CN117606656A - Workpiece stress detection method, device, equipment and medium - Google Patents
Workpiece stress detection method, device, equipment and medium Download PDFInfo
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L1/00—Measuring force or stress, in general
- G01L1/20—Measuring force or stress, in general by measuring variations in ohmic resistance of solid materials or of electrically-conductive fluids; by making use of electrokinetic cells, i.e. liquid-containing cells wherein an electrical potential is produced or varied upon the application of stress
- G01L1/22—Measuring force or stress, in general by measuring variations in ohmic resistance of solid materials or of electrically-conductive fluids; by making use of electrokinetic cells, i.e. liquid-containing cells wherein an electrical potential is produced or varied upon the application of stress using resistance strain gauges
- G01L1/2287—Measuring force or stress, in general by measuring variations in ohmic resistance of solid materials or of electrically-conductive fluids; by making use of electrokinetic cells, i.e. liquid-containing cells wherein an electrical potential is produced or varied upon the application of stress using resistance strain gauges constructional details of the strain gauges
- G01L1/2293—Measuring force or stress, in general by measuring variations in ohmic resistance of solid materials or of electrically-conductive fluids; by making use of electrokinetic cells, i.e. liquid-containing cells wherein an electrical potential is produced or varied upon the application of stress using resistance strain gauges constructional details of the strain gauges of the semi-conductor type
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L1/00—Measuring force or stress, in general
- G01L1/20—Measuring force or stress, in general by measuring variations in ohmic resistance of solid materials or of electrically-conductive fluids; by making use of electrokinetic cells, i.e. liquid-containing cells wherein an electrical potential is produced or varied upon the application of stress
- G01L1/22—Measuring force or stress, in general by measuring variations in ohmic resistance of solid materials or of electrically-conductive fluids; by making use of electrokinetic cells, i.e. liquid-containing cells wherein an electrical potential is produced or varied upon the application of stress using resistance strain gauges
- G01L1/2206—Special supports with preselected places to mount the resistance strain gauges; Mounting of supports
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L1/00—Measuring force or stress, in general
- G01L1/20—Measuring force or stress, in general by measuring variations in ohmic resistance of solid materials or of electrically-conductive fluids; by making use of electrokinetic cells, i.e. liquid-containing cells wherein an electrical potential is produced or varied upon the application of stress
- G01L1/22—Measuring force or stress, in general by measuring variations in ohmic resistance of solid materials or of electrically-conductive fluids; by making use of electrokinetic cells, i.e. liquid-containing cells wherein an electrical potential is produced or varied upon the application of stress using resistance strain gauges
- G01L1/225—Measuring circuits therefor
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L5/00—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes
- G01L5/0047—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes measuring forces due to residual stresses
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Abstract
The application discloses a workpiece stress detection method, device, equipment and medium, relates to the technical field of stress detection, and aims to solve the technical problem that assembly stress or residual stress of a workpiece cannot be detected in time in the assembly process of the existing stress detection method. The detection method comprises the following steps: pasting a graphene sensor on the surface of a workpiece to be detected, so as to output a detection signal when the workpiece to be detected is stressed; extracting the peak value of the detection signal as a characteristic signal value; based on the characteristic signal value and the standard characteristic signal value, obtaining a stress value of the workpiece to be detected; the standard characteristic signal value refers to a detection resistance value peak value output by the graphene sensor in a state that a workpiece to be detected is not stressed; and obtaining a stress detection result based on the stress value and the stress limit threshold value of the workpiece to be detected.
Description
Technical Field
The present disclosure relates to the field of stress detection technologies, and in particular, to a method, an apparatus, a device, and a medium for detecting stress of a workpiece.
Background
In the machine body assembly process, the existence of assembly stress easily causes connection stress concentration, so that the fatigue fracture of a connecting piece and the deformation of a machine body structure are caused, and the assembly quality and the aerodynamic appearance of an airplane are seriously influenced. In the assembly process of the machine body, because the aircraft parts are huge in size and irregular in shape, a large number of variable curvature areas exist, if excessive assembly stress and residual stress exist at the parts and cannot be detected in time, the problems of large stress deformation, composite material tearing, out-of-tolerance assembly quality and the like of the parts are caused in the assembly process and after the assembly is completed, and finally, large-scale and high-value parts are reworked, scrapped and other economic losses are caused, so that the aircraft manufacturing quality and the service life are seriously threatened.
The existing stress detection equipment is limited by the detection method, the detection principle, the geometric volume of the detection equipment and other factors, so that the existing stress detection equipment cannot be detected in time due to overlarge assembly stress and residual stress in the assembly process of various material parts, and the problems of larger stress deformation, composite material tearing, out-of-tolerance assembly quality and the like of the parts in the assembly process and after the assembly is completed are caused.
Disclosure of Invention
The main purpose of the application is to provide a workpiece stress detection method, a device, equipment and a medium, and aims to solve the technical problem that the assembly stress or residual stress of a workpiece cannot be detected in time in the assembly process of the existing stress detection method.
In order to solve the above technical problems, the embodiments of the present application provide: a method of workpiece stress detection comprising the steps of:
pasting a graphene sensor on the surface of a workpiece to be detected, so as to output a detection signal when the workpiece to be detected is stressed; the detection signal refers to a resistance value output by the graphene sensor when the workpiece to be detected is stressed;
extracting the peak value of the detection signal as a characteristic signal value;
based on the characteristic signal value and the standard characteristic signal value, obtaining a stress value of the workpiece to be detected; the standard characteristic signal value refers to a detection resistance value peak value output by the graphene sensor in a state that a workpiece to be detected is not stressed;
and obtaining a stress detection result based on the stress value and the stress limit threshold value of the workpiece to be detected.
As some optional embodiments of the present application, the obtaining the stress value of the workpiece to be detected based on the characteristic signal value and the standard characteristic signal value includes:
if the characteristic signal value is larger than the standard characteristic signal value, calculating based on a tensile stress formula to obtain a tensile stress value of the workpiece to be detected;
If the characteristic signal value is smaller than the standard characteristic signal value, calculating based on a compressive stress formula to obtain a compressive stress value of the workpiece to be detected;
and if the characteristic signal value is equal to the standard characteristic signal value, the stress value of the workpiece to be detected is zero.
As some optional embodiments of the present application, the tensile stress value satisfies the following relationship:
F=(R u -p2)/p1
wherein,Fit is meant that the tensile stress value is,R u it is meant that the characteristic signal value is,p1、p2 are all constants.
As some optional embodiments of the present application, the compressive stress value satisfies the following relationship:
F’=(p4-R u )/p3
wherein,F’it is meant that the value of the compressive stress,R u it is meant that the characteristic signal value is,p3、p4 are all constants.
As some optional embodiments of the present application, if the workpiece to be detected is a curved test piece, the standard characteristic signal value satisfies the following relationship:
R a =R a1 ±ΔR
wherein, theR a Representing the standard characteristic signal value; the saidR a1 The method comprises the steps of representing a peak value of a detection resistance value output by the graphene sensor in a non-stress state of a workpiece to be detected; the saidΔRIndicating that the sensor output value deviates from the unstressed state when the sensor is stuck to the surface of the curved memberR a1 Values.
As some optional embodiments of the present application, the extracting the peak value of the detection signal as the characteristic signal value includes:
Performing filtering noise reduction processing and signal amplification processing on the detection signal to obtain a target detection signal;
and extracting the peak value of the target detection signal to be used as a characteristic signal value.
As some optional embodiments of the present application, before the adhering the graphene sensor to the surface of the workpiece to be detected, so as to output the detection signal when the workpiece to be detected is stressed, the method further includes:
adhering a graphene sensor to the surface of a test piece, and applying tensile stress to the test piece to obtain a detection tensile characteristic signal value;
applying compressive stress to the test piece to obtain a detected compression characteristic signal value;
and obtaining a linear relation between the stress value received by the workpiece to be detected and the detection characteristic signal value based on the detection tensile characteristic signal value and the detection compression characteristic signal value.
As some optional embodiments of the present application, the applying a tensile stress to the test piece to obtain a detected tensile characteristic signal value includes:
applying a tensile stress to the test piece to obtain a detected tensile signal;
based on the detected stretching signal, obtaining a detected stretching characteristic signal value; the detected stretching characteristic signal value is a peak value of the detected stretching signal.
As some optional embodiments of the present application, the applying a compressive stress to the test piece to obtain a detected compressive characteristic signal value includes:
applying a compressive stress to the test piece to obtain a detected compressive signal;
based on the detected compression signal, obtaining a detected compression characteristic signal value; the detected compression characteristic signal value is a peak value of the detected compression signal.
As some optional embodiments of the present application, the obtaining the stress detection result based on the stress value and the stress limit threshold of the workpiece to be detected includes:
if the stress value of the workpiece to be detected is smaller than the stress limit threshold, the detection result is indicated as normal;
and if the stress value of the workpiece to be detected is greater than or equal to the stress limit threshold, the detection result is expressed as abnormal.
As some optional embodiments of the present application, the resistance value output by the graphene sensor satisfies the following relation:
wherein,R x representing the graphene sensor outputResistance value of (2);the resistivity of the graphene is represented by the formula,Lrepresents the graphene length;Srepresents the graphene cross-sectional area.
In order to solve the above technical problems, the embodiment of the present application further provides: a workpiece stress detection device comprising:
The first output module is used for pasting the graphene sensor on the surface of the workpiece to be detected so as to output a detection signal when the workpiece to be detected is stressed; the detection signal refers to a resistance value output by the graphene sensor when the workpiece to be detected is stressed;
the characteristic extraction module is used for extracting the peak value of the detection signal to be used as a characteristic signal value;
the calculation module is used for obtaining a stress value of the workpiece to be detected based on the characteristic signal value and the standard characteristic signal value; the standard characteristic signal value refers to a detection resistance value peak value output by the graphene sensor in a state that a workpiece to be detected is not stressed;
and the detection module is used for obtaining a stress detection result based on the stress value and the stress limit threshold value of the workpiece to be detected.
In order to solve the above technical problems, the embodiment of the present application further provides: an electronic device comprising a memory in which a computer program is stored and a processor executing the computer program to implement a method of workpiece stress detection as described above.
In order to solve the above technical problems, the embodiment of the present application further provides: a computer readable storage medium having a computer program stored thereon, a processor executing the computer program to implement a workpiece stress detection method as described above.
Compared with the prior art, in the workpiece stress detection method, the graphene sensor is stuck to the surface of the workpiece to be detected, so that a detection signal is output when the workpiece to be detected is stressed; the detection signal refers to a resistance value output by the graphene sensor when the workpiece to be detected is stressed; the graphene sensor is adhered to the surface of a workpiece to be detected, and when the workpiece to be detected is subjected to stress deformation, the graphene sensor is deformed together, so that the mobility of a current carrier is changed and the resistivity of the current carrier is changed due to the change of the electronic energy band structure in the graphene. Extracting the peak value of the detection signal as a characteristic signal value; based on the characteristic signal value and the standard characteristic signal value, obtaining a stress value of the workpiece to be detected; the standard characteristic signal value refers to a detection resistance value peak value output by the graphene sensor in a state that a workpiece to be detected is not stressed; comparing the characteristic signal value with the standard characteristic signal value to judge the type and the magnitude of the stress action; and obtaining a stress detection result based on the stress value and the stress limit threshold value of the workpiece to be detected. Therefore, the stress detection method realizes the stress detection of the workpiece in the assembly process, and effectively improves the assembly quality.
Drawings
Fig. 1 is a schematic structural diagram of a graphene flexible sensor according to an embodiment of the present application; in fig. 1, (a) is a stress-free state diagram, fig. 1, (b) is a tensile stress state diagram, and fig. 1, (c) is a compressive stress state diagram;
FIG. 2 is a schematic diagram of a workpiece stress detection system according to an embodiment of the present application; fig. 2 (a) is a schematic diagram of a stress calibration module, and fig. 2 (b) is a schematic diagram of a stress detection module;
FIG. 3 is a schematic diagram of the characteristic signal versus tensile stress according to an embodiment of the present application;
FIG. 4 is a schematic diagram of the relationship between the characteristic signal and the compressive stress according to an embodiment of the present application;
FIG. 5 is a flow diagram of a stress calibration flow according to an embodiment of the present application;
fig. 6 is a flow chart illustrating a stress detection flow according to an embodiment of the present application.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In the machine body assembly process, the existence of assembly stress easily causes connection stress concentration, so that the fatigue fracture of a connecting piece and the deformation of a machine body structure are caused, and the assembly quality and the aerodynamic appearance of an airplane are seriously influenced. In the assembly process of the machine body, because the aircraft parts are huge in size and irregular in shape, a large number of variable curvature areas exist, if excessive assembly stress and residual stress exist at the parts and cannot be detected in time, the problems of large stress deformation, composite material tearing, out-of-tolerance assembly quality and the like of the parts are caused in the assembly process and after the assembly is completed, and finally, large-scale and high-value parts are reworked, scrapped and other economic losses are caused, so that the aircraft manufacturing quality and the service life are seriously threatened.
Currently existing stress detection methods include both lossy and non-destructive detection methods. Because the surface quality of the parts is extremely high in the assembly process of the machine body structure, the pneumatic appearance and the service life of the aircraft are seriously affected by holes and scratches. Therefore, the method of detecting damage such as the drilling method, the peeling method, the ring core method, etc. cannot be applied to the detection of the body assembly stress. Nondestructive testing methods existing today include electromagnetic methods, ultrasonic methods, X-ray methods, electron shift interferometry, scanning electron acoustic microscopy, and the like. However, the above stress nondestructive testing method is limited by factors such as a testing principle, testing precision, physical properties of materials, high cost, damage to physical health, excessively heavy equipment and the like, and cannot realize high-efficiency, high-precision, rapid and convenient stress testing and real-time monitoring oriented to a production site.
Based on the above, according to the workpiece stress detection method, the graphene sensor is stuck to the surface of the workpiece to be detected, so that a detection signal is output when the workpiece to be detected is stressed; the detection signal refers to a resistance value output by the graphene sensor when the workpiece to be detected is stressed; the graphene sensor is adhered to the surface of a workpiece to be detected, and when the workpiece to be detected is subjected to stress deformation, the graphene sensor is deformed together, so that the mobility of a current carrier is changed and the resistivity of the current carrier is changed due to the change of the electronic energy band structure in the graphene. Extracting the peak value of the detection signal as a characteristic signal value; based on the characteristic signal value and the standard characteristic signal value, obtaining a stress value of the workpiece to be detected; the standard characteristic signal value refers to a detection resistance value peak value output by the graphene sensor in a state that a workpiece to be detected is not stressed; comparing the characteristic signal value with the standard characteristic signal value to judge the type and the magnitude of the stress action; and obtaining a stress detection result based on the stress value and the stress limit threshold value of the workpiece to be detected. Therefore, the stress detection method realizes the stress detection of the workpiece in the assembly process, and effectively improves the assembly quality.
The embodiment of the application also provides an electronic device of the hardware running environment, which can comprise: a processor, such as a central processing unit (Central Processing Unit, CPU), a communication bus, a user interface, a network interface, a memory. Wherein the communication bus is used to enable connection communication between these components. The user interface may comprise a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface may further comprise a standard wired interface, a wireless interface. The network interface may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable Non-Volatile Memory (NVM), such as a disk Memory. The memory may alternatively be a storage device separate from the aforementioned processor.
It will be appreciated by those skilled in the art that the foregoing is not limiting of the electronic device and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components. Such as an operating system, data storage modules, network communication modules, user interface modules, and electronic programs, may be included in the memory as a storage medium. In a specific embodiment, the network interface is mainly used for data communication with a network server; the user interface is mainly used for carrying out data interaction with a user; the processor and the memory in the electronic equipment can be arranged in the electronic equipment, and the electronic equipment calls the workpiece stress detection device stored in the memory through the processor and executes the workpiece stress detection method provided by the embodiment of the application.
The embodiment of the application provides: a method of workpiece stress detection comprising the steps of:
step S10, sticking a graphene sensor on the surface of a workpiece to be detected, so as to output a detection signal when the workpiece to be detected is stressed; the detection signal refers to a resistance value output by the graphene sensor when the workpiece to be detected is stressed.
The graphene sensor (also referred to as a graphene flexible sensor) is shown in fig. 1 (a), and the thickness of the graphene sensor ishThe graphene sensor is composed of graphene and an encapsulation layer, wherein the shape and the thickness of the graphene can be prepared according to stress detection requirements so as to obtain sensors with different detection sensitivities and detection capacities; the packaging layer is used for isolating and protecting graphene, and the graphene is positioned below the center line of the thickness of the packaging layer. The graphene flexible sensor has good bending and stretching properties and strong plasticity, and can be attached to the surfaces of various curvature workpieces to monitor micro stress in real time. Compared with the traditional strain sensors such as strain gauges, the graphene flexible sensor has higher sensing sensitivity and larger sensing linearity, and has the capabilities of high sensitivity, quick response, high stability and capability of detecting micro stress. When no external stress is applied, the sensor is insensitive to the stress, and the stress output value of the sensor is a certain fixed value; when the sensor is slightly bent and deformed under the action of stress, as shown in the deformation state shown in (b) of fig. 1, the graphene is positioned below the center line of the thickness of the packaging layer, and at the moment, the graphene is slightly stretched and deformed, and the internal electronic energy band structure of the graphene is changed, so that the carrier mobility is generated The resistivity of the sensor is changed, and in a certain range, the larger the stress strain is, the larger the resistance value of the sensor output is; when the sensor is in a deformation state as shown in (c) of fig. 1, the graphene in the packaging layer is slightly compressed and deformed, the internal electron energy band structure of the graphene is changed, so that the mobility of internal carriers of the graphene is changed, the resistivity of the graphene is changed, and at the moment, the graphene is compressed and deformed within a certain range, the larger the stress strain is, the smaller the resistance value of the sensor output is. The detection principle of the graphene flexible strain sensor is that the graphene is subjected to tiny stress, so that the geometric dimension (such as the length or the sectional area of the graphene) of the graphene is changed, and the output value of the sensor is causedR x A change is made whose expression is:
in the method, in the process of the invention,R x representing a resistance value of the graphene sensor output;in order to achieve the specific resistance of the graphene,Lis graphene length;Sis graphene cross-sectional area.
The sensitivity of the graphene flexible sensor is as follows: (ΔR 1 /R 0 ）/ΔVWhereinR 0 For the initial output resistance of the sensor,ΔR 1 in order to obtain the value of the change in resistance,ΔVis the strain of the sensor. According to the property of the sensor which is exhibited by tiny stretching and compression deformation, the real-time monitoring of the magnitude and the direction of the assembly stress can be realized.
In the practical application process, the graphene flexible sensor is stuck to the surface of the test piece in the state of (a) in fig. 1, one end of the test piece is fixed on the clamping device, and after the sensor pin is connected with the power supply, the stress is increased at the other end of the test piece, so that the sensor is subjected to the tensile stress shown in (b) in fig. 1 or the compressive stress shown in (c) in fig. 1.
In the practical application process, before the graphene sensor is adhered to the surface of the workpiece to be detected, so that when the workpiece to be detected is stressed, a detection signal is output, the method further comprises: adhering a graphene sensor to the surface of a test piece, and applying tensile stress to the test piece to obtain a detection tensile characteristic signal value; applying compressive stress to the test piece to obtain a detected compression characteristic signal value; and obtaining a linear relation between the stress value received by the workpiece to be detected and the detection characteristic signal value based on the detection tensile characteristic signal value and the detection compression characteristic signal value. Preferably, in order to improve the detection accuracy of the graphene sensor, the standard characteristic signal value can be output under the condition that the test piece has no stress.
It should be noted that the above steps may be collectively referred to as a stress calibration step, and the calibration is performed based on a stress calibration module shown in fig. 2 (a), where the stress calibration module is configured to calibrate a mapping relationship between stress and an output signal of the graphene flexible sensor, and perform stress detection with the mapping relationship as a detection reference. The stress calibration module consists of a stress calibration platform and a calibration data processing module, as shown in (a) of fig. 2, wherein the stress calibration platform comprises a test piece 1, a graphene flexible sensor 2, a clamping device 3, a power supply 4 and a calibration device 5, the stress calibration module is used for acquiring and calibrating stress strain conditions of the test piece, and the graphene flexible sensor is used for acquiring stress strain information of the test piece and converting the stress strain information into a resistance value for outputting; the clamping device is used for fixing the test piece; the power supply is used for providing excitation current for the sensor; the calibration device comprises, but is not limited to, a stress testing machine, is used for providing stress acting on the test piece in different sizes and directions when the stress is calibrated, and is used for simulating assembly stress (including but not limited to bending stress) born by the component in the assembly process of the component. The calibrated data is transmitted to a calibrated data processing module, and the calibrated data processing module consists of a signal conditioning circuit 6, a data acquisition and storage circuit 7 and a stress and characteristic signal value relation acquisition circuit 8; the signal conditioning circuit 6 is used for filtering high-frequency noise signals and low-frequency interference signals in the sensor output signals, and amplifying detection signals so as to improve the signal-to-noise ratio of the detection signals; the data acquisition and storage 7 is used for converting the analog signals output from the signal conditioning circuit into digital signals through A/D conversion and storing the digital signals; the stress and characteristic signal value relation obtaining 8 is used for extracting characteristic values of the sensor output signals in different calibrated stress states to obtain characteristic signal values (including but not limited to resistance signal peak values and differential peak values), and performing data fitting to obtain a relation expression of the stress values and the characteristic signal values.
Specifically, the applying a tensile stress to the test piece to obtain a detected tensile characteristic signal value includes: applying a tensile stress to the test piece to obtain a detected tensile signal; based on the detected stretching signal, obtaining a detected stretching characteristic signal value; the detected stretching characteristic signal value is a peak value of the detected stretching signal.
More specifically, the above-described process of applying a tensile stress to the test piece to obtain a detected tensile characteristic signal value may be repeated a plurality of times to obtain a more accurate detected tensile characteristic signal value. If the tensile stress is classified according to the stress, for example, the tensile stress is classified into 10 classes, namely No. 1-No. 10, the tensile stress is respectively F1, F2, F3, F4, F5, F6, F7, F8, F9 and F10 from small to large, the tensile stress is taken as a tensile stress value of a test piece, 10 groups of experiments are carried out, sensor resistance signals obtained when the test piece is subjected to different tensile stresses are recorded respectively, a resistance signal peak value is extracted as a characteristic signal value, and each group of experiments is repeated 5 times. Respectively extracting peak values of the detection signals as characteristic signal values, and recording asR 11 、R 12 、R 13 、R 14 、R 15 Obtaining the characteristic signal valueR 1 As shown in formula (1):
R 1 =（R 11 +R 12 +R 13 +R 14 +R 15 ）/5（1）
According to the method, the characteristic signal values obtained by the stress corresponding to No. 1-No. 10 are respectively recorded asR 1 、R 2 、R 3 、R 4 、R 5 、R 6 、R 7 、R 8 、R 9 、R 10 。
The method for acquiring the relation between stress and characteristic signal value comprises the following steps:
performing data fitting on the characteristic signal values obtained under different stresses, as shown in fig. 3, and obtaining a linear relation between the stress and the characteristic signal value, as shown in formula (2):
R u =p1*F+p2（2）
wherein,Fthe tensile stress of the test piece is measured;R u the characteristic signal value is output by the graphene flexible sensor;p1、p2 is a constant.
As can be seen from fig. 3, the linear correlation coefficient is 0.9997, which indicates that there is a good linear relationship between the tensile stress and the characteristic signal value, so as to establish a quantitative relationship between the tensile stress and the characteristic signal value.
Specifically, the applying compressive stress to the test piece to obtain a detected compression characteristic signal value includes: applying a compressive stress to the test piece to obtain a detected compressive signal; based on the detected compression signal, obtaining a detected compression characteristic signal value; the detected compression characteristic signal value is a peak value of the detected compression signal.
Likewise, the above-described process of applying compressive stress to the test piece to obtain the detected compressive characteristic signal value may be repeated a plurality of times to obtain a more accurate detected compressive characteristic signal value. Classifying the compressive stress according to the stress, for example, classifying the compressive stress into 10 classes, namely No. 11-No. 20, wherein the compressive stress is respectively from small to large F’ 1 、F’ 2 、F’ 3 、F’ 4 、F’ 5 、F’ 6 、F’ 7 、F’ 8 、F’ 9 、F’ 10 WhereinF’ i （i=1, 2, 3, … …, 10) andF i （i=1, 2, 3, … …, 10) are equal in size and opposite in direction.
Taking the measured value as a compression stress value of the test piece, carrying out 10 groups of experiments, respectively recording sensor resistance signals obtained when the test piece is subjected to different stresses, extracting resistance signal peak values as characteristic signal values, and repeating each group of experiments for 5 times. Respectively extracting peak values of the detection signals as characteristic signal values, and recording asR’ 11 、R’ 12 、R’ 13 、R’ 14 、R’ 15 Obtaining the characteristic signal valueR’ 1 As shown in formula (3):
R’ 1 =（R’ 11 +R’ 12 +R’ 13 +R’ 14 +R’ 15 ）/5（3）
according to the formula (3), the characteristic signal values obtained by the stress corresponding to the numbers No. 11-No. 20 are respectively recorded asR’ 1 、R’ 2 、R’ 3 、R’ 4 、R’ 5 。
The technical method for data fitting is as follows:
data fitting is performed on the characteristic signal values obtained under different stresses, as shown in fig. 4, and at this time, a linear relationship is formed between the compressive stress and the characteristic signal values:
R u =-p3*F’+p4（4）
wherein,R u the characteristic signal value is output by the graphene flexible sensor;F’the compression stress of the test piece is measured;p3、p4 is a constant.
As can be seen from fig. 4, the linear correlation coefficient is 0.9990, so that a good linear relationship between the compressive stress and the characteristic signal value is obtained, and a quantitative relationship between the compressive stress and the characteristic signal value is established.
The characteristic signal and stress relation diagram is obtained, when the sensor is not subjected to external stress, the sensor is insensitive to stress, and the initial value of the stress output of the sensor is a fixed value R a The method comprises the steps of carrying out a first treatment on the surface of the When the test piece is slightly bent and deformed under the action of stress, the sensor actually outputs a characteristic signal valueR u ＞R a When the graphene is slightly stretched and deformed, the stress on the member is a tensile stress, and the relationship between the characteristic signal and the stress follows the formula (2); actual output characteristic signal value of sensorR u ＜R a In this case, as can be seen from fig. 4, the graphene undergoes a slight compressive deformation, the stress applied to the member is a compressive stress, and the relationship between the characteristic signal and the stress follows equation (3). The direction of the stress to which the component is subjected can be discriminated by means of fig. 3 to 4. After the stress calibration is completed, repeated calibration is not needed when stress detection is carried out.
After the stress calibration process is finished, the stress detection process is performed based on a detection module shown in (b) of fig. 2, wherein the detection module comprises a graphene flexible sensor 2, a power supply 4 and a component 9, the graphene flexible sensor and the power supply have the same functions as the calibration module, and different numbers of sensors can be selected for use according to the size of a region to be detected during detection; the detection data processing module comprises a signal conditioning circuit 6, a data acquisition and storage 7 and a data inversion and stress value display 10, wherein the functions of the signal conditioning circuit 6 and the data acquisition and storage 7 are the same as those of the signal conditioning circuit 6 and the data acquisition and storage 7 in the calibration data processing module; and in the data inversion process, the characteristic signals and the stress relation expression obtained by the stress calibration module are utilized to calculate the assembly stress born by the component.
Namely, the stress detection flow comprises the following steps:
the stress calibration procedure is performed as shown in fig. 5: firstly, a stress calibration experiment platform is built as shown in (b) of fig. 1, a stress calibration flow is developed, and the stress calibration is divided into three parts, namely tensile stress calibration, stress-free calibration and compressive stress calibration; the signal output by the graphene flexible sensor is subjected to filtering noise reduction and signal amplification through a conditioning circuit; data acquisition and storage are carried out, and the analog signals output from the signal conditioning circuit are converted into digital signals through A/D conversion and stored; finally extracting characteristic informationNumber valueR u And respectively acquiring relational expressions among the tensile stress, the stress-free stress, the compressive stress and the characteristic signal value, and carrying out stress detection by taking the relational expressions as detection references.
The stress detection procedure is performed as shown in fig. 6: constructing a stress detection platform, and adhering a graphene flexible sensor to a member to be detected; normalizing the stress detection signal to make the output value of the sensor in the stress-free state be the initial valueR a The method comprises the steps of carrying out a first treatment on the surface of the Then stress detection is carried out, and signals output by the sensor are subjected to filtering noise reduction and signal amplification through a conditioning circuit; data acquisition and storage are carried out, and the analog signals output from the signal conditioning circuit are converted into digital signals through A/D conversion and stored; output characteristic value to sensor R u To make a discrimination, ifR u ＞R a Determining that the member is under tensile stress, obtaining a characteristic signal value according to the formula (5) based on the formula (2)R u Carrying out (5) to obtain the tensile stress born by the component in the assembly process; if it isR u ＜R a Determining the compressive stress of the component, obtaining a formula (6) according to a formula (4), and obtaining a characteristic signal valueR u Carrying out (6) to obtain the compression stress born by the component in the assembly process; further, the early warning and the discrimination of stress: setting an assembly stress limit threshold according to actual assembly requirementsσThe stress value output in the assembly process is monitored in real time, ifF(orF’）＜σThe assembly stress meets the requirement and the stress value is displayed; if it isF(orF’）≥σAnd the assembly stress is out of range, so that the member is deformed or torn, and the stress monitoring device gives out a buzzing early warning.
Wherein the formula (5) is as follows:
F=(R u -p2)/p1（5）
according to formula (5), the tensile stress actually born by the component can be obtained through the graphene flexible sensorF. Wherein F is a tensile stress value,R u it is meant that the characteristic signal value is,p1、p2 are all constants.
The formula (6) is as follows:
F’=(p4-R u )/p3（6）
according to formula (6), the compressive stress actually born by the component can be obtained through the graphene flexible sensor F’. Wherein,F’it is meant that the value of the compressive stress,R u it is meant that the characteristic signal value is,p3、p4 are all constants.
And step S20, extracting the peak value of the detection signal as a characteristic signal value.
In practical application, the extracting the peak value of the detection signal as the characteristic signal value includes: performing filtering noise reduction processing and signal amplification processing on the detection signal to obtain a target detection signal; and extracting the peak value of the target detection signal to be used as a characteristic signal value.
Specifically, the steps include: the detection signal is subjected to filtering of a high-frequency noise signal and a low-frequency interference signal in the graphene strain gauge output signal by a signal conditioning circuit, and the detection signal is amplified to improve the signal-to-noise ratio of the detection signal; then the detection signal after filtering, noise reduction and signal amplification is subjected to peak value extraction by a data acquisition card to be used as a characteristic signal value。
Step S30, obtaining a stress value of the workpiece to be detected based on the characteristic signal value and the standard characteristic signal value; the standard characteristic signal value refers to a detection resistance value peak value output by the graphene sensor in a state that a workpiece to be detected is not stressed.
The graphene flexible sensor has the characteristics of flexibility and easiness in attaching, can be effectively attached to a curved surface member, is attached to the surface of the curved surface member, and has an output value of R a ±ΔR. Therefore, the output signal of the sensor needs to be normalized before stress detection, and the change of the initial value of the output signal, which is introduced by the graphene flexible sensor due to the fact that the graphene flexible sensor is stuck on a curved surface member, is eliminated. As can be seen from fig. 3 to 4, graphene is flexibleThe sensor has higher sensing linearity, and before the sensor signal is output, whether the output value of the sensor is an initial value in a stress-free state is judgedR a If the initial value isR a ±ΔRThen the sensor output value in the unstressed state is subtracted (or added) from the linear relationship between stress and eigenvalue in FIGS. 3-4ΔRThe sensor output value is assigned to the initial value. Therefore, if the workpiece to be detected is a curved test piece, the standard characteristic signal value satisfies the following relation:
R a =R a1 ±ΔR
wherein, theR a Representing the standard characteristic signal value; the saidR a1 The method comprises the steps of representing a peak value of a detection resistance value output by the graphene sensor in a non-stress state of a workpiece to be detected; the saidΔRIndicating that the sensor output value deviates from the unstressed state when the sensor is stuck to the surface of the curved memberR a1 A value; i.e.ΔRIndicating that when the sensor is adhered to the surface of a curved member, the sensor is deformed under the condition of not receiving assembly stress due to the curved surface structure of the member, and the output value of the sensor deviates from the unstressed (deformation-free) state R a1 The amount of the value.
In practical application, the obtaining the stress value of the workpiece to be detected based on the characteristic signal value and the standard characteristic signal value includes: if the characteristic signal value is larger than the standard characteristic signal value, calculating based on a tensile stress formula to obtain a tensile stress value of the workpiece to be detected; and if the characteristic signal value is smaller than the standard characteristic signal value, calculating based on a compressive stress formula to obtain a compressive stress value of the workpiece to be detected.
Wherein the tensile stress value satisfies the following relationship:
F=(R u -p2)/p1
wherein F is a tensile stress value,R u it is meant that the characteristic signal value is,p1、p2 are all constants.
Wherein the compressive stress value satisfies the following relationship:
F’=(p4-R u )/p3
wherein,F’it is meant that the value of the compressive stress,R u it is meant that the characteristic signal value is,p3、p4 are all constants.
It should be noted that the number of the substrates,P3、P4and (3) withP1、P2Is a source of data that is different from the source of data,P1、P2in order to obtain the relation between the tensile stress and the sensor output signal when the workpiece and the sensor are subjected to different tensile stresses F,P3、P4in order to obtain the relationship between the compressive stress and the sensor output signal when the workpiece and the sensor are subjected to different compressive stresses F',P3、P4also depending on the sensitivity of the sensor itself, the magnitude of the stress, different values may be obtained for sensors of different sensitivities. The output values of the sensor show different change trends under the condition of bearing compressive stress and tensile stress, so that whether the sensor and the workpiece bear tensile stress or compressive stress is judged.
And step S40, obtaining a stress detection result based on the stress value and the stress limit threshold value of the workpiece to be detected.
In practical application, the obtaining the stress detection result based on the stress value and the stress limit threshold of the workpiece to be detected includes: if the stress value of the workpiece to be detected is smaller than the stress limit threshold, the detection result is indicated as normal; and if the stress value of the workpiece to be detected is greater than or equal to the stress limit threshold, the detection result is expressed as abnormal.
Specifically, when the stress value of the workpiece to be detected is greater than or equal to the stress limit threshold, the detection result shows that the assembly stress exceeds the range and causes deformation or tearing of the component, and at the moment, the stress monitoring device gives out buzzing early warning to prompt a worker to check the assembly condition.
In general, the method according to the embodiment of the present application may include the following steps:
stress calibration flow:
step 1: sticking the lower surface of the graphene flexible sensor shown in (a) in fig. 1 to a position to be detected of the surface stress of the test piece, and fixing one end of the test piece on a clamping device;
step 2: pins at two ends of the graphene flexible sensor are connected with a power supply, and the signal output ends of the pins are sequentially connected with a signal conditioning circuit, a data acquisition and storage module and a data fitting module;
Step 3: calibration of tensile stress, namely, applying a stress No.1 to a test piece to subject the test piece and the sensor to tensile stress as shown in (b) of FIG. 1, wherein the tensile stress isF 1 The test piece being subjected to bending stressesF 1 The graphene flexible sensor generates extremely weak deformation, the graphene flexible sensor with high sensitivity detects the weak deformation, and a detection signal is output;
step 4: the detection signal is filtered by the signal conditioning circuit to remove a high-frequency noise signal and a low-frequency interference signal in the sensor output signal, and the detection signal is amplified to improve the signal-to-noise ratio of the detection signal;
step 5: the detection signals after filtering, noise reduction and signal amplification are subjected to A/D conversion into digital signals through a data acquisition card for display and storage;
step 6; extracting peak value of the detection signal as characteristic signal value, and recording asR 11 ；
Step 7: repeating the steps 3-6, performing five groups of experiments, respectively extracting the peak value of the detection signal as a characteristic signal value, and recording asR 11 、R 12 、R 13 、R 14 、R 15 Obtaining the characteristic signal valueR 1 As shown in formula (1):
R 1 =（R 11 +R 12 +R 13 +R 14 +R 15 ）/5（1）
step 8: repeating the steps 3-7 to obtain characteristic signal values respectivelyR 1 、R 2 、R 3 、R 4 、R 5 、R 6 、R 7 、R 8 、R 9 、R 10 ；
Step 9: stress and characteristic signal value relation acquisition: data fitting is carried out on the characteristic signal values obtained under different tensile stresses, as shown in fig. 3, the tensile stress and the characteristic signal values are in a linear relation, and a fitting formula is as follows:
R u =p1*F+p2（2）
Wherein,Fthe tensile stress of the test piece is measured;R u the characteristic signal value is output for the graphene strain gauge;p1、p2 is a constant. It can be known from calculation that the linear correlation coefficient is 0.9997, namely the linear correlation coefficient is more than 0.999, so that the quantitative relation between the tensile stress and the characteristic signal value is established.
Step 10: calibrating a detection signal output by the graphene flexible sensor in a stress-free state, and acquiring a characteristic signal value output by the sensor in the stress-free state through a signal conditioning circuit, data acquisition and storage;
step 11: calibration of compressive stress, namely, applying a stress No.11 to the test piece, and subjecting the test piece and the sensor to compressive stress as shown in FIG. 1 (c), wherein the compressive stress is the value ofF’ 1 The test piece being subjected to bending stressesF’ 1 The graphene flexible sensor generates extremely weak deformation, the graphene flexible sensor with high sensitivity detects the weak deformation, and a detection signal is output;
step 12: the detection signal is filtered by the signal conditioning circuit to remove a high-frequency noise signal and a low-frequency interference signal in the sensor output signal, and the detection signal is amplified to improve the signal-to-noise ratio of the detection signal;
Step 13: the detection signals after filtering, noise reduction and signal amplification are subjected to A/D conversion into digital signals through a data acquisition card for display and storage;
step 14; extracting peak value of the detection signal as characteristic signal value, and recording asR’ 11 ；
Step (a)15: repeating the steps 11-14, performing five groups of experiments, respectively extracting peak values of the detection signals as characteristic signal values,R’ 11 、R’ 12 、R’ 13 、R’ 14 、R’ 15 obtaining the characteristic signal valueR’ 1 As shown in formula (3):
R’ 1 =（R’ 11 +R’ 12 +R’ 13 +R’ 14 +R’ 15 ）/5（3）
step 16: repeating the steps 11-15 to obtain characteristic signal values respectivelyR’ 1 、R’ 2 、R’ 3 、R’ 4 、R’ 5 ；
Step 17: stress and characteristic signal value relation acquisition: data fitting is carried out on the characteristic signal values obtained under different stresses, as shown in fig. 4, at the moment, the stresses and the characteristic signal values are in a linear relation, and a fitting formula is as follows:
R u =-p3*F’+p4（4）
wherein,F’the compression stress of the test piece is measured;p3、p4 is a constant.
The linear correlation coefficient is 0.9990, so that a quantitative relation between the compressive stress and the characteristic signal value is established.
Stress detection flow: after the stress calibration is completed, the repeated calibration is not needed for a specific assembly scene and a specific component, and the stress quantitative detection is carried out only according to a stress relation diagram (shown as fig. 3-4) obtained by the stress calibration;
step 18: attaching a graphene flexible sensor to a member, wherein the sensor can be added according to the size of the member and the detected part to form a sensor group, as shown in (b) of fig. 1;
Step 19: connecting pins at two ends of a graphene strain gauge with a power supply, and sequentially connecting a signal conditioning circuit, a data acquisition and storage module, a data inversion module and a stress value display with the signal output ends of the pins;
step 20: sensor arrangementForce detection signal normalization: when the sensor is adhered to the surface of the curved member, the sensor outputs a value ofR a ±ΔR；
Step 21: according to the high sensing linearity of the graphene flexible sensor, before the sensor signal is output, whether the sensor output value is an initial value in a stress-free state is judgedR a If the initial value isR a ±ΔRThen the sensor output value in the unstressed state is subtracted (or added) from the linear relationship between stress and eigenvalue in FIGS. 3-4ΔRThe sensor output value is attributed to the initial value;
step 22: stress detection, wherein the component is subjected to bending stress in the assembly process, the graphene flexible sensor is used for sensitively detecting micro deformation of the component caused by the assembly stress, and outputting a detection signal;
step 23: the detection signal is subjected to filtering of a high-frequency noise signal and a low-frequency interference signal in the graphene strain gauge output signal by a signal conditioning circuit, and the detection signal is amplified to improve the signal-to-noise ratio of the detection signal;
Step 24: the detection signals after filtering, noise reduction and signal amplification are subjected to A/D conversion into digital signals through a data acquisition card for display and storage;
step 25: extracting peak value of the detection signal as characteristic signal value, and recording asR u ；
Step 26: output characteristic value to sensorR u And (3) judging:
when the component generates micro bending deformation under the stress effect, the sensor actually outputs the characteristic signal valueR u ＞R a In this case, as can be determined from fig. 3, the graphene undergoes a slight tensile deformation, the stress on the member is a tensile stress, the relationship between the characteristic signal and the stress follows the equation (2), and the equation (2) is converted into the equation (5), namely:
F=(R u -p2)/p1（5）
according to formula (5), can be communicatedObtaining tensile stress actually born by component through graphene flexible sensorF。
When the component sensor actually outputs the characteristic signal valueR u ＜R a In this case, as can be determined from fig. 4, the graphene undergoes a slight compressive deformation, the stress on the member is a compressive stress, the relationship between the characteristic signal and the stress follows the formula (4), and the formula (6) can be written as:
F’=(p4-R u )/p3（6）
according to formula (6), the compressive stress actually born by the component can be obtained through the graphene flexible sensorF’。
Step 27: and (5) stress early warning and judging: setting an assembly stress limit threshold according to actual assembly requirements σThe stress value output in the assembly process is monitored in real time;
if it isF(orF’）＜σThe assembly stress meets the requirement and the stress value is displayed;
if it isF(orF’）≥σAnd the stress monitoring device sends out buzzing early warning to prompt workers to check the assembly condition.
It can be seen that the method solves the problems that the existing parts cannot be detected in time due to overlarge assembly stress and residual stress in the assembly process of the existing parts, and the parts generate larger stress deformation, composite material tearing, and ultra-poor assembly quality in the assembly process and after the assembly is completed. The graphene strain gauge adopted by the detection method has high flexibility and is easy to paste, can be suitable for the surfaces of the curvature members with various shapes, and solves the problem that the detection result is inaccurate due to the fact that the existing stress detection equipment cannot conform to the shape of the member; meanwhile, the structural characteristics of the graphene flexible strain sensor are fully utilized, and as the graphene is positioned below the middle line of the thickness of the packaging layer, the graphene is slightly stretched and compressed to deform when the sensor is stressed and bent, so that different numerical changes of resistivity occur, the accurate judgment of the stress magnitude and the stress direction of the component can be effectively realized, and compared with the traditional strain sensors such as strain gauges, the sensing sensitivity and the sensing linearity are higher. The detection device has higher sensitivity, detects extremely weak deformation of a component, outputs a detection signal in a form of a resistance value, is simple, portable and easy to operate, realizes integration of a power supply, a signal conditioning circuit, data acquisition and storage, data fitting, inversion and display, and solves the problems of heavy weight, inconvenient operation, complex signal processing and need of guidance of professional skill personnel of the conventional stress detection equipment; in addition, the detection performance of the detection method is not limited by the physical performance of the detected component, ferromagnetic materials, nonferromagnetic materials and nonmetallic materials (such as composite materials) can be detected, and stress values can be obtained rapidly and quantitatively through data fitting and inversion.
The detection method and the detection device are high in universality, are not limited by space and physical properties of the detected materials, are quick in stress detection response, low in manufacturing cost, light, small in size and high in sensitivity, can realize real-time monitoring and stress early warning of the stress and the stress direction in the whole assembly process of the curved surface component (such as an aircraft air inlet passage), effectively solve the problems that the existing parts cannot be detected in time due to overlarge assembly stress and residual stress in the assembly process, cause larger stress deformation, composite material tearing, out-of-tolerance assembly quality and the like in the assembly process and after the assembly is completed, effectively avoid economic losses of reworking, scrapping and the like of large-sized and high-value parts, greatly save the cost and have great application value.
In order to solve the above technical problems, the embodiment of the present application further provides: a workpiece stress detection device comprising:
the first output module is used for pasting the graphene sensor on the surface of the workpiece to be detected so as to output a detection signal when the workpiece to be detected is stressed; the detection signal refers to a resistance value output by the graphene sensor when the workpiece to be detected is stressed;
The characteristic extraction module is used for extracting the peak value of the detection signal to be used as a characteristic signal value;
the calculation module is used for obtaining a stress value of the workpiece to be detected based on the characteristic signal value and the standard characteristic signal value; the standard characteristic signal value refers to a detection resistance value peak value output by the graphene sensor in a state that a workpiece to be detected is not stressed;
and the detection module is used for obtaining a stress detection result based on the stress value and the stress limit threshold value of the workpiece to be detected.
It should be noted that, each module in the workpiece stress detection device in this embodiment corresponds to each step in the workpiece stress detection method in the foregoing embodiment one by one, so the specific implementation manner and the achieved technical effect of this embodiment may refer to the implementation manner of the workpiece stress detection method, and will not be described herein again.
Furthermore, in an embodiment, the present application also provides a computer storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the method in the previous embodiment.
In some embodiments, the computer readable storage medium may be FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; but may be a variety of devices including one or any combination of the above memories. The computer may be a variety of computing devices including smart terminals and servers.
In some embodiments, the executable instructions may be in the form of programs, software modules, scripts, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and they may be deployed in any form, including as stand-alone programs or as modules, components, subroutines, or other units suitable for use in a computing environment.
As an example, the executable instructions may, but need not, correspond to files in a file system, may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a hypertext markup language (HTML, hyper Text Markup Language) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code).
As an example, executable instructions may be deployed to be executed on one computing device or on multiple computing devices located at one site or, alternatively, distributed across multiple sites and interconnected by a communication network.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
From the above description of embodiments, it will be clear to a person skilled in the art that the above embodiment method may be implemented by means of software plus a necessary general hardware platform, but may of course also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read-only memory/random-access memory, magnetic disk, optical disk), comprising several instructions for causing a multimedia terminal device (which may be a mobile phone, a computer, a television receiver, or a network device, etc.) to perform the method described in the embodiments of the present application.
The foregoing disclosure is merely a partial embodiment of the present application, and it is not intended to limit the scope of the claims of the present application.
Claims (14)
1. The workpiece stress detection method is characterized by comprising the following steps of:
pasting a graphene sensor on the surface of a workpiece to be detected, so as to output a detection signal when the workpiece to be detected is stressed; the detection signal refers to a resistance value output by the graphene sensor when the workpiece to be detected is stressed;
extracting the peak value of the detection signal as a characteristic signal value;
based on the characteristic signal value and the standard characteristic signal value, obtaining a stress value of the workpiece to be detected; the standard characteristic signal value refers to a detection resistance value peak value output by the graphene sensor in a state that a workpiece to be detected is not stressed;
and obtaining a stress detection result based on the stress value and the stress limit threshold value of the workpiece to be detected.
2. The method according to claim 1, wherein the obtaining the stress value of the workpiece to be inspected based on the characteristic signal value and the standard characteristic signal value includes:
If the characteristic signal value is larger than the standard characteristic signal value, calculating based on a tensile stress formula to obtain a tensile stress value of the workpiece to be detected;
if the characteristic signal value is smaller than the standard characteristic signal value, calculating based on a compressive stress formula to obtain a compressive stress value of the workpiece to be detected;
and if the characteristic signal value is equal to the standard characteristic signal value, the stress value of the workpiece to be detected is zero.
3. The workpiece stress detection method as recited in claim 2, wherein said tensile stress value satisfies the following relationship:
F=(R u -p2)/p1
wherein,Fit is meant that the tensile stress value is,R u it is meant that the characteristic signal value is,p1、p2 are all constants.
4. The workpiece stress detection method as recited in claim 2, wherein said compressive stress value satisfies the following relationship:
F’=(p4-R u )/p3
wherein,F’it is meant that the value of the compressive stress,R u it is meant that the characteristic signal value is,p3、p4 are all constants.
5. The method according to claim 1, wherein if the workpiece to be inspected is a curved specimen, the standard characteristic signal value satisfies the following relation:
R a =R a1 ±ΔR
wherein the saidR a Representing the standard characteristic signal value; the saidR a1 The method comprises the steps of representing a peak value of a detection resistance value output by the graphene sensor in a non-stress state of a workpiece to be detected; the said ΔRIndicating that the sensor output value deviates from R in the unstressed state when the sensor is stuck on the surface of the curved member a1 Value of。
6. The method of claim 1, wherein extracting the peak value of the detection signal as a characteristic signal value comprises:
performing filtering noise reduction processing and signal amplification processing on the detection signal to obtain a target detection signal;
and extracting the peak value of the target detection signal to be used as a characteristic signal value.
7. The method according to claim 1, further comprising, before the attaching the graphene sensor to the surface of the workpiece to be inspected to output the inspection signal when the workpiece to be inspected is subjected to the stress:
adhering a graphene sensor to the surface of a test piece, and applying tensile stress to the test piece to obtain a detection tensile characteristic signal value;
applying compressive stress to the test piece to obtain a detected compression characteristic signal value;
and obtaining a linear relation between the stress value received by the workpiece to be detected and the detection characteristic signal value based on the detection tensile characteristic signal value and the detection compression characteristic signal value.
8. The method of claim 7, wherein applying a tensile stress to the test piece to obtain a detected tensile characteristic signal value comprises:
Applying a tensile stress to the test piece to obtain a detected tensile signal;
based on the detected stretching signal, obtaining a detected stretching characteristic signal value; the detected stretching characteristic signal value is a peak value of the detected stretching signal.
9. The method of claim 7, wherein applying a compressive stress to the test piece to obtain a detected compressive characteristic signal value comprises:
applying a compressive stress to the test piece to obtain a detected compressive signal;
based on the detected compression signal, obtaining a detected compression characteristic signal value; the detected compression characteristic signal value is a peak value of the detected compression signal.
10. The method according to claim 1, wherein the obtaining a stress detection result based on the stress value and the stress limit threshold of the workpiece to be detected includes:
if the stress value of the workpiece to be detected is smaller than the stress limit threshold, the detection result is indicated as normal;
and if the stress value of the workpiece to be detected is greater than or equal to the stress limit threshold, the detection result is expressed as abnormal.
11. The method of claim 1, wherein the resistance value output by the graphene sensor satisfies the following relationship:
Wherein,R x representing a resistance value of the graphene sensor output;the resistivity of the graphene is represented by the formula,Lrepresents the graphene length;Srepresents the graphene cross-sectional area.
12. A workpiece stress detection device, comprising:
the first output module is used for pasting the graphene sensor on the surface of the workpiece to be detected so as to output a detection signal when the workpiece to be detected is stressed; the detection signal refers to a resistance value output by the graphene sensor when the workpiece to be detected is stressed;
the characteristic extraction module is used for extracting the peak value of the detection signal to be used as a characteristic signal value;
the calculation module is used for obtaining a stress value of the workpiece to be detected based on the characteristic signal value and the standard characteristic signal value; the standard characteristic signal value refers to a detection resistance value peak value output by the graphene sensor in a state that a workpiece to be detected is not stressed;
and the detection module is used for obtaining a stress detection result based on the stress value and the stress limit threshold value of the workpiece to be detected.
13. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the method of workpiece stress detection as claimed in any of claims 1-11.
14. A computer readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the method of workpiece stress detection as claimed in any of claims 1 to 11.
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| CN102253285A (en) * | 2011-05-06 | 2011-11-23 | 淮阴工学院 | Technology for detecting graphene piezoresistance factor by adopting iso-stress beam method |
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| CN111664985A (en) * | 2020-06-15 | 2020-09-15 | 华东交通大学 | Curved surface sensing device for train bogie air pressure detection |
| CN113390540A (en) * | 2021-05-28 | 2021-09-14 | 北京理工大学 | Self-powered high-sensitivity bridge stress detection system and detection method |
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| CN102253285A (en) * | 2011-05-06 | 2011-11-23 | 淮阴工学院 | Technology for detecting graphene piezoresistance factor by adopting iso-stress beam method |
| CN102539035A (en) * | 2012-01-17 | 2012-07-04 | 江苏物联网研究发展中心 | Lattice type flexible pressure distribution sensor and manufacturing method thereof |
| CN111664985A (en) * | 2020-06-15 | 2020-09-15 | 华东交通大学 | Curved surface sensing device for train bogie air pressure detection |
| CN113390540A (en) * | 2021-05-28 | 2021-09-14 | 北京理工大学 | Self-powered high-sensitivity bridge stress detection system and detection method |
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