Optimization method for plasma spraying parameters

Abstract

The invention relates to a plasma spraying parameter optimization method, which comprises the following steps of: determining process parameters needing to be controlled in a plasma spraying process as test factors, providing microscopic structure key characteristics of a coating and a corresponding Delphi method weighted quantization score, and obtaining a characteristic quantization value of a microscopic structure through the weighted quantization score; and carrying out a DOE test on the test factor, taking the characteristic quantized value as a response variable, obtaining a function relationship between the test factor and the response variable, and determining an overall optimal process parameter combination according to the function relationship. According to the method, the period and the test workload of the plasma spraying process parameter development process can be effectively improved, and the quality of the coating prepared by the plasma spraying process is improved.

Description

Technical Field

[0001] The invention belongs to the field of material surface treatment, and in particular relates to a method for optimizing plasma spraying parameters. Background Art

[0002] The rapid development of the aviation industry has put forward higher requirements on material performance. Atmospheric plasma spraying process is an effective technical means for surface strengthening of advanced materials. It has been widely used in the aviation industry due to its many advantages such as high heat source temperature, concentrated energy, controllable spraying atmosphere, wide process range, and good process stability. About 80% of the thermal sprayed parts of aviation engines use plasma spraying process. However, the plasma spraying process involves a wide variety of coatings and complex process parameters. Plasma spraying process parameters that meet the performance requirements of aviation coatings often require a lot of test verification and repeated iterative optimization. At present, the development of plasma spraying process depends on the experience of technicians, with a large amount of test workload, a long verification cycle, and low development efficiency. Therefore, providing an optimization method that can quickly and efficiently achieve plasma spraying parameter matching is of great value for plasma spraying process development and engineering applications. Summary of the invention

[0003] The object of the present invention is to provide a method for optimizing plasma spraying parameters to improve the efficiency of process parameter matching during the plasma spraying process development.

[0004] According to an embodiment of the present invention, a method for optimizing plasma spraying parameters is provided, and the method comprises the following steps: determining process parameters that need to be controlled in a plasma spraying process as main influencing factors; providing key microstructural features of a coating prepared by plasma spraying and Delphi method weighted quantitative scores of the key microstructural features, and obtaining characteristic quantitative values ​​of the microstructural features through weighted quantitative scores; using the main influencing factors as experimental factors, conducting DOE experiments, using the characteristic quantitative values ​​as response variables, and establishing a functional relationship between the experimental factors and the response variables; and determining an overall optimal combination of the main influencing factors according to the functional relationship between the experimental factors and the response variables.

[0005] By using the above-mentioned method to perform weighted quantification on the microstructure of the coating, a quantitative functional relationship between the microstructure characteristics and the spraying process parameters is established, which can more intuitively reflect the influence of the spraying process parameters on the coating quality, and then quickly identify the key variables affecting the coating quality among many process parameters, thereby achieving efficient optimization of plasma spraying process parameters, shortening the R&D cycle, and reducing test costs.

[0006] Furthermore, in some embodiments, the main influencing factors include a combination of at least two of the main gas flow rate, auxiliary gas flow rate, spraying current, spraying voltage, powder feeding carrier gas flow rate, powder feeding rate, powder feeding plate rotation speed, spraying distance and spray gun moving speed.

[0007] Further, in some embodiments, the key microstructural features include a combination of at least two of porosity, oxides, unmelted particles, interface contamination, microcracks and delamination.

[0008] Furthermore, in some embodiments, the DOE experiment adopts a full factorial experiment or a partial factorial experiment. The full factorial experiment has higher accuracy, but the experiment amount and experiment cost are also higher; when the number of experimental factors under investigation is large, the partial factorial experiment can optimize the experiment cycle and experiment cost.

[0009] Furthermore, in some embodiments, the DOE experiment is a two-level experiment.

[0010] Furthermore, in some embodiments, the functional relationship between the test factor and the response variable is obtained by calculating the regression equation of the response variable with respect to the test factor.

[0011] Furthermore, in some embodiments, the overall optimal combination of the main influencing factors is obtained by DOE factor response optimization prediction.

[0012] Furthermore, in some embodiments, the highest weight value among the main influencing factors is 0.45-0.65.

[0013] Furthermore, in some embodiments, the weighted quantitative scores of the key microstructural features are all expected large variables or all expected small variables.

[0014] Furthermore, in some embodiments, after determining the overall optimal combination of the main influencing factors, it also includes experimental verification and optimization iteration steps. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a metallographic microstructure photograph of a plasma sprayed cobalt-chromium-molybdenum alloy coating before optimization in an embodiment;

[0016] Figure 2 This is an effect diagram of factors related to the quality of the cobalt-chromium-molybdenum alloy coating in an embodiment;

[0017] Figure 3 Parameter factor effect diagram after optimization of the cobalt-chromium-molybdenum alloy coating variables in one embodiment;

[0018] Figure 4a It is a normalized residual diagram of the quantified value of the microstructure of the cobalt-chromium-molybdenum alloy coating in one embodiment;

[0019] Figure 4b It is a diagram of residual fitting values ​​of quantified values ​​of microstructure of cobalt-chromium-molybdenum alloy coating in one embodiment;

[0020] Figure 4c A histogram of normalized residuals of quantified values ​​of microstructure of a cobalt-chromium-molybdenum alloy coating in an embodiment;

[0021] Figure 4d It is a sequence diagram of the standardized residual observation values ​​of the microstructure quantization value of the cobalt-chromium-molybdenum alloy coating in an embodiment;

[0022] Figure 5 It is a process parameter optimization diagram for quantitative prediction of microstructure of cobalt-chromium-molybdenum alloy coating in one embodiment;

[0023] Figure 6 The metallographic microstructure photograph of the cobalt-chromium-molybdenum alloy coating after process parameter optimization in one embodiment.

[0024] The purpose of the above drawings is to explain the present invention in detail so that those skilled in the art can understand the technical concept of the present invention, but it is not intended to limit the present invention. DETAILED DESCRIPTION

[0025] The present invention is further described in detail below through specific embodiments in conjunction with the accompanying drawings.

[0026] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The phrase appearing in various locations in the specification does not necessarily refer to the same embodiment, nor is it limited to mutually exclusive independent or alternative embodiments. Those skilled in the art should be able to understand that the embodiments herein may be combined with other embodiments without causing structural conflicts.

[0027] In the description of this article, the terms "first", "second" and the like are only used to distinguish different objects, and cannot be understood as indicating relative importance or limiting the quantity, specific order or primary and secondary relationship of the described technical features. In the description of this article, the meaning of "plurality" is at least two.

[0028] Thermal spraying technology, with its process and performance advantages, has become an extremely important surface treatment technology in the design and overhaul of modern aircraft engines. A modern aircraft engine has thousands of parts with more than 3,000 surfaces coated with thermal spray coatings. In the thermal spraying process, atmospheric plasma spraying (referred to as plasma spraying) has many advantages such as high heat source temperature, concentrated energy, controllable spraying atmosphere, wide process range and good process stability, and has been widely used. About 80% of thermal sprayed parts in aircraft engines use plasma spraying technology. However, there are many types of coatings in the manufacturing or maintenance of aircraft engine parts, involving more than twenty different powder materials; at the same time, even for the same spraying material, due to the structural differences between the plasma spraying equipment used, the plasma spraying process parameters of different spraying equipment do not have a good reference and inheritance, and even some coating powder material suppliers recommend spraying process parameters that cannot obtain spraying effects that meet the design requirements in actual applications. Therefore, in engineering practice, the development of spraying processes for different materials and different equipment is difficult, the workload is large, the development cycle is long, and a lot of costs are consumed. At present, the development of plasma spraying process parameters relies on the engineering experience of technicians. It is necessary to make corresponding adjustments to the spraying parameters by examining the mechanical properties, hardness and microstructural state of the coating. The optimization process usually adopts conventional trial and error method, which lacks direction and purpose, has a low degree of standardization, complex uncertain factors and low overall efficiency.

[0029] In order to solve the above problems, an embodiment of the present invention provides a method for optimizing plasma spraying parameters, which can quickly and effectively achieve the matching of plasma spraying process parameters and optimize the spraying quality of the coating.

[0030] Some existing quantitative technical solutions for optimizing spraying process parameters focus on performance parameters such as coating hardness, bonding strength between coating and substrate, and elastic modulus of coating, which can directly measure specific values, as response variables of experimental design to analyze test results and optimize parameters. However, for plasma spray coatings, microstructural characteristics including internal porosity, oxide level, unmelted particle size, interface contamination, microcracks and delamination of coatings have a more significant impact on the performance of coatings. In one embodiment, the metallographic photograph of the cobalt-chromium-molybdenum alloy coating is as follows: Figure 1As shown, the coating 2 is attached to the surface of the alloy substrate 1. The chemical composition of the coating 2 is calculated by mass percentage: no more than 0.08% C, 27%-30% Mo, 3%-3.8% Si, no more than 1.5% Fe, no more than 0.03% P, no more than 1.5% Ni, no more than 0.03% S, no more than 0.15% O, 16.5%-18.5% Cr, and the balance is Co. The coating 2 is prepared by spraying using a Praxair 7700 plasma spraying equipment using an SG-100 internal powder spray gun. In the metallographic photograph, it can be observed that there are relatively obvious defects in the coating 2, such as interface contamination 3, unmelted particles 4, pores 5 and oxide clusters 6. It is necessary to optimize the spraying process parameters to improve the coating quality.

[0031] The specific optimization process is as follows:

[0032] First, determine the process parameters that need to be controlled in the plasma spraying process as the main influencing factors. In a preferred embodiment, the main influencing factors can be selected from a combination of several parameters including main gas flow, auxiliary gas flow, spraying current, spraying voltage, powder carrier gas flow, powder feeding rate, powder feeding disk speed, spraying distance and spray gun moving speed. In this embodiment, the output variable Y is defined by the relationship Y=F(A, B, C, D, E), where A is the main gas flow, B is the auxiliary gas flow, C is the spraying current, D is the powder feeding rate, and E is the spraying distance; the output variable Y can include Y m (response variable after quantification of coating microstructure characteristics, which is a large output variable), Y h (response variable of coating hardness value, which is a large output variable), Y s (The response variable of the tensile strength of the coating is a large output variable). In the method provided in this embodiment, Y m as the response variable.

[0033] Next, the key features of the microstructure of the coating 2 are determined. In different embodiments, the key features may include a combination of porosity, oxides, unmelted particles, cross-sectional contamination, microcracks, and delamination. In this embodiment, porosity P, unmelted particles U, and oxide level O are used. The Delphi method is used to quantify and weight the above key features: Y m=a1P+a2U+a3O, in the preferred embodiment, a1 is the main key feature weight, set to 0.45-0.65. Delphi method is an expert consultation method. In the embodiment, the metallographic microstructure characteristics are quantified by submitting the metallographic photos of the coating to the technicians for scoring according to the same standard. Among them, the weighted quantitative scoring standards of different microstructural features should be consistent, and each variable should be set as a large variable (the higher the score, the better) or set as a small variable (the lower the score, the better). In this embodiment, it is set to 0.6, a2 is set to 0.25, and a3 is set to 0.15; porosity P, unmelted particles U and oxide level O are divided into three grade intervals, excellent for 80-100 points, good for 65-80 points, and poor for 35-60 points. Among them, the specific scoring criteria can be formulated for cobalt-chromium-molybdenum alloy coatings. Special scoring maps can also be used to divide grade intervals and determine scoring criteria by referring to industry public standards such as GB / T36591-2018 and other documents. In some embodiments, the "expert" in the Delphi scoring process of microstructural features can also be image recognition software or a specially trained algorithm program.

[0034] The three main influencing factors P, U, and O are used as experimental factors to carry out DOE (Design of experiment) test design. DOE is a statistical method for arranging experiments and analyzing experimental data. It mainly arranges experiments reasonably to obtain ideal experimental results and scientific conclusions with a smaller experimental scale, shorter cycle and lower cost. For the Praxair7700 plasma spraying equipment, after setting the gas flow rate and spraying current, the spraying voltage and spraying power are automatically generated by the equipment. Therefore, the main influencing factors affecting the coating quality characteristics in this embodiment include the main gas flow rate A (argon), auxiliary gas flow rate B (helium), spraying current C, powder feeding rate D and spraying distance E. Due to the large number of experimental factors, if a full factor two-level experiment is carried out, Xu conducted 2 5 That is, 32 process tests, the cycle and cost are high, so a partial factorial experiment is used to find the key variables with a smaller number of tests. In other embodiments, when the number of test factors is small, a full factorial experiment can also be used.

[0035] Mintab software was used to conduct a 5-factor, 2-level 1 / 2 fractional factorial experiment, with the center point and block group set to 1. An orthogonal test table as shown in Table 1 was established, and the process test was carried out according to the test scheme sequence in Table 1, and the metallographic microscopic characteristic value Y of the coating after the test was calculated. m The calculation is performed according to the quantitative weighted scoring formula, and the results are shown in Table 1.

[0036]

[0037] Table 1 Orthogonal test design table of cobalt-chromium-molybdenum alloy coating

[0038] The test was carried out according to the test sequence shown in Table 1, and the metallographic structure of the coating was quantitatively assigned. The scoring results were compared with the metallographic microscopic characteristic value Y m The calculation results are shown in Table 2.

[0039] Test No. Porosity Unmelted particles U Oxide Ym: Microstructure quantitative value 1 65 60 60 63 2 70 70 65 69.25 3 75 70 70 73 4 65 65 60 64.25 5 70 65 65 68 6 75 72 70 73.5 7 70 65 65 68 8 80 80 70 78.5 9 70 65 70 68.75 10 90 85 85 88 11 65 65 65 65 12 70 60 50 64.5 13 80 85 85 82 14 70 70 65 69.25 15 80 80 80 80 16 80 80 75 79.25 17 75 75 70 74.25

[0040] Table 2 Quantitative values ​​of metallographic microstructural characteristics of coating tests

[0041] Mintab software was used to perform factor analysis on the experimental data in Table 1 to obtain the effect Pareto chart of variable factors, as shown in Figure 2 As shown, factor A is the main gas flow-Ar (SCFH), B is the auxiliary gas flow-He (SCFH), C is the spraying current (A), D is the powder feeding rate (g / min), E is the spraying distance (mm), the response is the microstructure quantification value, α = 0.05, and Lenth's PSE = 0.515625. According to Figure 2 It can be seen that the importance of factors affecting the metallographic quality of the coating is ranked as auxiliary gas flow rate B, main gas flow rate A, the interaction between auxiliary gas flow rate B and main gas flow rate A, spraying current C, spraying distance E, and powder feeding rate D. Among them, the interaction between auxiliary gas flow rate B, main gas flow rate A, spraying current C, and AB is the main significant factor. Remove the non-significant items and perform DOE analysis on the experimental data to obtain the following Figure 3 The standardized effects plots shown and Figure 4a-4d The quantitative residual value diagram of the microstructure is shown. Since the powder feeding rate D and the spraying distance E have little effect on the quality of the coating within the test level range, their average values ​​can be taken to determine that the powder feeding rate of the cobalt-chromium-molybdenum alloy coating during plasma spraying is set to 45g / min and the spraying distance is set to 105mm. Figure 4a It can be seen that the residuals in the four-in-one residual graph obey the normal distribution, and Figure 4b The scatter plot of the residuals and fitted values ​​shown in the figure shows that the distribution of the three points has no obvious funnel or trumpet shape, and the residuals maintain equal variance, which can be determined to be within the normal range. Figure 4c shows the frequency distribution histogram of the standardized residuals, Figure 4d Standardized residuals for the order of observations are shown.

[0042] The regression equation of the microstructure quantitative value of the cobalt-chromium-molybdenum alloy coating was obtained by DOE factor analysis. The calculated regression equation is in the form of Y m =80.1-0.623A-0.469B+0.015C+0.00917AB+16.72.

[0043] Through response optimization, the optimal spraying process parameters of the cobalt-chromium-molybdenum alloy coating can be predicted. For the cobalt-chromium-molybdenum alloy coating after spraying, the quantitative value of the coating microstructure is expected to be a large response variable. The DOE factor response optimization prediction is performed through Mintab software, and the following is obtained: Figure 5 As shown in the process parameter optimization diagram, it can be seen that when the main gas flow rate A = 105 (SCFH), the auxiliary gas flow rate B = 85 (SCFH), and the spraying current C = 975 (A), it is expected that Y m The overall optimal value is 88.

[0044] Finally, in an optional embodiment, the predicted overall optimal main influencing factor parameters are verified and optimized iteratively. The plasma spraying process test of the cobalt-chromium-molybdenum alloy coating is carried out, with the main gas flow A=105 (SCFH), the auxiliary gas flow B=85 (SCFH), the spraying current C=975 (A), the powder feeding rate D=45 (g / min), and the spraying distance E=105 (mm). The metallographic structure of the cobalt-chromium-molybdenum alloy coating obtained is as follows: Figure 6 As shown, the metallographic microstructure is good, with no obvious pores, unmelted particles, oxide aggregation or interface contamination; the bonding strength between the coating 2 and the alloy substrate 1 is measured to be 22.15-26.69Mpa, meeting the process requirement of 18.6MPa; the Rockwell hardness HRN15 of the coating is 83.4-84.3, and the composite hardness value HRN15 ≥ 82. The test results meet the coating quality expectations.

[0045] In other embodiments, the plasma spraying parameter optimization method provided by the present invention can be applied to the optimization of coating spraying process parameters of other materials, and can also be used to quickly optimize other types of spraying equipment and other different spraying process parameters, effectively improving the development efficiency of plasma spraying process parameters and improving spraying quality.

[0046] The purpose of the above embodiments is to further explain the present invention in detail in conjunction with the accompanying drawings so that those skilled in the art can understand the technical concept of the present invention. Within the scope of the present invention, the optimization or equivalent replacement of the method steps involved, or the combination of the implementation methods in different embodiments without any conflict of principles, all fall within the protection scope of the present invention.

Claims

1. A method for optimizing plasma spraying parameters, characterized in that: The following steps are involved: Determine the process parameters that need to be controlled in the plasma spraying process as the main influencing factors; Providing key microstructural features of a coating prepared by plasma spraying and Delphi method weighted quantitative scores of the key microstructural features, and obtaining characteristic quantitative values ​​of the microstructural features through weighted quantitative scores; Taking the main influencing factors as experimental factors, conducting DOE experiments, taking the characteristic quantified values ​​as response variables, and establishing a functional relationship between the experimental factors and the response variables; The overall optimal combination of the main influencing factors is determined according to the functional relationship between the experimental factors and the response variables.

2. The method for optimizing plasma spraying parameters according to claim 1, characterized in that: The main influencing factors include a combination of at least two of the main gas flow rate, auxiliary gas flow rate, spraying current, spraying voltage, powder feeding carrier gas flow rate, powder feeding rate, powder feeding plate rotation speed, spraying distance and spray gun moving speed.

3. The method for optimizing plasma spraying parameters according to claim 1, characterized in that: The key microstructural features include a combination of at least two of porosity, oxides, unmelted particles, interface contamination, microcracks and delamination.

4. The method for optimizing plasma spraying parameters according to claim 1, 2 or 3, characterized in that: The DOE experiment adopts a full factorial experiment or a fractional factorial experiment.

5. The method for optimizing plasma spraying parameters according to claim 4, characterized in that: The DOE experiment was a two-level experiment.

6. The method for optimizing plasma spraying parameters according to claim 1, 2 or 3, characterized in that: The functional relationship between the experimental factor and the response variable is obtained by calculating the regression equation of the response variable with respect to the experimental factor.

7. The method for optimizing plasma spraying parameters according to claim 1, 2 or 3, characterized in that: The overall optimal combination of the main influencing factors is obtained through DOE factor response optimization prediction.

8. The method for optimizing plasma spraying parameters according to claim 1, 2 or 3, characterized in that: The highest weight value among the main influencing factors is 0.45-0.

65.

9. The method for optimizing plasma spraying parameters according to claim 1, 2 or 3, characterized in that: The weighted quantitative scores of the key microstructural characteristics are all large-scale variables or all small-scale variables.

10. The method for optimizing plasma spraying parameters according to claim 1, 2 or 3, characterized in that: After determining the overall optimal combination of the main influencing factors, it also includes experimental verification and optimization iteration steps.

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