Particle reinforced composite material processing damage quantitative evaluation method based on local measurement and parameter correction

Abstract

The invention provides a particle reinforced composite material processing damage quantitative evaluation method based on local measurement and parameter correction. The method comprises the following steps: firstly, scanning damage feature images at different positions of the surface of a machined workpiece, and calculating the area ratio of each damage size to a scanned local area through binarization processing to obtain the measured crack density, the particle breakage rate and the particle extraction rate; then, establishing a regression model of the damage characteristics, the processing material structure parameters and the processing technology parameters, and correcting the damage characteristic data by means of the regression model to enable the damage characteristic data to better conform to the actual damage conditions under different processing conditions; then, through weighted average and correction coefficients, the damage characteristics of the local area are expanded to the whole machining area, and a mathematical model among machining material structure parameters, machining process parameters and machining damage is established; and finally, comprehensively evaluating the crack density, the particle breakage rate and the particle pull-out rate to obtain a comprehensive damage evaluation value of the whole processing area. The method can effectively evaluate the quality of the machined surface.

Description

Technical Field

[0001] The present invention belongs to the field of evaluation of composite material processing damage, and particularly relates to a method for quantitatively evaluating the processing damage of particle-reinforced composite materials based on local measurement and parameter correction. Background Art

[0002] By adding particle reinforcement phases into the matrix material, particle-reinforced composite materials significantly improve the hardness, strength and wear resistance of the materials, and thus are widely used in fields such as aviation, automotive and machinery, especially suitable for engineering components requiring high strength, lightweight and high temperature resistance. Silicon carbide particle-reinforced aluminum matrix composite material (SiC p / Al), as a typical particle-reinforced composite material, has excellent physical and mechanical properties and is commonly used in high-performance engineering components. However, due to the presence of hard and brittle reinforcement particles, the interface between the particles and the matrix may cause stress concentration, thereby increasing the possibility of defects such as crack generation, particle fragmentation, and particle pull-out during the material processing.

[0003] Wang Wenhu et al. from Northwestern Polytechnical University proposed a method for quantitatively evaluating the machining surface quality of SiC f / SiC ceramic matrix composite materials in the invention patent with the publication number of CN114740005A. This method uses the three-dimensional surface roughness S a as the characteristic parameter of the machining surface quality, and introduces the overall damage factor of the machining surface as the second quantitative parameter for evaluating the machining surface quality.

[0004] Yu Xiaolin et al. from Shenyang Ligong University proposed a grinding method and a machining surface quality evaluation method for aluminum matrix silicon carbide composite materials in the invention patent with the publication number of CN117840905A. This method uses computer software to process scanning electron microscope (SEM) images and characterizes the surface quality by calculating the defect rate and the average size of the defects.

[0005] Traditional damage characterization methods usually focus on the contour morphology of the machining surface, mainly through two-dimensional surface roughness R a or three-dimensional surface roughness S aParameters such as Summary of the Invention

[0006] To solve the problem of insufficient accuracy in the evaluation of the machining quality of particulate-reinforced composites, the present invention proposes a method for quantitatively evaluating the machining damage of particulate-reinforced composites based on local measurement and parameter correction. This method first quantitatively measures the damage characteristics in a specific area of the machined surface, and then corrects the measured damage data to make it more in line with the actual damage situation under different machining conditions; through weighted averaging and correction coefficients, the local damage characteristics are extended to the entire machining area, thereby obtaining a comprehensive damage evaluation value. This method provides a systematic process from local damage measurement to overall machining quality evaluation, effectively improving the accurate evaluation ability of the machined surface quality.

[0007] The present invention provides a method for quantitatively evaluating the machining damage of particulate-reinforced composites based on local measurement and parameter correction, and the machining includes but is not limited to grinding, turning, and milling;

[0008] The particulate-reinforced composites include but are not limited to particulate-reinforced metal matrix composites, ceramic matrix composites, or polymer matrix composites.

[0009] Specifically, it includes the following steps:

[0010] Step 1: Use a scanning electron microscope to scan the inlet, outlet, and middle positions of the surface of the particulate-reinforced composite machining workpiece to obtain damage characteristic images of crack density, particle breakage rate, and particle pull-out rate, and perform binary processing on the obtained damage characteristic images to convert them into damage data of the local area;

[0011] Step 2: Combine the regression model of the damage characteristics with the material structure parameters and machining process parameters of the machining workpiece, and correct the measured local area damage characteristic data to the corresponding machining conditions to make it more in line with the actual damage situation;

[0012] Step 3: Based on the damage characteristic data of the local area, the local damage characteristics are expanded and extrapolated to the entire processing area by means of weighted average and correction coefficient, and then a mathematical model between the workpiece material structure parameters, processing process parameters and processing damage is established;

[0013] Step 4: Based on the expanded damage feature data, a comprehensive evaluation is performed on multiple damage features such as crack density, particle breakage rate, and particle pullout rate, and finally a comprehensive damage evaluation value of the entire processing area is obtained.

[0014] In step 1, a scanning electron microscope is used to select a surface area at low magnification, identify the damage type at medium magnification, and obtain characteristic images of surface cracks, particle crushing, and particle extraction at high magnification. The low magnification is 500 to 1k times; the medium magnification is 1k to 3k times; and the high magnification is 3k to 10k times.

[0015] The inlet position is within a range of 0 to 20% from the processing starting point; the outlet position is within a range of 0 to 20% from the processing end point; and the intermediate position is within a range of 40 to 60% from the processing starting point.

[0016] The damage data of the local area obtained by the conversion is to perform binarization processing on the damage characteristic image taken by the scanning electron microscope, analyze the image by computer software Image Pro Plus, set the scale of the image, and count and calculate the size of various types of damage. The ratio of the total length of the crack to the area of ​​the local area is calculated to obtain the crack density; the ratio of the area of ​​the broken particles to the area of ​​the local area is calculated, that is, the particle breakage rate; the ratio of the area of ​​the pulled out particles to the area of ​​the local area is calculated, that is, the particle pull-out rate.

[0017] The material structure parameter in step 2 is the particle volume fraction (V p ), the machining process parameters are cutting speed (v s )、Cutting depth (a p ), feed speed (v w ), processing width (b). Through experiments or theoretical analysis, a regression model of damage characteristics (crack density, particle breakage rate, particle pullout rate) and material structure parameters and processing parameters is established. p )、Cutting speed(v s )、Cutting depth (a p ), feed speed (v w ), and the regression model of processing width (b) is shown in formula (1).

[0018] D adj,j =D mea,j ×f j (Vp , v s , a p , v w , b)(1)

[0019] Where D adj,j is the adjusted damage feature value of the j-th damage feature, and D mea,j is the damage feature value measured in the local area of the j-th damage feature, and f j (V p , v s , a p , v w , b) is the regression model of the material structure and processing parameters on the j-th damage feature.

[0020] By means of the regression model, the damage feature data measured in the local area is corrected to make it more in line with the actual damage situation under different processing conditions

[0021] In step 3, the weighting coefficient (w i ) of each local area is defined, and the weight is assigned according to the damage area contribution of the local area, as shown in formula (2).

[0022]

[0023] Where A i is the area of the i-th local area, and A tot is the area of the entire processing area.

[0024] The correction coefficient can be used to further adjust the damage feature value of the local area, reflect the influence of different local areas on the damage feature due to different processing states during the actual processing, clearly distinguish the actual differences between different areas, and thus introduce the correction coefficient γ i , which can be determined by experimental fitting, theoretical derivation or actual processing experience, as shown in formula (3).

[0025]

[0026] Where D i is the damage feature value of the local area, γ i is the correction coefficient, is the damage feature value after correction.

[0027] According to the weighting coefficient w i of each local area and the corrected damage feature value The damage feature data of each local area is extended to the entire processing area by weighted average, as shown in formula (4).

[0028]

[0029] In the formula, is the damage characteristic value of the entire machining area, and n is the number of local areas.

[0030] In step 4, the crack density, particle fragmentation rate, and particle pull-out rate after weighted averaging are comprehensively evaluated, and weighted fusion is performed to obtain the comprehensive damage evaluation value of the entire machining area, as shown in formula (5).

[0031]

[0032] In the formula, is the weighted average value of the jth damage characteristic, and ω j is the weight coefficient of the jth damage characteristic, and m is the number of types of damage characteristics.

[0033] Beneficial effects:

[0034] (1) By separately scanning the crack density, particle fragmentation rate, and particle pull-out rate of multiple damage characteristic images at the entrance, exit, and middle positions of the machining surface, binary processing is performed on the scanned damage characteristic images, and the scale is set for the images through Image ProPlus. The size of various damages and their ratios in the local area are statistically calculated and converted to obtain the damage characteristic data of the local area. It can comprehensively and systematically capture different types of machining damages, ensuring a multi-dimensional and accurate evaluation of the machining surface quality;

[0035] (2) Innovatively incorporate the material structure characteristic parameters and machining process parameters of the workpiece into the damage evaluation, correct the damage characteristics under different machining conditions, reduce the data error when expanding from local damage to the whole, so as to better reflect the complex influence in the actual machining process, avoid the problem that the existing methods lack full consideration of machining conditions, and enable the damage characteristics to be more closely combined with the actual machining process;

[0036] (3) Through weighted averaging and correction coefficients, the damage characteristic data of the local area (at the entrance, exit, and middle positions) are extended to the entire machining area, and the overall damage situation can be calculated more accurately. This extension method ensures the comprehensiveness and representativeness of the machining surface quality evaluation;

[0037] (4) The present invention comprehensively considers the influences of various damage characteristics, workpiece material structure characteristic parameters, and machining process parameters. Based on the local area damage measurement results, and in combination with the relationship model between damage characteristics and material structure parameters and machining process parameters, the local damage data is corrected to make it more consistent with the actual damage conditions under different machining conditions. This evaluation method can more accurately reflect the damage in the actual machining process and reduce the possible errors of existing methods. Compared with traditional evaluation methods, this method has higher accuracy and reliability, and is particularly suitable for the machining quality evaluation of multiphase materials such as particle-reinforced composites. The proposed machining damage evaluation method shows significant advantages in terms of comprehensiveness, accuracy, and adaptability, not only improving the evaluation accuracy of machining quality, but also providing scientific and accurate support for quality control in the machining process. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a micrograph of a silicon carbide particle-reinforced aluminum matrix composite with a volume fraction of 60% in Example 1;

[0039] Figure 2 It is a schematic diagram of grinding machining;

[0040] Figure 3 It is a scanning electron micrograph of the local area damage on the surface of the machined workpiece;

[0041] Figure 4 It is a scanning electron micrograph of the surface crack damage characteristics in Example 1;

[0042] Figure 5 It is a scanning electron micrograph of the particle crushing damage characteristics in Example 1;

[0043] Figure 6 It is a scanning electron micrograph of the particle pulling-out damage characteristics in Example 1;

[0044] Figure 7 It is a binary scanning electron micrograph of the surface crack damage characteristics in Example 1;

[0045] Figure 8 It is a binary scanning electron micrograph of the particle crushing damage characteristics in Example 1;

[0046] Figure 9 It is a binary scanning electron micrograph of the particle pulling-out damage characteristics in Example 1;

[0047] Figure 10 It is a process flow chart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0048] The present invention will be specifically described below in conjunction with the accompanying drawings. The accompanying drawings form a part of the present invention and, together with the description of the invention, explain its principles. The embodiments shown in the drawings are only examples and are intended to help explain the present invention and should not be construed as a limitation of the present invention.

[0049] The present invention provides a method for quantitatively evaluating the machining damage of particle-reinforced composites based on local measurement and parameter correction. First, damage feature images are scanned in different regions on the surface of the machined workpiece, and the images are converted into quantified damage data. A regression model of the workpiece material structure characteristics parameters, machining process parameters, and various damage features is established. With the help of this regression model, the measured damage feature data is corrected to make it more accurately reflect the actual damage conditions under different machining conditions. Through weighted average and correction coefficients, the local damage features are extended to the entire machining area, thereby obtaining a comprehensive damage evaluation value. This method provides a systematic process, from local damage feature scanning to overall machining quality evaluation, effectively improving the evaluation accuracy of the surface quality of the machined workpiece.

[0050] In this embodiment, the workpiece to be machined is a silicon carbide particle-reinforced aluminum matrix composite with a volume fraction of 60%, and the diameter of the silicon carbide particles is 20 - 30 μm. The microstructure of this composite material is as Figure 1 shown. The grinding process is adopted, and the grinding speed v s = 8 m / s, the feed speed v w = 160 mm / min, the grinding depth a p = 20 μm, and the grinding width b = 6 mm. The machining schematic diagram is as Figure 2 shown. The following are the specific steps for quantitatively evaluating the damage condition on the surface of the machined workpiece:

[0051] Step 1: Use a scanning electron microscope to randomly select regions at the entrance, exit, and middle positions on the surface of the machined workpiece for scanning local damage features, ensuring that different parts of the machining area can be covered, so as to reflect the damage features of each region. The entrance, exit, and middle positions on the machining surface are reasonably defined according to the machining area range to ensure the representativeness and measurability of each region. The machining area is divided along the feed direction. The entrance position is in the range of 0 - 20% from the machining starting point; the exit position is in the range of 0 - 20% from the machining end point; the middle position is in the range of 40% - 60% from the machining starting point. Only surface cracks, particle breakage, and particle pull-out are considered as damage features. Use a scanning electron microscope (SEM) to observe the surfaces of these regions, and select appropriate magnification factors for image acquisition at different scales. Low magnification (500 - 1k times) is used for the selected regions to ensure that a large surface range can be covered; medium magnification (1k - 3k times) is used for initially identifying the damage types and confirming the positions and distributions of cracks, broken particles, and pulled-out particles, as Figure 3 shown; high magnification (3k - 10k times) is used for carefully observing and accurately measuring the specific morphologies and sizes of cracks, particle breakage, and particle pull-out to provide accurate image data for subsequent analysis, as Figures 4 to 6 shown.

[0052] The acquired damage feature images are analyzed by computer software Image Pro Plus (any software with the same functions can be used). First, binary processing is performed to ensure that the edges of the damage areas in the images are clearly distinguishable, as Figures 7 to 9 shown. Then, a scale is set in the image, and the sizes of cracks, particle breakage, and particle pull-out are accurately measured, and the sizes and quantities of various damage features are accurately recorded. For the calculation of crack density, the total length of the cracks is measured and the ratio is calculated with the area of the local area; the particle breakage rate is calculated by measuring the area of the broken particles and calculating the ratio with the area of the local area; the particle pull-out rate is obtained by calculating the ratio of the area of the pulled-out particles to the area of the local area. Finally, based on the calculation results, the crack density, particle breakage rate, and particle pull-out rate in the local area are obtained, as shown in Table 1, providing basic data for subsequent damage quantitative evaluation and machining quality analysis.

[0053] Table 1 Quantitative measurement results of surface damage of machined workpieces

[0054]

[0055] Step 2: Based on the known material structure characteristics (particle volume fraction), machining parameters (such as grinding speed, grinding depth, feed speed, grinding width), and local area damage feature data, a regression model is established. This model is used to describe the relationship between material structure, machining parameters, and different damage features, and provides a theoretical basis for the correction of damage features through regression analysis. The form of the regression model can be linear or non-linear, and the specific form depends on the complexity of the machining process and damage mechanism.

[0056] The local area damage feature data measured in Step 1 are input into the regression model, and corrections are made in combination with the corresponding material structure characteristics and machining parameters. During the correction process, the damage features are adjusted through model prediction to make them more conform to the damage conditions under actual machining conditions. Specifically, the relationship models between the particle volume fraction (V p ), grinding speed (v s ), grinding depth (a p ), feed speed (v w ), grinding width (b) and the three damage features of crack density, particle breakage rate, and particle pull-out rate are used, as shown in Equation (1), to adjust and correct the measured local area damage feature data to the corresponding machining conditions, as shown in Table 2, to make it more in line with the actual damage situation and reduce the error when extending from local damage features to overall damage.

[0057] D adj,j = D mea,j × f j (V p , v s, a p , v w , b) (1)

[0058] Wherein, D adj,j is the adjusted damage feature value of the j-th damage feature, and D mea,j is the damage feature value measured in the local area of the j-th damage feature, and f j (V p , v s , a p , v w , b) is the regression model of the material structure and processing parameters on the j-th damage feature.

[0059] Table 2 Results after machining surface damage correction

[0060]

[0061] Step 3: Calculate the weight coefficient (w i ) of each local area in the entire machining surface according to the areas of the local areas selected at the inlet, outlet, and middle positions of the machining surface, and assign weights according to the damage area contribution of the local area, as shown in Equation (2).

[0062]

[0063] Wherein, A i is the area of the i-th local area, and A tot is the area of the entire machining area;

[0064] In order to reflect the influence of different local areas on the damage features due to different machining states during the actual machining process, a correction coefficient γ i is introduced, as shown in Equation (3), and it can be determined through experimental fitting, theoretical derivation, or actual machining experience.

[0065]

[0066] Wherein, D i is the damage feature value of the local area, γ i is the correction coefficient, is the damage feature value after correction; γ 入口 is 0.8, γ 中间 is 1, γ 出口 is 0.9;

[0067] For the damage feature data (crack density, particle breakage rate, particle pull-out rate) in each area, according to the weighting coefficient w i of each local area and the corrected damage feature value D i adj, in the way of weighted average and correction coefficient, as shown in Equation (4), the local damage characteristics are extended and extrapolated to the entire machining area, as shown in Table 3, and then a mathematical model between the material structure, machining parameters and machining damage is established.

[0068]

[0069] In the formula, is the damage characteristic value of the entire machining area, and n is the number of local areas.

[0070] Table 3 Results of the extension and extrapolation of the surface damage of the machined workpiece

[0071]

[0072] Step 4: Based on the damage characteristic data extended and extrapolated in Step 3, including crack density, particle breakage rate and particle pull-out rate, first, it is necessary to determine the contribution weights of each damage characteristic to the machining quality. These weights can be set according to experimental data, theoretical analysis or engineering experience to ensure that the influence of each damage characteristic in the comprehensive evaluation conforms to the actual situation. The multiple damage characteristics such as the weighted average crack density, particle breakage rate and particle pull-out rate are comprehensively evaluated and weighted and fused, as shown in Equation (5), to obtain the comprehensive damage evaluation value of the entire machining area, as shown in Table 4.

[0073]

[0074] In the formula, is the weighted average value of the j-th damage characteristic, ω j is the weight coefficient of the j-th damage characteristic, and m is the number of types of damage characteristics.

[0075] Table 4 Comprehensive evaluation results of the machining surface damage

[0076]

[0077] The content described in the embodiments of the present invention is only a simple example of the implementation form of the inventive concept. The protection scope of the present invention should not be limited to the specific form described in the embodiments. Any simple modification made according to the present invention should be within the protection scope of the present invention.

Claims

1. A quantitative evaluation method for particle reinforced composite material processing damage based on local measurement and parameter correction, characterized in that: The following steps are involved: Step 1: Use a scanning electron microscope to scan the crack density, particle breakage rate, and particle pull-out rate damage feature images at different locations on the workpiece surface, perform binary processing on the obtained damage feature images, set the scale of the image through Image ProPlus, calculate the ratio of each damage feature size to the area of ​​the scanned local area, and obtain the damage data of the local area; Step 2: Establish a regression model between the damage data and the material structure parameters and processing parameters of the workpiece, and use the regression model to correct the local area damage characteristic data obtained in step 1 to the corresponding processing conditions to make it more consistent with the actual damage situation; the processing technology includes but is not limited to grinding, turning, and milling; Step 3: Based on the damage characteristic data of the local area, the local damage characteristics are expanded and extrapolated to the entire processing area by means of weighted average and correction coefficient, and then a mathematical model between material structure parameters, processing technology parameters and processing damage is established; Step 4: Based on the expanded damage feature data, a comprehensive evaluation is performed on multiple damage features including crack density, particle breakage rate, and particle pullout rate, and finally a comprehensive damage evaluation value of the entire processing area is obtained.

2. The method for quantitatively evaluating machining damage of particle reinforced composite materials based on local measurement and parameter correction according to claim 1, characterized in that: In step 1, a scanning electron microscope is used to select a surface area at low magnification, identify the damage type at medium magnification, and obtain characteristic images of surface cracks, particle breakage, and particle pullout at high magnification.

3. The method for quantitatively evaluating machining damage of particle reinforced composite materials based on local measurement and parameter correction according to claim 2, characterized in that: The low magnification of the scanning electron microscope is 500 to 1k times; the medium magnification is 1k to 3k times; and the high magnification is 3k to 10k times. The different positions of the workpiece surface are an entrance position within a range of 0-20% from the processing starting point; an exit position within a range of 0-20% from the processing end point; and a middle position within a range of 40%-60% from the processing starting point.

4. A method for quantitatively evaluating processing damage of particle reinforced composite materials based on local measurement and parameter correction according to claim 1 or 2, characterized in that: In step 1, the ratio of the total length of the crack to the area of ​​the local region is calculated to obtain the crack density; Calculate the ratio of the area of ​​the broken particles to the area of ​​the local area, that is, the particle breakage rate; The ratio of the area of ​​the pulled-out particles to the area of ​​the local region is calculated, i.e., the particle pulling rate.

5. The method for quantitatively evaluating machining damage of particle reinforced composite materials based on local measurement and parameter correction according to claim 1, characterized in that: In step 2, the structural parameter of the workpiece material is the particle volume fraction V p , the machining process parameter is the cutting speed v s , cutting depth a p , feed speed v w , processing width b.

6. A method for quantitatively evaluating processing damage of particle reinforced composite materials based on local measurement and parameter correction according to claim 1 or 5, characterized in that: The regression model of damage characteristics, workpiece material structure parameters and machining process parameters is shown in formula (1); D adj,j =D mea,j ×f j (V p ,v s ,a p ,v w ,b) (1) Where D adj,j is the damage characteristic value after adjustment of the jth damage characteristic, D mea,j is the damage characteristic value measured in the jth damage characteristic local area, f j (V p ,v s ,a p ,v w ,b) is the regression model of material structure parameters and processing technology parameters for the jth damage feature.

7. The method for quantitatively evaluating machining damage of particle reinforced composite materials based on local measurement and parameter correction according to claim 1, characterized in that: The particle-reinforced composite material in step 1 includes, but is not limited to, a particle-reinforced metal-based composite material, a ceramic-based composite material or a polymer-based composite material.

8. The method for quantitatively evaluating machining damage of particle reinforced composite materials based on local measurement and parameter correction according to claim 1, characterized in that: In step 3, the weight coefficient (w i ), as shown in formula (2), the weight is assigned according to the damage area contribution of the local area; In the formula, A i is the area of ​​the ith local region, A tot is the area of ​​the entire processing area; The correction coefficient can be used to further adjust the damage characteristic value of the local area to reflect the actual differences between different areas, as shown in formula (3); Where D i is the damage characteristic value of the local area, γ i is the correction factor, is the corrected damage characteristic value; According to the weighting coefficient w of each local area i and the corrected damage characteristic value The damage feature data of each local area is extended to the entire processing area by weighted averaging, as shown in formula (4); In the formula, is the damage characteristic value of the entire processing area, and n is the number of local areas.

9. The method for quantitatively evaluating machining damage of particle reinforced composite materials based on local measurement and parameter correction according to claim 1, characterized in that: In step 4, the weighted averaged damage characteristics of crack density, particle breakage rate, and particle pullout rate are comprehensively evaluated and weighted fused to obtain a comprehensive damage evaluation value of the entire processing area, as shown in formula (5); In the formula, is the weighted average of the jth damage feature, ω j is the weight coefficient of the jth damage feature, and m is the number of types of damage features.

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