CN113984500A - A material failure monitoring device and its classification method - Google Patents
A material failure monitoring device and its classification method Download PDFInfo
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Abstract
本发明提供一种材料失效监测装置及利用该装置的分类方法，属于新材料力学性能测试与表征技术领域。该材料失效监测装置包括旋转平台，旋转平台的左侧设置有X射线发射仪，右侧设置有X射线探测器；旋转平台上设置有用于放置材料试样的加载装置，材料试样的表面设置声发射探测器，声发射探测器依次与信号放大器、声发射信号分析仪和电脑控制系统相连接；X射线探测器与电脑控制系统相连接。本发明通过光学与声学相结合，从材料内部测量到表面测量的失效监测装置，并利用该装置所采集的信号开展主成分分析和聚类分析，建立材料不同损伤模式与信号特征参数之间的内在联系，从而达到能够区分不同形式的失效模式以及各种损伤模式所占比例的目的。
The invention provides a material failure monitoring device and a classification method using the device, belonging to the technical field of mechanical property testing and characterization of new materials. The material failure monitoring device includes a rotating platform, an X-ray transmitter is arranged on the left side of the rotating platform, and an X-ray detector is arranged on the right side; a loading device for placing a material sample is arranged on the rotating platform, and the surface of the material sample is arranged The acoustic emission detector is connected with the signal amplifier, the acoustic emission signal analyzer and the computer control system in turn; the X-ray detector is connected with the computer control system. The invention combines optics and acoustics to measure a failure monitoring device from the inside of the material to the surface measurement, and uses the signals collected by the device to carry out principal component analysis and cluster analysis to establish the relationship between different damage modes of the material and signal characteristic parameters. Intrinsic connection, so as to achieve the purpose of distinguishing different forms of failure modes and the proportion of various damage modes.
Description
Technical Field
The invention belongs to the technical field of mechanical property testing and characterization of new materials, and particularly relates to a material failure monitoring device and a classification method using the same.
Background
The particle reinforced metal matrix composite material is widely applied to the field of high-end equipment such as aerospace, automobiles and the like, and has more failure modes due to complex components. The existing failure analysis of the particle reinforced metal matrix composite material is based on macroscopic mechanical properties, microscopic components, fracture analysis and the like, and the failure of particles, particle-matrix interfaces and the like in the particle reinforced metal matrix composite material is not deeply known. The main reason for this is the lack of effective experimental devices and methods to detect the failure process of particle-reinforced metal matrix composites, especially to monitor the failure process of the inner particles, interfaces and matrices. In addition, the particle reinforced metal matrix composite material is failed by a multi-mode coupling process, and how to distinguish different damage modes and what damage mode is dominant are difficult to realize by the conventional device and method. This hinders the deep understanding of the mechanical properties of the particle-reinforced metal matrix composite material and affects the safety of the particle-reinforced metal matrix composite material in service.
Disclosure of Invention
The purpose of the invention is:
in order to solve the defects in the prior art, one of the objectives of the present invention is to provide a material failure monitoring device, which combines optics and acoustics to measure from the inside of the material to the surface, and uses the signals acquired by the device to perform principal component analysis and cluster analysis to establish the internal relation between different damage modes of the material and the characteristic parameters of the signals, thereby achieving the purpose of distinguishing the different types of failure modes and the proportion of the various damage modes.
In order to solve the technical problem, the technical scheme of the invention is as follows:
on one hand, the invention provides a material failure monitoring device which comprises a rotary platform, wherein an X-ray emission instrument is arranged on the left side of the rotary platform, and an X-ray detector is arranged on the right side of the rotary platform;
the rotary platform is provided with a loading device for placing a material sample, the surface of the material sample is provided with an acoustic emission detector, and the acoustic emission detector is sequentially connected with a signal amplifier, an acoustic emission signal analyzer and a computer control system;
the X-ray detector is connected with the computer control system.
Preferably, the material is a multiphase composite material.
Preferably, the multiphase composite is a particle reinforced metal matrix composite.
Preferably, the signal analysis software in the computer control system is MATLAB software.
Preferably, the MATLAB software includes X-ray imaging internal information analysis and acoustic emission signal time and frequency domain analysis.
On the other hand, the invention also provides a classification method using the material failure monitoring device, which comprises the following steps:
s1, carrying out three-dimensional scanning on the in-situ loaded sample through an X-ray emission instrument to obtain internal deformation and damage information before and after loading the sample, wherein the internal deformation and damage information specifically comprises the internal porosity distribution and particle distribution of the sample and the bonding condition of particles and an interface;
s2, receiving the acoustic emission signal of the sample when loaded by an acoustic emission detector, and carrying out signal analysis on the acoustic emission signal;
s3 identifies the failure of particles, interfaces and matrixes by combining the clustering analysis result with the three-dimensional internal information, and distinguishes the failure of different forms and the proportion of various damage modes.
Preferably, in S2, the signal analysis includes performing principal component analysis on the acquired time domain signal, finding out a corresponding principal component that affects the material failure mode, and performing frequency domain transformation on the principal component related to the material failure to obtain frequency domain characteristics of different principal components.
Preferably, the frequency domain transform methods include fourier transforms, short-time fourier transforms and hilbert transforms.
Preferably, in S2, the signal analysis further includes performing cluster analysis on the principal components of the signal by using a clustering method according to the types of common failure modes of the material, so as to obtain a principal component signal corresponding to each failure mode.
Preferably, the proportion of the different failure modes in the current damage state is calculated according to the proportion of each main component signal.
Compared with the prior art, the invention has the following beneficial effects:
1. the material failure monitoring device provided by the invention combines optics and acoustics, measures from the inside of the material to the surface, performs principal component analysis and cluster analysis by using signals acquired by the device, and establishes the internal relation between different damage modes of the material and characteristic parameters of the signals, thereby achieving the purpose of distinguishing different types of failure modes and the proportion of various damage modes.
2. The material failure monitoring device and the classification method thereof provided by the invention can identify the failure of particles, interfaces and matrixes, and can distinguish different failure modes and the proportion of various damage modes.
Drawings
FIG. 1 is a schematic structural diagram of a material failure monitoring device according to the present invention;
in the figure: the system comprises an X-ray emission instrument, an X-ray detector, a rotary platform, a loading device, a material sample, an acoustic emission detector, a signal amplifier, an acoustic emission signal analyzer and a computer control system, wherein the X-ray emission instrument comprises 1, the X-ray detector 2, the rotary platform 3, the loading device 5, the material sample 6, the acoustic emission detector 7, the signal amplifier 8, the acoustic emission signal analyzer 9 and the computer control system 9.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention; it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work are within the scope of the present invention.
As shown in fig. 1, the invention provides a material failure monitoring device, which comprises a rotary platform 3, wherein an X-ray emitter 1 is arranged on the left side of the rotary platform 3, and an X-ray detector 2 is arranged on the right side of the rotary platform 3;
the rotary platform 3 is provided with a loading device 4 for placing a material sample 5, the surface of the material sample 5 is provided with an acoustic emission detector 6, and the acoustic emission detector 6 is sequentially connected with a signal amplifier 7, an acoustic emission signal analyzer 8 and a computer control system 9;
the X-ray detector 2 is connected with a computer control system 9.
In the device, the rotating platform can rotate 360 degrees, the loading device 4 can provide tension, compression, bending and fatigue loading, and can provide different loading environments such as high temperature, low temperature, damp heat, corrosion and the like.
In the present invention, the material is a multiphase composite material.
In the present invention, the multiphase composite is a particle reinforced metal matrix composite.
In the present invention, the signal analysis software in the computer control system 9 is MATLAB software.
In the invention, the MATLAB software comprises the analysis of the internal information of X-ray imaging and the time domain and frequency domain analysis of acoustic emission signals.
The invention also provides a classification method using the material failure monitoring device, which comprises the following steps:
s1, carrying out three-dimensional scanning on the in-situ loaded sample through an X-ray emission instrument to obtain internal deformation and damage information before and after loading the sample, wherein the internal deformation and damage information specifically comprises the internal porosity distribution and particle distribution of the sample and the bonding condition of particles and an interface;
s2, receiving the acoustic emission signal of the sample when loaded by an acoustic emission detector, and carrying out signal analysis on the acoustic emission signal;
s3 identifies the failure of particles, interfaces and matrixes by combining the clustering analysis result with the three-dimensional internal information, and distinguishes the failure of different forms and the proportion of various damage modes.
In the invention, in S2, the signal analysis includes performing principal component analysis on the acquired time domain signal, finding out a corresponding principal component that affects the material failure mode, and performing frequency domain transformation on the principal component related to the material failure to obtain frequency domain characteristics of different principal components.
In the present invention, the frequency domain transformation method includes fourier transformation, short-time fourier transformation, and hilbert transformation, and the frequency domain transformation method is merely exemplary, and those skilled in the art may select other frequency domain transformation methods.
In the invention S2, the signal analysis further comprises the steps of carrying out cluster analysis on the principal components of the signals by using a clustering method according to the types of the common failure modes of the materials to obtain principal component signals corresponding to each failure mode; the clustering method includes, but is not limited to, a fuzzy clustering method, a C-means clustering method, and the like.
In the invention, the proportion of different failure modes in the current damage state is calculated according to the proportion of each main component signal; wherein, the ratio includes but is not limited to frequency domain signal energy ratio, time-frequency domain amplitude ratio, etc.
The using method of the invention is as follows:
the material sample 5 is placed on a loading device 4, the loading device 4 is fixed on a rotary platform 3, an X-ray emitter 1 on the left side of the rotary platform 3 emits X-rays, the X-rays penetrate through the material sample 5 and then are imaged on an X-ray detector 2, ultrasonic signals emitted in the loading process of the material sample 5 are received by an acoustic emission detector 6 fixed on the surface of the sample and reach an acoustic emission signal analyzer 8 after passing through a signal amplifier 7, and the acoustic emission signal analyzer 8 and the X-ray detector 2 are connected with a computer control system 9. The image processing software in the computer control system 9 reconstructs the three-dimensional image of the material sample 5 to obtain three-dimensional internal information of the material sample 5 and can calculate the internal deformation by a numeric-type correlation method. The signal analysis software (MATLAB) in the computer control system 9 may perform filtering processing on the acoustic emission signals, and further perform principal component analysis and fuzzy clustering to obtain acoustic emission signal characteristics corresponding to different damage types.
The digital image correlation method can calculate the internal deformation field and the stress strain field through the internal information of X-ray imaging before and after the sample deformation.
The signal analysis software is MATLAB commercial software, and the required component analysis and fuzzy clustering method is a mathematical and signal processing field calculation method.
The material failure monitoring device provided by the invention combines optics and acoustics, measures from the inside of the material to the surface, performs principal component analysis and cluster analysis by using signals acquired by the device, and establishes the internal relation between different damage modes of the material and characteristic parameters of the signals, thereby achieving the purpose of distinguishing different types of failure modes and the proportion of various damage modes.
The above are merely preferred embodiments of the present invention; the scope of the invention is not limited thereto. Any person skilled in the art should be able to cover the technical scope of the present invention by equivalent or modified solutions and modifications within the technical scope of the present invention.
Claims (10)
1. The material failure monitoring device is characterized by comprising a rotary platform, wherein an X-ray emission instrument is arranged on the left side of the rotary platform, and an X-ray detector is arranged on the right side of the rotary platform;
the rotary platform is provided with a loading device for placing a material sample, the surface of the material sample is provided with an acoustic emission detector, and the acoustic emission detector is sequentially connected with a signal amplifier, an acoustic emission signal analyzer and a computer control system;
the X-ray detector is connected with the computer control system.
2. A material failure monitoring device as claimed in claim 1 wherein the material is a multi-phase composite material.
3. A material failure monitoring apparatus as claimed in claim 2, wherein the multi-phase composite material is a particle reinforced metal matrix composite material.
4. A material failure monitoring device as claimed in claim 1, wherein the signal analysis software in the computer control system is MATLAB software.
5. A material failure monitoring device as claimed in claim 4, wherein the MATLAB software includes X-ray imaging internal information analysis and acoustic emission signal time and frequency domain analysis.
6. A method of sorting a material failure monitoring apparatus according to any of claims 1 to 5, the method comprising the steps of:
s1, carrying out three-dimensional scanning on the in-situ loaded sample through an X-ray emission instrument to obtain internal deformation and damage information before and after loading the sample, wherein the internal deformation and damage information specifically comprises the internal porosity distribution and particle distribution of the sample and the bonding condition of particles and an interface;
s2, receiving the acoustic emission signal of the sample when loaded by an acoustic emission detector, and carrying out signal analysis on the acoustic emission signal;
s3 identifies the failure of particles, interfaces and matrixes by combining the clustering analysis result with the three-dimensional internal information, and distinguishes the failure of different forms and the proportion of various damage modes.
7. The classification method according to claim 6, wherein in step S2, the signal analysis includes performing principal component analysis on the collected time domain signals to find out corresponding principal components affecting the material failure mode, and performing frequency domain transformation on the principal components related to the material failure to obtain frequency domain characteristics of different principal components.
8. The classification method according to claim 6, characterised in that the frequency domain transformation methods comprise Fourier transformation, short-time Fourier transformation and Hilbert transformation.
9. The classification method according to claim 7, wherein in step S2, the signal analysis further includes performing cluster analysis on principal components of the signal according to the types of common failure modes of the material by using a clustering method to obtain principal component signals corresponding to each failure mode.
10. The classification method according to claim 9, wherein the proportion of the different failure modes in the current damage state is calculated according to the proportion of each principal component signal.
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