Grinding wheel wear prediction method for difficult-to-machine materials

Abstract

The application provides a grinding wheel wear prediction method for difficult-to-machine materials. The application comprises the following steps: step S1: selecting a sample piece of the difficult-to-machine material to be analyzed and a grinding wheel matched with the sample piece; step S2: determining the grinding thickness of each layer of the grinding cycle experiment according to the thickness of the sample piece, collecting the grinding dust and collecting the data in the grinding cycle experiment; step S3: characterizing the multidimensional experimental results based on the collected data and the grinding dust data; and step S4: establishing a grinding wheel wear prediction model based on the characterized multidimensional experimental results. By using the method of the application, the grinding wheel wear in the grinding process of the difficult-to-machine material can be systematically studied, and the grinding wheel wear prediction model can be established, thereby providing a theoretical basis for the machining precision control of the difficult-to-machine material and the optimization of the machining process of the difficult-to-machine material, such as the grinding wheel selection and the machining parameters.

Description

Technical Field

[0001] This invention relates to the field of grinding technology for difficult-to-machine materials, and in particular to a method for predicting grinding wheel wear for difficult-to-machine materials. Background Technology

[0002] In recent years, the rapid development of the aerospace field has placed higher demands on the service performance of core components and increased the importance of materials processing technology. Difficult-to-machine materials such as artificial crystals, engineering ceramics, and ceramic matrix composites are increasingly widely used in aerospace and other fields due to their advantages such as high hardness, high temperature resistance, wear resistance, and good chemical stability. Difficult-to-machine materials are typically precision-machined using grinding, but their high hardness and wear resistance lead to workpiece breakage and severe tool wear during processing, seriously restricting their engineering applications. Currently, research on grinding of difficult-to-machine materials both domestically and internationally mainly focuses on material removal mechanisms, processing damage, and processing parameter optimization. For example, the quantitative characterization method for grinding burns proposed by Nanjing University of Aeronautics and Astronautics (Chinese Patent CN109872316A) can quantitatively characterize the degree of grinding burns, providing a basis for optimizing process parameters for difficult-to-machine materials from the perspective of processing damage. The ultrasonic vibration-assisted grinding device proposed by China University of Geosciences (Wuhan) (Chinese Patent CN108788974A) improves the grinding efficiency and surface quality of difficult-to-machine materials from the perspective of processing technology. However, current research on grinding wheel wear in the grinding process of difficult-to-machine materials is not systematic and in-depth enough, making it difficult to provide scientific theoretical and technical support for the machining of difficult-to-machine materials from the perspective of cutting tools. On the other hand, a reasonable grinding wheel life also has a positive effect on the grinding efficiency and surface quality of difficult-to-machine materials, but there is currently little research on grinding wheel wear prediction, and the formulation of grinding wheel life lacks scientific guidance.

[0003] Therefore, there is an urgent need for a systematic method to study grinding wheel wear and predict grinding wheel wear, so as to provide theoretical guidance for the precision control and process of grinding difficult-to-machine materials. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for predicting grinding wheel wear on difficult-to-machine materials. The technical means employed in this invention are as follows:

[0005] A method for predicting grinding wheel wear of difficult-to-machine materials, used to study the grinding wheel wear during the grinding process of difficult-to-machine materials, includes the following steps:

[0006] Step S1: Select the difficult-to-machine material sample to be analyzed and the matching grinding wheel;

[0007] Step S2: Determine the grinding thickness of each layer in the grinding cycle experiment based on the sample thickness, collect grinding debris and collect data from the grinding cycle experiment;

[0008] Step S3: Characterize the multi-dimensional experimental results based on the collected data and wear debris data;

[0009] Step S4: Establish a grinding wheel wear prediction model based on the multi-dimensional experimental results of the characterization.

[0010] Further, step S1 includes the following steps:

[0011] S11: Select difficult-to-machine materials and prepare experimental samples;

[0012] S12: Select the matching grinding wheel model according to the selected material;

[0013] S13: Measure the initial radius R0 of the grinding wheel and observe the initial surface morphology of the grinding wheel.

[0014] Further, step S2 includes the following steps:

[0015] S21: Grind the sample according to the sample thickness and the thickness of each grinding layer until the grinding length L = L. i (i = 1, 2, ..., n);

[0016] S22: Collect grinding debris and measure grinding force F. i (i = 1, 2, ..., n), measuring the grinding depth H i (i = 1, 2, ..., n), measure the radius R of the grinding wheel. i (i = 1, 2, ..., n);

[0017] S23: If the grinding length L = L n Measure the surface roughness Sa of the sample i (i = 1, 2, ..., n), observe the surface morphology of the processed sample and further observe the surface morphology of the grinding wheel. The cycle ends; otherwise, return to S21.

[0018] Furthermore, in step S3, the characterization of the experimental results includes the following steps:

[0019] S31: Characterizing the wear degree of the grinding wheel: grinding wheel surface morphology, grinding wheel radial wear ΔR;

[0020] S32: Characterizing the grinding performance of the grinding wheel: specific grinding energy e s Grinding ratio G;

[0021] S33: Characterizing processing accuracy: theoretical removal amount V0 and actual removal amount V;

[0022] S34: Characterizing surface quality: sample surface morphology, surface roughness Sa;

[0023] S35: Characterizes processing efficiency: processing time t, material removal rate MMR.

[0024] Furthermore, in step S4, the grinding wheel wear prediction model is established, which includes the following steps:

[0025] S41: Analyze grinding wheel wear parameters;

[0026] S42: Establish a grinding wheel wear prediction model.

[0027] Furthermore, difficult-to-machine materials include, but are not limited to, quartz crystals, optical glass, ceramic materials, high-performance alloys, particle-reinforced metal matrix composites, whisker-reinforced ceramic matrix composites, and fiber-reinforced ceramic matrix composites.

[0028] Furthermore, the abrasives used in grinding wheels include, but are not limited to, corundum, silicon carbide, cubic boron nitride, and synthetic diamond, while the bonding agents used in grinding wheels include, but are not limited to, ceramics, resins, rubber, and metals.

[0029] Furthermore, in step S21, the grinding conditions include, but are not limited to, dry grinding and wet grinding, end grinding and side grinding, and the specific experimental parameters can be selected according to the specific circumstances.

[0030] Furthermore, in step S41, the grinding wheel wear parameters include, but are not limited to, radial wear of the grinding wheel per unit length, abrasive grain protrusion height, and abrasive grain wear area.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] (1) The present invention provides a method for predicting grinding wheel wear for difficult-to-machine materials. It adopts a cyclic test method, with a clear operation process and simple implementation method, which can reduce the consumption of test materials and improve test efficiency.

[0033] (2) The present invention provides a method for predicting the wear of grinding wheels for difficult-to-machine materials, which can observe the wear of grinding wheels in the grinding process of difficult-to-machine materials in multiple steps, record the wear morphology of grinding wheels in the processing process more accurately, and reveal the wear mechanism of grinding wheels in the grinding of difficult-to-machine materials.

[0034] (3) The present invention provides a method for predicting grinding wheel wear for difficult-to-machine materials. Through a process-oriented experimental method, it can systematically study the impact of grinding wheel wear on machining accuracy, surface quality and machining efficiency. It provides a theoretical basis for the control of machining accuracy of difficult-to-machine materials and the optimization of machining processes such as grinding wheel selection, grinding wheel wear prediction and machining parameters, thereby improving the machining accuracy, surface quality and machining efficiency of difficult-to-machine materials.

[0035] (4) The present invention provides a method for predicting grinding wheel wear for difficult-to-machine materials. Through systematic research on grinding wheel wear, a grinding wheel wear prediction model for difficult-to-machine materials can be proposed, providing theoretical guidance for error compensation in actual production and processing.

[0036] Based on the above reasons, this invention can be widely applied in fields such as grinding of difficult-to-machine materials. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart of a method for predicting grinding wheel wear on difficult-to-machine materials, according to a specific embodiment of the present invention.

[0039] Figure 2 This is a schematic diagram of a cyclic experiment in a specific embodiment of the present invention.

[0040] Figure 3 This is a schematic diagram of grinding wheel radius measurement in a specific embodiment of the present invention.

[0041] Figure 4 This is a schematic diagram of grinding wheel wear in a specific embodiment of the present invention.

[0042] Figure 5 This is a schematic diagram of grinding depth measurement in a specific embodiment of the present invention.

[0043] Figure 6 This is an observation diagram of the surface morphology of the grinding wheel in a specific embodiment of the present invention.

[0044] Figure 7 This is a sample surface roughness measurement diagram in a specific embodiment of the present invention.

[0045] Figure 8 The following is a line graph representing the experimental results in a specific embodiment of the present invention, wherein (a) represents the wear degree of the grinding wheel; (b) represents the grinding performance of the grinding wheel; (c) represents the machining accuracy; (d) represents the machining quality; and (e) represents the machining efficiency.

[0046] Figure 9 This is a diagram of the grinding wheel wear prediction model in a specific embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Example 1

[0049] like Figure 1 As shown in the figure, this invention discloses a method for predicting grinding wheel wear for difficult-to-machine materials, including the following steps:

[0050] Step S1: Select the difficult-to-machine material sample to be analyzed and the matching grinding wheel;

[0051] Step S2: Determine the grinding thickness of each layer in the grinding cycle experiment based on the sample thickness, collect grinding debris and collect data from the grinding cycle experiment;

[0052] Step S3: Characterize the multi-dimensional experimental results based on the collected data and wear debris data;

[0053] Step S4: Establish a grinding wheel wear prediction model based on the multi-dimensional experimental results of the characterization.

[0054] Step S1 includes the following steps:

[0055] S11: Selected SiC f / SiC ceramic matrix composite material was cut into samples with H0×L0×b0=16×25×6mm using a diamond wire saw;

[0056] S12: Based on the characteristics of the selected material, such as high hardness, anisotropy and heterogeneity, a diamond metal bond grinding wheel with a diameter of 8mm, height (thickness) T=9mm, grit size of 100 and concentration of 75% is selected.

[0057] S13: As Figure 3 As shown, the initial radius R0 of the grinding wheel was measured to be 3.987 mm using a tool adjuster, and the initial surface morphology of the grinding wheel was observed using a super depth-of-field microscope.

[0058] like Figure 2 As shown, step S2 includes the following steps:

[0059] S21: Set the spindle speed n = 5000 r / min, and the feed rate v w =100mm / min, single-stroke grinding depth a p=0.02mm, grinding thickness b=4mm, clamp the sample and perform sample grinding. Based on the sample thickness and the grinding thickness of each layer, set k=4, meaning four cycles are required. Removing the kth layer of material requires a unidirectional grinding length L along the negative X direction. i (i = 1, 2, 3, 4);

[0060] S22: Collect grinding debris, measure the grinding force using a piezoelectric force meter, measure a set of data, take the values ​​of three stable intervals from the set of data, calculate the average value, and obtain the normal grinding force F based on the average value of the three stable values. ni (i = 1, 2, 3, 4), the tangential grinding force is F. ti (i=1,2,3,4), such as Figure 5 As shown, the actual grinding depth H in the y-axis of the sample was measured using a machine tool probe. i (i = 1, 2, 3, 4), the radius of the grinding wheel is measured as R using a tool adjuster. i (i = 1, 2, 3, 4), grinding wheel wear is as follows Figure 4 As shown;

[0061] S23: If the grinding length L = L4, such as Figure 7 As shown, the surface roughness of the sample was measured using a white light interferometer 3D surface profilometer. The surface roughness of three regions was collected each time, and the average value was taken to obtain Sa. i (i = 1, 2, 3, 4), the surface morphology of the machined sample was observed using a super depth-of-field microscope, and the surface morphology of the grinding wheel was further observed, such as... Figure 6 As shown, the loop experiment ends; otherwise, return to S21.

[0062] Table 1 Measurement data in specific embodiments of the present invention

[0063]

[0064]

[0065] Among them, the unidirectional grinding length is not a measured value, but a set experimental parameter, which is used to analyze the relationship between the grinding length (i.e. how much the grinding wheel wears) and other characterization data in the future.

[0066] Step S3 includes the following steps:

[0067] S31: Characterizing the wear degree of the grinding wheel: grinding wheel surface morphology, radial wear ΔR of the grinding wheel. i =R0-R i (i=1,2,3,4), such as Figure 8 As shown in (a), the radial wear of the grinding wheel and the radial wear per unit feed of the grinding wheel vary with the unidirectional grinding length.

[0068] S32: Characterizes the grinding performance of the grinding wheel: specific grinding energy Grinding ratio T is the thickness of the grinding wheel, such as Figure 8 As shown in (b), the specific grinding energy and grinding ratio vary with the unidirectional grinding length.

[0069] S33: Characterizing processing accuracy: Actual removal error ΔV i =L i ba p -H i L0b(i=1,2,3,4), such as Figure 8 As shown in (c), the actual removal error varies with the unidirectional grinding length;

[0070] S34: Characterizing surface quality: sample surface morphology, surface roughness Sa i (i=1,2,3,4), such as Figure 8 As shown in (d), the surface roughness varies with the unidirectional grinding length;

[0071] S35: Characterizing processing efficiency: Processing time Material removal rate like Figure 8 As shown in (e), the material removal rate varies with the unidirectional grinding length.

[0072] Step S4 includes the following steps:

[0073] S41: Based on accuracy requirements, the radial wear ε of the feed wheel per unit length is... i As the main parameters of the grinding wheel wear prediction model,

[0074] S42: Calculate the radial wear of the feed wheel per unit length, such as Figure 9 As shown, the analysis of the fitted data curves yields a grinding wheel wear prediction model, providing theoretical guidance for error compensation in actual production and processing.

[0075] Table 2 Characterization data in specific embodiments of the present invention

[0076]

[0077]

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting grinding wheel wear on difficult-to-machine materials, characterized in that, Includes the following steps: Step S1: Select the difficult-to-machine material sample to be analyzed and a matching grinding wheel; the difficult-to-machine material sample is a SiCf / SiC ceramic matrix composite material, cut into dimensions using a diamond wire saw. The sample; the grinding wheel is a diamond-bonded grinding wheel with a diameter of 8mm and a thickness of T =9 mm Particle size 100, concentration 75%; Measure the initial radius of the grinding wheel. R 0, and observe the initial surface morphology of the grinding wheel using a super depth-of-field microscope; Step S2: Determine the grinding thickness of each layer in the grinding cycle experiment according to the sample thickness, collect grinding debris and collect data in the grinding cycle experiment; The grinding thickness of each layer is 0.02 mm, and a total of 4 layers of cycle experiments are set, with the grinding length of each layer being respectively L 1=4175 mm , L 2=4100 mm , L 3=4025 mm , L 4=3950 mm The collected data includes: measuring the grinding force using a piezoelectric force gauge, measuring a set of data, taking the average value of three stable intervals from the set of data, and obtaining the normal grinding force based on the average of the three stable values. The tangential grinding force is The actual grinding depth in the y-axis of the sample was measured using a machine tool probe. The radius of the grinding wheel was measured using a tool adjuster. ; Step S3: Characterize the multi-dimensional experimental results based on the collected data and wear debris data; Specifically, this includes: S31: Characterizing the wear degree of the grinding wheel: grinding wheel surface morphology, grinding wheel radial wear. ; S32: Characterizes the grinding performance of the grinding wheel: specific grinding energy Grinding ratio T is the thickness of the grinding wheel; S33: Characterizing machining accuracy: Actual removal error ; S34: Characterizing surface quality: sample surface morphology, surface roughness Sa; S35: Characterizing processing efficiency: Processing time Material removal rate ; Step S4: Establish a grinding wheel wear prediction model based on the multi-dimensional experimental results of the characterization, the specific expression of which is as follows: y =3.462-0.376 x +0.011 x 2 .

2. The method for predicting grinding wheel wear of difficult-to-machine materials according to claim 1, characterized in that, Step S1 includes the following steps: S11: Select difficult-to-machine materials and prepare experimental samples; S12: Select the matching grinding wheel model according to the selected material; S13: Measure the initial radius R0 of the grinding wheel and observe the initial surface morphology of the grinding wheel.

3. The method for predicting grinding wheel wear for difficult-to-machine materials according to claim 1, characterized in that, Step S2 includes the following steps: S21: Grind the sample according to the sample thickness and the thickness of each grinding layer until the grinding length is reached. ; S22: Collect grinding debris and measure grinding force. Measuring grinding depth Measuring the radius of the grinding wheel ; S23: If grinding length Measure the surface roughness of the sample The morphology of the machined sample surface is observed, and the morphology of the grinding wheel surface is further observed. The cycle experiment ends; otherwise, return to S21. In the above formula, .

4. The method for predicting grinding wheel wear for difficult-to-machine materials according to claim 1, characterized in that, Step S4 includes the following steps: S41: Analyze grinding wheel wear parameters; S42: Establish a grinding wheel wear prediction model.

5. The method for predicting grinding wheel wear for difficult-to-machine materials according to claim 2, characterized in that, Difficult-to-machine materials include quartz crystals, optical glass, ceramic materials, high-performance alloys, particle-reinforced metal matrix composites, whisker-reinforced ceramic matrix composites, and fiber-reinforced ceramic matrix composites.

6. The method for predicting grinding wheel wear for difficult-to-machine materials according to claim 2, characterized in that, Abrasives used in grinding wheels include corundum, silicon carbide, cubic boron nitride, and synthetic diamond. Grinding wheel binders include ceramics, resins, rubber, and metals.

7. The method for predicting grinding wheel wear for difficult-to-machine materials according to claim 3, characterized in that, In step S21, the grinding conditions include dry grinding and wet grinding, end grinding and side grinding.

8. The method for predicting grinding wheel wear for difficult-to-machine materials according to claim 4, characterized in that, In step S41, the grinding wheel wear parameters include radial wear per unit length of feed grinding wheel, abrasive grain protrusion height, and abrasive grain wear area.

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