CN115615782A - Evaluation and prediction method for fiber reinforced composite subsurface damage - Google Patents
Evaluation and prediction method for fiber reinforced composite subsurface damage Download PDFInfo
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- 238000011156 evaluation Methods 0.000 title claims abstract description 19
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- 238000012545 processing Methods 0.000 claims abstract description 43
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- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
- G01N1/32—Polishing; Etching
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- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
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- G01N21/84—Systems specially adapted for particular applications
- G01N2021/8444—Fibrous material
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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Abstract
The invention provides a method for evaluating and predicting subsurface damage of a fiber reinforced composite material, which comprises the following steps: preparing a subsurface damage detection sample; observing the sub-surface damage degree of the composite material through a microscope, wherein the evaluation standard is the maximum crack depth h 1 Maximum fiber extraction depth h 2 Maximum breaking pit depth h 3 And width l 1 (ii) a Processing tests under multiple preset process conditions are carried out, and the maximum crack depth h of the composite material under different process conditions is counted 1 Maximum fiber extraction depth h 2 Maximum pit collapse depth h 3 And a width of l 1 Constructing a damage database according to different damage parameters; based on damage dataAnd the library is used for establishing a fiber reinforced composite material subsurface damage prediction model under different process parameters by utilizing BP neural network training. The method can master the processing damage characteristics of the fiber reinforced composite material on the basis of small-batch experiments, and greatly saves the material cost in the experiment process.
Description
Technical Field
The invention relates to the technical field of composite material processing, in particular to a method for evaluating and predicting subsurface damage of a fiber reinforced composite material.
Background
The fiber reinforced Ceramic Matrix Composites (CMC) comprises a Ceramic Matrix, a reinforcement, an interface layer and other structural units, namely, the fiber reinforcement is introduced into the Ceramic Matrix, and the defects of high sensitivity, poor reliability, poor toughness and the like of a single-phase Ceramic Matrix material are overcome through the action of the interface layer which is properly and weakly combined, so that the toughening and reinforcing effects of the reinforcement on the Ceramic Matrix are realized. The material has the characteristics of low density, high specific action strength, good thermal mechanical use function, high ablation resistance, high heat insulation capability and the like, is widely applied to an aviation and aerospace thermal protection component, and realizes the integration of heat insulation and support, thereby greatly reducing the weight of the system and improving the task efficiency.
At present, the ceramic matrix composite material member is mostly prepared by adopting a near-net forming technology, and in order to meet the structural and performance requirements of the member, a blank member needs to be subjected to secondary processing so as to achieve proper size and accuracy. However, the fiber reinforced ceramic matrix composite has poor processability and obvious anisotropy, and is easy to generate severe processing damage with complex cause.
In order to control the machining damage, a method for detecting and predicting the machining damage needs to be established. At present, processing damage detection and prediction are related, such as a damage detection method and a system of a multilayer composite material with the publication number of CN110286155B, a detection method and a system of a composite material layered damage with the publication number of CN112763452A and the like, but the damage detection method does not consider a damage evaluation method after the composite material is processed and evaluation standards under different material performance requirements.
Based on the above, it is necessary to research a composite subsurface damage evaluation standard and evaluation method.
Disclosure of Invention
According to the technical problems, the invention provides the method for evaluating and predicting the subsurface damage of the fiber reinforced composite material, which can effectively evaluate the subsurface damage degree of the processed fiber reinforced composite material, predict the damage degree under different process conditions, save the experimental cost and provide a technical basis for the quality control of the processed surface. The technical means adopted by the invention are as follows:
a method for evaluating and predicting subsurface damage of a fiber reinforced composite material comprises the following steps:
step 1: preparing a subsurface damage detection sample;
and 2, step: observing the sub-surface damage degree of the composite material through a microscope, wherein the evaluation standard is the maximum crack depth h 1 Maximum fiber extraction depth h 2 Maximum pit collapse depth h 3 And width l 1 ；
And step 3: processing tests under multiple preset process conditions are carried out, and the maximum crack depth h of the composite material under different process conditions is counted 1 Maximum fiber extraction depth h 2 Maximum pit collapse depth h 3 And width l 1 Constructing a damage database according to different damage parameters;
and 4, step 4: and (4) according to the damage database, utilizing BP neural network training to establish a fiber reinforced ceramic matrix composite subsurface damage prediction model under different process parameters.
Further, the step 1 specifically includes the following steps:
step 12, pasting the accompanying sheets: cutting a polished silicon wafer with the same size as the sample as a companion wafer, putting the sample and the companion wafer into alcohol for ultrasonic cleaning and drying, and then adhering the processing surface of the sample and the polished surface of the companion wafer together;
step 13, grinding and polishing the section: and fixing the adhered sample on a balancing weight, enabling the section to be observed to be vertical to the plane where the accompanying sheet is located and the processing direction, and grinding and polishing the section to be observed of the sample on a grinding polishing machine.
Further, in the step 12, a certain pressure is applied to the accompanying sheet and the sample in the pasting process, so that the adhesive layer between the accompanying sheet and the sample is uniform and fine.
Further, in the step 13, the time of each process in the grinding process is long enough and has a certain removal amount, so that the damage introduced by the previous process is completely removed, the polished surface to be observed is smooth, and the crack information of the subsurface can be clearly seen.
Further, in the step 3, the preset process conditions include a processing mode, a linear speed of the tool, a feeding speed of the tool, a processing depth of the tool, and a model of the tool.
Further, in the step 4, the neural network model construction process includes the following steps:
step 41, determining a proper number of training samples;
step 42, dividing training samples;
43, carrying out normalization processing on the samples of the input layer and the output layer;
step 44, designing a BP network structure;
and step 45, establishing a fiber reinforced ceramic matrix composite subsurface damage prediction model under different process parameters through selection of the number of layers of the BP neural network, the excitation function, the training function, the learning rate and the learning function of the hidden layer and the output layer.
The method is based on the fiber fracture mechanism and the matrix crushing mechanism in the fiber reinforced composite material processing process, obtains the damage evaluation standard of the fiber reinforced composite material after processing under different performance requirements, establishes the damage prediction model of the fiber reinforced composite material after processing by utilizing a neural network, can master the processing damage characteristics of the fiber reinforced composite material on the basis of a small batch experiment, greatly saves the material cost in the experimental process, simultaneously supplements and perfects the damage prediction model under different process conditions, predicts the evaluation standard under specific process parameters through the damage prediction model in the actual production process, obtains the optimal solution meeting the preset evaluation standard by adjusting a certain process parameter, provides a basis for optimizing the processing process parameters, is favorable for the quality control of the processing surface of the composite material, and provides technical support for the high-quality and high-efficiency processing of the composite material.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the damage evaluation and prediction method of the composite material of the present invention.
FIG. 2 is a schematic diagram of a composite material surface damage detection sample of the present invention.
FIG. 3 shows the maximum crack depth h of the composite material of the present invention 1 And maximum fiber extraction depth h 2 Schematic representation of (a).
FIG. 4 shows the maximum crushed pit depth h of the composite material of the present invention 3 And width l 1 Schematic representation of (a).
FIG. 5 is a schematic diagram of the BP neural network structure according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the embodiment of the invention discloses a method for evaluating and predicting subsurface damage of a fiber reinforced composite material, which comprises the following steps:
step 1: preparing a subsurface damage detection sample by using a sample preparation method in a section microscopic observation method;
1): the sample was cut. And cutting the processed sample into a sub-surface damage sample with a proper size to be observed. The process of cutting the sample to be observed needs to carefully protect the processing surface, so as to avoid damaging the original processing surface and introducing new damage.
2): and sticking the accompanying sheet. As shown in fig. 2, the sample and the coupon are placed in alcohol for ultrasonic cleaning and drying, and then the processed surface of the sample and the polished surface of the coupon are adhered together.
3): and (5) grinding and polishing the section. And fixing the adhered sample on a balancing weight. And grinding and polishing the section to be observed of the sample (the section to be observed is vertical to the plane where the accompany piece is located and the processing direction) on a grinding polishing machine.
Step 2: observing the sub-surface damage degree of the composite material after grinding and polishing treatment by a microscope, and obtaining the evaluation standard of the maximum crack depth h 1 Maximum fiber extraction depth h 2 Maximum pit collapse depth h 3 And width l 1 ；
Maximum crack depth h of composite material 1 And maximum fiber extraction depth h 2 As shown in fig. 3, the maximum chipping depth h 3 And a width of l 1 As shown in fig. 4, the fiber reinforced composite tends to crack first during processing because the fiber-matrix interface strength is much less than the fiber and matrix strength. Cracks propagate along the fiber-matrix interface. And after the fiber-matrix interface is cracked, the fiber and the interface are debonded, if the cutting force is greater than the tensile strength of the fiber, the fiber is broken and pulled out of the matrix. The damage form often occurs in the processing process of the fiber reinforced composite material and easily causes obvious damage defects, so the index is selected as an evaluation index for evaluating the damage of the fiber reinforced composite material.
And step 3: counting different technological conditions (processing mode, linear speed of cutter, feed speed of cutter, cutter)Machining depth, tool type) maximum crack depth h of the composite material 1 Maximum fiber extraction depth h 2 Maximum breaking pit depth h 3 And width l 1 Constructing a damage database according to different damage parameters;
and (3) establishing a process database of the process parameters (such as processing mode, linear speed of the cutter, feeding speed of the cutter, processing depth of the cutter and model of the cutter) and the damage parameters obtained in the step (2) so as to obtain the relation between the process parameters and the damage parameters.
And 4, step 4: according to the damage database, a BP neural network training is utilized to establish a fiber reinforced ceramic matrix composite subsurface damage prediction model under different process parameters, as shown in FIG. 5, the method specifically comprises the following steps:
1): determining a proper number of training samples;
2): dividing training samples;
3): carrying out normalization processing on the samples of the input layer and the output layer;
4): and (4) designing a BP network structure.
5): and establishing a fiber reinforced ceramic matrix composite subsurface damage prediction model under different process parameters through selection of the number of layers of the BP neural network, excitation functions, training functions, learning rates, learning functions and the like of the hidden layer and the output layer. The technological parameters and the evaluation standards obtained by experiments are a training set and a test set, in the actual production process, the evaluation standards under the specific technological parameters are predicted through a damage prediction model, the technological parameters (such as processing mode, linear speed of a cutter, feeding speed of the cutter, processing depth of the cutter and the type of the cutter) are used as input layers, and the output layer of a BP neural network model comprises the maximum crack depth h 1 Maximum fiber extraction depth h 2 Maximum pit collapse depth h 3 And width l 1 . And obtaining an optimal solution meeting a preset evaluation standard by adjusting a certain process parameter.
The method is oriented to the requirements of high-efficiency low-damage processing of the fiber reinforced ceramic matrix composite member, solves the problem that a method for detecting and evaluating the subsurface damage of the fiber reinforced ceramic matrix composite is lacked at present, can effectively predict the subsurface damage degree under different processing conditions, has strong applicability and reliability, and provides theoretical method support for the suppression of the processing damage of the fiber reinforced ceramic matrix composite.
Example 1
In the embodiment, a processing verification experiment is carried out on the fiber reinforced ceramic matrix composite, the cutter is an electroplated diamond grinding wheel, and the reliability of the prediction method provided by the invention is verified according to the side grinding test result.
The grinding processing test of the fiber reinforced ceramic matrix composite material comprises the following steps:
step 1: grinding the surface of the workpiece by using cup-shaped grinding wheels of 100#, 500#, and 1500# respectively to ensure that the surface of the workpiece is not obviously damaged;
step 2: grinding a workpiece by using the side surface of an electroplated diamond grinding wheel with the diameter of 6mm, setting processing parameters, setting the grinding depth to be 0.03mm, setting the feeding speed to be 10mm/min, and setting the rotating speed of a main shaft to be 4000r/min.
And step 3: and (5) replacing the workpiece, changing the processing parameters and grinding the workpiece.
A method for evaluating and predicting subsurface damage of a fiber reinforced composite material comprises the following steps:
step 1: and preparing a sub-surface damage detection sample by using a cross-section microscopic observation method.
1) And cutting the sample wafer. Cutting the ground sample into 5mm by 10mm pieces with a diamond wire saw
The sample, wherein 5mm x 10mm is the ground surface, the machined surface is carefully protected during the cutting process, and the original machined surface is prevented from being damaged and new damage is prevented from being introduced;
2) And sticking the accompanying sheet. And cutting a polished silicon wafer with the same size as the sample wafer as a companion wafer, putting the sample wafer and the companion wafer into alcohol for ultrasonic cleaning, drying and then adhering the ground surface and the polished surface of the companion wafer together by using special glue for a transmission electron microscope. The adhesive layer between the accompanying sheet and the sample sheet is uniform and fine by applying a certain pressure to the accompanying sheet and the sample sheet in the pasting process. The accompanying sheet can play a role in protecting the grinding surface, avoids introducing new damage to the edge effect in the grinding process, and can also be used as a measuring reference when the damage depth is measured.
3) Grinding and polishing the section. And fixing the adhered sample on a circular balancing weight by paraffin. The method comprises the steps of grinding the section of a sample on a grinding polishing machine by using W20 and W5 diamond grinding discs in sequence, wherein each process has a certain removal amount to ensure that the damage of the previous process is completely removed, then polishing the section by using W0.5 diamond grinding paste, and the polished section has a smooth surface and can clearly see the crack information of the subsurface.
Step 2: observing the sub-surface damage degree of the composite material through a microscope, wherein the evaluation standard is the maximum crack depth h 1 Maximum fiber extraction depth h 2 Maximum pit collapse depth h 3 And width l 1 。
And 3, step 3: counting the maximum crack depth h of the composite material under different process conditions 1 Maximum fiber extraction depth h 2 Maximum breaking pit depth h 3 And width l 1 And constructing a damage database according to different damage parameters.
And 4, step 4: and (4) according to the damage database, utilizing BP neural network training to establish a fiber reinforced ceramic matrix composite subsurface damage prediction model under different process parameters.
1): determining a suitable number of training samples;
2): dividing training samples;
3): carrying out normalization processing on the samples of the input layer and the output layer;
4): and (4) designing a BP network structure.
5): and establishing a fiber reinforced ceramic matrix composite subsurface damage prediction model under different process parameters through selection of the number of layers of the BP neural network, excitation functions, training functions, learning rates, learning functions and the like of the hidden layer and the output layer.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. A method for evaluating and predicting subsurface damage of a fiber reinforced composite material is characterized by comprising the following steps:
step 1: preparing a subsurface damage detection sample;
step 2: observing the sub-surface damage degree of the composite material through a microscope, wherein the evaluation standard is the maximum crack depth h 1 Maximum fiber extraction depth h 2 Maximum pit collapse depth h 3 And width l 1 ；
And step 3: processing tests under multiple preset process conditions are carried out, and the maximum crack depth h of the composite material under different process conditions is counted 1 Maximum fiber extraction depth h 2 Maximum breaking pit depth h 3 And width l 1 Constructing a damage database according to different damage parameters;
and 4, step 4: and (4) according to the damage database, utilizing BP neural network training to establish a fiber reinforced ceramic matrix composite subsurface damage prediction model under different process parameters.
2. The method for evaluating and predicting the subsurface damage of the fiber-reinforced composite material according to claim 1, wherein the step 1 specifically comprises the following steps:
step 11, cutting a sample: cutting the processed sample into a sub-surface damage sample with a proper size to be observed;
step 12, pasting the accompanying sheets: cutting a polished silicon wafer with the same size as the sample as a companion wafer, putting the sample and the companion wafer into alcohol for ultrasonic cleaning and drying, and then adhering the processing surface of the sample and the polished surface of the companion wafer together;
step 13, grinding and polishing the section: and fixing the adhered sample on a balancing weight, enabling the section to be observed to be vertical to the plane where the accompanying sheet is located and the processing direction, and grinding and polishing the section to be observed of the sample on a grinding polishing machine.
3. The method for evaluating and predicting the subsurface damage of the fiber reinforced composite material as claimed in claim 2, wherein in the step 12, a certain pressure is applied to the coset and the sample in the pasting process to ensure that the glue layer between the coset and the sample is uniform and fine.
4. The method for evaluating and predicting the subsurface damage of the fiber reinforced composite material according to claim 2, wherein in the step 13, the time of each process in the grinding process is long enough and has a certain removal amount, so that the damage introduced in the previous process is completely removed, the polished surface to be observed is smooth, and the crack information of the subsurface can be clearly seen.
5. The method for evaluating and predicting the subsurface damage of the fiber reinforced composite material according to claim 1, wherein the preset process conditions in the step 3 comprise a processing mode, a linear speed of a cutter, a feeding speed of the cutter, a processing depth of the cutter and a model of the cutter.
6. The method for evaluating and predicting the subsurface damage of the fiber reinforced composite material according to claim 1, wherein in the step 4, the neural network model construction process comprises the following steps:
step 41, determining a proper number of training samples;
step 42, dividing training samples;
43, normalizing the samples of the input layer and the output layer;
step 44, designing a BP network structure;
and step 45, establishing a fiber reinforced ceramic matrix composite subsurface damage prediction model under different process parameters through selection of the number of layers of the BP neural network, the excitation function, the training function, the learning rate and the learning function of the hidden layer and the output layer.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105842265A (en) * | 2016-05-30 | 2016-08-10 | 大连理工大学 | Surface treatment method of damage below machining face of carbon-fiber-reinforced composite material |
| CN108897925A (en) * | 2018-06-11 | 2018-11-27 | 华中科技大学 | A kind of casting technological parameter optimization method based on casting defect prediction model |
| CN112748115A (en) * | 2020-12-23 | 2021-05-04 | 沈阳航空航天大学 | Evaluation method for subsurface damage of fiber reinforced composite material hole making |
| CN112818582A (en) * | 2021-03-24 | 2021-05-18 | 上海邦麟复合材料科技有限公司 | Fiber reinforced composite material performance judgment method and application thereof |
| CN114741744A (en) * | 2022-05-13 | 2022-07-12 | 北京航空航天大学 | Microscopic modeling and multi-scale analysis method suitable for needling composite material |
-
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- 2022-11-10 CN CN202211406611.7A patent/CN115615782A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105842265A (en) * | 2016-05-30 | 2016-08-10 | 大连理工大学 | Surface treatment method of damage below machining face of carbon-fiber-reinforced composite material |
| CN108897925A (en) * | 2018-06-11 | 2018-11-27 | 华中科技大学 | A kind of casting technological parameter optimization method based on casting defect prediction model |
| CN112748115A (en) * | 2020-12-23 | 2021-05-04 | 沈阳航空航天大学 | Evaluation method for subsurface damage of fiber reinforced composite material hole making |
| CN112818582A (en) * | 2021-03-24 | 2021-05-18 | 上海邦麟复合材料科技有限公司 | Fiber reinforced composite material performance judgment method and application thereof |
| CN114741744A (en) * | 2022-05-13 | 2022-07-12 | 北京航空航天大学 | Microscopic modeling and multi-scale analysis method suitable for needling composite material |
Non-Patent Citations (1)
| Title |
|---|
| 黄松林;崔建忠;: "基于人工神经网络的铝合金铸锭裂纹倾向预测", 中国有色金属学报, no. 09, 15 September 2008 (2008-09-15), pages 1699 - 1705 * |
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