A comprehensive evaluation method for bimetallic composite based on multi-attribute decision-making

Abstract

The application discloses a kind of based on multi-attribute decision-making bimetallic composite material comprehensive evaluation method, belong to material performance comprehensive evaluation field.The application implementation method is: considering the multiple properties of multiple regions of bimetal, the performance comprehensive evaluation model of bimetallic composite material under multiple alternative process schemes is established;Establish the mapping relationship between evaluation index and the performance comprehensive evaluation model of bimetallic composite material;Fully consider the internal law between designer's subjective demand and index data, construct bimetallic composite material performance comprehensive evaluation index system;The subjective weight and objective weight of metal composite material evaluation index are combined;The performance of bimetallic composite material under multiple alternative schemes is scored and sorted using TOPSIS method, finally determine the optimal process scheme that both meets design requirement and can quantitatively weigh bimetal index, process bimetallic composite material according to optimal process scheme.The application can improve the precision and efficiency of bimetallic composite material comprehensive evaluation.

Description

Technical Field

[0001] This invention belongs to the field of comprehensive evaluation of material properties, and in particular relates to a comprehensive evaluation method for bimetallic composite materials based on multi-attribute decision-making. Background Technology

[0002] The development of industry and technology has posed greater challenges to traditional single-material components. In actual production applications, these components need to possess certain strength and toughness, as well as wear resistance, corrosion resistance, and so on, to improve their service life while ensuring economic efficiency. Finding a single material that can meet multiple requirements is difficult; therefore, with the development of manufacturing technology, bimetallic composite materials have gradually gained widespread attention. Bimetallic composite materials are composed of two materials with different physicochemical properties. The two sides of the interface retain their original materials, while a metallurgical bond is formed at the interface, allowing the advantages of both metal materials to be utilized.

[0003] Bimetallic composites contain multiple regions, including the diffusion zone and heat-affected zone (located at the bonding interface), the original metal region, and the manufacturing material region, all of which simultaneously influence the overall performance and service life of the bimetal. The bonding interface of bimetallic composites undergoes element diffusion, phase transformation, and new phase formation during manufacturing, resulting in a complex forming mechanism that differs from that of the original metal. Furthermore, the properties at the bonding interface also differ from those of the original metal, and the sensitivity of each property at the bonding interface to process parameters varies. When applying bimetallic composites in engineering, the different properties of different regions of the bimetal should be considered simultaneously to achieve a holistic understanding of the composite's performance. Therefore, conducting performance evaluations of bimetallic composites is of great significance.

[0004] Current research on the performance evaluation of bimetallic composites is limited to single-performance evaluations at specific locations, such as the bonding strength at the interface, hardness, wear resistance, corrosion resistance, and fatigue performance. This evaluation method has limitations and cannot reflect the overall characteristics of bimetallic composites. Performance requirements for bimetallic composites vary across different applications, and a single evaluation standard cannot be used to measure their quality. Based on the design requirements of bimetallic composites, a comprehensive evaluation of their performance under different manufacturing processes is crucial for understanding the overall performance of bimetallic composites during the development phase, thus saving manufacturing costs and time.

[0005] Therefore, there is currently a lack of suitable methods for the comprehensive evaluation of bimetallic composite materials, which leads to the main method of trial and error in the comprehensive evaluation, resulting in high time, human and material costs. In addition, the evaluation standards for bimetallic composite materials mainly rely on experimental experience and the subjective judgment of R&D personnel, which lack a certain degree of objectivity and a unified process to guide the comprehensive evaluation of bimetallic composite materials. Summary of the Invention

[0006] To address the lack of suitable comprehensive evaluation methods and unified procedures for guiding the comprehensive evaluation of bimetallic composite materials in engineering applications, this invention aims to provide a comprehensive evaluation method for bimetallic composite materials based on multi-attribute decision making (MADM), which can improve the accuracy and efficiency of comprehensive evaluation of bimetallic composite materials.

[0007] The objective of this invention is achieved through the following technical solution.

[0008] The comprehensive evaluation method for bimetallic composite materials based on multi-attribute decision-making disclosed in this invention includes the following steps:

[0009] Step 1: Based on the pre-set design requirements for bimetals, and considering the various properties of multiple regions of bimetals, establish a comprehensive performance evaluation model for bimetallic composite materials under multiple alternative process schemes.

[0010] Step 2: Based on the comprehensive performance evaluation model described in Step 1, determine the evaluation indicators and establish the mapping relationship between the evaluation indicators and the comprehensive performance evaluation model of bimetallic composite materials; construct a comprehensive performance evaluation indicator system for bimetallic composite materials.

[0011] Step 3: Assign subjective weights to the evaluation indicators of bimetallic composite materials based on design requirements;

[0012] Step 4: Assign objective weights to the evaluation indicators of bimetallic composite materials based on the characteristics of each indicator data;

[0013] Step 5: Combining the subjective weights from Step 3 and the objective weights from Step 4, the subjective and objective weights are combined to obtain the combined weights of the comprehensive performance evaluation index of bimetallic composite materials.

[0014] Step 6: Using the combined weights obtained in Step 5, score and rank the performance of bimetallic composite materials under multiple alternative schemes using the TOPSIS method, determine the optimal process scheme that meets the design requirements and can quantitatively weigh the bimetallic indicators, and process the bimetallic composite material according to the optimal process scheme.

[0015] Among the various properties of the multiple regions mentioned in Step 1, the division method of the multiple regions is as follows: In the laser cladding process, the bimetallic composite material is divided into the matrix region, heat-affected zone, semi-melted zone, diffusion zone, and cladding zone; In the powder bed melting process, the composite material is divided into the A metal region, semi-melted zone, diffusion zone, and B metal region; In the welding process, the bimetallic composite material is divided into the matrix region, heat-affected zone, and weld zone.

[0016] The various properties include: microhardness, nanohardness, elastic modulus, nanoelastic modulus, yield strength, tensile strength, elongation after fracture, reduction of area, impact toughness, fracture toughness, fatigue strength, fatigue life, thermal conductivity, coefficient of thermal expansion, resistivity, corrosion resistance, oxidation resistance, and wear resistance.

[0017] The specific method for constructing the comprehensive evaluation index system for the performance of bimetallic composite materials in step two is as follows:

[0018] Step 1) Prepare bimetallic test samples under multiple alternative process parameters through process experiments, determine multiple regions of the bimetal and corresponding properties, and use each property of each region as an evaluation index.

[0019] Step 2) Obtain data for each evaluation indicator in Step 1) using the corresponding testing methods for the performance determined in Step 1);

[0020] Step 3) Calculate the dispersion of the data for each evaluation indicator in Step 2), where α represents the dispersion of the data. The smaller α is, the less dispersion the data. Here, D is the sum of the variances of all data, N is the number of data points, and μ is the average value. The calculation formula is as follows:

[0021]

[0022] Step 4) Based on the α value of each evaluation index calculated in Step 3), filter out the indexes with an α value less than 0.1, and use the remaining indexes as indicators for comprehensive evaluation of the performance of bimetallic composite materials.

[0023] Step 5) Perform dimensionless and normalized processing on the data of each indicator in Step 4), establish a mapping relationship between the evaluation indicators and the bimetallic composite material performance comprehensive evaluation model, and perform consistency processing on the types of indicators to complete the construction of the comprehensive performance evaluation indicator system for bimetallic composite materials.

[0024] The subjective weights mentioned in step three are determined using the analytic hierarchy process (AHP); the objective weights mentioned in step four are determined using the entropy method.

[0025] The specific method for combining subjective and objective weights as described in step five is as follows:

[0026] Step 1) Write the subjective weights determined in Step 3 and the objective weights determined in Step 4 into vector form. The indicator system in Step 2 has n indicators. The vector form of the subjective weights is denoted as: ω a ={a1, a2, ..., a n}, where ω a Let a be the subjective weight vector, a1, a2, ..., a n Let ω represent the subjective weights of the n indicators; the objective weights are denoted as a vector form: ω b ={b1, b2, ..., b n}, where ω b Let b1, b2, ..., b be the objective weight vector. n These are the objective weights of the n indicators;

[0027] Step 2), the weights of subjective weight and objective weight are denoted as β respectively. a β b ,β a The weight of subjective weight, β b The weight assigned to objective weights;

[0028] Step 3) Solve for β in step 2) using matrix form. a β b Value, ωa T ω is the transpose of the vector of subjective weights. b T Given the transpose of the objective weight vector, solve for β. a β b The matrix of values ​​is as follows:

[0029]

[0030] Step 4) β obtained in Step 3) a β b The value is normalized, β a * The weight of the normalized subjective weights, β b * The weights of the normalized objective weights are processed as follows:

[0031]

[0032]

[0033] Step 5) Obtain the combined weight vector set of subjective and objective weights as ω, ω = {β} a * ·a1+β b * ·b1,βa * ·a2+β b * ·b2,...,β a * ·a n +β b * ·b n}. Wherein, β a * ·a1+β b * ·b1,β a * ·a2+β b * ·b2,...,β a * ·a n +β b * ·b n These are the combined weights of n indicators.

[0034] Beneficial effects:

[0035] 1. The comprehensive evaluation method for bimetallic composite materials based on multi-attribute decision-making disclosed in this invention summarizes and analyzes multiple regional partition types and multiple properties in multiple regions according to actual engineering application requirements. Based on the multiple properties of bimetallic materials in multiple regions, a comprehensive performance evaluation model considering multiple regions and multiple properties of bimetallic composite materials is constructed. This model fully considers the influence of key regions and important properties of bimetal on the overall performance of bimetal, and provides a more detailed and accurate evaluation of the overall performance of bimetal, and can characterize the important material performance requirements of actual engineering applications.

[0036] 2. The comprehensive evaluation method for bimetallic composite materials based on multi-attribute decision-making disclosed in this invention has the following effect: the smaller the dispersion of the data, the less impact the index has on the final evaluation, or even the negative effect on the evaluation result. The index is screened according to the dispersion of the performance index data, and a comprehensive evaluation index system for the performance of bimetallic composite materials is constructed based on the screened indexes, thereby improving the evaluation accuracy and efficiency of the comprehensive evaluation method for bimetallic composite materials.

[0037] 3. The comprehensive evaluation method for bimetallic composite materials based on multi-attribute decision-making disclosed in this invention assigns subjective and objective weights to the evaluation indicators respectively. The design requirements for bimetallic performance are characterized by subjective weights, while the characteristics of bimetallic index data are characterized by objective weights. By assigning subjective and objective weights, the design requirements are met, and the objective laws of bimetallic index data are reflected.

[0038] 4. The comprehensive evaluation method for bimetallic composite materials based on multi-attribute decision-making disclosed in this invention combines subjective and objective weights. Subjective weights are influenced by the designer's personal cognition and engineering experience, lacking a certain degree of objectivity; while objective weights are obtained only by formulas and data processing, which can easily lead to inconsistencies between the evaluation results and design requirements. The subjective and objective weight coefficients are in competition with each other, but they also need to be coordinated and unified. Combining subjective and objective weights can overcome the limitations of assigning weights by a single method.

[0039] 5. The comprehensive evaluation method for bimetallic composite materials based on multi-attribute decision-making disclosed in this invention supplements the existing performance evaluation methods for bimetallic composite materials and solves the problem of the lack of comprehensive evaluation methods for bimetallic composite materials in engineering applications. Attached Figure Description

[0040] Figure 1 This is a flowchart of a comprehensive evaluation method for bimetallic composite materials based on multi-attribute decision-making;

[0041] Figure 2 This is a flowchart of the process experiment and performance test in Example 1;

[0042] Figure 3 This is a diagram showing the test locations for the performance indicators of the bimetallic composite material in Example 1;

[0043] Figure 4 This is a flowchart of the weight calculation method for the comprehensive evaluation of the properties of bimetallic composite materials in Example 1;

[0044] Figure 5 This is a flowchart of the TOPSIS method for comprehensive evaluation of bimetallic composite materials in Example 1. Detailed Implementation

[0045] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. It is important to note that the terminology used herein is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0046] See Figure 1This paper presents a comprehensive evaluation method for bimetallic composite materials based on multi-attribute decision-making. Based on the design requirements of bimetal, considering multiple regions of the bimetal, a comprehensive performance evaluation model is established for bimetallic composite materials under multiple alternative process schemes. Based on this comprehensive evaluation model, evaluation indicators are determined, and a mapping relationship is established between the evaluation indicators and the performance evaluation model of bimetallic composite materials. On this basis, a comprehensive performance evaluation indicator system for bimetallic composite materials is constructed. Subjective weights are assigned to the comprehensive performance evaluation indicators of bimetallic composite materials based on design expectations and requirements, while objective weights are assigned based on the characteristics of the indicator data. The inherent laws between the designer's subjective needs and the indicator data are fully considered, and the subjective and objective weights are combined to obtain the combined weights of the comprehensive performance evaluation indicators of bimetallic composite materials. Based on the mapping relationship between the evaluation model and the comprehensive performance evaluation indicator system of bimetallic composite materials, and the combined weights of the comprehensive performance evaluation indicators, the TOPSIS method is used to comprehensively evaluate and rank the performance of bimetallic composite materials.

[0047] Example 1:

[0048] This embodiment will determine the evaluation indicators based on the thermal fatigue design requirements of cast iron / GH4169 bimetallic composite materials manufactured by powder bed fusion (PBF) under different process parameters, establish the mapping relationship between the evaluation indicators and the performance evaluation model of cast iron / GH4169 bimetallic composite materials, and on this basis, construct a comprehensive evaluation index system for the thermal fatigue performance of cast iron / GH4169 bimetallic composite materials; calculate the subjective and objective combination weights of each evaluation index in the evaluation index system, and use the TOPSIS method to comprehensively evaluate and rank the thermal fatigue performance of cast iron / GH4169 bimetallic composite materials.

[0049] This embodiment is based on the design requirements for thermal fatigue performance when cast iron / GH4169 bimetallic composite material is applied to high power density cylinder head. It considers the various properties of the manufacturing material region (GH4169), diffusion region and heat-affected zone of cast iron / GH4169 bimetallic composite material and establishes a comprehensive evaluation model.

[0050] The above comprehensive evaluation model is as follows: the less thermal stress generated at the interface of the cast iron / GH4169 bimetallic composite material under thermal load, the better; and the stronger the wear resistance and deformation resistance of the GH4169 surface of the cast iron / GH4169 bimetallic composite material, the better.

[0051] This embodiment, based on the design requirements of cast iron / GH4169 bimetallic composite materials, determines the comprehensive performance evaluation indicators of cast iron / GH4169 bimetallic composite materials:

[0052] Considering that the bonding interface size of the cast iron / GH4169 bimetallic composite material prepared by PBF is only between 100-200μm, this embodiment selects seven indicators: GH4169 surface nano-hardness (A1), GH4169 surface elastic modulus (A2), diffusion zone nano-hardness (B1), diffusion zone elastic modulus (B2), cast iron heat-affected zone nano-hardness (C1), cast iron heat-affected zone elastic modulus (C2), and density (D) of the cast iron / GH4169 bimetallic composite material.

[0053] The mapping relationship between the above comprehensive evaluation model and the above evaluation indicators is as follows:

[0054] (1) The density of the GH4169 additive layer is over 95%: The density of the additive layer is negatively correlated with defects and positively correlated with the performance of the additive layer. Low density will lead to poor thermal and mechanical properties of the additive layer. Mario Valdez's research shows that when the density of the SLM additive part reaches 95%, its yield strength and tensile strength can reach 90% of that of GH4169 manufactured by the traditional method.

[0055] (2) The hardness of the GH4169 additive layer should not be lower than that of cast iron, and the higher the better: Hardness is positively correlated with wear resistance. The cylinder head will be subjected to wear from parts such as valve seats during service. Therefore, the surface of GH4169 should have good wear resistance.

[0056] (3) The elastic modulus of the GH4169 additive layer should not be lower than that of the elastic modulus of cast iron, and the higher the better: the elastic modulus characterizes the material’s ability to resist deformation, and the cylinder head has certain stiffness requirements during service.

[0057] (4) Considering the thermal fatigue design requirements, the elastic modulus of the interface (including the heat-affected zone of cast iron and the diffusion part) should be as low as possible: the interface is prone to failure under thermal load due to the difference in the thermal properties of the materials. The low elastic modulus at the interface will result in less thermal stress under thermal load, which is beneficial to improving the thermal fatigue performance of the interface and increasing the overall service life of the bimetal.

[0058] (5) Considering the design requirements for thermal fatigue, the hardness of the interface (including the heat-affected zone and diffusion part of cast iron) should be as close as possible to the hardness of cast iron: reduce stress concentration and uneven stress distribution caused by hard and brittle compounds such as martensite and ledeburite.

[0059] The comprehensive performance evaluation indexes and data of the aforementioned cast iron / GH4169 bimetallic composite material were obtained through PBF process and multi-region performance testing. The method for constructing the comprehensive performance evaluation index system of the bimetallic composite material is as follows:

[0060] Step 1: First, conduct PBF process experiments and prepare 6 groups of cast iron / GH4169 bimetallic composite material samples under different process parameters (Table 1).

[0061] Table 1 PBF process test parameters

[0062] Power W 225 240 255 270 285 300 speed mm / s 1000 1200 1100 950 1150 1050 Spacing mm 0.09 0.13 0.11 0.08 0.12 0.1

[0063] Step Two, Refer to Figure 2-3 The heat-affected zone and diffusion zone of cast iron at the interface of each sample, as well as the nanohardness and nanoelastic modulus of GH4169, were tested. The density of each cast iron / GH4169 bimetallic sample was also tested, and the data for each performance index were used (Table 2). Except for density (D), the other data are dimensionless.

[0064] Table 2 Data for each performance indicator

[0065] 1 0.4502 0.2977 0.7271 -0.0131 0.3588 0.0179 95.96％ 2 0.4379 0.0948 0.7795 0.1434 0.3370 0.0383 97.10％ 3 0.6357 0.0535 1.0884 0.0878 0.9302 0.1016 95.09％ 4 0.5121 0.0326 0.9084 -0.0028 0.0674 -0.0207 97.50％ 5 0.4926 0.0382 0.7009 -0.0480 0.2768 0.0900 94.30％ 6 0.5877 0.0291 0.9870 -0.0211 0.1250 0.0412 95.68％

[0066] Step 3: Calculate the dispersion α of the data for each evaluation indicator. Calculate the dispersion α of the seven performance indicators in the indicator system separately. The selection criterion is: if the dispersion α of an indicator is less than 0.1, it can be considered that the indicator has little impact on the final evaluation result data, or has a negative effect, and can be deleted. The dispersion of the seven indicators in the above comprehensive performance evaluation indicator system for cast iron / GH4169 bimetallic composite materials is listed in Table 3.

[0067] Table 3. Dispersion of the seven indicators

[0068] Discreteness α 0.1370 1.0444 0.1634 2.7860 0.8039 0.9285 0.0115

[0069] Step 4: The dispersion of index D is the smallest, only 0.0115. The variation of index D within the experimental process range is very small, and the change of index D data has little impact on the final evaluation result. The average value of index D is 95.94%, which already meets the requirements of (1) in the mapping relationship between the comprehensive evaluation model and the evaluation index. Therefore, index D is deleted from the seven indicators of the comprehensive evaluation index system of cast iron / GH4169 bimetallic composite material performance in this embodiment.

[0070] Step 5: Normalize the filtered indicator data (the data is now dimensionless); based on the mapping relationship between the comprehensive evaluation model and the evaluation indicators, standardize the type of the processed indicator data, transforming extremely small, interval-type, and intermediate-type indicator data into extremely large indicator data. The transformed data is listed in Table 4:

[0071] Table 4. Data after conversion

[0072]

[0073] See Figure 4 Calculate the combined weights of the performance indicators of the cast iron / GH4169 bimetallic composite material.

[0074] In this embodiment, the subjective and objective weights of the performance indicators of the cast iron / GH4169 bimetallic composite material were calculated, and the subjective and objective weights were combined and weighted based on game theory.

[0075] The subjective weight is calculated as follows:

[0076] The Analytic Hierarchy Process (AHP) is a subjective evaluation method proposed by American operations researcher Saaty in the early 1970s. It is suitable for decision-making schemes that require subjective decision-making or judgment based on experience.

[0077] Step 1: Determine the index system. The index system determined in this embodiment is as follows: GH4169 surface nano-hardness (A1), GH4169 surface elastic modulus (A2), diffusion zone nano-hardness (B1), diffusion zone elastic modulus (B2), cast iron heat-affected zone nano-hardness (C1), and cast iron heat-affected zone elastic modulus (C2).

[0078] Step 2: Construct the judgment matrix. Typically, the judgment matrix is ​​constructed by experts evaluating each indicator pairwise and assigning values ​​based on the relative importance of the two indicators, according to the analytic hierarchy process (AHP) evaluation system table in Table 5. Indicators with higher values ​​are considered more important.

[0079] Table 5 Evaluation System of Analytic Hierarchy Process

[0080] 1 Equally important 3 Slightly more important 5 Obviously important 7 Very important 9 Extremely important 2，4，6，8 Between the above levels of importance

[0081] Based on the scoring results above, a judgment matrix A can be constructed. All elements in the judgment matrix are greater than 0, the diagonal elements are equal to 1, and the values ​​in the upper and lower triangles are reciprocals of each other. Based on the scoring rules and the characteristics of the judgment matrix, the judgment matrix A in this embodiment is shown below.

[0082]

[0083] Step 3: Calculate the weights. Based on the judgment matrix above, the square root method is used. First, the product of each element in each row is calculated, and then the nth root is taken to obtain the row vector of the index. Then, the row vectors are normalized to calculate the weight vector W, thus obtaining the weights of the indicators.

[0084] Table 6 Subjective weights of each indicator in Example 1

[0085] Weight 0.1204 0.0346 0.1051 0.1868 0.2294 0.3236

[0086] Step 4: Consistency Check. The consistency check is used to determine whether there are logical problems in the constructed judgment matrix. For example, if a judgment matrix is ​​constructed using three indicators a, b, and c, and the scale of a relative to b is 3, and the scale of b relative to c is also 3 (indicating that the importance order is a > b > c), then if the scale of a relative to c in the judgment matrix is ​​1 / 3, then there is a logical error. Calculate the consistency index (CI value) based on the largest eigenvalue, obtain the random consistency index (RI value) from Table 7, and calculate the consistency ratio (CR value) to determine if the consistency has passed. When CR is less than 0.1, it indicates that the consistency of the judgment matrix is ​​within the acceptable range, and the consistency check has passed; when CR is greater than 0.1, it indicates that the constructed judgment matrix has failed the consistency check and contains a logical error, requiring adjustment of the judgment matrix until it passes the consistency check.

[0087] Table 7 Random Consistency Index (RI) Values

[0088] RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.54 1.56

[0089] After obtaining the weights, the largest eigenvalue λ can be calculated. max :

[0090]

[0091] Where n is the order, A is the judgment matrix, and W is the weight vector.

[0092] The above maximum eigenvalue λ max Subtracting the order n of the matrix and then dividing by the order of the matrix minus one (n-1) yields the consistency index (CI value). Then, based on Table 7, the random consistency index (RI value) is obtained, and the consistency ratio (CR value) is calculated.

[0093]

[0094]

[0095] The consistency ratio (CR value) in this embodiment is 0.0336, which is less than 0.1. Therefore, the judgment matrix established in this embodiment is logically reasonable, and the subjective weights obtained have passed the consistency evaluation.

[0096] The objective weights are calculated as follows:

[0097] Entropy method is a mathematical method used to determine the degree of dispersion of an indicator, providing objective weights for comprehensive evaluation of multiple indicators. By calculating the entropy value of an indicator, the degree of dispersion and information carrying capacity of the indicator are judged. The smaller the entropy value, the greater the degree of dispersion of the indicator, the greater the information carrying capacity, the greater the influence of the indicator on the comprehensive evaluation, and the greater its weight, and vice versa.

[0098] Step 1: Handling the positive and negative correlation of data. When the data units and data types are inconsistent, the data should be standardized and dimensionless. To avoid the logarithm being meaningless when calculating entropy, a small order of magnitude real number, such as 0.001, can be added to each value.

[0099] Step 2: Calculate the magnitude of variation P for each data point of the indicator. ij Calculate the weight of each indicator within the overall index, using the data x for each indicator. ij Divide by the sum of the j-index data Obtain the variation size P of each data point for this indicator. ij .

[0100] Step 3: Calculate the entropy value e j The entropy value of each indicator is calculated using the following formula, where n is the number of samples, and P... ij The variation size for each data point.

[0101]

[0102] Step 4: Calculate the utility value d of the indicator information j The information utility value d of a certain indicator j Equal to the entropy value e j The difference between 1 and d is the information utility value. j The larger the value, the greater its importance in the evaluation, and the greater its weight.

[0103] Step 5: Calculate the objective weights of the indicators. Assume there are m indicators, and the information utility value d for each indicator. j The total information utility value of all indicators The ratio is the objective weight of the indicator. The objective weights in this embodiment are listed in Table 8.

[0104]

[0105] Table 8. Objective weights of each indicator in Example 1

[0106] <![CDATA[A1]]> 0.3786 0.6214 0.1692 <![CDATA[A2]]> 0.2420 0.7580 0.2064 <![CDATA[B1]]> 0.4237 0.5763 0.1569 <![CDATA[B2]]> 0.4324 0.5676 0.1545 <![CDATA[C1]]> 0.4458 0.5542 0.1509 <![CDATA[C2]]> 0.4046 0.5954 0.1621

[0107] Subjective and objective weight coefficients are in competition with each other, yet they need to be coordinated and unified. Combining these weights can satisfy the design requirements of the cylinder head while reflecting the objective patterns of various performance data. The calculation method for combining subjective and objective weights is as follows:

[0108] Step 1: The vector form of subjective weights and objective weights is denoted as:

[0109] ω a ={0.1204, 0.0346, 0.1051, 0.1868, 0.2294, 0.3236}

[0110] ω b ={0.1692, 0.2064, 0.1569, 0.1545, 0.1509, 0.1621}

[0111] Step 2: The weights of the subjective weight and the objective weight are denoted as β. a β b ,β a The weight of subjective weight, β b The weight assigned to objective weights;

[0112] Step 3: Establish the solution for subjective and objective weights β a β b Given the matrix shown below, solve for β. a β b The values ​​are 0.8759 and 0.1699, respectively.

[0113]

[0114] Step 4: Normalize the above combined weights. In this embodiment, the subjective weight and objective weight are calculated to be 0.8375 and 0.1625 respectively, as obtained through the above steps.

[0115] Step 5: After calculation, the combined weight vector set of subjective and objective weights is obtained as follows:

[0116] ω={0.1283, 0.0625, 0.1135, 0.1816, 0.2167, 0.2974}

[0117] The combined weights of the following indices in the index system are 0.1283, 0.0625, 0.1135, 0.1816, 0.2167, and 0.2974, respectively: GH4169 surface nano hardness (A1), GH4169 surface elastic modulus (A2), diffusion zone nano hardness (B1), diffusion zone elastic modulus (B2), cast iron heat-affected zone nano hardness (C1), and cast iron heat-affected zone elastic modulus (C2).

[0118] See Figure 5 The TOPSIS method was used to score and rank the indicators.

[0119] TOPSIS, also known as the Approximation to Ideal Solution Ranking Method or the Distance Method between Best and Worst Solutions, is a multi-attribute decision analysis method. Its core idea is to construct the ideal solution for the evaluation object, that is, the optimal and worst values ​​among the evaluation indicators, calculate the Euclidean distance between each evaluation object and the ideal solution, obtain the relative closeness to the ideal solution, and rank the evaluation objects according to the relative closeness.

[0120] Step 1: Establish the initial matrix and preprocess the matrix data. Ensure that all indicator data are dimensionless and that the data type has been converted to extremely large; then standardize the matrix, denoted as Z.

[0121] x ij For a given indicator, under one of the process schemes, each element in Z is:

[0122]

[0123] Step 2: Define the optimal and worst values. The optimal and worst values ​​are obtained using the following method:

[0124] optimal value

[0125]

[0126] Worst value

[0127]

[0128] Step 3: Calculate the distance between the i-th evaluation object and the optimal and worst values, obtained as follows:

[0129] The distance between the i-th (i = 1, 2, ..., n) evaluation object and the optimal value

[0130]

[0131] The distance between the i-th (i = 1, 2, ..., n) evaluation object and the worst value

[0132]

[0133] Step 4: The results obtained through the above calculations The comprehensive evaluation score S of the i-th object can be calculated. i And sort them according to their scores. The score S of the i-th (i = 1, 2, ..., n) evaluation object.i The calculation method is as follows:

[0134]

[0135] The comprehensive evaluation scores and rankings of the cast iron / GH4169 bimetallic composite material samples under six sets of process parameters were calculated through the above steps. The results show that the ranking of the six sets of process parameters is as follows: No. 1 > No. 4 > No. 6 > No. 5 > No. 2 > No. 3. The process parameters of No. 1 better meet the thermal fatigue design requirements of the cast iron / GH4169 bimetallic composite material.

[0136] Table 9. Overall Evaluation Score and Ranking

[0137]

[0138] In this embodiment, a comprehensive evaluation method is proposed to assess the performance of cast iron / GH4169 bimetallic composite materials to meet thermal fatigue design requirements. Considering multiple regions of the cast iron / GH4169 bimetallic composite material, a comprehensive performance evaluation model is established for the bimetallic composite material under multiple alternative process schemes. Combining the mapping relationship between evaluation indicators and the bimetallic composite material performance evaluation model, a comprehensive performance evaluation index system for bimetallic composite materials is constructed. Subjective and objective weights are combined to satisfy both the thermal fatigue design requirements of the cast iron / GH4169 bimetallic composite material and to reflect the objective laws governing each performance data. Finally, the TOPSIS method is used to comprehensively evaluate and rank the performance of the bimetallic composite material, obtaining the process parameters that best meet the design requirements. The comprehensive evaluation method provided by this invention solves the problem of lacking a suitable comprehensive evaluation method and a unified process to guide the comprehensive evaluation of bimetallic composite materials in engineering applications.

[0139] The above examples, in conjunction with the accompanying drawings, have described specific embodiments of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention, used to explain the present invention, and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made based on the technical solutions and concepts of the present invention should be included within the scope of protection of the present invention.

Claims

1. A comprehensive evaluation method for bimetallic composite materials based on multi-attribute decision-making, characterized in that: Includes the following steps, Step 1: Based on the pre-set design requirements for bimetals, and considering the various properties of multiple regions of bimetals, establish a comprehensive performance evaluation model for bimetallic composite materials under multiple alternative process schemes. Step 2: Based on the comprehensive performance evaluation model described in Step 1, determine the evaluation indicators and establish the mapping relationship between the evaluation indicators and the comprehensive performance evaluation model of bimetallic composite materials; construct a comprehensive performance evaluation indicator system for bimetallic composite materials. The specific method for constructing a comprehensive performance evaluation index system for bimetallic composite materials is as follows: Step 1) Prepare bimetallic test samples under multiple alternative process parameters through process experiments, determine multiple regions of the bimetal and corresponding properties, and use each property of each region as an evaluation index. Step 2) Obtain data for each evaluation metric in Step 1) using the corresponding performance testing methods determined in Step 1). Step 3) Calculate the dispersion of the data for each evaluation indicator in Step 2). Indicates the degree of dispersion of the data. The smaller the value, the less dispersed the data. Here, D is the sum of the variances of all data points, and N is the number of data points. The average value is calculated using the following formula: Step 4) Based on the data of each evaluation indicator calculated in Step 3) Values, filtering out data The remaining indicators with values ​​less than 0.1 are used as indicators for comprehensive evaluation of the performance of bimetallic composite materials. Step 5) Perform dimensionless and normalized processing on the data of each indicator in Step 4). Based on the mapping relationship between the evaluation indicators and the comprehensive evaluation model of bimetallic composite material performance, perform consistency processing on the types of indicators to realize the construction of the comprehensive evaluation indicator system for bimetallic composite material performance. Step 3: Assign subjective weights to the evaluation indicators of bimetallic composite materials based on design requirements; Step 4: Assign objective weights to the evaluation indicators of bimetallic composite materials based on the characteristics of each indicator data; Step 5: Combining the subjective weights from Step 3 and the objective weights from Step 4, the subjective and objective weights are combined to obtain the combined weights of the comprehensive performance evaluation index of bimetallic composite materials. The specific method for combining subjective weights and objective weights is as follows: Step 1) Express the subjective and objective weights determined in Steps 3 and 4 as vectors. The indicator system in Step 2 has n indicators. The vector form of the subjective weights is denoted as: ,in, For subjective weight vectors, These are the subjective weights of the n indicators; the objective weights are denoted in vector form as follows: ,in For objective weight vectors, These are the objective weights of the n indicators; Step 2), the weights of subjective weight and objective weight are denoted as follows: , , The weighting is determined by subjective factors. The weight assigned to objective weights; Step 3) Solve the problem in Step 2) using matrix form. , value, This is the transpose of the vector of subjective weights. Solve for the transpose of the objective weight vector. , The matrix of values ​​is as follows: Step 4) The result obtained in Step 3) , The values ​​are normalized. The weights are the normalized subjective weights. The weights of the normalized objective weights are processed as follows: Step 5) Obtain the combined weight vector set of subjective and objective weights as follows: , ,in, , , ..., These are the combined weights of n indicators; Step 6: Using the combined weights obtained in Step 5, the TOPSIS method is used to comprehensively evaluate and rank the performance of bimetallic composite materials under multiple alternative schemes, and the optimal process scheme that meets the design requirements and can quantitatively weigh the bimetallic indicators is determined. The bimetallic composite material is then processed according to the optimal process scheme.

2. The comprehensive evaluation method for bimetallic composite materials based on multi-attribute decision-making as described in claim 1, characterized in that: Among the various properties of the multiple regions mentioned in Step 1, the division method of the multiple regions is as follows: In the laser cladding process, the bimetallic composite material is divided into the matrix region, heat-affected zone, semi-melted zone, diffusion zone, and cladding zone; In the powder bed melting process, the composite material is divided into the A metal region, semi-melted zone, diffusion zone, and B metal region. In the welding process, bimetallic composite materials are divided into the matrix zone, the heat-affected zone, and the weld zone; The various properties include: microhardness, nanohardness, elastic modulus, nanoelastic modulus, yield strength, tensile strength, elongation after fracture, reduction of area, impact toughness, fracture toughness, fatigue strength, fatigue life, thermal conductivity, coefficient of thermal expansion, resistivity, corrosion resistance, oxidation resistance, and wear resistance.

3. The comprehensive evaluation method for bimetallic composite materials based on multi-attribute decision-making as described in claim 1, characterized in that: The subjective weights mentioned in step three are determined using the analytic hierarchy process (AHP); the objective weights mentioned in step four are determined using the entropy method.

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