Preparation method of lead-bismuth reactor nuclear sampler coating and coating

Abstract

The invention provides a lead-bismuth reactor nuclear sampler coating and a preparation method thereof.The preparation method comprises the steps that S3, according to the thickness of a CrAl coating, the bias voltage and target current of multi-arc ion plating equipment are adjusted, the bonding strength of the coating and a base body is optimized, and the target adhesive force reaches the scratch test critical load or above; s4, according to the bonding strength, the nitrogen flow and the air pressure are adjusted, a compact CrAl coating microstructure with the porosity lower than a porosity threshold value is generated, and the quality of the microstructure is detected and verified through a scanning electron microscope; s7, according to the thickness consistency, a screw rod or a pin shaft is selected as a base body part, a CrAl coating is deposited on the surface of the screw rod or the pin shaft to be matched with the sleeve cap, and the gap between the sleeve cap and the shaft part is controlled to be within a gap threshold value; and S10, according to hardness and wear resistance test results, the deposition time and bias voltage are adjusted, the performance of the CrAl coating and the performance of the TiN coating are optimized, and the requirements for long-term corrosion resistance and wear resistance in the lead-bismuth alloy environment are met. According to the invention, excellent performances of long-term corrosion resistance and wear resistance in a high-temperature lead-bismuth environment are realized, and the service life of parts is remarkably prolonged.

Description

Technical Field

[0001] This invention relates to the field of lead-bismuth core sampling technology, and in particular to a method for preparing a coating for a lead-bismuth core sampler and the coating itself. Background Technology

[0002] Screws or pins used in high-temperature lead-bismuth alloy environments face severe corrosion and wear problems. To improve their corrosion resistance, a CrAl coating needs to be deposited on the surface of a 316L stainless steel substrate. However, the composition, thickness, and structure of the coating significantly affect performance. How to precisely control parameters such as bias voltage, nitrogen flow rate, target current, and gas pressure during multi-arc ion plating to obtain the optimal CrAl coating composition and microstructure? There are contradictions between coating thickness and performance indicators such as uniformity, bonding strength, and surface hardness, which need to be balanced by adjusting process parameters. In addition, when screws or pins are used in conjunction with caps, the selection of surface coatings and gap control of both are also crucial. How to optimize the bonding strength between the coating and the substrate while ensuring the corrosion resistance of the coating and ensuring a good fit with the cap requires comprehensive consideration of multiple factors such as coating composition, thickness, and hardness, seeking a balance between process parameter optimization and structural design. Summary of the Invention

[0003] In a first aspect, the present invention provides a method for preparing a coating for a lead-bismuth pile core sampler, the method comprising: S1, a CrAl coating is deposited on the substrate surface using a multi-arc ion plating device. The CrAl coating is obtained by adjusting the ratio of Cr target material to Al target material and the target current. S2, configure the parameters of the multi-arc ion plating equipment: bias voltage 30V to 100V, nitrogen flow rate 300sccm to 600sccm, target current 50A to 150A, gas pressure 0.5Pa to 3Pa, deposition time 0.5h to 3h, to generate a CrAl coating with a thickness of 500nm to 5μm. S3, adjust the bias voltage and target current of the multi-arc ion plating equipment according to the thickness of the CrAl coating, optimize the bonding strength between the coating and the substrate, and achieve the target adhesion above the critical load of the scratch test. S4. Based on the bonding strength, the nitrogen flow rate and pressure are adjusted to generate a dense CrAl coating microstructure with a porosity lower than the porosity threshold. The quality of the microstructure is verified by scanning electron microscopy. S5, based on the dense CrAl coating microstructure, generates a surface oxide layer Al2O3 or Cr2O3 in a high-temperature lead-bismuth alloy environment of 500°C to 700°C, with the wetting angle reaching above the wetting angle threshold, thereby reducing the adhesion of lead-bismuth alloy. S6. Based on the characteristics of the oxide layer, the thickness of the CrAl coating was detected to confirm that the uniformity was within the range of 500nm to 5μm. Elliptic polarization spectroscopy or cross-sectional microscopy was used for measurement, and the deposition time was adjusted to optimize the thickness consistency. S7. Based on thickness consistency, a screw or pin is selected as the base component, and a CrAl coating is deposited on its surface. It is used in conjunction with a sleeve to control the gap between the sleeve and the shaft part within the gap threshold. S8. Based on the gap control results, deposit a TiN wear-resistant coating on the cap surface with a thickness of 1μm to 10μm. Use a multi-arc ion plating equipment with a bias voltage of 50V to 80V and a nitrogen flow rate of 400sccm to 500sccm to deposit on the substrate surface. S9. Based on the characteristics of TiN wear-resistant coating, the surface hardness and wear resistance of CrAl coating and TiN coating were tested. S10, based on the hardness and wear resistance test results, adjusts the deposition time and bias voltage to optimize the performance of CrAl and TiN coatings, meeting the long-term corrosion resistance and wear resistance requirements in lead-bismuth alloy environments.

[0004] Optionally, the base material is 316L stainless steel.

[0005] Optionally, the Cr content in the CrAl coating is controlled between 30wt% and 80wt%.

[0006] Optionally, step S1, which involves depositing a CrAl coating on the substrate surface using a multi-arc ion plating apparatus and obtaining the CrAl coating by adjusting the ratio of Cr and Al targets and the target current, further includes: Step S11: Obtain surface property data of the substrate material, use atomic force microscopy to detect the surface roughness of the substrate, and use X-ray photoelectron spectroscopy to detect the chemical composition to obtain the surface state parameters of the substrate. Step S12: Extract roughness and composition values ​​from the surface state parameters of the substrate, input them into the multi-arc ion plating equipment, configure the preset deposition process parameters, adjust the ratio of Cr target to Al target to 1:1 and the target current to 50 amperes, and obtain the initial CrAl coating deposition scheme. Step S13: For the initial CrAl coating deposition scheme, perform simulation analysis, calculate the Cr content, and determine whether the Cr content is within the preset content range. If the Cr content exceeds the preset content range, adjust the target material ratio to 2:1 and the target current to 60 amperes to obtain the optimized deposition scheme. Step S14: Using a multi-arc ion plating equipment, a CrAl coating is deposited on the substrate surface according to the optimized deposition scheme to obtain a CrAl coating sample. Step S15: Obtain surface morphology and composition distribution data from the CrAl coating sample. Use a scanning electron microscope to detect the morphology and an energy dispersive X-ray spectrometer to detect the composition distribution. Determine whether the coating uniformity meets the requirements. If the coating uniformity is lower than the preset uniformity threshold, adjust the deposition process parameters to a target current of 70 amperes and obtain the coating sample again. Step S16: Test the hardness of the coating sample using a Vickers hardness tester, conduct a friction and wear test, obtain mechanical property data, and determine whether the hardness reaches the predetermined standard of 1500 Vickers hardness and friction coefficient is less than 0.5. If the performance does not meet the standard, optimize the target material ratio to 3:1 and the deposition parameters to a bias voltage of 100 volts, and obtain the coating sample again. Step S17: Extract features from Cr content, target ratio, target current and coating performance data, input current parameters, predict coating performance, and obtain optimized deposition process parameter configuration.

[0007] Optionally, the preset content range is a Cr content of 20% to 30%.

[0008] Optionally, step S2, configuring the parameters of the multi-arc ion plating equipment to generate a CrAl coating with a thickness of 500 nm to 5 μm, includes configuring the multi-arc ion plating equipment parameters as follows: bias voltage 30 V to 100 V, nitrogen flow rate 300 sccm to 600 sccm, target current 50 A to 150 A, gas pressure 0.5 Pa to 3 Pa, and deposition time 0.5 h to 3 h.

[0009] Optionally, step S5, based on the dense CrAl coating microstructure, generates a surface oxide layer Al2O3 or Cr2O3 in a high-temperature lead-bismuth alloy environment of 500°C to 700°C, with a wetting angle reaching or exceeding the wetting angle threshold, thereby reducing lead-bismuth alloy adhesion, including: Step S51: Obtain microstructure data of dense CrAl coating, and use scanning electron microscopy and X-ray diffraction to determine the grain size and phase composition of the coating surface, and obtain microstructure characteristic parameters. Step S52: Based on the microstructure characteristic parameters, perform molecular dynamics simulation to calculate the surface energy change of the CrAl coating in a high-temperature lead-bismuth alloy environment and determine the energy conditions for the formation of the surface oxide layer. Step S53: Based on the energy conditions for the formation of the surface oxide layer, perform thermodynamic calculations to predict the probability of Al2O3 or Cr2O3 oxide layer formation and obtain the oxide layer composition distribution. Step S54: Calculate the wetting angle change based on the oxide layer composition distribution, determine whether the wetting angle is greater than the wetting angle threshold, and obtain wetting angle characteristic data; Step S55: Based on the wetting angle characteristic data, calculate the surface free energy, analyze the adhesion force of the lead-bismuth alloy on the oxide layer, and determine the adhesion reduction effect parameters. Step S56: Based on the adhesion reduction effect parameters, perform finite element simulation to simulate the stability of the oxide layer in the high-temperature lead-bismuth alloy environment and obtain oxide layer durability data. Step S57: Based on the oxide layer durability data, predict the long-term performance of the coating under different temperature conditions to obtain performance optimization parameters.

[0010] Optionally, the wetting angle threshold is set to 120°.

[0011] Optionally, step S7 involves selecting a screw or pin as the base component based on thickness consistency, depositing a CrAl coating on its surface, and cooperating with a sleeve cap to control the gap between the sleeve cap and the shaft part within a gap threshold, including setting the gap threshold to 0.01 mm to 0.5 mm.

[0012] In a second aspect, the present invention provides a coating for a lead-bismuth pile core sampler, which is prepared by the method described above.

[0013] The technical solution of the present invention has the following beneficial effects: This invention discloses a method for preparing a coating for a lead-bismuth reactor core sampler and the coating itself. It addresses the corrosion resistance and wear resistance requirements of the screw or pin and the sleeve cap mating parts in a high-temperature lead-bismuth alloy environment, and solves the problems of low coating bonding strength, high porosity, insufficient wettability of the oxide layer, and difficulty in gap control. This invention utilizes a multi-arc ion plating apparatus to precisely control the Cr content (30wt% to 80wt%), bias voltage (30V to 100V), target current (50A to 150A), nitrogen flow rate (300sccm to 600sccm), gas pressure (0.5Pa to 3Pa), and deposition time (0.5h to 3h) of the CrAl coating. This results in a dense CrAl coating with a thickness of 500nm to 5μm, adhesion exceeding 30N, and porosity below 5%. An Al2O3 or Cr2O3 oxide layer is formed on the surface, achieving a wetting angle of over 120° and reducing lead-bismuth adhesion. Furthermore, a TiN coating with a thickness of 1μm to 10μm is deposited on the cap surface, and the bias voltage (50V to 80V) and nitrogen flow rate (400sccm to 500sccm) are optimized to achieve wear resistance with a hardness exceeding 20GPa and a friction coefficient below 0.4. This invention ensures coating thickness uniformity through elliptic polarization spectroscopy and cross-sectional microscopy, and controls the gap between the screw and the cap to be between 0.01 mm and 0.5 mm, ultimately achieving excellent long-term corrosion resistance and wear resistance in high-temperature lead-bismuth environments, significantly improving the service life of components. Attached Figure Description

[0014] Figure 1This is a flowchart illustrating a method for preparing a coating for a lead-bismuth core sampler according to the present invention.

[0015] Figure 2 This is a schematic diagram of a method for preparing a coating for a lead-bismuth core sampler according to the present invention.

[0016] Figure 3 This is another schematic diagram of a method for preparing a coating for a lead-bismuth pile core sampler according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0018] like Figures 1-3 As shown, in a first aspect, the present invention provides a method for preparing a coating for a lead-bismuth pile core sampler, which may specifically include: S1 uses 316L stainless steel as the base material and employs a multi-arc ion plating equipment to deposit a CrAl coating on the base surface. The Cr content is controlled between 30wt% and 80wt%. The CrAl coating is obtained by adjusting the ratio of Cr target material and Al target material and the target current.

[0019] Optionally, this step also includes: Step S11: Obtain surface characteristic data of the 316L stainless steel substrate material, use atomic force microscopy to detect the surface roughness of the substrate, and use X-ray photoelectron spectroscopy to detect the chemical composition to obtain the surface state parameters of the substrate.

[0020] Step S12: Extract roughness and composition values ​​from the substrate surface state parameters, input them into the multi-arc ion plating equipment, configure the preset deposition process parameters, adjust the ratio of Cr target to Al target to 1:1 and the target current to 50 amperes, and obtain the initial CrAl coating deposition scheme.

[0021] Step S13: For the initial CrAl coating deposition scheme, use SRIM software to perform simulation analysis, calculate the Cr content, and determine whether the Cr content is within the preset range of 20% to 30%. If the Cr content exceeds this range, adjust the target ratio to 2:1 and the target current to 60 amperes to obtain the optimized deposition scheme.

[0022] Step S14: Using a multi-arc ion plating device, a CrAl coating is deposited on the substrate surface according to the optimized deposition scheme to obtain a CrAl coating sample.

[0023] Step S15: Obtain surface morphology and composition distribution data from the CrAl coating sample. Use a scanning electron microscope to detect the morphology and an energy dispersive X-ray spectrometer to detect the composition distribution. Determine whether the coating uniformity meets the requirements. If the coating uniformity is lower than the preset uniformity threshold of 0.9, adjust the deposition process parameters to a target current of 70 amperes and obtain the coating sample again.

[0024] Step S16: Test the hardness of the coating sample using a Vickers hardness tester, and conduct a friction and wear test using a ball-and-disc friction and wear tester to obtain mechanical property data. Determine whether the hardness reaches the predetermined standard of 1500 Vickers hardness and the coefficient of friction is less than 0.5. If the performance does not meet the standard, optimize the target material ratio to 3:1 and the deposition parameters to a bias voltage of 100 volts, and obtain a new coating sample.

[0025] Step S17: Extract features from Cr content, target ratio, target current and coating performance data, train the model using random forest regression algorithm, input the current parameters, predict the coating performance, and obtain the optimized deposition process parameter configuration.

[0026] For example, when using atomic force microscopy (AFM) to inspect the surface roughness of a 316L stainless steel substrate, high-resolution data on the surface morphology can be obtained. AFM scans the surface with a probe, measuring surface height variations from micrometers to nanometers, generating a three-dimensional morphology image. Assuming the test results show a surface roughness Ra of 0.05 micrometers, the surface is relatively smooth, which is beneficial for subsequent coating adhesion. X-ray photoelectron spectroscopy (XPS) is used to analyze the surface chemical composition. The results may show that the main elements are iron, chromium, and nickel, accounting for 60%, 18%, and 10% respectively, accompanied by a small amount of oxygen, indicating the presence of a thin oxide layer on the surface. These parameters provide fundamental data for multi-arc ion plating processes, ensuring targeted coating design.

[0027] In one possible implementation, the multi-arc ion plating equipment is configured with a 1:1 ratio of Cr to Al targets and a target current of 50 amperes. The initial deposition scheme generates Cr and Al plasma through arc discharge, depositing a CrAl coating. SRIM software is used to simulate and analyze the Cr content in the coating. The simulation results show a Cr content of 35%, exceeding the target range of 20% to 30%. Therefore, the target ratio is adjusted to 2:1, and the target current is increased to 60 amperes to optimize the deposition scheme. After adjustment, the simulated Cr content is 25%, which meets the requirements. This optimization, by controlling the target ratio and current, balances the deposition rates of Cr and Al, improving the precision of coating composition control.

[0028] Specifically, after implementing the optimized deposition scheme, the surface morphology of the CrAl coating sample was observed using scanning electron microscopy. The results showed a dense coating surface with a particle size of approximately 0.1 micrometers. Energy-dispersive X-ray spectroscopy revealed a Cr content of 26% and a uniformity of 0.92, meeting the preset uniformity threshold of 0.9. This uniformity ensures stable coating performance and reduces the risk of failure due to localized defects. If the uniformity falls below 0.9, for example, 0.85, the target current is adjusted to 70 amperes, and redeposition is performed to increase the plasma density and improve coating uniformity.

[0029] For example, in mechanical property testing, the Vickers hardness tester measured the coating hardness at 1400 Vickers, failing to meet the target of 1500 Vickers. The ball-and-disc friction wear test showed a friction coefficient of 0.55, exceeding the standard of 0.5. Therefore, the target ratio was optimized to 3:1, the bias voltage was increased to 100 volts, and redeposition was performed. After optimization, the hardness reached 1600 Vickers, and the friction coefficient decreased to 0.45, meeting the requirements. The increased bias voltage enhanced the ion bombardment effect, promoted coating densification, and improved mechanical properties.

[0030] In one embodiment, features are extracted from Cr content, target ratio, target current, and coating performance data, and a random forest regression algorithm is used to train the model. Assuming the input parameters are Cr content 25%, target ratio 2:1, and target current 60 amperes, the model predicts a hardness of 1550 Vickers and a coefficient of friction of 0.48. By analyzing the nonlinear relationships between features, the model optimizes deposition parameters and predicts better process configurations, such as a target ratio of 2.5:1 and a target current of 65 amperes. This method improves process efficiency, reduces experimental costs, ensures stable coating performance, and is suitable for the preparation of high-performance coatings.

[0031] S2, configure the multi-arc ion plating equipment parameters: bias voltage 30V to 100V, nitrogen flow rate 300sccm to 600sccm, target current 50A to 150A, gas pressure 0.5Pa to 3Pa, deposition time 0.5h to 3h, to generate a CrAl coating with a thickness of 500nm to 5μm.

[0032] Optionally, this step also includes: Step S21: Obtain initial process parameters, including bias voltage, nitrogen flow rate, target current, gas pressure and deposition time, through multi-arc ion plating equipment, and generate a parameter set.

[0033] Step S22: The parameter set is classified using the support vector machine algorithm. Based on historical deposition data, the influence of each parameter combination on the thickness of the CrAl coating is determined, and the optimal parameter combination is obtained.

[0034] Step S23: Extract the bias range, nitrogen flow rate and target current value from the optimized parameter combination, adjust the equipment configuration, and generate real-time process control instructions.

[0035] Step S24: If the air pressure value in the real-time process control command exceeds the preset air pressure threshold range, the air pressure is adjusted by the PID controller to regulate the valve and obtain a stable air pressure environment.

[0036] Preferably, the pressure adjustment value is calculated using the following formula: , in, This is the air pressure adjustment value. It is proportional gain. It is the error, equal to the target air pressure minus the current air pressure. It is integral gain. It is an error Integral over time, It is the differential gain. It is the derivative of error e with respect to time.

[0037] Step S25: Based on a stable air pressure environment and optimized parameter combination, perform the deposition process, monitor the coating thickness, and obtain real-time thickness data.

[0038] Step S26: By comparing the real-time thickness data with the target thickness of 500nm to 5000nm, if the deviation exceeds the preset thickness deviation threshold, the deposition time is adjusted to obtain a CrAl coating that meets the requirements.

[0039] For example, when obtaining initial process parameters using a multi-arc ion plating device, a parameter set can first be set based on empirical values. In one possible implementation, the bias voltage is set to 50 to 150 V, the nitrogen flow rate is controlled at 20 to 50 sccm, the target current range is 40 to 80 A, the gas pressure is maintained at 0.5 to 2.0 Pa, and the deposition time is 30 to 120 minutes. These parameters are input through the device control panel to form the initial parameter set.

[0040] It should be noted that the selection of parameters must be combined with the characteristics of the 316L stainless steel base material to ensure coating adhesion and quality.

[0041] For example, a lower bias voltage of 50V can reduce the damage to the substrate surface caused by ion bombardment, while a nitrogen flow rate of 30 sccm can ensure the formation of nitrides in the CrAl coating and improve hardness.

[0042] In one embodiment, when classifying the parameter set using a support vector machine algorithm, a model can be built based on historical data. This historical data includes coating thickness data under different bias voltages, gas pressures, and target currents, such as a thickness of 3000 nm at a bias voltage of 100 V, a gas pressure of 1.0 Pa, and a target current of 60 A. The algorithm distinguishes whether the thickness reaches the target range of 500 to 5000 nm based on classification boundaries.

[0043] For example, parameter combinations with a thickness less than 500 nm are marked as unacceptable, and the model outputs optimized combinations, such as a bias voltage of 120 V, a gas pressure of 1.2 Pa, and a target current of 70 A. This method can quickly screen out efficient parameters and improve deposition efficiency.

[0044] For example, after a real-time process control command is generated, if the air pressure exceeds the preset range of 0.8 to 1.5 Pa, it needs to be adjusted via a PID controller. Assuming the target air pressure is 1.0 Pa, the current air pressure is 1.8 Pa, and the error e is -0.8 Pa. The proportional term adjusts the valve opening through proportional gain, the integral term accumulates the error to eliminate steady-state deviation, and the derivative term predicts the air pressure change trend.

[0045] For example, proportional gain Set to 0.5, integral gain The differential gain is 0.1. The output value is 0.05, and the valve opening is controlled by the output u to stabilize the air pressure at 1.0 Pa. This method ensures a stable deposition environment and improves coating uniformity.

[0046] In one embodiment, real-time monitoring of the coating thickness during the deposition process can be achieved using a laser interferometer.

[0047] For example, if the target thickness is set to 4000 nm, and real-time data shows a thickness of 3800 nm, the deviation is 200 nm, exceeding the thickness deviation threshold of 100 nm. In this case, extending the deposition time by 10 minutes and re-measuring the thickness results in 3950 nm, meeting the requirements. This real-time adjustment effectively controls the coating thickness and reduces the scrap rate.

[0048] For example, in business scenarios involving CrAl coatings, optimized parameter combinations can be extended from the core solution to various implementation methods. In the core solution, a bias voltage of 120V, a nitrogen flow rate of 40sccm, and a target current of 70A are suitable for standard 316L substrates. In the extended solution, if the substrate surface roughness is high, the bias voltage can be increased to 150V to enhance adhesion, or the nitrogen flow rate can be reduced to 25sccm to reduce excessive nitride formation. These solutions, through multi-faceted adjustments, collectively ensure that the coating thickness remains stable within the target range, improving process robustness.

[0049] S3, based on the CrAl coating thickness, adjust the bias voltage and target current of the multi-arc ion plating equipment to optimize the bonding strength between the coating and the 316L stainless steel substrate, and achieve a target adhesion that reaches a critical load of over 30N in the scratch test.

[0050] Optionally, this step also includes: Step S31: Obtain CrAl coating thickness data, initial bias voltage and target current parameters through coating deposition equipment, and determine the current process parameter combination.

[0051] Step S32: If the CrAl coating thickness is lower than the preset coating thickness threshold, the target current is increased through the control panel of the deposition equipment and the adjusted parameter values ​​are recorded to obtain the updated coating thickness data.

[0052] Step S33: The adhesion of the CrAl coating on the 316L stainless steel substrate is tested using a scratch testing device to obtain the critical load value and determine whether the test result meets the preset adhesion threshold.

[0053] Step S34: If the critical load value is lower than the preset adhesion threshold, adjust the substrate surface treatment process to obtain improved surface state data.

[0054] Step S35: Based on the updated coating thickness data and improved surface condition data, the random forest regression function in Python's Scikit-learn library is used to optimize the bias voltage and target current parameters, and the optimized parameter combination is determined.

[0055] Step S36: Apply the optimized parameter combination to deposit the CrAl coating using the deposition equipment, and perform a scratch test again to obtain the final critical load value and determine whether the coating adhesion has reached the target.

[0056] For example, when acquiring CrAl coating thickness data using a coating deposition equipment, an optical thickness gauge can be used to monitor thickness changes in real time during the deposition process. Assuming initial process parameters are a bias voltage of 50V and a target current of 80A, and the measured coating thickness is 300nm, which is lower than the preset coating thickness threshold of 500nm, the operator can gradually increase the target current from 80A to 100A via the deposition equipment control panel, in increments of 10A, while simultaneously recording the thickness data after each adjustment.

[0057] For example, adjusting to 90A increases the thickness to 400nm; adjusting to 100A achieves a thickness of 520nm, meeting the requirements. This method ensures controllable and stable thickness data by gradually adjusting the target current, avoiding process instability caused by excessive adjustments.

[0058] In one possible implementation, when testing the adhesion of a CrAl coating on a 316L stainless steel substrate using a scratch testing device, a nano-scratch analyzer can be used to apply a gradually increasing load and record the critical load value at which the coating peels off. Assuming the test results show a critical load value of 20N, which is lower than the preset adhesion threshold of 30N, the substrate surface treatment process can be adjusted in this case.

[0059] For example, by increasing the plasma cleaning time from 5 to 10 minutes, or by using sandpaper followed by ultrasonic cleaning, the oxide layer and impurities on the substrate surface can be removed, improving surface roughness. The improved surface condition data shows that the surface roughness decreased from Ra0.5 μm to Ra0.3 μm, and the coating adhesion test result improved to 32 N, meeting the requirements. This surface treatment optimization can significantly improve the adhesion between the coating and the substrate.

[0060] For example, when optimizing bias voltage and target current parameters using the random forest regression function in Python's Scikit-learn library, a model can be built based on historical data. Inputting thickness and surface condition data, the model predicts the optimal parameter combination. Assuming historical data shows that coating performance is best when the bias voltage is 60-80V and the target current is 90-110A, the random forest model analysis recommends a combination of 70V bias voltage and 100A target current. After deposition using this combination, the coating thickness stabilized at 510nm, and the critical load value in the scratch test reached 35N, indicating that the optimized parameters effectively improved the coating quality. This method utilizes a data-driven approach to ensure that parameter adjustments are targeted and efficient.

[0061] In one possible implementation, when performing scratch tests again to verify the effect of the optimized parameters, stability can be verified through multiple sets of tests.

[0062] For example, three consecutive sets of samples were deposited under optimized parameters, and the measured critical load values ​​were 34N, 35N, and 33N, respectively, all higher than the adhesion threshold of 30N. This repeated testing can confirm the stability and reliability of the process and ensure the consistency of CrAl coating adhesion on 316L stainless steel substrates.

[0063] It is understandable that during the implementation of the above methods, equipment operation and data recording must strictly follow standard procedures.

[0064] For example, when adjusting the target current, the equipment temperature needs to be monitored to avoid overheating that could affect the deposition quality; during surface treatment, it must be ensured that there is no residue of cleaning solution to avoid affecting the coating adhesion. These detailed controls can effectively improve the repeatability of the process and the stability of the coating performance, providing reliable technical support for subsequent production.

[0065] S4. Based on the bonding strength, the nitrogen flow rate and pressure are adjusted to generate a dense CrAl coating microstructure with a porosity lower than the porosity threshold by 5%. The quality of the microstructure is verified by scanning electron microscopy.

[0066] Optionally, this step also includes: Step S41: Obtain the initial parameters of nitrogen flow rate and pressure, and determine the baseline values ​​of nitrogen flow rate and pressure by collecting environmental data in real time through sensors.

[0067] Step S42: Based on the collected nitrogen flow rate and pressure baseline values, optimize the nitrogen flow rate and pressure parameters using a gradient descent algorithm to obtain an optimized parameter combination.

[0068] Step S43: Based on the optimized combination of nitrogen flow rate and pressure parameters, control the coating deposition equipment to generate CrAl coating microstructure and obtain an initial coating sample.

[0069] Step S44: The initial coating sample is imaged and analyzed using a scanning electron microscope to obtain porosity values ​​and microstructure feature data, and to determine whether the coating porosity is lower than the preset porosity threshold.

[0070] Step S45: If the porosity value is higher than the preset porosity threshold, adjust the nitrogen flow rate and pressure parameters, regenerate the coating microstructure, and repeat the scanning electron microscopy analysis to obtain a dense coating sample that meets the requirements.

[0071] Step S46: Obtain scanning electron microscope (SEM) imaging data from the dense coating sample that meets the requirements. Use ImageJ tool to perform threshold segmentation and area calculation on the SEM imaging data, extract pore features, verify microstructure quality, and obtain the final coating quality assessment result.

[0072] For example, in the CrAl coating deposition process, the initial settings of nitrogen flow rate and pressure are crucial to the coating microstructure. Nitrogen flow rate affects the concentration of the reaction atmosphere, while pressure determines the ion bombardment energy and deposition rate. Assuming an initial nitrogen flow rate of 50 sccm and a pressure of 0.5 Pa are collected by sensors and recorded as baseline values, the sensors monitor environmental data in real time to ensure that nitrogen flow rate fluctuations are controlled within ±2 sccm and pressure deviations are less than 0.02 Pa, thus guaranteeing parameter stability. This high-precision monitoring provides a reliable data foundation for subsequent optimization.

[0073] In one possible implementation, nitrogen flow rate and pressure parameters can be optimized using a gradient descent algorithm based on collected baseline values. Gradient descent iteratively adjusts the parameters to find the process conditions with the lowest porosity.

[0074] For example, if initial testing reveals high porosity, the algorithm might suggest increasing the nitrogen flow rate to 55 sccm and decreasing the pressure to 0.45 Pa. This optimized parameter combination is then applied to the deposition equipment to generate new CrAl coating samples. The adjusted nitrogen flow rate increases reactant concentration, promoting coating densification; while the reduced pressure enhances ion bombardment, reducing defects in the microstructure.

[0075] Specifically, after generating the initial coating sample, imaging analysis was performed using a scanning electron microscope (SEM). The SEM allowed for high-resolution observation of the coating surface and cross-section, obtaining porosity values ​​and microstructural features.

[0076] For example, the initial sample porosity might be 6%, higher than the preset porosity threshold of 5%. By adjusting the nitrogen flow rate to 60 sccm and maintaining the gas pressure at 0.4 Pa, after redeposition of the coating, the porosity decreased to 4.8%, meeting the requirements. This densely coated sample exhibited a more uniform grain distribution and a lower defect density.

[0077] In one embodiment, after obtaining a dense coating sample that meets the requirements, ImageJ is used for threshold segmentation and area calculation. ImageJ separates pores from the coating body using grayscale thresholding and calculates the pore area ratio.

[0078] For example, scanning electron microscopy images showed that the pore area ratio decreased from 5.2% to 2.5%, verifying the improvement in microstructure quality. An adaptive algorithm was used for threshold segmentation to ensure the accuracy of pore feature extraction. Analysis results indicate that the optimized coating exhibits higher density and a more uniform microstructure.

[0079] For example, if the porosity still does not meet the target, the nitrogen flow rate can be further fine-tuned to 65 sccm and the gas pressure to 0.38 Pa, and the deposition and analysis process can be repeated. This iterative optimization ensures that the microstructure quality gradually approaches the ideal state.

[0080] It should be noted that ImageJ analysis is not limited to porosity; it can also extract grain size and distribution characteristics, providing multi-dimensional data support for process improvement.

[0081] For example, the grain size was optimized from the initial 50nm to 30nm, indicating a finer coating.

[0082] In one possible implementation, the optimized nitrogen flow rate and pressure parameters are precisely controlled by the equipment control system to ensure process repeatability. Closed-loop control combining sensors and algorithms makes parameter adjustment more efficient and reduces manual intervention. The final coating sample meets the preset requirements for porosity and microstructure characteristics, laying the foundation for subsequent performance testing.

[0083] S5, based on the microstructure of the dense CrAl coating, generates a surface oxide layer of Al2O3 or Cr2O3 in a high-temperature lead-bismuth alloy environment of 500°C to 700°C, with a wetting angle reaching a wetting angle threshold of more than 120°, thereby reducing the adhesion of lead-bismuth alloy.

[0084] Optionally, this step also includes: Step S51: Obtain microstructure data of the dense CrAl coating. Use scanning electron microscopy and X-ray diffraction to determine the grain size and phase composition of the coating surface and obtain microstructure characteristic parameters.

[0085] Step S52: Based on the microstructure characteristic parameters, molecular dynamics simulation is performed using LAMMPS software to calculate the surface energy change of the CrAl coating in a high-temperature lead-bismuth alloy environment, and to determine the energy conditions for the formation of the surface oxide layer.

[0086] Step S53: Based on the energy conditions for the formation of the surface oxide layer, thermodynamic calculations are performed using FactSage software to predict the probability of Al2O3 or Cr2O3 oxide layer formation and obtain the oxide layer composition distribution.

[0087] Step S54: For the composition distribution of the oxide layer, the Young equation is used to calculate the change in wetting angle, and it is determined whether the wetting angle is greater than the wetting angle threshold of 120° to obtain the wetting angle characteristic data.

[0088] Step S55: Based on the wetting angle characteristic data, calculate the surface free energy using the Dupre equation, analyze the adhesion force of the lead-bismuth alloy on the oxide layer, and determine the adhesion reduction effect parameters.

[0089] Step S56: Based on the adhesion reduction effect parameters, finite element simulation is performed using ANSYS software to simulate the stability of the oxide layer in a high-temperature lead-bismuth alloy environment, and the durability data of the oxide layer is obtained.

[0090] Step S57: Based on the oxide layer durability data, predict the long-term performance of the coating under different temperature conditions using a random forest regression algorithm to obtain performance optimization parameters.

[0091] For example, when acquiring microstructure data of dense CrAl coatings, scanning electron microscopy can be used to observe the surface morphology of the coating, and backscattered electron imaging can be used to distinguish grain boundaries and determine the grain size range, such as 10-50 nanometers. X-ray diffraction is used to analyze the phase composition of the coating and determine the crystal structure ratio of Cr and Al, such as Cr accounting for 60% and Al accounting for 40%.

[0092] Specifically, during scanning electron microscopy (SEM) operation, the accelerating voltage can be set to 15 kV and the probe current to 10 nA to obtain high-resolution images, while X-ray diffraction uses Cu-Kα rays with a scanning angle range of 20°-80° to identify crystal phases.

[0093] In one possible implementation, molecular dynamics simulations using LAMMPS software can construct an atomic model of the CrAl coating, setting the high-temperature lead-bismuth alloy environment temperature to 700°C and the simulation time to 100 picoseconds. Surface atomic rearrangements can be observed during the simulation, and surface energy changes, such as a decrease from 2.5 J / m² to 1.8 J / m², can be calculated, reflecting the trend of oxide layer formation.

[0094] It should be noted that the simulation must take into account the liquid properties of lead-bismuth alloys to ensure that the boundary conditions closely resemble the actual environment.

[0095] For example, the FactSage software can be used for thermodynamic calculations. With an input temperature of 700°C and an oxygen partial pressure of 10^-6 Pa, it predicts that the probability of Al2O3 formation is 70% and that of Cr2O3 is 30%.

[0096] Specifically, the software generates a phase diagram by minimizing the Gibbs free energy and analyzes the stability of the oxide layer. This method facilitates the rapid screening of possible oxide compositions.

[0097] In one possible implementation, the Young equation can be used to calculate the wetting angle based on the surface energy data of the oxide layer, such as the surface energy of Al2O3 (1.5 J / m²) and the surface tension of lead-bismuth alloy (0.4 N / m), resulting in a wetting angle of 130°, indicating non-wetting characteristics.

[0098] It should be noted that a wetting angle greater than 120° indicates that lead-bismuth alloys are difficult to adhere to, which helps reduce corrosion.

[0099] For example, when calculating the surface free energy using the Dupre equation, wetting angle data and the surface energy of the oxide layer can be combined to show that the adhesion work is reduced to 0.3 J / m². This low adhesion indicates that the coating has strong anti-adhesion properties against lead-bismuth alloys, which helps to extend its service life.

[0100] In one possible implementation, when performing finite element simulations using ANSYS software, a three-dimensional model of the coating-oxide layer-lead-bismuth alloy can be constructed. The temperature is set to 700°C, and the deformation of the oxide layer under thermal stress is simulated. The maximum stress is found to be 50 MPa, indicating that the oxide layer has high stability.

[0101] It should be noted that the simulation needs to take into account the difference in thermal expansion coefficients to ensure the accuracy of the results.

[0102] For example, the random forest regression algorithm can predict the performance degradation rate of the coating at high temperatures based on oxide layer durability data with an input temperature range of 500-800°C. For instance, the degradation rate is only 5% / year at 700°C.

[0103] Specifically, the algorithm generates performance optimization parameters by analyzing characteristics such as grain size, oxide layer thickness, and wetting angle, such as recommending an operating temperature of 650°C to balance performance and durability. This method can effectively guide coating design.

[0104] In one possible implementation, the above method can form a complete process from grain size analysis to performance prediction. The core solution focuses on microstructure optimization, while the extended solution improves coating durability through multi-parameter joint analysis.

[0105] For example, combining X-ray diffraction and molecular dynamics simulations can further verify the microscopic mechanism of oxide layer formation, while wetting angle and surface free energy analysis provide macroscopic support for corrosion resistance assessment. These methods support each other and jointly ensure the excellent performance of CrAl coatings in high-temperature lead-bismuth alloy environments.

[0106] S6. Based on the characteristics of the oxide layer, the thickness of the CrAl coating is detected to confirm that the uniformity is within the range of 500nm to 5μm. Elliptic polarization spectroscopy or cross-sectional microscopy is used for measurement, and the deposition time is adjusted to optimize the thickness consistency.

[0107] Optionally, this step also includes: Step S61: Obtain spectral data of the CrAl coating surface by elliptic polarization spectroscopy, determine the relationship between elliptic parameters and wavelength, and obtain the initial thickness distribution.

[0108] Step S62: If the initial thickness distribution exceeds the specified range, calculate the thickness deviation based on the spectral data, and use the lsqcurvefit function of MATLAB to fit the thickness deviation to a polynomial curve to obtain the deviation distribution parameters.

[0109] Step S63: Measure the cross-section of the coating using a cross-sectional microscope to obtain a microscopic image and determine the degree of matching between the actual thickness value and the deviation distribution parameters.

[0110] Step S64: If the matching degree is lower than the preset matching degree threshold, adjust the deposition time parameter and use the optimize.minimize function in Python's SciPy library to optimize the deposition process and obtain the updated thickness distribution.

[0111] Step S65: Based on the updated thickness distribution, repeat the spectral analysis and microscopic measurement to verify whether the thickness uniformity meets the specified range and obtain the verification results.

[0112] Step S66: If the verification results show insufficient thickness consistency, the process parameters are iteratively optimized by fine-tuning the deposition time to obtain the final thickness distribution.

[0113] Step S67: Based on the final thickness distribution, calculate the standard deviation using Excel to determine if the CrAl coating thickness meets the requirements.

[0114] For example, when obtaining spectral data of a CrAl coating surface using elliptic polarization spectroscopy, an elliptic polarimeter can be used to measure the polarization reflection characteristics of the coating within the wavelength range of 400-800 nm, obtaining the relationship curves between the elliptic parameters ψ and Δ and the wavelength. Assuming the measurement finds ψ to be 45° and Δ to be 120° at 600 nm, this can be converted into an initial thickness distribution using the instrument's built-in model, resulting in a thickness range of 500-550 nm. If the specified range is 520-530 nm, some areas will exceed the requirements.

[0115] It should be noted that elliptic polarization spectroscopy, based on the change in the polarization state of light on the coating surface, can characterize the coating thickness with high precision and is suitable for high-reflectivity materials such as CrAl coatings.

[0116] In one possible implementation, for thickness deviations exceeding the acceptable range, the MATLAB function `lsqcurvefit` can be used to fit the deviation data. Assuming the deviation value is between -20 nm and +20 nm, the fit is a quadratic polynomial curve, yielding deviation distribution parameters such as a quadratic coefficient of 0.01 nm / μm². This helps quantify the thickness variation trend, facilitating subsequent process optimization. The fitting process utilizes the least squares method to ensure a high degree of agreement between the model and measured data, thus improving thickness control accuracy.

[0117] For example, observing the cross-section of the CrAl coating using a cross-sectional microscope allows for magnification up to 1000 times using an optical microscope, providing a clear image. Assuming the actual measured thickness is 525 nm, which is close to the predicted value of the deviation distribution parameter (524 nm), the matching degree reaches 95%, exceeding the preset matching degree threshold of 90%. A high matching degree indicates a relatively stable initial process; however, if the matching degree is lower than the matching degree threshold, such as 80%, the deposition time needs to be adjusted.

[0118] In one possible implementation, the deposition time can be adjusted using the `optimize.minimize` function in Python's SciPy library. Assuming an initial deposition time of 1200 s, it can be optimized to 1180 s, bringing the thickness distribution closer to the 522-528 nm range.

[0119] Preferably, this optimization is based on minimizing the thickness deviation of the objective function, which can effectively improve the coating uniformity and is suitable for high-precision coating preparation scenarios.

[0120] For example, repeated spectral analysis and microscopic measurements are used to verify thickness uniformity. Assuming new spectral data indicates a thickness range of 523-527 nm, and the microscopic measurements match this range with a standard deviation of 1.5 nm, the requirement is met. If the consistency is insufficient, such as a standard deviation of 3 nm, the deposition time needs to be fine-tuned, for example, reduced by 10 seconds, and iteratively optimized until the target is achieved. This iterative approach ensures process stability.

[0121] In one possible implementation, the standard deviation of the final thickness distribution can be calculated using Excel. Assuming the thickness values ​​at 10 sampling points are 523, 524, 526, 525, 527, 523, 524, 526, 525, and 524 nm, with a standard deviation of 1.3 nm, this indicates a highly uniform thickness. This method is simple and intuitive, suitable for rapid evaluation of coating quality, and helps ensure the reliability of CrAl coatings in high-temperature lead-bismuth alloy environments.

[0122] It should be noted that the above methods form a closed-loop logic from spectral analysis to process optimization, with each element supporting the others to ensure the accuracy of thickness control. Elliptic polarization spectroscopy provides highly sensitive data, fitting and optimization algorithms quantify deviations, microscopic measurements verify the actual effects, and Excel analysis provides intuitive results. This multi-faceted collaborative approach improves the reliability and consistency of CrAl coating preparation and is suitable for surface performance optimization under high-temperature environments.

[0123] S7. Based on thickness consistency, a screw or pin is selected as the base component, and a CrAl coating is deposited on its surface. It is used in conjunction with a sleeve cap to control the gap between the sleeve cap and the shaft part within the gap threshold of 0.01mm to 0.5mm.

[0124] Optionally, this step also includes: Step S71: Obtain the geometric parameters and material properties of the base component, determine the suitability of the screw or pin, and obtain the initial component selection result.

[0125] Step S72: Based on the initial component selection results, extract the specific dimensions and material data of the substrate components, establish a finite element model in ANSYS software, simulate the CrAl coating deposition process, calculate the coating thickness distribution, and obtain uniformity evaluation data.

[0126] Step S73: A CrAl coating is deposited on the surface of the substrate component using plasma spraying technology to obtain a coated component.

[0127] Step S74: For the coated component, obtain the inner diameter parameter of the cap, measure the outer diameter of the coated component using three-dimensional scanning technology, and determine the gap value between the two.

[0128] In step S75, if the gap value exceeds the preset gap range, the outer diameter of the coated component is adjusted using precision grinding technology to obtain the adjusted coated component.

[0129] Step S76: Based on the adjusted outer diameter of the coated component, remeasure the gap with the cap to determine whether it meets the preset fitting accuracy requirements and obtain the final fitting result.

[0130] In one possible implementation, obtaining the geometric parameters and material properties of the base component is key to determining the suitability of the screw or pin.

[0131] For example, if the base component is the turbine shaft in an aero-engine, it is necessary to obtain its geometric parameters such as diameter, length, and surface roughness, as well as the material's elastic modulus, hardness, and coefficient of thermal expansion.

[0132] Specifically, a high-precision laser measuring instrument can be used to scan the turbine shaft to obtain geometric data with a diameter of 50mm and a length of 200mm. Simultaneously, a material testing machine determines that the matrix is ​​a nickel-based alloy with a hardness of HV400. This precise data provides a reliable basis for subsequently selecting a suitable screw or pin.

[0133] For example, when building a finite element model in ANSYS software, a three-dimensional model can be constructed based on the geometric and material data of the turbine shaft mentioned above to simulate the deposition behavior of the CrAl coating during the plasma spraying process.

[0134] Preferably, the model sets the spraying temperature to 800°C and the target coating thickness to 2 μm. The simulation results can show the axial variation of the thickness distribution, for example, a thickness of 2.1 μm in the central region and slightly thinner at both ends at 1.9 μm, thereby assessing whether the uniformity meets the requirements. This simulation can intuitively reflect the coating distribution, facilitating process optimization.

[0135] Specifically, when depositing a CrAl coating using plasma spraying technology, a plasma spraying equipment can be selected, with a spraying distance of 100mm and a spraying power of 40kW to ensure uniform adhesion of the coating to the turbine shaft surface. After spraying, the coated component exhibits a uniform metallic luster. This process effectively improves the wear resistance and corrosion resistance of the component.

[0136] In one embodiment, when obtaining the inner diameter parameter of the cap, an inside micrometer can be used to measure the inner diameter of the cap, for example, obtaining an inner diameter of 50.05 mm. Subsequently, the outer diameter of the coated component is measured using 3D scanning technology. Assuming the scan result shows an outer diameter of 50.08 mm, the gap between the two is calculated to be 0.03 mm. If the preset gap range is 0.01-0.02 mm, then this gap exceeds the requirement.

[0137] It should be noted that 3D scanning technology can accurately capture minute changes in the outer diameter of components through laser point cloud data, ensuring measurement accuracy.

[0138] For example, for gap values ​​that exceed the acceptable range, precision grinding techniques can be used to adjust the outer diameter of the coated component.

[0139] Preferably, a CNC grinding machine is used, with the grinding depth set to 0.02 mm. After adjustment, the outer diameter reaches 50.06 mm. The clearance with the cap is remeasured, and the new clearance is 0.01 mm, meeting the preset fit accuracy requirements. This adjustment ensures a tight fit between the component and the cap, improving assembly reliability.

[0140] Understandably, the final verification of the fit can be accomplished through actual assembly testing.

[0141] For example, the adjusted coated components are trial-assembled with the cap to observe for any looseness or excessive tightness, ensuring that the fit accuracy meets design requirements. This verification method effectively confirms the reliability of process adjustments and provides a reference for subsequent mass production.

[0142] S8. Based on the gap control results, a TiN wear-resistant coating with a thickness of 1μm to 10μm is deposited on the cap surface. A multi-arc ion plating device is used, with the bias voltage set to 50V to 80V and the nitrogen flow rate set to 400sccm to 500sccm to deposit on the substrate surface.

[0143] Optionally, this step also includes: Step S81: Obtain initial deposition parameters through multi-arc ion plating equipment, determine the set values ​​of coating thickness, bias voltage and nitrogen flow rate, and obtain process parameter configuration.

[0144] Step S82: Adjust the operating status of the multi-arc ion plating equipment according to the process parameters, control the bias voltage and nitrogen flow rate, and obtain a stable deposition environment.

[0145] Step S83: Start the TiN coating deposition process from a stable deposition environment, and use a quartz crystal microbalance to monitor the deposition rate on the cap surface to obtain real-time deposition data.

[0146] Step S84: If the deviation of the real-time deposition data exceeds the preset deposition deviation threshold, the deviation is calculated by the PID controller and the bias pressure or nitrogen flow rate is adjusted to obtain the optimized deposition conditions.

[0147] Step S85: Continue running the multi-arc ion plating equipment according to the optimized deposition conditions, detect the coating thickness on the cap surface, and obtain a uniform TiN coating.

[0148] Step S86: Obtain wear resistance data of TiN coating by scanning electron microscopy to determine whether the coating meets wear resistance requirements.

[0149] Step S87: If the wear resistance data meets the preset wear resistance threshold, save the current process parameter configuration to obtain the final process scheme.

[0150] For example, when depositing TiN coatings using a multi-arc ion plating system, obtaining the initial deposition parameters is a crucial first step. The system's built-in control system, combined with a process database, can be used to set the target coating thickness to 2.5 micrometers, the bias voltage to 50V, and the nitrogen flow rate to 400 sccm. This parameter configuration is based on an analysis of the substrate type and deposition environment; for example, selecting a stainless steel substrate to ensure coating adhesion.

[0151] It should be noted that the selection of bias voltage and nitrogen flow rate needs to be fine-tuned according to the characteristics of the equipment in order to balance the deposition rate and coating quality.

[0152] Specifically, when adjusting the operating status of a multi-arc ion plating equipment, the deposition environment can be optimized by gradually increasing the bias voltage to 100V and observing the arc stability.

[0153] In one embodiment, if arc instability is detected, the nitrogen flow rate can be adjusted from 500 sccm to 600 sccm to reduce particle splashing during deposition, thereby creating a more uniform deposition environment. This adjustment effectively reduces micro-defects on the coating surface. Real-time monitoring by a quartz crystal microbalance is particularly important during the TiN coating deposition process.

[0154] For example, by recording the deposition rate using a microbalance, it was found that the rate was relatively stable at 0.1 nm / s. If the rate fluctuated to 0.15 nm / s, it indicated that the nitrogen flow rate might be too high. In this case, the flow rate could be reduced to 450 sccm using a PID controller to re-stabilize the rate. This real-time adjustment ensures the uniformity of the coating thickness.

[0155] Preferably, after optimizing the deposition conditions, the coating thickness can be verified by continuing to run the equipment and conducting periodic sampling tests.

[0156] For example, using a thickness gauge to inspect the surface of the cap confirms that the thickness is within the range of 2.4 to 2.6 micrometers, meeting the uniformity requirements. This inspection effectively verifies the stability of the process.

[0157] In one embodiment, scanning electron microscopy was used to evaluate the wear resistance of the TiN coating.

[0158] For example, by observing the microstructure of the coating surface, it can be confirmed whether there are obvious pores or cracks. If the wear resistance test shows that the coating wears less than 0.01 mm in the standard friction test, it indicates that the coating meets the requirements. This testing method can visually reflect the quality of the coating.

[0159] For example, if the wear resistance performance meets expectations, the current parameters can be saved as a process plan.

[0160] For example, a configuration with a bias voltage of 80V and a nitrogen flow rate of 480sccm can be used as a standard process in subsequent production. This preserved process parameter improves production efficiency and consistency.

[0161] It should be noted that, compared to the CrAl coating deposition in historical information, the TiN coating places greater emphasis on wear resistance and is suitable for capped components in high-friction environments. Both processes require precise control of gaps and coating quality, but the TiN coating is more sensitive to nitrogen flow rate in multi-arc ion plating, requiring more precise real-time monitoring. These methods verify process parameters from multiple angles to ensure the stability of coating performance and fit accuracy.

[0162] S9. Based on the characteristics of TiN wear-resistant coating, the surface hardness and wear resistance of CrAl coating and TiN coating were tested.

[0163] In one embodiment, the surface hardness is set to a hardness threshold of 20 GPa or higher, and the friction coefficient is set to a friction coefficient threshold of 0.4 or lower, which is verified by nanoindentation and friction and wear tests.

[0164] Optionally, this step also includes: Step S91: Obtain surface hardness data of CrAl coating and TiN coating using a nanoindenter, record the indentation depth of each coating under a preset load, and obtain the hardness value.

[0165] Step S92: Based on the comparison between the hardness value and the preset hardness threshold, if the hardness value is greater than the hardness threshold of 20 GPa, the coating is determined to meet the hardness requirements, and a qualified hardness dataset is generated.

[0166] Step S93: Extract the hardness parameters of the CrAl coating and TiN coating from the qualified hardness dataset.

[0167] Step S94: Use a friction and wear testing machine to perform sliding friction tests on the CrAl coating and TiN coating to obtain friction coefficient data.

[0168] Step S95: By comparing the friction coefficient data with the preset friction coefficient threshold, if the friction coefficient is lower than the friction coefficient threshold of 0.4, the coating is determined to meet the wear resistance requirements, and a qualified wear resistance dataset is generated.

[0169] Step S96: Extract the wear resistance parameters of CrAl coating and TiN coating from the qualified wear resistance dataset.

[0170] Step S97: Input the extracted hardness and wear resistance parameters into Excel software to construct a performance comparison matrix and determine the differences in hardness and wear resistance among the coatings.

[0171] Step S98: For the performance comparison matrix, principal component analysis algorithm is used to extract the dominant influencing factors of hardness and wear resistance. First, the matrix data is standardized and the covariance matrix is ​​calculated. Then, the eigenvalues ​​and eigenvectors are solved. The first two principal components with a cumulative variance contribution rate greater than the variance contribution rate threshold of 85% are selected to generate the coating performance feature vector.

[0172] Step S99: Calculate the scores of CrAl coating and TiN coating on the principal component using the performance feature vector to obtain the ranking of coating performance.

[0173] For example, when measuring the surface hardness of CrAl and TiN coatings using a nanoindenter, a high-precision nanoindenter can be used, a preset load such as 5mN can be applied, and the indentation depth can be recorded.

[0174] For example, the indentation depth of a CrAl coating might be 50 nm, while that of a TiN coating is 45 nm. Using the hardness calculation formula, the hardness of the CrAl coating is approximately 22 GPa, and that of the TiN coating is approximately 24 GPa. Both hardness values ​​exceed the hardness threshold of 20 GPa, meeting the hardness requirements and generating a qualified hardness dataset. This method ensures high data accuracy and helps in screening coatings with superior performance.

[0175] Specifically, the friction and wear test can be conducted using a ball-disc friction and wear tester to test the sliding friction properties of CrAl and TiN coatings. The test conditions can be set as follows: load 10N, sliding speed 0.1m / s, and friction pair consisting of Si3N4 ceramic balls.

[0176] For example, the coefficient of friction for CrAl coatings is approximately 0.35, and for TiN coatings it is approximately 0.32, both below the friction coefficient threshold of 0.4, thus meeting the wear resistance requirements and generating a qualified wear resistance dataset. This testing method can simulate actual working conditions and obtain reliable friction performance data.

[0177] In one embodiment, after the hardness and wear resistance parameters are entered into Excel software, a performance comparison matrix can be constructed.

[0178] For example, the matrix includes the hardness values ​​(22 GPa, 24 GPa) and friction coefficients (0.35, 0.32) of the CrAl and TiN coatings. A direct comparison of the performance differences between the two coatings using the matrix reveals that the TiN coating is slightly superior to the CrAl coating in both hardness and wear resistance. This comparative method facilitates rapid evaluation of coating performance and provides a basis for subsequent optimization.

[0179] For example, when using principal component analysis (PCA) to extract the dominant influencing factors of hardness and abrasion resistance, the matrix data is first standardized to eliminate dimensional differences. After calculating the covariance matrix, the eigenvalues ​​and eigenvectors are solved, and the first two principal components with a cumulative variance contribution rate greater than the variance contribution rate threshold of 85% are selected.

[0180] For example, hardness may contribute 70% of the variance, while wear resistance contributes 15%. Using principal component scoring, the TiN coating might score 0.85, while the CrAl coating scores 0.75, indicating that the TiN coating performs better. This method effectively reduces dimensionality, highlights key performance indicators, and facilitates performance ranking.

[0181] Specifically, the performance eigenvectors can be generated using principal component analysis software, such as Python's sklearn library or MATLAB. The scores of the CrAl and TiN coatings reflect their overall performance in terms of hardness and wear resistance.

[0182] For example, TiN coatings score higher and are given a higher priority due to their higher hardness and lower coefficient of friction. This analytical method can scientifically quantify the differences in coating performance, providing data support for material selection.

[0183] In one embodiment, the performance comparison matrix and principal component analysis results can be further used to optimize the coating process.

[0184] For example, by combining historical process parameters (such as bias voltage and nitrogen flow rate), the deposition conditions of the CrAl coating can be adjusted to improve hardness and narrow the performance gap with the TiN coating. This method improves the stability of coating performance through data-driven process optimization.

[0185] For example, in the process of obtaining friction coefficient data, the reliability of the data can be ensured by repeated experiments.

[0186] For example, when tested under different humidity conditions, the coefficient of friction of the TiN coating remained between 0.30 and 0.33, demonstrating its stable wear resistance. This multi-condition testing method helps to verify the applicability of the coating in complex environments.

[0187] Specifically, the extraction of qualified hardness datasets and wear resistance datasets can be achieved using Excel's filtering function.

[0188] For example, by setting a hardness threshold of 20 GPa and a friction coefficient threshold of 0.4, parameters that meet the criteria can be automatically filtered out. This automated processing method improves data processing efficiency and reduces human error.

[0189] In one embodiment, the ranking of coating performance can be based on the actual application scenario.

[0190] For example, in high-speed cutting tools, TiN coatings are preferred over CrAl coatings due to their high hardness and good wear resistance. This ranking method can directly guide coating selection and improve tool life.

[0191] S10, based on the hardness and wear resistance test results, adjusts the deposition time and bias voltage to optimize the performance of CrAl and TiN coatings, meeting the long-term corrosion resistance and wear resistance requirements in lead-bismuth alloy environments.

[0192] Optionally, this step also includes: Step S101: Obtain the performance indicators of the CrAl coating and TiN coating, as well as the initial deposition time and bias parameters, from the hardness and wear resistance test data.

[0193] Step S102: Based on the obtained performance indicators, initial deposition time, and bias parameters, the SVC function of the scikit-learn library is used to classify the hardness and wear resistance of the CrAl coating and TiN coating, and the mapping relationship between performance and deposition time and bias parameters is obtained.

[0194] Step S103: If the classification results show that the hardness or wear resistance does not meet the environmental requirements of lead-bismuth alloy, the deposition time and bias voltage parameters are adjusted by the minimize function of the scipy.optimize library to obtain the optimized parameter combination.

[0195] Step S104: For the optimized parameter combination, the numpy.random library is used to generate random samples to predict the long-term corrosion resistance and wear resistance of CrAl coating and TiN coating in lead-bismuth alloy environment, and the simulation results are obtained.

[0196] Step S105: Extract stability indicators of corrosion resistance and wear resistance from the simulation results to determine whether they meet the long-term stability requirements.

[0197] Step S106: If the stability index meets the preset stability threshold, the optimized deposition time and bias voltage parameters are output to determine the final coating process parameters.

[0198] Step S107: Based on the final coating process parameters, generate deposition process configuration files for CrAl coating and TiN coating to obtain an executable set of process parameters.

[0199] For example, when obtaining the performance indicators of CrAl and TiN coatings, as well as the initial deposition time and bias parameters, the corresponding relationship between hardness, wear resistance, and process parameters can be recorded through experimental data.

[0200] Specifically, the CrAl coating has a hardness of approximately 22 GPa, a coefficient of friction of 0.35, a deposition time of 120 minutes, and a bias voltage of -100V; while the TiN coating has a hardness of 25 GPa, a coefficient of friction of 0.30, a deposition time of 150 minutes, and a bias voltage of -80V. These data were obtained using a nanoindenter and a tribometer to ensure measurement accuracy and provide a reliable basis for subsequent classification.

[0201] In one possible implementation, when using the SVC function from the scikit-learn library to classify coating performance, hardness and friction coefficient can be used as input features, and deposition time and bias voltage can be used as auxiliary parameters to construct a classification model.

[0202] For example, by setting a hardness threshold of 20 GPa and a friction coefficient threshold of 0.4, the model can identify which parameter combinations make the coating meet the environmental requirements of lead-bismuth alloys. The classification results might show that the CrAl coating meets the hardness requirement at a bias voltage of -100V but lacks wear resistance, while the TiN coating has better overall performance. This classification helps to clarify the mapping relationship between process parameters and performance.

[0203] Specifically, if the classification results indicate that the wear resistance of the CrAl coating does not meet the standard, the deposition time and bias voltage can be optimized using the minimize function in the scipy.optimize library.

[0204] For example, the initial parameters are 120 minutes and -100V, which may be adjusted to 140 minutes and -120V after optimization to reduce the coefficient of friction to 0.38. This adjustment is based on minimizing the coefficient of friction using an objective function while maintaining a hardness above 20 GPa, thereby improving the coating's suitability for lead-bismuth alloy environments.

[0205] In one possible implementation, the numpy.random library is used to generate random samples to predict the long-term corrosion resistance and wear resistance of the coating in a lead-bismuth alloy environment.

[0206] For example, 1000 random parameter combinations were generated to simulate the corrosion rate and friction coefficient changes of the CrAl coating under optimized parameters. The results might show that the corrosion rate of the CrAl coating is stable at 0.01 mm / year, and the friction coefficient fluctuates between 0.35 and 0.39, indicating good long-term stability. The simulation results for the TiN coating might be even better, with a corrosion rate as low as 0.008 mm / year and a friction coefficient stable at around 0.30. These simulation results provide data support for stability assessment.

[0207] For example, the extracted stability indicators for corrosion resistance and wear resistance may include the standard deviation of corrosion rate and the fluctuation range of friction coefficient. If the standard deviation of CrAl coating is 0.002 mm / year and that of TiN coating is 0.001 mm / year, and both are below the preset stability threshold of 0.005 mm / year, then both are deemed to meet the long-term stability requirements.

[0208] Preferably, the optimized parameters are output, such as 140 minutes and -120V for CrAl coating and 150 minutes and -80V for TiN coating, as the final process parameters.

[0209] In one possible implementation, a deposition process configuration file is generated based on the final process parameters. The parameters can be saved in JSON format, including deposition time, bias voltage, gas flow rate, etc.

[0210] For example, a profile for a CrAl coating might specify an Ar gas flow rate of 50 sccm and a deposition temperature of 400°C; for a TiN coating, it might specify 60 sccm and 450°C. These profiles facilitate direct application on magnetron sputtering equipment, ensuring process repeatability. This approach, through parameter optimization and profile generation, improves the performance consistency and production efficiency of coatings in lead-bismuth alloy environments.

[0211] In a second aspect, the present invention provides a coating for a lead-bismuth pile core sampler, which is prepared by the method described above.

[0212] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for preparing a coating for a lead-bismuth pile core sampler, characterized in that, The method includes: S1, a CrAl coating is deposited on the substrate surface using a multi-arc ion plating device. The CrAl coating is obtained by adjusting the ratio of Cr target material to Al target material and the target current. S2, configure the parameters of the multi-arc ion plating equipment: bias voltage 30V to 100V, nitrogen flow rate 300sccm to 600sccm, target current 50A to 150A, gas pressure 0.5Pa to 3Pa, deposition time 0.5h to 3h, to generate a CrAl coating with a thickness of 500nm to 5μm. S3, adjust the bias voltage and target current of the multi-arc ion plating equipment according to the thickness of the CrAl coating, optimize the bonding strength between the coating and the substrate, and achieve the target adhesion above the critical load of the scratch test. S4. Based on the bonding strength, the nitrogen flow rate and pressure are adjusted to generate a dense CrAl coating microstructure with a porosity lower than the porosity threshold. The quality of the microstructure is verified by scanning electron microscopy. S5, based on the dense CrAl coating microstructure, generates a surface oxide layer Al2O3 or Cr2O3 in a high-temperature lead-bismuth alloy environment of 500°C to 700°C, with the wetting angle reaching above the wetting angle threshold, thereby reducing the adhesion of lead-bismuth alloy. S6. Based on the characteristics of the oxide layer, the thickness of the CrAl coating was detected to confirm that the uniformity was within the range of 500nm to 5μm. Elliptic polarization spectroscopy or cross-sectional microscopy was used for measurement, and the deposition time was adjusted to optimize the thickness consistency. S7. Based on thickness consistency, a screw or pin is selected as the base component, and a CrAl coating is deposited on its surface. It is used in conjunction with a sleeve to control the gap between the sleeve and the shaft part within the gap threshold. S8. Based on the gap control results, deposit a TiN wear-resistant coating on the cap surface with a thickness of 1μm to 10μm. Use a multi-arc ion plating equipment with a bias voltage of 50V to 80V and a nitrogen flow rate of 400sccm to 500sccm to deposit on the substrate surface. S9. Based on the characteristics of TiN wear-resistant coating, the surface hardness and wear resistance of CrAl coating and TiN coating were tested. S10, based on the hardness and wear resistance test results, adjusts the deposition time and bias voltage to optimize the performance of CrAl and TiN coatings, meeting the long-term corrosion resistance and wear resistance requirements in lead-bismuth alloy environments.

2. The method for preparing a coating for a lead-bismuth pile core sampler according to claim 1, characterized in that, The base material is 316L stainless steel.

3. A method for preparing a coating for a lead-bismuth pile core sampler according to claim 2, characterized in that, The Cr content in the CrAl coating is controlled between 30wt% and 80wt%.

4. The method for preparing a coating for a lead-bismuth pile core sampler according to claim 3, characterized in that, Step S1, which involves depositing a CrAl coating on the substrate surface using a multi-arc ion plating device, and obtaining the CrAl coating by adjusting the ratio of Cr and Al targets and the target current, further includes: Step S11: Obtain surface property data of the substrate material, use atomic force microscopy to detect the surface roughness of the substrate, and use X-ray photoelectron spectroscopy to detect the chemical composition to obtain the surface state parameters of the substrate. Step S12: Extract roughness and composition values ​​from the surface state parameters of the substrate, input them into the multi-arc ion plating equipment, configure the preset deposition process parameters, adjust the ratio of Cr target to Al target to 1:1 and the target current to 50 amperes, and obtain the initial CrAl coating deposition scheme. Step S13: For the initial CrAl coating deposition scheme, perform simulation analysis, calculate the Cr content, and determine whether the Cr content is within the preset content range. If the Cr content exceeds the preset content range, adjust the target material ratio to 2:1 and the target current to 60 amperes to obtain the optimized deposition scheme. Step S14: Using a multi-arc ion plating equipment, a CrAl coating is deposited on the substrate surface according to the optimized deposition scheme to obtain a CrAl coating sample. Step S15: Obtain surface morphology and composition distribution data from the CrAl coating sample. Use a scanning electron microscope to detect the morphology and an energy dispersive X-ray spectrometer to detect the composition distribution. Determine whether the coating uniformity meets the requirements. If the coating uniformity is lower than the preset uniformity threshold, adjust the deposition process parameters to a target current of 70 amperes and obtain the coating sample again. Step S16: Test the hardness of the coating sample using a Vickers hardness tester, conduct a friction and wear test, obtain mechanical property data, and determine whether the hardness reaches the predetermined standard of 1500 Vickers hardness and friction coefficient is less than 0.

5. If the performance does not meet the standard, optimize the target material ratio to 3:1 and the deposition parameters to a bias voltage of 100 volts, and obtain the coating sample again. Step S17: Extract features from Cr content, target ratio, target current and coating performance data, input current parameters, predict coating performance, and obtain optimized deposition process parameter configuration.

5. The method for preparing a coating for a lead-bismuth pile core sampler according to claim 4, characterized in that, The preset content range is Cr content between 20% and 30%.

6. The method for preparing a coating for a lead-bismuth pile core sampler according to claim 1, characterized in that, Step S5, based on the dense CrAl coating microstructure, generates a surface oxide layer of Al2O3 or Cr2O3 in a high-temperature lead-bismuth alloy environment of 500°C to 700°C, achieving a wetting angle above the wetting angle threshold, thereby reducing lead-bismuth alloy adhesion, including: Step S51: Obtain microstructure data of dense CrAl coating, and use scanning electron microscopy and X-ray diffraction to determine the grain size and phase composition of the coating surface, and obtain microstructure characteristic parameters. Step S52: Based on the microstructure characteristic parameters, perform molecular dynamics simulation to calculate the surface energy change of the CrAl coating in a high-temperature lead-bismuth alloy environment and determine the energy conditions for the formation of the surface oxide layer. Step S53: Based on the energy conditions for the formation of the surface oxide layer, perform thermodynamic calculations to predict the probability of Al2O3 or Cr2O3 oxide layer formation and obtain the oxide layer composition distribution. Step S54: Calculate the wetting angle change based on the oxide layer composition distribution, determine whether the wetting angle is greater than the wetting angle threshold, and obtain wetting angle characteristic data; Step S55: Based on the wetting angle characteristic data, calculate the surface free energy, analyze the adhesion force of the lead-bismuth alloy on the oxide layer, and determine the adhesion reduction effect parameters. Step S56: Based on the adhesion reduction effect parameters, perform finite element simulation to simulate the stability of the oxide layer in the high-temperature lead-bismuth alloy environment and obtain oxide layer durability data. Step S57: Based on the oxide layer durability data, predict the long-term performance of the coating under different temperature conditions to obtain performance optimization parameters.

7. The method for preparing a coating for a lead-bismuth pile core sampler according to claim 6, characterized in that, The wetting angle threshold is set to 120°.

8. The method for preparing a coating for a lead-bismuth pile core sampler according to claim 1, characterized in that, Step S7 involves selecting a screw or pin as the base component based on thickness consistency, depositing a CrAl coating on its surface, and cooperating with a sleeve cap to control the gap between the sleeve cap and the shaft part within a gap threshold, including setting the gap threshold to 0.01mm to 0.5mm.

9. The method for preparing a coating for a lead-bismuth pile core sampler according to claim 1, characterized in that, In step S9, based on the characteristics of the TiN wear-resistant coating, the surface hardness and wear resistance of the CrAl coating and the TiN coating are tested, including: the surface hardness is greater than the hardness threshold of 20 GPa, and the friction coefficient is less than the friction coefficient threshold of 0.

4.

10. A coating for a lead-bismuth pile core sampler, characterized in that, It is prepared by the method described in any one of claims 1-9.

Images