A method for testing dynamic vibration characteristics of an aluminum alloy during friction stir welding

Abstract

The application discloses a kind of aluminum alloy friction stir welding whole process dynamic vibration characteristic test methods, it is related to the field of friction stir welding process monitoring, the method includes the following steps: (1) test point planning and sensor arrangement;(2) test system builds and parameter configuration, sensor is connected to dynamic signal acquisition instrument;(3) whole process dynamic vibration signal real-time acquisition, records from the multichannel vibration acceleration original signal of friction stir welding whole process;(4) signal pre-processing and segmentation, original signal is filtered and denoised;(5) dynamic characteristic extraction and correlation analysis;Beneficial effect is that the method can realize the vibration characteristic capture of aluminum alloy friction stir welding whole life cycle, provides data support for process parameter optimization and defect early warning.

Description

Technical Field

[0001] This invention relates to the field of aluminum alloy friction stir welding technology, and in particular to a method for testing the dynamic vibration characteristics of the entire aluminum alloy friction stir welding process. Background Technology

[0002] Friction stir welding (FSW), as an advanced solid-state joining technology, has become a key process for achieving highly reliable connections in high-end manufacturing fields such as aerospace, rail transportation, and high-power electronics for lightweight structures made of aluminum alloys and magnesium alloys, thanks to its excellent characteristics such as low welding temperature, low residual stress at the joint, and absence of porosity and cracks. In actual production, the FSW process involves periodic excitation caused by the high-speed rotation of the stirring head and complex thermoplastic rheological processes, resulting in a system exhibiting extremely strong nonlinear dynamic response. Real-time monitoring and analysis of the vibration characteristics during this process is crucial for identifying welding defects and optimizing process parameters.

[0003] Current industrial monitoring methods primarily focus on quasi-static measurements of axial force, torque, and temperature field during the welding process. While these parameters reflect the basic thermodynamic cycle of welding, their low sampling frequency and strong time lag often make it difficult to capture the dynamic response fluctuations generated by the intense interaction between the stirring pin and the metal. Existing vibration studies are mostly limited to macroscopic measurements of spindle axial pressure, torque, or temperature field, remaining at the level of theoretical simulation or local analysis for the stable phase, lacking a standardized testing method for dynamic vibration characteristics that can cover the entire cycle of "pressure application - preheating - stable welding - finishing."

[0004] To address the aforementioned issues, this invention proposes a method for testing the dynamic vibration characteristics of aluminum alloy friction stir welding throughout the entire process. This method enables real-time, high-precision capture of system vibration signals throughout the entire welding cycle. By analyzing time-frequency domain characteristics in stages, it fills a gap in the field of dynamic experimental monitoring of aluminum alloy friction stir welding throughout the entire process. This method not only provides scientific data for the refined adjustment of process parameters but also helps improve the overall quality consistency of aluminum alloy welded structures, demonstrating significant engineering application prospects in promoting digital monitoring of high-end equipment manufacturing processes. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies and fill related technological gaps, this invention provides a method for testing the dynamic vibration characteristics of the entire process of aluminum alloy friction stir welding. This method constructs a multi-dimensional vibration signal acquisition method covering the entire cycle of the friction stir welding machine spindle and workpiece system, and synchronously records the raw dynamic acceleration signals in real time throughout the complete process cycle from the downward pressure of the stirring head, the dwell preheating, the stable welding, to the retraction and finalization. Then, by filtering and preprocessing the multi-channel non-stationary signals and extracting time-frequency domain features, the dynamic vibration response characteristics of the entire aluminum alloy friction stir welding process under different process parameters and staged loads are obtained.

[0006] The technical solution adopted by this invention to solve its technical problem is as follows: A method for testing the dynamic vibration characteristics of the entire process of aluminum alloy friction stir welding, characterized by comprising the following steps:

[0007] Step (1): Measurement point planning and sensor layout. Triaxial accelerometers are placed on the friction stir welding equipment and the aluminum alloy workpiece system respectively.

[0008] The sensor arrangement is as follows:

[0009] A first triaxial accelerometer is installed on the outside of the spindle head of the friction stir welding equipment near the spindle bearing to monitor the vibration along the X-axis (feed direction), Y-axis (lateral direction), and Z-axis (axial direction of the stirring needle spindle) when the stirring head interacts with the workpiece.

[0010] A second triaxial accelerometer is installed on the bottom back plate of the fixed aluminum alloy workpiece to monitor the structural response vibration of the workpiece side under the action of thermo-mechanical coupling.

[0011] Step (2): Test system setup and parameter configuration. Connect the triaxial accelerometer to the dynamic signal acquisition system. Set the sampling frequency according to the spindle speed range of friction stir welding to ensure that the sampling frequency can cover the vibration signal generated by spindle rotation and friction heat.

[0012] Step (3): Real-time acquisition of dynamic vibration signals throughout the entire process, recording the original multi-channel vibration acceleration signals from the start of the friction stir welding process; starting the friction stir welding equipment for welding, and the dynamic signal acquisition system synchronously recording the original vibration acceleration signals from the start of welding to the end of welding. The entire cycle includes: the spindle rotation and pressing stage, the dwell and preheating stage, the stable welding stage, and the retraction and finishing stage.

[0013] The criteria for determining signal segmentation at different welding stages are as follows:

[0014] During the spindle rotation and pressing stage: the Z-axis load rapidly increases from zero to the set pressing force, and the vibration signal exhibits short-time high-frequency impact characteristics.

[0015] During the preheating phase: the Z-axis feed stops, the spindle continues to rotate, the Z-axis load shows a slight decrease and tends to stabilize, and the vibration amplitude gradually decreases to the stable range;

[0016] Stable welding stage: X-axis feed starts, and the vibration signal is a stable random signal with periodic amplitude modulation characteristics;

[0017] During the final pullback stage: the X-axis feed stops, the Z-axis load rapidly decreases to zero, and the vibration signal gradually disappears after the unloading impact.

[0018] Step (4): Signal preprocessing and segmentation. The original vibration acceleration signal is processed by removing the mean and filtering for noise reduction. A bandpass filter is used to eliminate low-frequency interference and high-frequency noise from the servo motor of the equipment. A detrending term algorithm is used to eliminate baseline drift caused by sensor temperature drift. Combined with the time axis of the process action command of the machine tool equipment and the Z-axis load change of the spindle, the vibration signal is accurately segmented into the corresponding welding stages.

[0019] Step (5): Dynamic characteristic extraction and correlation analysis. For the vibration signals of each segmented welding stage, time domain and frequency domain analysis are performed respectively. Time domain feature values ​​that characterize the vibration intensity of the system are extracted. The root mean square value, peak-to-peak value and peak factor of the vibration signal in the three-axis direction in the stable welding stage are calculated, as well as the frequency domain feature values ​​that characterize the energy distribution characteristics. By comparing the feature value changes under different process parameters, the process stability of the entire friction stir welding process is evaluated and the joint quality performance is mapped. When the variance of the root mean square value of vibration in the stable welding stage is within the set tolerance threshold, it indicates that the plastic flow of the material is stable and the process parameters are well matched. When the proportion of high-frequency band energy of a certain non-principal axis rotational harmonic in the frequency domain features suddenly increases, it is determined that abnormal and severe friction or local instability of material rheology has occurred, and an early warning signal is generated to indicate that welding defects such as holes, tunnels or flash will appear in the weld.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: The method constructs a multi-node dynamic vibration testing system for aluminum alloy friction stir welding, and simultaneously collects the original acceleration signals throughout the entire process cycle. Then, digital filtering and time-frequency feature decomposition algorithms are used to process and analyze the non-stationary vibration signals to obtain the dynamic vibration response characteristics of the system under different welding action stages and process parameters. This fills the technical gap in experimental monitoring of the entire process of aluminum alloy friction stir welding, helps to accurately reveal process stability and provides a scientific basis for preventing welding defects, thereby improving welding quality and production efficiency. Attached Figure Description

[0021] Figure 1 This is a flowchart of the dynamic vibration characteristic testing method for the entire process of aluminum alloy friction stir welding;

[0022] Figure 2This is a schematic diagram of the hardware composition and vibration sensor arrangement of a dynamic vibration characteristic testing system for the entire process of aluminum alloy friction stir welding.

[0023] Figure 3 This is a schematic diagram showing the dynamic vibration time-domain stages of the entire aluminum alloy friction stir welding process;

[0024] Figure 4 These are time-domain and spectrum diagrams of vibration signals during the stable welding stage under different conditions in aluminum alloy friction stir welding. Detailed Implementation

[0025] Embodiments of the present invention will be described with reference to the accompanying drawings, which will be further described below. Figure 1 — Figure 4 The specific embodiments of the present invention will be described in detail below.

[0026] Figure 1 The diagram shows the flowchart of the dynamic vibration characteristic test method for the entire process of aluminum alloy friction stir welding.

[0027] The method and procedure for testing the dynamic vibration characteristics of aluminum alloy friction stir welding throughout the entire process includes the following steps:

[0028] Step (1): Measurement point planning and sensor layout. Triaxial accelerometers are placed on the friction stir welding equipment and the aluminum alloy workpiece system respectively.

[0029] like Figure 2 The diagram shows the hardware configuration and vibration sensor arrangement of a dynamic vibration characteristic testing system for the entire process of aluminum alloy friction stir welding. The sensor arrangement is as follows:

[0030] A first triaxial accelerometer is installed on the outside of the spindle head of the friction stir welding equipment near the spindle bearing to monitor the vibration along the X-axis (feed direction), Y-axis (lateral direction), and Z-axis (axial direction of the stirring needle spindle) when the stirring head interacts with the workpiece.

[0031] A second triaxial accelerometer is installed on the bottom back plate of the fixed aluminum alloy workpiece to monitor the structural response vibration of the workpiece side under the action of thermo-mechanical coupling.

[0032] Step (2): Test system setup and parameter configuration. Connect the triaxial accelerometer to the dynamic signal acquisition system. Set the sampling frequency according to the spindle speed range of friction stir welding to ensure that the sampling frequency can cover the vibration signal generated by spindle rotation and friction heat.

[0033] Step (3): Real-time acquisition of dynamic vibration signals throughout the entire process, recording the original multi-channel vibration acceleration signals from the start of the friction stir welding process; starting the friction stir welding equipment for welding, the dynamic signal acquisition system synchronously records the original vibration acceleration signals from the start of welding to the end of welding. Figure 3The diagram shows the time-domain stages of dynamic vibration acceleration during the entire process of aluminum alloy friction stir welding. The entire cycle includes: the spindle rotation and pressing stage, the dwell and preheating stage, the stabilization welding stage, and the retraction and finishing stage.

[0034] The criteria for determining signal segmentation at different welding stages are as follows:

[0035] During the spindle rotation and pressing stage: the Z-axis load rapidly increases from zero to the set pressing force, and the vibration signal exhibits short-time high-frequency impact characteristics.

[0036] During the preheating phase: the Z-axis feed stops, the spindle continues to rotate, the Z-axis load shows a slight decrease and tends to stabilize, and the vibration amplitude gradually decreases to the stable range;

[0037] Stable welding stage: X-axis feed starts, and the vibration signal is a stable random signal with periodic amplitude modulation characteristics;

[0038] During the final pullback stage: the X-axis feed stops, the Z-axis load rapidly decreases to zero, and the vibration signal gradually disappears after the unloading impact.

[0039] Step (4): Signal preprocessing and segmentation. The original vibration acceleration signal is processed by removing the mean and filtering for noise reduction. A bandpass filter is used to eliminate low-frequency interference and high-frequency noise from the servo motor of the equipment. A detrending term algorithm is used to eliminate baseline drift caused by sensor temperature drift. Combined with the time axis of the process action command of the machine tool equipment and the Z-axis load change of the spindle, the vibration signal is accurately segmented into the corresponding welding stages.

[0040] Step (5): Dynamic characteristic extraction and correlation analysis. For the vibration signals of each segmented welding stage, time-domain and frequency-domain analyses are performed respectively. Time-domain feature values ​​characterizing the vibration intensity of the system are extracted. The root mean square value, peak-to-peak value, and peak factor of the three-axis vibration signals in the stable welding stage are calculated, as well as the frequency-domain feature values ​​characterizing the energy distribution. By comparing the changes in feature values ​​under different process parameters, the process stability of the entire friction stir welding process is evaluated and the joint quality performance is mapped. When the variance of the root mean square value of vibration in the stable welding stage is within the set tolerance threshold, it indicates that the material plastic flow is stable and the process parameters are well matched. When the proportion of high-frequency band energy of a specific non-principal axis rotational harmonic in the frequency domain features suddenly increases, it is determined that abnormal and severe friction or local instability and rheology of the material has occurred, and an early warning signal is generated to indicate that welding defects such as holes, tunnels, or flash will appear in the weld. Figure 4 The image shows the time-domain and spectrum diagrams of vibration signals during the stable welding stage under different conditions in aluminum alloy friction stir welding.

Claims

1. A method for testing the dynamic vibration characteristics of aluminum alloy friction stir welding throughout the entire process, characterized in that, Includes the following steps: Step (1): Measurement point planning and sensor layout, triaxial accelerometers are placed on the friction stir welding equipment and the aluminum alloy workpiece system respectively; The sensor arrangement is as follows: A first triaxial accelerometer is installed on the outside of the spindle head of the friction stir welding equipment near the spindle bearing to monitor the vibration along the X-axis (feed direction), Y-axis (lateral direction), and Z-axis (axial direction of the stirring needle spindle) when the stirring head interacts with the workpiece. A second triaxial accelerometer is installed on the bottom back plate of the fixed aluminum alloy workpiece to monitor the structural response vibration of the workpiece side under the action of thermo-mechanical coupling. Step (2): Test system setup and parameter configuration, connect the triaxial accelerometer to the dynamic signal acquisition system, set the sampling frequency according to the spindle speed range of friction stir welding, and ensure that the sampling frequency can cover the vibration signal excited by the spindle rotation and friction heat generation; Step (3): Real-time acquisition of dynamic vibration signals throughout the entire process, recording the original multi-channel vibration acceleration signals from the entire friction stir welding process; The friction stir welding equipment is started for welding, and the dynamic signal acquisition system synchronously records the original vibration acceleration signal of the entire cycle from the start of welding to the end of welding; The entire cycle includes: the spindle rotation and pressing stage, the dwell and preheating stage, the stable welding stage, and the retraction and finishing stage; The criteria for determining signal segmentation at different welding stages are as follows: During the spindle rotation and pressing stage: the Z-axis load rapidly increases from zero to the set pressing force, and the vibration signal exhibits short-time high-frequency impact characteristics. During the preheating phase: the Z-axis feed stops, the spindle continues to rotate, the Z-axis load shows a slight decrease and tends to stabilize, and the vibration amplitude gradually decreases to the stable range; Stable welding stage: X-axis feed starts, and the vibration signal is a stable random signal with periodic amplitude modulation characteristics; During the final pullback stage: the X-axis feed stops, the Z-axis load drops rapidly to zero, and the vibration signal gradually disappears after the unloading impact. Step (4): Signal preprocessing and segmentation. The original vibration acceleration signal is processed by removing the mean and filtering for noise reduction. A bandpass filter is used to eliminate low-frequency interference and high-frequency noise from the servo motor of the equipment. A detrending term algorithm is used to eliminate baseline drift caused by sensor temperature drift. Combined with the time axis of the process action command of the machine tool equipment and the Z-axis load change of the spindle, the vibration signal is accurately segmented into the corresponding welding stages. Step (5): Dynamic characteristic extraction and correlation analysis. For the vibration signals of each segmented welding stage, time domain and frequency domain analysis are performed respectively. Time domain feature values ​​that characterize the vibration intensity of the system are extracted. The root mean square value, peak-to-peak value and peak factor of the vibration signals in the three-axis direction during the stable welding stage are calculated, as well as the frequency domain feature values ​​that characterize the energy distribution characteristics. By comparing the feature value changes under different process parameters, the process stability of the entire friction stir welding process is evaluated and the joint quality performance is mapped.

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