Method for measuring residual stress of microstructure with large slenderness ratio

Abstract

The invention discloses a method for measuring residual stress of a microstructure with a large slenderness ratio, which comprises the following steps of: etching a rectangular grating on the surface of a sample by adopting a focused ion beam technology, and synchronously marking an X-ray detection point; the integrated white light interferometer generates a curvature change curve in real time along a spiral track scanning path, and processes data by applying an empirical mode decomposition algorithm; a six-axis robot control system is used for compensating the position offset of the mark point, the incident angle of the X-ray is adjusted to adapt to the warping surface geometry, and multi-angle stress detection is carried out; establishing a mapping relation through grating-X-ray joint calibration, and generating a two-dimensional stress distribution diagram by using an interpolation algorithm; according to the grating curvature stability and the X-ray diffraction peak signal-to-noise ratio, the weight is dynamically distributed, a three-dimensional residual stress cloud picture is output, and a high-risk area is marked, so that the residual stress analysis capability of a material with a complex shape is effectively improved, more accurate and comprehensive stress distribution information is provided, and the potential material failure problem can be identified and prevented.

Description

Technical Field

[0001] The invention relates to the technical field of stress detection, and in particular to a method for measuring residual stress of a microstructure with a large slenderness ratio. Background Art

[0002] In the fields of micro-electromechanical systems, micro-machining and advanced materials research, the application of microstructures with large aspect ratio is becoming more and more extensive. However, in the manufacturing process of these microstructures, such as through process steps such as welding or etching, a significant technical challenge is often encountered: due to factors such as thermal effects and uneven material removal during welding or etching, the sample is easily deformed locally or overall. This deformation not only changes the original geometry of the sample, but may also introduce additional residual stress.

[0003] When trying to use an X-ray diffraction residual stress instrument to test the residual stress of such processed high aspect ratio microstructures, the above deformation problem will lead to inaccurate measurement results. Specifically, the warping or distortion of the sample surface makes it difficult for the X-rays to accurately enter the predetermined detection point, thus affecting the position and intensity of the diffraction peak; at the same time, the deformation may also cause the actual stress state to be inconsistent with the theoretical model, further exacerbating the measurement error.

[0004] Therefore, it is necessary to provide a method for measuring the residual stress of microstructures with a large slenderness ratio to solve the above technical problems. Summary of the invention

[0005] The present invention overcomes the deficiencies of the prior art and provides a method for measuring residual stress of a microstructure with a large slenderness ratio.

[0006] To achieve the above object, the technical solution adopted by the present invention is: a method for measuring residual stress of a microstructure with a large slenderness ratio, comprising the following steps:

[0007] S1: Use focused ion beam technology to etch a rectangular grating on the sample surface and synchronously mark the X-ray detection points;

[0008] S2: The integrated white light interferometer sets the spiral trajectory scanning path, generates the curvature change curve in real time, and applies the empirical mode decomposition algorithm to process the curvature data;

[0009] S3: Use the six-axis robot control system to compensate for the position offset of the marking point, adjust the X-ray incident angle to adapt to the warped surface geometry, and perform multi-angle stress detection;

[0010] S4: establishing a mapping relationship between the two through grating-X-ray joint calibration, and generating a two-dimensional stress distribution map using an interpolation algorithm;

[0011] S5: Dynamically assign weights based on grating curvature stability and X-ray diffraction peak signal-to-noise ratio, and output a three-dimensional residual stress cloud map with high-risk areas marked.

[0012] In a preferred embodiment of the present invention, in S1, the grating etching period ranges from 3 to 8 μm, the depth ranges from 100 to 300 nm, and the grating direction forms an angle of 40 to 50° with the long axis of the sample.

[0013] In a preferred embodiment of the present invention, in S1, the diameter of the X-ray detection point is in the range of 40-60 μm, and the distance between each detection point is in the range of 100-300 μm.

[0014] In a preferred embodiment of the present invention, in S2, the scanning parameters of the white light interferometer include a spiral trajectory, a scanning radius ranging from 0.5 to 1.5 mm, and an axial feed speed ranging from 0.2 to 0.8 mm / s.

[0015] In a preferred embodiment of the present invention, in S2, the curvature data is separated into low-frequency deformation components and high-frequency residual stress components, and a pre-compensation correction of 30-50% is performed on the measured curvature.

[0016] In a preferred embodiment of the present invention, in S3, the six-axis robot control system automatically compensates for the maximum position deviation of the marking point of ±200 μm and adjusts the X-ray incident angle to 75°±5°.

[0017] In a preferred embodiment of the present invention, in S3, the multi-angle stress detection includes performing 7 diffraction measurements at different inclination angles at each marking point, detecting the curvature gradient in the area where the curvature changes gently.

[0018] <5% / mm, infrared thermal imaging is performed on the detection point, the infrared thermal imaging accuracy is ±0.2℃, and the temperature drift effect is deducted in real time.

[0019] In a preferred embodiment of the present invention, in S4, the interpolation algorithm generates a two-dimensional stress distribution map with a resolution range of 10-30 μm, and performs nanoindentation verification on the high gradient stress area.

[0020] In a preferred embodiment of the present invention, in S5, the fluctuation of the grating curvature stability is less than 5%, and the signal-to-noise ratio SNR of the X-ray diffraction peak is greater than 30 dB.

[0021] In a preferred embodiment of the present invention, in S5, the grating data weight range is 60-80%, the X-ray data weight range is 20-40%, and the high-risk area is where the stress exceeds 70% of the material yield strength.

[0022] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0023] (1) The present invention provides a method for measuring residual stress of a microstructure with a large aspect ratio. By adjusting the grating period and depth and the angle with the long axis of the sample to 45° in step S1, the deformation information in different directions can be captured more accurately, thereby providing a more comprehensive and true residual stress analysis result. It can also minimize the new residual stress introduced by the preparation process and ensure the purity of the original data.

[0024] (2) The present invention provides a method for measuring residual stress of a microstructure with a large slenderness ratio. In step S2 and step S3, a spiral trajectory scanning path is set by using an integrated white light interferometer to generate a curvature change curve in real time, and a six-axis robot control system is used to compensate for the position offset of the marking point, and multi-angle stress detection is performed to adapt to the geometry of the warped surface, thereby improving the ability to monitor the surface deformation of microstructures with a large slenderness ratio. In particular, when dealing with complex or irregular shapes, the empirical mode decomposition algorithm helps to separate low-frequency deformation components and high-frequency residual stress components. The dynamic adjustment function of the six-axis robot ensures that even if the sample surface is warped, the X-ray incident angle can be accurately controlled, greatly improving the accuracy of residual stress measurement, and the introduction of infrared thermal imaging technology effectively eliminates the influence of temperature fluctuations, further enhancing the reliability of the data.

[0025] (3) The present invention provides a method for measuring residual stress in a microstructure with a large aspect ratio. Step S4 and step S5 combine the advantages of two different measurement techniques, use an interpolation algorithm to generate a high-resolution two-dimensional stress distribution map, and perform nanoindentation verification on high-gradient stress areas to ensure data accuracy. By dynamically allocating weights and outputting a three-dimensional residual stress cloud map, not only can the stress distribution be visualized, but also high-risk areas can be intuitively marked to facilitate subsequent analysis and processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 It is a schematic structural diagram of the process of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0030] like Figure 1 As shown, the present invention provides a method for measuring residual stress of a microstructure with a large slenderness ratio, comprising the following steps:

[0031] S1: Use focused ion beam technology to etch a rectangular grating on the sample surface and synchronously mark the X-ray detection points;

[0032] Focused ion beam technology can accurately create rectangular gratings on the sample surface at the micrometer scale and simultaneously mark the detection points for X-ray diffraction analysis, thereby providing a high-precision measurement benchmark to ensure that the basic data for subsequent stress analysis is accurate. In addition, it can effectively reduce the errors caused by manual marking, and due to the precision of FIB operations, avoid unnecessary damage to the sample or the introduction of additional residual stress.

[0033] S2: The integrated white light interferometer sets the spiral trajectory scanning path, generates the curvature change curve in real time, and applies the empirical mode decomposition algorithm to process the curvature data;

[0034] Through the rectangular grating in S1, a white light interferometer is used to scan the sample surface along a specific spiral trajectory, which can capture the subtle deformation of the sample surface in real time, which not only improves the monitoring efficiency, but also provides continuous curvature change information. The application of the empirical mode decomposition algorithm further enhances the ability of data analysis. The empirical mode decomposition algorithm can separate data with different frequency components, making the deformation characteristics related to residual stress more obvious.

[0035] S3: Use the six-axis robot control system to compensate for the position offset of the marking point, adjust the X-ray incident angle to adapt to the warped surface geometry, and perform multi-angle stress detection;

[0036] Based on the curvature change information obtained in S2, the six-axis robot system is used to dynamically adjust the X-ray incident angle to adapt to the warping of the sample surface, ensuring that each preset X-ray detection point can be accurately aligned, improving the accuracy of residual stress measurement, especially when dealing with large slenderness ratio microstructures with surface irregularities. In addition, the multi-angle stress detection strategy can comprehensively evaluate the stress distribution from different directions, thereby obtaining a more complete description of the stress state.

[0037] S4: establishing a mapping relationship between the two through grating-X-ray joint calibration, and generating a two-dimensional stress distribution map using an interpolation algorithm;

[0038] Combining grating and X-ray diffraction data, a mapping relationship is established, allowing the distribution of residual stress to be analyzed at the microscopic level. The application of interpolation algorithms fills the gaps between direct measurement points and generates detailed two-dimensional stress distribution maps, improving the resolution of the data.

[0039] S5: Dynamically assign weights based on grating curvature stability and X-ray diffraction peak signal-to-noise ratio, and output a three-dimensional residual stress cloud map with high-risk areas marked.

[0040] Taking into account the stability of grating curvature and the quality of X-ray diffraction peaks, the generation process of 3D residual stress cloud map is optimized by dynamically allocating weights, which can highlight those areas with concentrated or abnormally high stress and help identify possible high-risk areas. 3D stress cloud map not only provides a three-dimensional perspective of stress distribution, but also provides an important reference for engineering design and material selection, which helps to prevent potential failure problems.

[0041] In this embodiment, in S1, the grating etching period ranges from 3 to 8 μm, the depth ranges from 100 to 300 nm, the grating direction forms an angle of 40 to 50° with the long axis of the sample, the diameter of the X-ray detection point ranges from 40 to 60 μm, and the distance between each detection point ranges from 100 to 300 μm.

[0042] Through focused ion beam technology, a rectangular grating with a period of 3-8μm and a depth of 100-300nm is etched on the surface of the sample, and the grating direction is at an angle of 40-50° with the long axis of the sample. In addition, X-ray detection points with a diameter of 40-60μm and a spacing of 100-300μm are synchronously marked. This process ensures a high-precision and repeatable measurement benchmark, while providing the ability to capture deformation information in multiple directions.

[0043] In this embodiment, in S2, the scanning parameters of the white light interferometer include a spiral trajectory, a scanning radius range of 0.5-1.5 mm, an axial feed speed range of 0.2-0.8 mm / s, and curvature data for separating low-frequency deformation components and high-frequency residual stress components, and performing a 30-50% pre-compensation correction on the measured curvature.

[0044] The sample surface is scanned along a spiral trajectory by a white light interferometer, with a scanning radius range of 0.5-1.5 mm and an axial feed speed range of 0.2-0.8 mm / s. The empirical mode decomposition algorithm is used to separate the low-frequency deformation component and the high-frequency residual stress component, and a 30-50% pre-compensation correction is performed on the measured curvature.

[0045] Among them, the empirical mode decomposition algorithm is to decompose the complex signal into multiple intrinsic mode functions, the formula is as follows:

[0046]

[0047] Where s(t) is the original signal, IMF I represents the i-th eigenmode function, r n This step enhances the accuracy of curvature data processing and helps to analyze residual stress more accurately.

[0048] In this embodiment, in S3, the six-axis robot control system automatically compensates for the maximum position offset of the marking point of ±200μm, adjusts the X-ray incident angle to 75°±5°, and the multi-angle stress detection includes 7 diffraction measurements at different inclination angles at each marking point, detecting the area with gentle curvature changes, and the curvature gradient is <5% / mm. Infrared thermal imaging is performed on the detection point with an infrared thermal imaging accuracy of ±0.2℃, and the influence of temperature drift is deducted in real time.

[0049] The six-axis robot control system automatically compensates for the maximum position offset of the marking point of ±200μm and adjusts the X-ray incident angle to 75°±5°. Each marking point is measured for diffraction at 7 different inclination angles (ψ=0°, ±15°, ±30°, ±45°), and the area with a curvature gradient of <5% / mm is selected for detection. Infrared thermal imaging technology is used to deduct the influence of temperature drift in real time, with an accuracy of ±0.2°C. The key to this step is to dynamically adapt to the geometric changes of the sample surface to ensure the accuracy of the X-ray diffraction measurement, while considering the influence of temperature factors on the measurement results.

[0050] In this embodiment, in S4, an interpolation algorithm generates a two-dimensional stress distribution map with a resolution range of 10-30 μm, and nanoindentation verification is performed on the high gradient stress area.

[0051] The mapping relationship between the two is established through grating-X-ray joint calibration, and the interpolation algorithm is used to generate a two-dimensional stress distribution map with a resolution range of 10-30μm, and nanoindentation verification is performed on the high gradient stress area. Commonly used interpolation algorithm (inverse distance weighted method). The inverse distance weighted method predicts the stress value of unknown points through known data points, thereby improving the resolution and reliability of the stress distribution map.

[0052] Among them, the inverse distance weighted method (IDW) has the following basic form:

[0053]

[0054] Among them, Z(x0) is the value of the point x0 to be predicted, ω i is the weight, usually defined as d i is the distance from the predicted point to the known point, p is a power parameter, Z(x i ) is a known point x i The value of .

[0055] In this embodiment, in S5, the fluctuation of the grating curvature stability is less than 5%, the signal-to-noise ratio SNR of the X-ray diffraction peak is greater than 30dB, the grating data weight range is 60-80%, the X-ray data weight range is 20-40%, and the high-risk area is where the stress exceeds 70% of the material yield strength.

[0056] Dynamically assign weights based on grating curvature stability and X-ray diffraction peak signal-to-noise ratio, output a three-dimensional residual stress cloud map, and mark high-risk areas (stress exceeds 70% of the material yield strength).

[0057] The weight distribution formula is:

[0058]

[0059] Among them, W g and W x are the weights of the grating data and X-ray data, respectively, and C g and C x They are grating curvature stability and X-ray diffraction peak signal-to-noise ratio. This method not only improves the accuracy of residual stress analysis, but also can intuitively display the stress concentration area.

[0060] Embodiment 1:

[0061] Use shape memory alloy and piezoelectric ceramic composite fixtures, adjust the fixture spacing to 85% of the total length according to the sample length (for example, if the sample length is 20mm, 7.5% free deformation area is left at both ends). Clamp the sample with 19N pressure to ensure that the clamping force is evenly distributed to avoid local stress concentration.

[0062] S1: Use focused ion beam technology to etch a rectangular grating on the sample surface, where the grating etching period is 5μm, the depth is 200nm, and the grating direction is at a 45° angle to the long axis of the sample; synchronously mark the X-ray detection points, the diameter of these points ranges from 50μm, and the distance between each detection point is 200μm.

[0063] S2: The integrated white light interferometer sets the spiral trajectory scanning path with a scanning radius of 1 mm and an axial feed speed of 0.5 mm / s, and generates a curvature change curve in real time; the empirical mode decomposition algorithm is applied to process the curvature data, separate the low-frequency deformation component and the high-frequency residual stress component, and perform a 40% pre-compensation correction on the measured curvature.

[0064] S3: Use the six-axis robot control system to automatically compensate for the maximum deviation of the marking point position of ±200μm, and adjust the X-ray incident angle to 75°±5° to adapt to the warped surface geometry. Perform 7 diffraction measurements at different tilt angles at each marking point, especially in the area where the curvature changes gently, with a curvature gradient of <5% / mm; perform infrared thermal imaging of the detection point with an accuracy of ±0.2℃, and deduct the temperature drift effect in real time.

[0065] S4: The mapping relationship between the two is established through grating-X-ray joint calibration, and the two-dimensional stress distribution map with a resolution range of 20μm is generated using an interpolation algorithm. Nanoindentation verification is performed on high gradient stress areas.

[0066] S5: Dynamically assign weights based on grating curvature stability (fluctuation < 5%) and X-ray diffraction peak signal-to-noise ratio (SNR > 30dB), with grating data weighting 70% and X-ray data weighting 30%. Output a three-dimensional residual stress cloud map and mark high-risk areas where stress exceeds 70% of the material yield strength.

[0067] In summary: First, the rectangular grating is accurately prepared by focused ion beam technology and the X-ray detection points are synchronously marked to ensure a high-precision measurement benchmark. Then, the sample surface is scanned along a spiral trajectory using an integrated white light interferometer, and the curvature data is processed using the empirical mode decomposition algorithm to effectively separate the low-frequency deformation and high-frequency residual stress components, thereby improving monitoring efficiency and data analysis capabilities. Then, a six-axis robot control system is used to dynamically adjust the X-ray incident angle to adapt to the surface warping of the sample, and multi-angle stress detection is performed to further improve the measurement accuracy. In addition, a two-dimensional stress distribution map is generated by grating-X-ray joint calibration and interpolation algorithm to verify the high-gradient stress area and enhance data resolution and reliability. Finally, according to the grating curvature stability and the X-ray diffraction peak signal-to-noise ratio, the weight is dynamically allocated, and a three-dimensional residual stress cloud map is output and the high-risk area is marked to achieve a comprehensive stress analysis from micro to macro. This method not only solves the problem of deformation during the preparation process affecting the test results, but also provides a more accurate and reliable residual stress analysis, significantly improving the understanding and control capabilities of complex-shaped materials.

[0068] The above is based on the ideal embodiment of the present invention. Through the above description, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the content in the specification, and the technical scope must be determined according to the scope of the claims.

Claims

1. A method for measuring residual stress in a microstructure with a large slenderness ratio, characterized in that: The following steps are involved: S1: Use focused ion beam technology to etch a rectangular grating on the sample surface and synchronously mark the X-ray detection points; S2: The integrated white light interferometer sets the spiral trajectory scanning path, generates the curvature change curve in real time, and applies the empirical mode decomposition algorithm to process the curvature data; S3: Use the six-axis robot control system to compensate for the position offset of the marking point, adjust the X-ray incident angle to adapt to the warped surface geometry, and perform multi-angle stress detection; S4: establishing a mapping relationship between the two through grating-X-ray joint calibration, and generating a two-dimensional stress distribution map using an interpolation algorithm; S5: Dynamically assign weights based on grating curvature stability and X-ray diffraction peak signal-to-noise ratio, and output a three-dimensional residual stress cloud map with high-risk areas marked.

2. A method for measuring residual stress of a microstructure with a large slenderness ratio according to claim 1, characterized in that: In S1, the grating etching period ranges from 3 to 8 μm, the depth ranges from 100 to 300 nm, and the grating direction forms an angle of 40 to 50° with the long axis of the sample.

3. A method for measuring residual stress of a microstructure with a large slenderness ratio according to claim 1, characterized in that: In S1, the diameter of the X-ray detection point is in the range of 40-60 μm, and the distance between each detection point is in the range of 100-300 μm.

4. The method for measuring residual stress of a microstructure with a large slenderness ratio according to claim 1, characterized in that: In S2, the scanning parameters of the white light interferometer include a spiral trajectory, a scanning radius ranging from 0.5 to 1.5 mm, and an axial feed speed ranging from 0.2 to 0.8 mm / s.

5. The method for measuring residual stress of a microstructure with a large slenderness ratio according to claim 1, characterized in that: In S2, the curvature data is separated into low-frequency deformation components and high-frequency residual stress components, and a pre-compensation correction of 30-50% is performed on the measured curvature.

6. The method for measuring residual stress of a microstructure with a large slenderness ratio according to claim 1, characterized in that: In S3, the six-axis robot control system automatically compensates for the maximum position deviation of the marking point of ±200 μm and adjusts the X-ray incident angle to 75°±5°.

7. The method for measuring residual stress of a microstructure with a large slenderness ratio according to claim 1, characterized in that: In S3, the multi-angle stress detection includes performing diffraction measurements at different inclination angles 7 times at each marked point, detecting the area with gentle curvature change, with a curvature gradient of <5% / mm, performing infrared thermal imaging on the detection point, with an infrared thermal imaging accuracy of ±0.2°C, and deducting the influence of temperature drift in real time.

8. The method for measuring residual stress of a microstructure with a large slenderness ratio according to claim 1, characterized in that: In S4, the interpolation algorithm generates a two-dimensional stress distribution map with a resolution range of 10-30 μm, and nanoindentation verification is performed on the high gradient stress area.

9. The method for measuring residual stress of a microstructure with a large slenderness ratio according to claim 1, characterized in that: In S5, the fluctuation of the grating curvature stability is less than 5%, and the signal-to-noise ratio (SNR) of the X-ray diffraction peak is greater than 30 dB.

10. The method for measuring residual stress of a microstructure with a large slenderness ratio according to claim 1, characterized in that: In S5, the grating data weight range is 60-80%, the X-ray data weight range is 20-40%, and the high-risk area is where the stress exceeds 70% of the material yield strength.

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