlTiSiN material is suitable for applications with high-temperature resistance and oxidation resistance requirements, and the (CoCrCuFeNi)-(TiZrNbTaMo)C is suitable for working conditions with high wear resistance requirements.

[0075] In the materials of the high-entropy alloy / high-entropy ceramic composite tough layer, the change in the proportion of the ceramic phase (HEC) mainly affects the toughness balance, wear resistance, heat resistance, and impact resistance of the material. According to different processing conditions and usage environments, the proportion of the ceramic phase can be divided into three cases: low proportion, medium proportion, and high proportion. For example: A low ceramic phase proportion (HEC content 30%, HEA content 70%) is suitable for working conditions with large impact loads (such as interrupted cutting, high-speed milling), heavy-duty cutting (such as machining high-strength steel, cast iron), and high toughness requirements (such as machining brittle materials like ceramics, fiberglass-reinforced composites). A medium ceramic phase proportion (HEC content 50%, HEA content 50%) is suitable for environments that require a balance between wear resistance and toughness (such as working conditions combining continuous cutting and interrupted cutting), medium-load machining (such as titanium alloys, stainless steels, hardened steels), applications with relatively high tool life requirements but still certain toughness requirements. A high ceramic phase proportion (HEC content 70%, HEA content 30%) is suitable for machining ultra-high-hardness workpieces (such as hardened steel with HRC>50, cemented carbide), high-speed cutting, high-temperature cutting (>1000°C), and scenarios that require an extremely low wear rate (such as ultra-precision machining).

[0076] Step 3: Use a thermo-mechanical coupling calculation method to analyze the interface performance of the bionic gradient coating. According to the calculation and analysis results, optimize the gradient transition design of the interface components and determine the gradient transition scheme of the interface material components.

[0077] The purpose of Step 3 is to perform thermo-mechanical coupling calculations using Thermo-Calc software, analyze the interfacial properties of the bio-inspired gradient coating, evaluate the bonding strength, residual stress distribution, thermal expansion matching, and phase stability of the coating interface, optimize the bonding, toughness, and thermal expansion coefficient (CTE) matching, and determine the best material composition gradient transition scheme (the composition gradient transition scheme is the composition change and thickness distribution scheme) to ensure the bonding strength, thermodynamic stability, and service environment adaptability between the coating and the substrate, thereby improving the durability and anti-failure ability of the coating.

[0078] It should be noted that Thermo-Calc is a thermodynamics calculation and phase diagram simulation software widely used in the fields of materials science, metallurgical engineering, and chemical engineering. Based on the CALPHAD (Calculation of Thermodynamics) method, it can be used to calculate the phase stability, thermodynamic properties, diffusion behavior, kinetic characteristics, etc. of multi-component alloy systems. In the design of high-entropy alloy (HEA) / high-entropy ceramic (HEC) coatings, Thermo-Calc can be used to optimize the matrix-coating interface phase matching, ensure strong bonding, and avoid brittle phases at the interface; calculate the thermal expansion coefficient (CTE), optimize the gradient transition layer, and prevent cracks caused by temperature changes; predict the high-temperature stability to ensure that the high-entropy alloy / high-entropy ceramic does not decompose or form brittle phases at high temperatures; combine with DICTRA (Diffusion-Controlled Transformations, a calculation module in Thermo-Calc software used to simulate diffusion-controlled phase transformation processes) to calculate the diffusion behavior and optimize the coating thickness to ensure a smooth gradient transition.

[0079] Gradient layer transition refers to the gradual change of composition, microstructure, or performance parameters between different materials in a multi-layer coating or composite material structure, rather than forming a sudden interface, to reduce interface stress, optimize the bonding strength, and improve the overall stability. In the design of bio-inspired gradient coatings, the role of the gradient transition layer is to relieve the performance mismatch between the substrate and the HEA plastic layer, between the HEA / HEC tough layer and the HEA plastic layer, and between the nanocrystalline HEC superhard layer, thereby optimizing the interface binding energy, thermal expansion matching (CTE), and residual stress distribution.

[0080] Specifically, Step 3 includes:

[0081] Step 3.1: Select a database suitable for the bio-inspired gradient coating in Thermo-Calc software, establish a structural model of the bio-inspired gradient coating, and set the required simulation environment parameters.

[0082] The databases applicable to bionic gradient coatings include the high-entropy alloy database (TCHEA), the ceramic database (TCTI / TCNI), the interface energy database (TCINTERFACE), and the mechanical property database (TCSMECH). Among them, the high-entropy alloy database is used to calculate the thermodynamic properties and phase stability of the HEA plastic layer. The ceramic database is used to calculate the structural stability of the HEC toughening layer and the nanocrystalline superhard layer. The interface energy database is used to calculate the binding energy between layers of the coating and the substrate, and optimize the interfacial adhesion. The mechanical property database is used to provide data on the residual stress and coefficient of thermal expansion (CTE) of gradient materials.

[0083] The bionic gradient coating structure model includes a tool substrate of high-entropy alloy material, and from the inside out, a HEA plastic layer, a HEA / HEC toughening layer, and a nanocrystalline HEC superhard layer.

[0084] The environmental parameters include the temperature range and the loading conditions. According to the working temperature range of the tool, the temperature range is defined as 25°C - 1000°C. The loading conditions include thermal load, isostatic pressure environment, and mechanical shock. Among them, the thermal load calculates the CTE matching of each layer, the isostatic pressure environment simulates the residual stress, and the mechanical shock evaluates the actual cutting stress.

[0085] Step 3.2: Calculate the interfacial binding energy of the substrate, the HEA plastic layer, the HEA / HEC toughening layer, and the nanocrystalline HEC superhard layer, and adjust the composition of the interfacial material according to the calculation results.

[0086] Specifically, CALPHAD + interfacial binding energy calculation is used to evaluate the binding energy W ad between the connected layers. The calculation targets include calculating the binding energy between the substrate and the HEA plastic layer, calculating the binding energy between the HEA plastic layer and the HEA / HEC toughening layer, and calculating the binding energy between the HEA / HEC toughening layer and the nanocrystalline HEC superhard layer.

[0087] Taking the calculation process of the binding energy W ad between the substrate and the HEA plastic layer as an example:

[0088] W ad = E interface - (E bulk1 + E bulk2 )(1)

[0089] In the formula: E interface represents the total energy of the interface between the substrate and the HEA plastic layer; E bulk1 represents the bulk energy of the substrate; E bulk2 represents the bulk energy of the HEA plastic layer.

[0090] First, determine the material parameters of the substrate and the HEA plastic layer. In Thermo-Calc, select the high-entropy alloy database (TCHEA) and the carbide database (TCTI). Select the substrate material as TiC-CrFeCoNi, where TiC is the hard phase, providing high hardness and wear resistance; CrFeCoNi is the bonding phase, providing good strength and toughness. In Thermo-Calc, select the TCHEA+TCNI material database, and select the HEA plastic layer material as CoCrFeNiTi, whose structure is FCC (face-centered cubic crystal structure).

[0091] Then, calculate the total energy E of the interface between the substrate and the HEA plastic layer interface . Use VASP (Vienna Ab-initio Simulation Package) or Quantum ESPRESSO for calculation, and construct the interface supercell: for the substrate part, select the (100) FCC crystal plane for TiC (ceramic phase) and the (111) FCC crystal plane for CrFeCoNi (metal bonding phase). For the HEA plastic layer part, select the (100) FCC crystal plane for matching. Calculate the binding energy under different interface mismatches. First, calculate the energy E of the original unoptimized interface unrelaxed interface , and then use the relaxation calculation to calculate the optimized interface energy E relaxed interface = E unrelaxed interface - E relaxation energy . Further calculate the interface binding energy E interface = E relaxed interface / A, where A represents the interface area.

[0092] Then calculate the bulk energies E bulk1 and E bulk2 of the substrate and the HEA plastic layer. Use the first-principles calculation (DFT) to calculate the bulk energy of the TiC-CrFeCoNi composite structure. Select the (100) TiC crystal plane to calculate the carbide formation energy, and then calculate the total energy E CoCrFeNi of the FCC-structured CoCrFeNi alloy and the bulk energy E TiC of TiC (ceramic phase), E bulk1 = E CoCrFeNi + E TiC . Use DFT to calculate the bulk energy E bulk2 of CoCrFeNiTi (HEA plastic layer).

[0093] The calculated E interface and Ebulk1 , E bulk2 Substitute into formula (1) to calculate the matrix-HEA plastic layer binding energy W ad . If W ad is negative (W ad < 0), it indicates that the interface bonding is strong and the coating is stable. If W ad is positive (W ad > 0), it indicates that the interface bonding is weak and the interface composition needs to be optimized.

[0094] Furthermore, the interface composition is optimized by adjusting the alloying elements of the HEA plastic layer. Since in this example, the selected matrix material is TiC-CrFeCoNi and the HEA plastic layer material is CoCrFeNiTi, it can be optimized in the following ways: 1. Add Mo or Nb to the HEA plastic layer material. Mo and Nb can form transition phases (such as NbC, MoC) with C in the matrix material, improving the metal-ceramic bonding ability at the TiC-HEA interface. 2. Reduce the Ti content in the HEA plastic layer material. Excessive Ti may lead to interface embrittlement. 3. Optimize the ratio of Co / Cr / Ni / Fe / Ti in the HEA plastic layer to make it compatible with the crystal structure of the matrix.

[0095] It should be noted that FCC (Face-Centered Cubic) and BCC (Body-Centered Cubic) are two common crystal structures of metals and alloys. These structures describe the arrangement of atoms in the lattice and have important influences on the physical, mechanical, and thermodynamic properties of materials. The FCC-FCC structure means that the materials on both sides of the interface adopt the FCC lattice (for example, Ni, Co, Fe, Al mainly exist in the FCC structure). The BCC-BCC structure means that the materials on both sides of the interface adopt the BCC lattice (for example, Nb, Mo, V mainly exist in the BCC structure). When the interfaces of two materials have the same lattice structure (FCC-FCC or BCC-BCC), their interface matching degree is higher, the binding energy is usually lower, and the adhesion is stronger. If the lattice structures of the two materials are different (such as the FCC-BCC structure), the lattice mismatch is larger, the interface binding energy is usually higher, and residual stresses and microcracks are likely to occur.

[0096] Step 3.3: Calculate the interface residual stresses of each layer of the bionic gradient coating under thermal cycling and mechanical load conditions.

[0097] Calculate the residual stress distribution of each layer under thermal cycling and mechanical loading through Step 3.3, and identify the stress concentration regions. Evaluate the influence of the interface layer thickness change on the residual stress, optimize the coating thickness and the design of the transition layer, and avoid stress concentration. Optimize the interface stress distribution of the coating material to avoid structural failure or the precipitation of new phases (such as Laves phase) caused by excessive stress. If the stress is too high, it is necessary to calculate the high-temperature phase stability in Step 3.5 and adjust the element content in the coating material.

[0098] Specifically, Step 3.3 includes:

[0099] Step 3.3.1: Implement multi-physics thermo-mechanical coupling simulation on the specimen using a finite element software platform to calculate the thermal stress distribution under different temperature conditions and the residual stress under different coating thickness conditions.

[0100] The finite element software platform includes Abaqus, ANSYS, etc. Through simulation, the stress response information of the coating interface under thermal load and mechanical load can be obtained.

[0101] The material models for multi-physics thermo-mechanical coupling simulation include: the matrix material is TiC-CrFeCoNi, the HEA plastic layer material is CoCrFeNiTi, the HEA / HEC tough and strong layer material is CoCrFeNi-TiC / NbC, and the nanocrystalline HEC superhard layer material is Al2O3-ZrC-TiC.

[0102] Set the simulation parameters of thermal load and mechanical load. Thermal load: 25°C → 1000°C → 25°C, cycle 100 times. Mechanical load: Simulate the impact force and shear force of the tool during the cutting process.

[0103] Calculate the thermal stress distribution at different temperatures through the following formula:

[0104] σ thermal =E×α×ΔT(2)

[0105] In the formula: E represents Young's modulus (with different values for different layers); α represents the coefficient of thermal expansion (CTE); ΔT represents the temperature change.

[0106] Calculate the residual stress under different coating thicknesses (5 - 20 μm) through the following formula:

[0107]

[0108] In the formula: σ interface represents the interface stress; v represents the Poisson's ratio; E1, E2 represent the Young's moduli of adjacent coatings; α1, α2 represent the coefficients of thermal expansion of adjacent coatings.

[0109] Step 3.3.2: Based on the influence of different layer thicknesses on residual stress calculated from the thermo-mechanical coupling simulation results, select the optimal coating thickness, improve the interfacial bonding ability by optimizing the content of each component element in the interfacial region, and optimize the gradient layer transition scheme by calculating the interfacial misfit degree so that the lattice matching degree of the gradient layer reaches or exceeds 95%.

[0110] The thicknesses of different layers are, for example, 5μm, 10μm, 15μm. According to the influence of these thicknesses on the residual stress, select the coating with the smallest influence on the thickness as the optimal thickness. Optimize the interfacial element content, for example, by reducing the Ti content (reducing the interfacial brittleness) or increasing the Nb content (improving the flexibility) and other methods to optimize the interfacial bonding ability.

[0111] Step 3.4: Calculate the differences in the coefficients of thermal expansion (CTE) between the substrate and the HEA plastic layer in the bionic gradient coating, as well as between adjacent layers in the bionic gradient coating, and optimize the interfacial thermal matching accordingly.

[0112] Specifically, select TCHEA (High Entropy Alloy Database) + TCTI (Carbide Database), and use Thermo-Calc to calculate the CTE at different temperatures:

[0113]

[0114] In the formula: α(T) represents the linear coefficient of thermal expansion of the material at temperature T, with the unit 1 / K; L represents the initial length of the material (usually measured at room temperature); dL represents the change in length of the material during the temperature change dT; dT represents the temperature change.

[0115] Calculate the CTE change curves of each layer material in the range of 25°C - 1000°C.

[0116] Taking the substrate and the HEA plastic layer as an example, calculate the CTE matching degree between the substrate - TiC - CrFeCoNi and the HEA plastic layer:

[0117] ΔCTE = |CTE 基体 -CTE HEA塑性层 |(5)

[0118] If ΔCTE < 4×10 -6 / K, it indicates that the risk of interfacial cracking caused by thermal cycling can be reduced.

[0119] If ΔCTE ≥ 4×10 -6 / K, then by increasing the content of Ti / Zr / Nb in the HEA plastic layer, reduce the CTE of the coating to match it with the substrate. Or add a Ti - Zr transition layer (CTE ≈ 10×10 -6 / K) between the substrate and the HEA plastic layer to relieve the CTE mismatch.

[0120] Step 3.5: Calculate the phase stability between the substrate and the bio-inspired gradient coating under high-temperature environment.

[0121] The phase stability calculated in Step 3.5 is used to finally verify the feasibility of the coating optimization scheme, ensure the stable operation of the optimization scheme in the actual application environment, and ensure that no brittle phases (such as Laves phase, σ phase) precipitate in the bio-inspired gradient coating above 1000 °C, and output the final optimized design scheme.

[0122] Specifically, Thermo-Calc is used to calculate the phase stability of the substrate-HEA plastic layer, HEA / HEC tough and strong layer, and nanocrystalline HEC superhard layer at different temperatures.

[0123] G 涂层 (T)=H - TS (6)

[0124] In the formula: H represents enthalpy; TS represents the entropy term, ensuring that no non-equilibrium phases are generated in the coating.

[0125] Calculate whether σ phase precipitates above 1000 °C:

[0126] ΔG σ =G HEA -G σ (7)

[0127] If ΔG σ >0, then σ phase may precipitate and the composition needs to be optimized.

[0128] Examples of composition optimization: Reduce the Cr content to avoid the precipitation of σ phase, control the Cr content within 15%-20% to reduce the formation of σ phase. Or optimize the Nb / Ti content to improve the high-temperature stability, increase the Nb content (>10%) to stabilize the high-entropy alloy structure. Or optimize the high-temperature phase transformation of the gradient coating, calculate the Gibbs free energy under different Ti / Nb combinations, and ensure that no brittle phases precipitate in the coating at 1200 °C.

[0129] Through the optimization process of Steps 3.2 - 3.5, it can be ensured that the bio-inspired gradient coating has excellent properties such as high bonding strength, low thermal stress, strong thermal stability, and high temperature resistance.

[0130] Step 4: Construct an atomic model reflecting the interface structures of the high-entropy alloy plastic layer, high-entropy alloy / high-entropy ceramic composite tough and strong layer, and nanocrystalline high-entropy ceramic superhard layer. Under the set temperature, pressure, and boundary conditions, perform molecular dynamics simulation on the atomic system to obtain data on the lattice and grain parameters, grain boundary width, and phase ratio varying with position. Based on the obtained data, determine a gradual transition scheme for the microstructure, enabling smooth connection between the layers of the coating in terms of lattice, grain, grain boundary, and phase structures.

[0131] The purpose of Step 4 is to construct an atomic-scale model through molecular dynamics simulation, deeply revealing the lattice structure, grain boundary distribution, and local changes in phase composition at each layer interface. Clearly define the microscopic transition characteristics at the interface, providing a basis for designing a microscopic structure scheme with smooth transition, thereby improving the bonding strength and crack resistance of the interface. This step lays the foundation for achieving continuous and smooth structural transition of the coating at the atomic level, reducing the weak bonding problems caused by microscopic discontinuities.

[0132] Specifically, Step 4 includes:

[0133] Step 4.1: Construct an atomic model. Step 4.1 includes:

[0134] Step 4.1.1: Material selection and crystal structure determination. According to the specific materials of the high-entropy alloy plastic layer, high-entropy alloy / high-entropy ceramic composite toughening layer, and nanocrystalline high-entropy ceramic superhard layer (such as CoCrFeNiTi and CoCrFeNi–TiC / NbC), obtain their respective lattice parameters, crystal structures, and defect information.

[0135] Step 4.1.2: Interface region model design. During the modeling process, construct atomic arrangement models between adjacent two layers (such as the high-entropy alloy plastic layer and the composite toughening layer, the composite toughening layer and the superhard layer), focusing on the atomic arrangement at the interface, the width of the interface transition zone, and possible grain boundary and phase mixing regions.

[0136] Refer to the attached Figure 4 , Figure 4 shows the distribution, size, and geometric shape of grain 9 and grain boundary 10 in the microscopic structure layer. In addition, in high-resolution microscopy techniques or other nano-scale analysis methods, the specific arrangement of the lattice and the microscopic structure can also be analyzed, and the influence laws of these microscopic structure factors on interface damage behaviors such as lattice distortion, stress concentration, and cracks inside the coating and at the interface can be analyzed through molecular dynamics simulation.

[0137] Step 4.1.3: Multi-scale model integration. For the local region, construct a representative atomic model using periodic boundary conditions, and at the same time, for the interface region, construct a larger-sized aperiodic model to fully capture local structural discontinuities and lattice distortion.

[0138] Step 4.2: Set molecular dynamics simulation parameters. Step 4.2 includes:

[0139] Step 4.2.1: Selection of potential function. Select an applicable interatomic potential (such as EAM potential, MEAM potential, or Tersoff potential) according to different material systems.

[0140] Step 4.2.2: Initial Conditions and Temperature Control. Set the initial atomic positions and velocities, obtain the equilibrium configuration of the system using the energy minimization method, and perform simulations under isothermal or temperature gradient conditions using the Nosé-Hoover temperature controller to simulate the actual preparation or heat treatment process.

[0141] Step 4.2.3: Simulation Steps and Time Scale. Refine the time step and determine the total number of simulation steps to capture the processes of interfacial atomic rearrangement, grain boundary diffusion, and local phase transformation. When necessary, use multi-step simulations (such as first energy minimization, then temperature increase, holding, and decrease simulations) to reflect the real process.

[0142] Step 4.3: Data Collection and Analysis. Step 4.3 includes:

[0143] Step 4.3.1: Analyze Atomic Arrangement and Local Structure. Use methods such as the radial distribution function (RDF), coordination number statistics, and atomic displacement vector analysis to quantitatively describe the continuity of atomic arrangement and the distribution of local defects in the interfacial region.

[0144] Step 4.3.2: Calculate Interfacial Energy and Bonding Strength. Calculate the total energy under different interfacial configurations, compare the interfacial energies of different schemes, and calculate the interfacial bonding strength using the interatomic interaction energy. Introduce the interfacial diffusion coefficient and atomic rearrangement rate as auxiliary indicators.

[0145] Step 4.3.3: Evaluate Lattice Matching Degree and Grain Boundary Width. Use local strain analysis and calculation of lattice misfit parameters to quantitatively evaluate the lattice matching degree at the interface. Further judge the rationality of the transition scheme by statistically analyzing the grain boundary width and the proportion of discontinuous regions.

[0146] Step 4.4: According to the data analysis results in Step 4.3, compare multiple schemes of the microstructures of different interface designs (such as the width of the transition zone, element gradient distribution), select the design scheme with the highest interfacial binding energy and the highest lattice matching degree, feedback the selected microstructure transition scheme to the overall coating design, adjust the atomic model parameters, conduct iterative simulations, and output the gradual transition scheme of the microstructure.

[0147] Step 5: Based on the actual process parameters and the temperature-dependent mechanical properties of each layer of material, establish a finite element model including the tool substrate and each layer of the gradient coating, apply thermo-mechanical coupling load conditions in the model, simulate the thermal cycle and mechanical load during the working process, obtain the distributions of stress, strain, and residual stress at the interface, analyze the stress concentration and deformation in the interfacial region, and determine an optimized interfacial mechanical property transition design scheme for adaptability.

[0148] The purpose of Step 5 is to evaluate the mechanical response of the coating interface at the macroscopic scale using finite element simulation, especially the stress, strain, and residual stress distributions under thermo-mechanical coupling. By simulating the mechanical behavior under actual working conditions, stress concentrations or weak areas that may occur at the interface are identified, and then the interface design is optimized to ensure good mechanical stability of the interface under load. This step provides quantitative analysis and verification of the mechanical properties for the coating design, ensuring the reliability and durability of the overall structure during long-term use.

[0149] Specifically, Step 5 includes:

[0150] Step 5.1: Establish a three-dimensional geometric model that includes the tool substrate and each layer of the gradient coating. Specifically divide the interface region to form independent interface elements or use bonded contact to clarify the transition region between different layers.

[0151] Each layer of the gradient coating includes a high-entropy alloy plastic layer, a high-entropy alloy / high-entropy ceramic composite toughening layer, and a nanocrystalline high-entropy ceramic superhard layer.

[0152] Step 5.2: Assign temperature-dependent elastic modulus, Poisson's ratio, coefficient of thermal expansion, yield strength, and other plastic parameters to each layer.

[0153] Step 5.3: Perform reasonable mesh division on the entire model. Use locally refined meshes in the interface and regions with high stress gradients. Through mesh independence verification, determine the optimal mesh size.

[0154] Regions with high stress gradients such as the junction between the coating and the substrate.

[0155] Step 5.4: Apply thermal loads according to the actual process flow, simulate the thermal cycle process, and form a temperature gradient.

[0156] Simulating the thermal cycle process includes heating, holding, and cooling.

[0157] Step 5.5: Apply fixed boundary conditions or appropriate constraints to the tool substrate, apply external forces or displacement loads under working conditions in the coating and interface regions, simulate the mechanical response generated during machining or use, and set the interface contact properties at the same time.

[0158] Interface contact properties such as slip, friction, or bond failure phenomena.

[0159] Step 5.6: Use a non-linear thermo-mechanical coupling solver to jointly solve the temperature field and stress field, and ensure convergence through step-by-step solution.

[0160] This step ensures that material non-linear behavior and large deformation effects are considered. Step-by-step solution such as performing thermal analysis first and then mechanical analysis, or coupled solution.

[0161] Step 5.7: Extract the stress, strain, residual stress, and local deformation data within the key areas, and use post-processing tools to generate distribution maps and local enlarged views of the temperature field and stress field to identify the stress concentration locations and magnitudes.

[0162] The key areas are mainly the interface areas between layers.

[0163] Step 5.8: Compare the maximum stress, strain distributions, and residual stress levels in the interface areas under different design schemes, establish stress concentration indicators, and use the constructed objective functions (such as minimizing the maximum interface stress, reducing the stress gradient, and decreasing the residual stress) as the optimization basis to analyze the effects of different interface contact parameters, layer thicknesses, and material transition zone designs on the objective functions.

[0164] Step 5.9: Conduct a sensitivity analysis of the key parameters to determine their influence on the mechanical response, propose a design improvement scheme based on the sensitivity analysis results, and optimize the interface transition design.

[0165] The key parameters include interface contact stiffness, interlayer coating thickness, temperature loading amplitude, etc.

[0166] Step 5.10: Conduct multi-condition simulation verification on the optimized design scheme, feedback the verification results into the model, and perform multiple iterative optimizations until an interface mechanical property transition scheme that meets the requirements of the actual working conditions is obtained.

[0167] The purpose of multi-condition simulation verification is to ensure that the interface can maintain low stress concentration and stable mechanical properties under different thermal and mechanical loads.

[0168] In Step 5.10, if there is experimental data support, the simulation results can also be compared with the experimental data, and the model parameters can be corrected if necessary.

[0169] Step 6: Preprocess and extract features from the obtained data on the material composition, microstructure, and mechanical properties of each layer to form a multi-dimensional data set. Use the BP neural network and particle swarm optimization algorithm to train and optimize the data, construct a mathematical model reflecting the interrelationships between the composition, structure, and properties of each layer of the gradient coating, and embed the mathematical model into the MATLAB mathematical modeling platform to achieve a gradual transition design between the tool substrate and the gradient coating and dynamically adjust the interface parameters.

[0170] The purpose of Step 6 is to integrate the aforementioned theoretical calculations and simulation data with the help of machine learning techniques such as BP neural network and particle swarm optimization algorithm, and establish a trinity collaborative regulation model of "composition entropy - structure entropy - performance entropy" for gradient coatings, that is, a collaborative regulation model reflecting the internal relationship among material composition, microstructure and mechanical properties. To achieve the precise design of the gradual transition between the tool substrate and the gradient coating, dynamically regulate the interface parameters, and optimize the overall interface bonding strength and toughness. This model provides theoretical support and optimization strategies for the multi-objective collaborative design of coatings through data-driven optimization methods, significantly improves the overall coating performance, and provides reference and improvement directions for the design of future similar systems.

[0171] Specifically, Step 6 includes:

[0172] Step 6.1: Collect the multi-scale data obtained in Steps 3 - 5, remove missing values, process outliers and perform normalization transformation on the original data. Use principal component analysis (PCA) or feature extraction technology to screen the key indicators related to interface bonding strength, toughness and overall performance, and construct an input data matrix as the basic data for the subsequent model.

[0173] The multi-scale data includes thermo-mechanical coupling simulation data, molecular dynamics simulation data, and finite element simulation data. Specifically, it includes but is not limited to: material composition, thickness of each layer and interface element distribution data; lattice parameters, grain boundary width, local defect information and atomic rearrangement data in the interface region; mechanical response data such as stress, strain, residual stress distribution, and stress concentration index at the interface.

[0174] Data cleaning and normalization of the original data can ensure that each variable is within the same order of magnitude, facilitating subsequent feature extraction and model training.

[0175] Step 6.2: Define composition entropy, structure entropy and performance entropy, and form a unified index system with the entropy values of composition entropy, structure entropy and performance entropy. Construct a multi-objective optimization model, and set the collaborative regulation objective as:

[0176] minJ = ω1·f(S c ) + ω2·f(S s ) + ω2·f(S p )

[0177] In the formula: ω1, ω2, ω2 are the weight factors of composition entropy, structure entropy and performance entropy respectively; f(S c ) represents the cost function of each index of composition entropy deviating from the ideal state; f(S s ) represents the cost function of each index of structure entropy deviating from the ideal state; f(S p ) represents the cost function of each index of performance entropy deviating from the ideal state.

[0178] The component entropy is used to reflect the uniformity and diversity of the composition and element distribution of each layer of materials, and the calculation formula can refer to the information entropy model. The structure entropy is used to describe the order degree of lattice matching, grain boundary width and defect distribution in the interface microstructure. The performance entropy is used to quantify the balance and robustness of the interface mechanical properties (such as interface bonding strength, residual stress, strain distribution).

[0179] The purpose of constructing the above entropy values into a unified index system is to characterize the overall collaborative regulation effect of the gradient coating.

[0180] Step 6.3: Select a multi-layer feedforward neural network as the basic model structure. The input layer receives the preprocessed composition, structure and performance data, and the output layer outputs the predicted values of the interface bonding strength and toughness. Set the number of hidden layers and nodes according to the data complexity, and adopt an activation function (such as ReLU or sigmoid) to improve the non-linear mapping ability of the model.

[0181] Step 6.4: Use the error backpropagation algorithm (BP algorithm) to train the network. Set the loss function as the mean square error or other appropriate metrics, and adopt the cross-validation method to evaluate the generalization ability of the model. Adjust the network parameters until both the training error and the validation error reach the expected requirements. Use the MATLAB mathematical modeling platform to implement the training process and generate training reports and performance evaluation charts.

[0182] Step 6.5: Use the particle swarm optimization (PSO) algorithm to globally search for the weights, biases and hyperparameters of the BP neural network to further reduce the training error.

[0183] The process of globally searching for the weights, biases and hyperparameters of the BP neural network by the particle swarm optimization (PSO) algorithm includes: initializing a certain number of particles (each particle represents a set of network parameters), defining the fitness function as the prediction error of the network on the validation set, and updating the velocity and position of each particle, iteratively searching until convergence, finally obtaining the optimal parameter set and applying it to the BP neural network model to form a comprehensively optimized collaborative regulation model.

[0184] Step 6.6: Integrate the "component entropy - structure entropy - performance entropy" collaborative regulation model after data preprocessing, BP neural network training and PSO tuning into the MATLAB platform, establish a systematic simulation calculation framework, use historical data and newly generated simulation data to verify the model, make the model prediction results coincide with the actual performance indicators, analyze the prediction error and sensitivity, and feedback to adjust the feature engineering, network structure and optimization parameters to form a closed-loop improvement process.

[0185] After the systematic simulation calculation framework is established, it can be used to predict the collaborative performance under different coating design schemes and guide the adjustment of actual process parameters. According to the real-time data obtained during the actual processing and use, the training data set is continuously updated, and the online learning mechanism is used to dynamically correct the model to ensure long-term stability and adaptability.

[0186] The above are only the embodiments of the present invention, and common knowledge such as specific structures and characteristics known in the solution is not described in detail here. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A cross-scale bionic gradient coating design method based on a multi-principal element high entropy system, characterized in that: The method comprises: Step 1: forming a high entropy alloy plastic layer, a high entropy alloy / high entropy ceramic tough layer, and a nanocrystalline high entropy ceramic superhard layer on the surface of the tool substrate in sequence to construct a bionic gradient coating with a gradient transition structure; Step 2: Determine the material composition of the tool substrate and each layer of the gradient coating, so that the material of the high entropy alloy plastic layer in the gradient coating matches the high entropy alloy bonding phase in the tool substrate, select the material of the nanocrystalline high entropy ceramic superhard layer according to the target processing object of the tool, and determine the material of the high entropy alloy / high entropy ceramic composite tough layer based on the material of the high entropy alloy plastic layer and the material of the nanocrystalline high entropy ceramic superhard layer; Step 3: Analyze the interface performance of the bionic gradient coating using a thermal-mechanical coupling calculation method. According to the calculation and analysis results, optimize the gradient transition design of the interface components and determine the gradual transition scheme of the interface material components. Step 4: Construct an atomic model that reflects the interface structure of the high-entropy alloy plastic layer, the high-entropy alloy / high-entropy ceramic composite tough layer, and the nanocrystalline high-entropy ceramic superhard layer. Perform molecular dynamics simulation on the atomic system under set temperature, pressure, and boundary conditions to obtain data on the changes in lattice and grain parameters, grain boundary width, and phase ratio with position. Based on the obtained data, determine a gradual transition scheme for the microstructure so that the coating layers can be smoothly connected in terms of lattice, grain, grain boundary, and phase structure. Step 5: According to the actual process parameters and the temperature-dependent mechanical properties of each layer of material, a finite element model including the tool substrate and each layer of the gradient coating is established. Thermal-mechanical coupling load conditions are applied to the model to simulate the thermal cycle and mechanical load during the working process, and the distribution of stress, strain and residual stress at the interface is obtained. The stress concentration and deformation in the interface area are analyzed to determine an adaptively optimized interface mechanical property transition design scheme; Step 6: Preprocess and extract features of the obtained material composition, microstructure and mechanical property data of each layer to form a multidimensional data set. Use BP neural network and particle swarm optimization algorithm to train and optimize the data, and construct a mathematical model that reflects the correlation between the composition, structure and performance of each layer of the gradient coating. The mathematical model is embedded in the MATLAB mathematical modeling platform to realize the gradual transition design between the tool substrate and the gradient coating, and dynamically control the interface parameters.

2. The cross-scale bionic gradient coating design method based on a multi-principal element high entropy system according to claim 1 is characterized in that: In step 2, the material of the tool substrate includes but is not limited to any one of TiC-CrFeCoNi, Ti(C,N)-CoCrCuFeNi, and TiC-CrMnFeCoNi.

3. The cross-scale bionic gradient coating design method based on a multi-principal element high entropy system according to claim 1 is characterized in that: In step 2, the material of the high entropy alloy plastic layer includes but is not limited to any one of CrFeCoNi, CoCrCuFeNi, and CrMnFeCoNi; the material of the nanocrystalline high entropy ceramic superhard layer includes but is not limited to any one of AlTiSiN, (TiZrNbTaMo)C / N, and (TiAlTaCrZr)N; the material of the high entropy alloy / high entropy ceramic composite tough layer includes but is not limited to any one of (CrFeCoNi)-AlTiSiN and (CoCrCuFeNi)-(TiZrNbTaMo)C.

4. The cross-scale bionic gradient coating design method based on a multi-principal element high entropy system according to claim 1 is characterized in that: Step 3 includes: Step 3.1: Select a database suitable for the bionic gradient coating in the Thermo-Calc software, establish a bionic gradient coating structure model, and set the required simulation environment parameters; Step 3.2: Calculate the interface binding energy of the substrate, high entropy alloy plastic layer, high entropy alloy / high entropy ceramic tough layer, and nanocrystalline high entropy ceramic superhard layer, and adjust the composition of the interface material according to the calculation results; Step 3.3: Under the conditions of thermal cycling and mechanical loading, calculate the interfacial residual stress of each layer of the bionic gradient coating; Step 3.4: Calculate the difference in thermal expansion coefficient between the substrate and the high entropy alloy plastic layer in the bionic gradient coating, and between adjacent layers in the bionic gradient coating, and optimize the interface thermal matching accordingly; Step 3.5: Calculate the phase stability between the substrate and the bionic gradient coating under high temperature environment.

5. The cross-scale bionic gradient coating design method based on multi-principal element high entropy system according to claim 1 is characterized in that: In step 2, the material of the tool substrate includes a composite high entropy alloy bonding phase and a ceramic hard phase, wherein the high entropy alloy bonding phase in the tool substrate material is the same as the high entropy alloy bonding phase in the material of the high entropy alloy plastic layer.

6. The cross-scale bionic gradient coating design method based on multi-principal element high entropy system according to claim 1 is characterized in that: Step 5 includes: Step 5.1: Establish a three-dimensional geometric model including the tool substrate and each layer of the gradient coating, divide the interface area specifically, form independent interface units or use bonding contact to clarify the transition area between different layers; Step 5.2: Assign temperature-dependent elastic modulus, Poisson's ratio, thermal expansion coefficient, yield strength and other plastic parameters to each layer; Step 5.3: Perform reasonable meshing of the entire model, use local mesh refinement at the interface and in areas with high stress gradients, and determine the optimal mesh size through mesh independence verification; Step 5.4: According to the actual process flow, apply heat load, simulate the thermal cycle process, and form a temperature gradient; Step 5.5: Apply fixed boundary conditions or appropriate constraints to the tool substrate, apply external forces or displacement loads under working conditions to the coating and interface areas, simulate the mechanical response generated during processing or use, and set the interface contact properties; Step 5.6: Use a nonlinear thermal-mechanical coupling solver to jointly solve the temperature field and the stress field, and solve the problem step by step to ensure convergence; Step 5.7: Extract stress, strain, residual stress and local deformation data in the key area, use post-processing tools to generate temperature field, stress field distribution map and local magnification map, and clarify the stress concentration location and amplitude; Step 5.8: Compare the maximum stress, strain distribution and residual stress level of the interface area under different design schemes, establish the stress concentration index, and analyze the influence of different interface contact parameters, layer thickness and material transition zone design on the objective function by constructing the objective function as the optimization basis; Step 5.9: Conduct sensitivity analysis on key parameters to determine their influence on mechanical response, propose design improvement schemes based on the sensitivity analysis results, and optimize the interface transition design; Step 5.10: Perform multi-operating condition simulation verification on the optimized design scheme, feed the verification results back to the model, and perform multiple iterative optimizations until an interface mechanical property transition scheme that meets the actual operating conditions is obtained.

7. The cross-scale bionic gradient coating design method based on multi-principal element high entropy system according to claim 1 is characterized in that: Step 6 includes: Step 6.1: Collect the multi-scale data obtained in steps 3-5, remove missing values, process outliers and normalize the original data, use principal component analysis or feature extraction technology to screen key indicators related to interface bonding strength, toughness and overall performance, and construct an input data matrix as the basic data for subsequent models; Step 6.2: Define component entropy, structural entropy and performance entropy, and use the entropy values ​​of component entropy, structural entropy and performance entropy to form a unified indicator system, build a multi-objective optimization model, and set collaborative control goals. Step 6.3: Select a multi-layer feedforward neural network as the basic model structure. The input layer receives the preprocessed composition, structure and performance data, and the output layer outputs the predicted values ​​of interface bonding strength and toughness. The number of hidden layers and nodes is set according to the data complexity, and the activation function is used to improve the nonlinear mapping ability of the model. Step 6.4: Use the error back propagation algorithm to train the network, set the loss function to mean square error or other appropriate indicators, use cross-validation to evaluate the generalization ability of the model, adjust the network parameters until the training error and validation error meet the expected requirements, use the MATLAB mathematical modeling platform to implement the training process, and generate a training report and performance evaluation chart; Step 6.5: Use the PSO algorithm to perform a global search on the weights, biases, and hyperparameters of the BP neural network to further reduce the training error; Step 6.6: Integrate the "component entropy-structure entropy-performance entropy" collaborative control model after data preprocessing, BP neural network training and PSO tuning into the MATLAB platform, establish a systematic simulation calculation framework, and use historical data and newly generated simulation data to verify the model so that the model prediction results are consistent with the actual performance indicators, analyze the prediction error and sensitivity, and feedback to adjust the feature engineering, network structure and optimization parameters to form a closed-loop improvement process.

8. The cross-scale bionic gradient coating design method based on multi-principal element high entropy system according to claim 1 is characterized in that: In step 4, the process of generating the optimal microstructure gradual transition scheme includes: first, according to the crystal structure, lattice parameters and defect information of the selected high-entropy alloy plastic layer, high-entropy alloy / high-entropy ceramic composite tough layer and nanocrystalline high-entropy ceramic superhard layer, an atomic model that meets the requirements of periodic and non-periodic boundary conditions is constructed, and the atomic arrangement and transition zone in the interface area are designed with emphasis; then, according to the characteristics of each material system, a suitable interatomic potential is selected, the initial atomic position and velocity are set, and the system equilibrium configuration is obtained by the energy minimization method, and then the Nosé-Hoover temperature controller is used to perform constant temperature or temperature gradient control under simulated real heat treatment conditions, and the interface atomic rearrangement, grain boundary diffusion and local phase change phenomena are captured by refining the time step and multi-step simulation; finally, the radial distribution function, coordination number statistics and local strain analysis methods are used to collect and quantitatively evaluate the simulation data, calculate the interface energy, bonding strength and lattice matching, and determine the optimal microstructure gradual transition scheme.

9. The cross-scale bionic gradient coating design method based on multi-principal element high entropy system according to claim 7, characterized in that: In step 6.5, the process of global search for the weights, biases and hyperparameters of the BP neural network through the PSO algorithm includes: initializing a certain number of particles, defining the fitness function as the prediction error of the network on the validation set, and updating the speed and position of each particle, iteratively searching until convergence, and finally obtaining the optimal parameter group, and applying it to the BP neural network model to form a comprehensive optimized collaborative control model.

10. The cross-scale bionic gradient coating design method based on multi-principal element high entropy system according to claim 7, characterized in that: In step 6.2, composition entropy is used to reflect the uniformity and diversity of the material composition and element distribution of each layer. The calculation formula can refer to the information entropy model; structural entropy is used to describe the order of lattice matching, grain boundary width and defect distribution in the interface microstructure; performance entropy is used to quantify the balance and robustness of the interface mechanical properties.

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