A stirring head power consumption detection method, server, storage medium and program product

Abstract

A method for detecting the power consumption of a stirring head, a server, a storage medium, and a program product, relating to the field of welding. In implementing this application, the server collects the sound field vibration signal during the welding process through a sound field sensor and performs a wavelet transform to obtain the main frequency band energy and phase information, and then calculates the sound field distortion factor and the sound field rotation angular velocity, which are input into the power consumption calculation model to obtain the effective power consumption of the stirring head. Compared with traditional power consumption monitoring that only relies on electrical parameters such as current and voltage, this method can accurately reflect the complex dynamics of mechanical contact, monitor the working status of the stirring head in multiple dimensions, quantify the uniformity of the sound field energy distribution (promptly detect abnormal contact of the stirring head, avoid damage to the workpiece or excessive loss of the stirring head due to energy concentration, and improve the stability of welding quality), track the sound field energy transfer efficiency, and improve the accuracy and real-time performance of power consumption calculation.

Description

Technical Field

[0001] The present application relates to the field of welding, and in particular to a method for detecting power consumption of a stirring head, a server, a storage medium, and a program product. Background Art

[0002] With the widespread application of friction stir welding technology in high-end manufacturing fields such as aerospace and rail transportation, people are placing increasingly higher demands on controlling the power consumption of the stir head during the welding process. The power consumption of the stir head during welding directly affects the weld quality and equipment life, so accurately measuring the power consumption of the stir head is of great significance.

[0003] The relevant technology mainly uses current sensors and angular velocity sensors to collect the current value and speed value of the stirring head motor during operation, and then inputs the collected current value and speed value into the set power calculation formula to obtain the power consumption value of the stirring head, and adjust the welding process parameters accordingly.

[0004] However, due to the complex and ever-changing state of the workpiece material during welding, relying solely on the stirrer motor parameters cannot accurately reflect the actual energy transfer between the stirrer and the workpiece. Under high-speed welding conditions, the softening and flow properties of the workpiece material can change dramatically, resulting in significant deviations between the calculated power consumption and the actual power consumption. Summary of the Invention

[0005] The present application provides a stirring head power consumption detection method, a server, a storage medium and a program product for improving the accuracy of stirring head power consumption detection.

[0006] In the first aspect, the present application provides a method for detecting the power consumption of a stirring head, which is applied to a server. The method includes: collecting the sound field vibration signal during the welding process through sound field sensors evenly arranged in the circumferential direction of the stirring head, and the sound field vibration signal is used to represent the sound field energy distribution of the contact surface between the stirring head and the workpiece; performing wavelet transform on the sound field vibration signal to obtain the main frequency band energy and phase information corresponding to the sound field vibration signal, the main frequency band energy is used to represent the intensity of the sound field vibration signal, and the phase information is used to represent the spatial position of the sound field vibration signal in the circumferential direction of the stirring head; based on the main frequency band energy, calculating the sound field distortion factor, the sound field distortion factor is used to represent the uniformity of the sound field energy distribution; calculating the phase difference between adjacent sound field sensors according to the phase information, and establishing a phase difference matrix, calculating the sound field rotation angular velocity based on the phase difference matrix, the phase difference is used to represent the sound field vibration position offset, and the sound field rotation angular velocity is used to characterize the energy transfer rate of the stirring head during the stirring process; inputting the sound field distortion factor and the sound field rotation angular velocity into the power consumption calculation model to obtain the effective power consumption of the stirring head.

[0007] By adopting the above technical solution, the server uses an acoustic field sensor to collect the acoustic field vibration signal during the welding process and performs a wavelet transform to obtain the main frequency band energy and phase information. It then calculates the acoustic field distortion factor and the acoustic field rotational angular velocity, which are then input into the power consumption calculation model to obtain the effective power consumption of the stirring head. Compared to traditional power consumption monitoring that relies solely on electrical parameters such as current and voltage, this method can accurately reflect the complex dynamics of mechanical contact, monitor the working status of the stirring head in multiple dimensions, quantify the uniformity of the acoustic field energy distribution (promptly detecting abnormal contact of the stirring head, avoiding damage to the workpiece or excessive wear of the stirring head due to energy concentration, and improving the stability of welding quality), track the efficiency of acoustic field energy transfer, and improve the accuracy and real-time performance of power consumption calculations.

[0008] In combination with some embodiments of the first aspect, in some embodiments, a wavelet transform is performed on the sound field vibration signal to obtain the main frequency band energy and phase information corresponding to the sound field vibration signal, specifically including: performing wavelet decomposition on the sound field vibration signal to obtain wavelet coefficients corresponding to multiple frequency bands, reconstructing the wavelet coefficients to obtain a reconstructed signal for each frequency band; determining the main frequency band range based on the energy distribution characteristics of the reconstructed signal, calculating the energy value within the main frequency band range, and obtaining the main frequency band energy; performing a Hilbert transform on the reconstructed signal within the main frequency band range to obtain an analytical signal; and calculating the phase angle based on the real part and imaginary part of the analytical signal to obtain phase information.

[0009] By adopting the above technical solution, the server performs wavelet decomposition and reconstruction on the sound field vibration signal, effectively separating the energy distribution characteristics of different frequency bands, facilitating the precise location of the main frequency band with the greatest impact on power consumption and avoiding interference from other frequency bands. The server demodulates the reconstructed signal within the main frequency band using the Hilbert transform, converting the time domain signal into an analytical signal containing phase information. The real and imaginary parts of the analytical signal are used to accurately calculate the phase angle, allowing real-time capture of the spatial position offset of the sound field vibration signal in the circumferential direction of the mixing head, providing high-precision energy intensity and spatial distribution data support for the calculation of the sound field distortion factor and the sound field rotational angular velocity.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the sound field distortion factor is calculated based on the main frequency band energy, and the sound field distortion factor is used to indicate the uniformity of the sound field energy distribution, specifically including: determining the maximum main frequency band energy and the minimum main frequency band energy in the main frequency band energy; dividing the difference between the maximum main frequency band energy and the minimum main frequency band energy by the maximum main frequency band energy to obtain the sound field distortion factor.

[0011] By employing this technical solution, the more uniform the acoustic energy distribution at the contact surface between the stirring head and the workpiece, the smaller the difference between the maximum and minimum mainband energies, and the smaller the calculated acoustic field distortion factor. This calculation method intuitively reflects the uniformity of energy transfer during the stirring process, allowing for timely detection of abnormalities such as stirring head eccentricity and wear, providing an important reference indicator for welding quality control.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the power consumption calculation model is: P=k×ω×(1-θ); wherein k is used to represent the characteristic coefficient corresponding to the geometric dimensions of the stirring head, ω is used to represent the angular velocity of the sound field rotation, and θ represents the sound field distortion factor.

[0013] By adopting the above technical solution, the power consumption calculation model takes into account multiple influencing factors such as the physical properties of the stirring head, the acoustic field energy transfer rate, and the uniformity of the acoustic field energy distribution, making the calculation results closer to actual working conditions. When the acoustic field distortion factor is smaller, the acoustic field energy distribution is more uniform, and the effective power consumption of the stirring head is greater; conversely, uneven acoustic field energy distribution will reduce the effective power consumption of the stirring head. This power consumption calculation model can accurately reflect the actual energy transfer between the stirring head and the workpiece, providing a scientific basis for process parameter optimization.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of inputting the sound field distortion factor and the sound field rotation angular velocity into the power consumption calculation model to obtain the effective power consumption of the stirring head, the method also includes: determining the power consumption interval based on the effective power consumption of the stirring head, the power consumption interval includes a normal power consumption interval, an overload power consumption interval and a critical power consumption interval; when the power consumption interval is the critical power consumption interval, determining the degree of eccentricity of the stirring head based on the sound field distortion factor; when the degree of eccentricity of the stirring head exceeds a preset first range, triggering automatic correction of the stirring head trajectory.

[0015] By adopting this technical solution, when the effective power consumption of the stirring head falls within the critical power consumption range, the server determines the degree of stirring head eccentricity based on the sound field distortion factor and automatically triggers stirring head trajectory correction when the eccentricity exceeds a preset first range. This automatic adjustment mechanism effectively prevents the stirring head from entering the overload power consumption range, avoiding equipment damage and welding quality issues. Simultaneously, the server monitors changes in the sound field distortion factor in real time, promptly detecting anomalies in the stirring head's motion trajectory and ensuring the stability of the welding process.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after inputting the sound field distortion factor and the sound field rotation angular velocity into the power consumption calculation model to obtain the effective power consumption of the stirring head, the method also includes: drawing a change trend curve of the effective power consumption of the stirring head, and determining the slope of the change trend curve as the power consumption change rate; when the effective power consumption of the stirring head exceeds the upper limit value of the normal power consumption range and the power consumption change rate is greater than the preset first threshold, predicting the remaining time for the effective power consumption of the stirring head to reach the lower limit value of the overload power consumption range; within the remaining time, reducing the stirring head speed according to the preset step size until the power consumption change rate is less than the preset second threshold, and the preset second threshold is less than the preset first threshold.

[0017] By adopting the above technical solution, the server plots and analyzes the changing trend curve of the effective power consumption of the stirring head in real time to establish a control strategy that actively prevents overload. When the effective power consumption of the stirring head exceeds the upper limit of the normal power consumption range and the power consumption change rate is greater than the preset first threshold, the server predicts the remaining time to reach the overload power consumption range and controls the power consumption growth by gradually reducing the stirring head speed during this period. This progressive speed regulation method based on trend prediction avoids the process impact that may be caused by sudden speed reduction, ensuring the stability of welding quality and effectively extending the service life of the equipment.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of collecting the sound field vibration signal during the welding process through the sound field sensors evenly arranged in the circumferential direction of the stirring head, the method also includes: establishing a sound field energy distribution thermogram based on the sound field vibration signal, and the sound field energy distribution thermogram is used to represent the energy distribution density of the contact surface between the stirring head and the workpiece; based on the sound field energy distribution thermogram, calculating the energy density gradient in the circumferential direction of the stirring head; when the energy density gradient is greater than the preset third threshold, calculating the wear position and wear degree of the stirring head based on the direction and magnitude of the energy density gradient; when the wear degree exceeds the preset second range, calculating the compensation inclination angle corresponding to the wear position, and adjusting the posture of the stirring head according to the compensation inclination angle.

[0019] By adopting the above technical solution, the server creates a heat map of the energy distribution of the acoustic field, achieving a visual representation of the energy distribution at the contact surface between the stirring head and the workpiece. The server calculates the energy density gradient in the circumferential direction of the stirring head, which can accurately locate and evaluate the wear condition of the stirring head. For example, when the energy density gradient is abnormal, the server can calculate the specific wear location and degree of wear based on its direction and size, and automatically calculate the compensation inclination angle for posture adjustment. This wear monitoring and compensation mechanism based on energy distribution characteristics can effectively extend the service life of the stirring head, ensure the stability of welding quality, and reduce equipment maintenance costs.

[0020] In a second aspect, an embodiment of the present application provides a server, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code comprising computer instructions, the one or more processors calling the computer instructions to enable the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when executed on a server, enables the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on a server, the server executes the method described in the first aspect and any possible implementation of the first aspect.

[0023] It is understandable that the server provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding methods and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. By adopting the above technical solution, the server uses an acoustic field sensor to collect the acoustic field vibration signal during the welding process and performs a wavelet transform to obtain the main frequency band energy and phase information. It then calculates the acoustic field distortion factor and the acoustic field rotational angular velocity, which are then input into the power consumption calculation model to obtain the effective power consumption of the stirring head. Compared to traditional power consumption monitoring that relies solely on electrical parameters such as current and voltage, this method can accurately reflect the complex dynamics of mechanical contact, monitor the working status of the stirring head in multiple dimensions, quantify the uniformity of the acoustic field energy distribution (promptly detecting abnormal stirring head contact, avoiding workpiece damage or excessive stirring head wear due to energy concentration, and improving welding quality stability), track the efficiency of acoustic field energy transfer, and improve the accuracy and real-time performance of power consumption calculations.

[0026] 2. By adopting the above technical solution, the server performs wavelet decomposition and reconstruction on the sound field vibration signal, effectively separating the energy distribution characteristics of different frequency bands, facilitating the precise location of the main frequency band with the greatest impact on power consumption, and avoiding interference from other frequency bands. The server demodulates the reconstructed signal within the main frequency band using the Hilbert transform, converting the time domain signal into an analytical signal containing phase information. The real and imaginary parts of the analytical signal are used to accurately calculate the phase angle, allowing real-time capture of the spatial position offset of the sound field vibration signal in the circumferential direction of the mixing head, providing high-precision energy intensity and spatial distribution data support for the calculation of the sound field distortion factor and the sound field rotational angular velocity.

[0027] 3. By adopting the above technical solution, the power consumption calculation model takes into account multiple influencing factors, including the physical properties of the stirring head, the rate of acoustic energy transfer, and the uniformity of acoustic energy distribution, making the calculation results more accurate to actual working conditions. A smaller acoustic field distortion factor indicates a more uniform acoustic energy distribution, resulting in a higher effective power consumption of the stirring head. Conversely, an uneven acoustic energy distribution reduces the effective power consumption of the stirring head. This power consumption calculation model accurately reflects the actual energy transfer between the stirring head and the workpiece, providing a scientific basis for optimizing process parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a method for detecting power consumption of a stirring head according to an embodiment of the present application;

[0029] Figure 2 This is another flow chart of the method for detecting the power consumption of a stirring head in an embodiment of the present application;

[0030] Figure 3 This is a schematic diagram of the physical device structure of the server in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular expressions "a", "an", "above", "the", and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations of one or more of the listed items.

[0032] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0033] The following is a description of the process of the method provided by this implementation. Figure 1 , which is a flow chart of the method for detecting the power consumption of the stirring head in an embodiment of the present application.

[0034] S101, collecting acoustic field vibration signals during the welding process through acoustic field sensors evenly arranged in the circumferential direction of the stirring head, where the acoustic field vibration signals are used to represent the acoustic field energy distribution at the contact surface between the stirring head and the workpiece;

[0035] Among them, the stirring head refers to the main tool used for stir friction welding, which has a cylindrical structure and has a specific thread or pattern on the bottom; the sound field sensor refers to a sensing device that can convert sound wave signals into electrical signals, usually using a piezoelectric crystal as a sensitive element; the sound field vibration signal refers to the sound wave signal formed by the mechanical vibration generated by the contact surface between the stirring head and the workpiece propagating in the medium, which contains characteristics such as amplitude, frequency and phase; the workpiece contact surface refers to the surface area where the stirring head and the workpiece to be welded actually rub against each other; the sound field energy distribution is used to represent the energy density change of the sound field vibration signal in space, which can be represented by an energy density contour map.

[0036] Specifically, the server confirms that multiple acoustic field sensors at preset locations around the mixing head are in normal working order. It then synchronously collects acoustic field vibration signals from each sensor at a preset sampling frequency (e.g., 1kHz). During the acquisition process, the server preprocesses the acoustic field vibration signals, including filtering out power frequency interference and eliminating random noise. The server then arranges the processed acoustic field vibration signals according to the spatial locations of the acoustic field sensors.

[0037] The following is a specific example of friction stir welding. Assume that eight acoustic field sensors (numbered T1-T8, with an angular interval of 45° between adjacent acoustic field sensors) are evenly spaced around a 20mm diameter stir head. When the stir head is welding at 2000 rpm, the server performs the following operations:

[0038] (1) Sensor status confirmation:

[0039] T1 status: normal, position: 0°;

[0040] T2 status: normal, position: 45°;

[0041] T3 status: normal, position: 90°;

[0042] T4 status: normal, position: 135°;

[0043] T5 status: normal, position: 180°;

[0044] T6 status: normal, position: 225°;

[0045] T7 status: normal, position: 270°;

[0046] T8 status: normal, position: 315°;

[0047] (2) Signal acquisition (using 1kHz sampling frequency and collecting 1ms data segments):

[0048] T1 raw data: [0.82, 0.85, 0.79, 0.83, …] mV;

[0049] T2 raw data: [0.75, 0.78, 0.73, 0.76, …] mV;

[0050] T3 raw data: [0.79, 0.81, 0.77, 0.80, …] mV;

[0051] T4 original data: [0.77, 0.79, 0.75, 0.78, …] mV;

[0052] T5 original data: [0.81, 0.84, 0.78, 0.82, …] mV;

[0053] T6 original data: [0.76, 0.78, 0.74, 0.77, …] mV;

[0054] T7 original data: [0.80, 0.83, 0.78, 0.81, …] mV;

[0055] T8 original data: [0.78, 0.80, 0.76, 0.79, …] mV;

[0056] (3) Signal preprocessing (power frequency interference filtering (50Hz notch filtering) and random noise elimination (wavelet threshold denoising)):

[0057] T1 processed data: [0.80, 0.80, 0.80, 0.81, …] mV;

[0058] T2 processed data: [0.75, 0.75, 0.75, 0.76, …] mV;

[0059] T3 processed data: [0.78, 0.78, 0.79, 0.79, …] mV;

[0060] T4 processed data: [0.77, 0.77, 0.77, 0.78, …] mV;

[0061] T5 processed data: [0.80, 0.80, 0.81, 0.81, …] mV;

[0062] T6 processed data: [0.76, 0.76, 0.76, 0.77, …] mV;

[0063] T7 processed data: [0.79, 0.79, 0.80, 0.80, …] mV;

[0064] Data after T8 processing: [0.77, 0.77, 0.78, 0.78, …] mV;

[0065] (4) Data arrangement (data matrix organized by the spatial position of the sound field sensor):

[0066] Table 1 Acoustic field sensor data collection table

[0067]

[0068] S102, performing wavelet transform on the sound field vibration signal to obtain main frequency band energy and phase information corresponding to the sound field vibration signal, where the main frequency band energy is used to represent the intensity of the sound field vibration signal, and the phase information is used to represent the spatial position of the sound field vibration signal in the circumferential direction of the stirring head;

[0069] The wavelet transform is a multi-resolution signal analysis method that can perform localized analysis of signals in both the time and frequency domains. It achieves time-frequency decomposition of signals by translating and scaling the basic wavelet function (mother wavelet). Compared to the traditional Fourier transform, the wavelet transform has better time-frequency localization and can effectively extract non-stationary characteristics of signals. For example, the commonly used discrete wavelet transform can gradually decompose a signal into high-frequency detail components and low-frequency approximation components.

[0070] The mainband energy refers to the sum of the energy within the frequency range where the signal energy is primarily concentrated after wavelet transformation. For friction stir welding, this mainband energy typically corresponds to the frequency range of the stir head speed and its multiples, reflecting the primary energy component of the welding process. The mainband range can be determined by calculating the energy contribution of different frequency bands.

[0071] Phase information refers to the relative variations in the acoustic field vibration signal across time and space. Around the circumference of the mixing head, phase information reflects the propagation delays and relative relationships of the acoustic field vibration at different locations. By analyzing the phase differences between signals detected by adjacent acoustic field sensors, the propagation direction and velocity of the acoustic field energy can be inferred.

[0072] The intensity of the acoustic field vibration signal is used to quantify the degree of change in the amplitude of the acoustic field vibration, which directly reflects the magnitude of the force between the stirring head and the workpiece.

[0073] Spatial position refers to the angular coordinate around the mixing head, used to pinpoint the specific location of the acoustic vibration signal. By establishing a correspondence between the placement of the acoustic sensor and the signal phase, the distribution of acoustic energy around the circumference can be tracked.

[0074] Optionally, in general, performing wavelet transform on the sound field vibration signal to obtain the main frequency band energy and phase information corresponding to the sound field vibration signal can be achieved in the following ways, which are not limited here: performing wavelet decomposition on the sound field vibration signal to obtain wavelet coefficients corresponding to multiple frequency bands, reconstructing the wavelet coefficients to obtain a reconstructed signal for each frequency band; determining the main frequency band range based on the energy distribution characteristics of the reconstructed signal, calculating the energy value within the main frequency band range, and obtaining the main frequency band energy; performing Hilbert transform on the reconstructed signal within the main frequency band range to obtain an analytical signal; calculating the phase angle based on the real and imaginary parts of the analytical signal to obtain phase information.

[0075] Specifically, the server selects an appropriate wavelet basis function (such as the Daubechies wavelet) and performs a wavelet transform on the acoustic field vibration signal. This involves performing a multi-layer wavelet decomposition of the acoustic field vibration signal to obtain high-frequency detail components and low-frequency approximate components in different frequency bands. After decomposition, the server analyzes the energy distribution of each frequency band and, based on the stirring head's rotational speed, determines the primary frequency band encompassing the fundamental frequency and its multiples. The decomposition results are reconstructed to obtain the signal within the primary frequency band and calculate the energy within that band.

[0076] The server then performs a Hilbert transform on the reconstructed mainband signal to extract its phase information. The server then maps this phase information to the placement of each acoustic field sensor along the circumference of the mixing head, establishing a phase-position mapping. By analyzing the phase differences and phase variations between adjacent acoustic field sensors, the server determines the propagation characteristics of the acoustic field vibration signal along the circumference of the mixing head, thereby characterizing the energy intensity and spatial location of the acoustic field vibration signal.

[0077] Following step S101, the server selects the db4 (Daubechies-4) wavelet basis function and performs a three-layer wavelet decomposition on the data after the signal preprocessing in step S101: the first layer decomposition obtains the detail coefficient d1 of the 250-500 Hz frequency band; the second layer decomposition obtains the detail coefficient d2 of the 125-250 Hz frequency band; the third layer decomposition obtains the detail coefficient d3 of the 62.5-125 Hz frequency band and the approximate coefficient a3 of the 0-62.5 Hz frequency band;

[0078] Taking the T1 processed data [0.80, 0.80, 0.80, 0.81, …] mV as an example, the energy distribution ratio of each frequency band obtained after decomposition is:

[0079] Table 2 Energy distribution ratio

[0080]

[0081] Since the stirring head rotates at 2000 rpm (fundamental frequency 33.33 Hz), 30-100 Hz is determined to be the main frequency band, which is mainly included in the d3 and a3 coefficients. Calculate the energy value of each sound field sensor within this main frequency band:

[0082] Table 3 Main frequency band energy table

[0083]

[0084] The server performs Hilbert transform on the main frequency band signal to obtain phase information:

[0085] Table 4 Phase information table

[0086]

[0087] S103. Calculate a sound field distortion factor based on the main frequency band energy, where the sound field distortion factor is used to indicate the uniformity of the sound field energy distribution.

[0088] Among them, the main frequency band energy refers to the energy value within the main frequency band range obtained by wavelet transform; the sound field distortion factor is a dimensionless parameter that represents the degree of uneven distribution of sound field energy; the uniformity of sound field energy distribution is used to represent the degree of consistency of the spatial distribution of sound field energy; the maximum main frequency band energy refers to the maximum value of the main frequency band energies measured by all sound field sensors; the minimum main frequency band energy refers to the minimum value of the main frequency band energies measured by all sound field sensors.

[0089] Specifically, the server finds the maximum and minimum values ​​of the main frequency band energy collected by all sound field sensors, calculates their difference, and divides it by the maximum value to obtain the sound field distortion factor. A sound field distortion factor values ​​closer to 0 indicate a more uniform sound field energy distribution, while values ​​closer to 1 indicate a more uneven sound field energy distribution. The server compares the calculated sound field distortion factor with a preset reference range (e.g., 0.1-0.3) to determine whether energy transfer during the current welding process is uniform, providing a basis for subsequent power consumption calculations and process parameter adjustments.

[0090] Optionally, in general, the sound field distortion factor is calculated based on the main frequency band energy. The sound field distortion factor is used to represent the uniformity of the sound field energy distribution and can be achieved in the following way, which is not limited here: determine the maximum main frequency band energy and the minimum main frequency band energy in the main frequency band energy; divide the difference between the maximum main frequency band energy and the minimum main frequency band energy by the maximum main frequency band energy to obtain the sound field distortion factor.

[0091] Following step S102, see Table 3, it can be determined that:

[0092] Maximum main frequency band energy: T1, T5 position, 0.60mV²;

[0093] Minimum main band energy: T2 position, 0.53mV²;

[0094] Calculate the sound field distortion factor θ: θ = (maximum main frequency band energy - minimum main frequency band energy) / maximum main frequency band energy = (0.60 - 0.53) / 0.60 = 0.117.

[0095] S104, calculating the phase difference between adjacent sound field sensors based on the phase information, and establishing a phase difference matrix, and calculating the sound field rotation angular velocity based on the phase difference matrix, where the phase difference is used to represent the sound field vibration position offset, and the sound field rotation angular velocity is used to characterize the energy transfer rate of the stirring head during the stirring process;

[0096] Among them, phase difference refers to the phase angle difference between the sound field vibration signals detected by two adjacent sound field sensors; phase difference matrix refers to the two-dimensional array representation of the phase difference between all adjacent pairs of sound field sensors; sound field vibration position offset is used to represent the relative displacement of sound field vibration in space; sound field rotation angular velocity refers to the rotation rate of the sound field energy distribution in the circumferential direction, measured in radians per second; energy transfer rate refers to the amount of energy transferred from the stirring head to the workpiece per unit time; adjacent sound field sensors refer to two sound field sensors arranged adjacent to each other in the circumferential direction of the stirring head.

[0097] Specifically, the server calculates the phase difference between each pair of adjacent sound field sensors according to the spatial numbering order of the sound field sensors. For n sound field sensors, an n×n phase difference matrix is ​​formed, and the element in the i-th row and j-th column of the phase difference matrix represents the phase difference between the i-th sound field sensor and the j-th sound field sensor. Then, the server calculates the rotation direction and angular velocity of the sound field by analyzing the change pattern of each element in the phase difference matrix. The spatial distribution angle and sampling time interval of the sound field sensors are taken into account during the calculation, and the phase difference is converted into an angular velocity value. If a sudden change or discontinuity in the phase difference between adjacent sound field sensors is detected, the server will mark the abnormality and trigger the verification process.

[0098] S105 , inputting the sound field distortion factor and the sound field rotation angular velocity into a power consumption calculation model to obtain the effective power consumption of the stirring head.

[0099] The power consumption calculation model is a mathematical expression used to calculate the effective power consumption of the mixing head. The power consumption calculation model is P = k × ω × (1-θ), where k represents the characteristic coefficient corresponding to the geometric dimensions of the mixing head, ω represents the angular velocity of the sound field, and θ represents the sound field distortion factor. The effective power consumption of the mixing head refers to the energy actually consumed by the mixing head for plasticizing and stirring the material.

[0100] Specifically, the server looks up or calculates the characteristic coefficient based on the geometric dimensions of the mixing head (such as shoulder diameter and needle length). The server then substitutes the sound field distortion factor, sound field rotation angular velocity, and characteristic coefficient into the power consumption calculation model to calculate the effective power consumption of the mixing head at the current moment.

[0101] By adopting the above technical solution, the server uses an acoustic field sensor to collect the acoustic field vibration signal during the welding process and performs a wavelet transform to obtain the main frequency band energy and phase information. It then calculates the acoustic field distortion factor and the acoustic field rotational angular velocity, which are then input into the power consumption calculation model to obtain the effective power consumption of the stirring head. Compared to traditional power consumption monitoring that relies solely on electrical parameters such as current and voltage, this method can accurately reflect the complex dynamics of mechanical contact, monitor the working status of the stirring head in multiple dimensions, quantify the uniformity of the acoustic field energy distribution (promptly detecting abnormal contact of the stirring head, avoiding damage to the workpiece or excessive wear of the stirring head due to energy concentration, and improving the stability of welding quality), track the efficiency of acoustic field energy transfer, and improve the accuracy and real-time performance of power consumption calculations.

[0102] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the stirring head power consumption detection method in an embodiment of the present application.

[0103] After step S101, the following steps may be performed or not performed, which is not limited here:

[0104] S201. Establish an acoustic field energy distribution thermogram based on the acoustic field vibration signal. The acoustic field energy distribution thermogram is used to represent the energy distribution density of the contact surface between the stirring head and the workpiece.

[0105] Among them, the acoustic field vibration signal refers to the acoustic wave signal formed by the mechanical vibration generated by the contact surface between the stirring head and the workpiece propagating in the medium; the acoustic field energy distribution heat map refers to a two-dimensional graph that intuitively displays the acoustic field energy density data through a color gradient, and different colors are usually used to represent different energy intensities; energy distribution density refers to the size of the acoustic field energy per unit area, reflecting the spatial distribution characteristics of energy transfer during the stirring process; color gradient refers to a visual method of using different shades of color to represent changes in energy size.

[0106] Specifically, the server uses an interpolation algorithm to generate continuous 360-degree sound field energy distribution data from discrete measurement points along the circumference of the mixing head (such as the locations of eight sound field sensors). The server then creates a two-dimensional grid in a rectangular coordinate system, with the horizontal axis representing the angular position of the mixing head along the circumference (0-360°) and the vertical axis representing the radial position (0-R, where R is the radius of the mixing head). The server maps the continuous sound field energy distribution data onto the two-dimensional grid, using different colors to represent the energy density at different locations to obtain a sound field energy distribution heat map.

[0107] S202. Calculate the energy density gradient in the circumferential direction of the stirring head based on the acoustic field energy distribution thermogram.

[0108] Among them, the acoustic field energy distribution thermogram refers to a two-dimensional visualization graph that characterizes the energy distribution at the contact surface between the stirring head and the workpiece; the energy density gradient refers to the rate of change of energy density in space, and its magnitude and direction are represented by vectors.

[0109] Specifically, the server calculates the energy density difference between adjacent grid points on a two-dimensional grid in an established rectangular coordinate system. The energy density change rate between adjacent angular points in the circumferential direction of the stirring head is calculated; in the radial direction, the energy density change rate between adjacent radial points is calculated. The energy density change rates in these two directions are combined to form an energy density gradient vector.

[0110] S203. When the energy density gradient is greater than a preset third threshold, the wear location and wear degree of the stirring head are calculated based on the direction and magnitude of the energy density gradient.

[0111] Among them, energy density gradient refers to the vector that describes the spatial variation characteristics of energy density; the preset third threshold refers to the safe upper limit of the energy density gradient determined based on process experience; the wear location refers to the specific location where wear occurs on the surface of the stirring head; the degree of wear refers to the degree to which the surface morphology of the stirring head deviates from the design contour; the gradient direction refers to the direction in which the energy density increases fastest; the gradient magnitude refers to the spatial rate of change of energy density.

[0112] Specifically, the server marks areas where the energy density gradient exceeds a preset third threshold. These areas typically correspond to abnormal energy concentration or dissipation. The server then analyzes the abnormal energy transfer pattern based on the divergence and curl characteristics of the energy density gradient vector. The server matches the abnormal pattern with typical wear patterns to identify possible wear locations. For each potential wear location, the server calculates the wear depth based on the magnitude of the energy density gradient and estimates the wear rate based on the accumulated operating time.

[0113] S204: When the degree of wear exceeds a preset second range, the compensation inclination angle corresponding to the wear portion is calculated, and the posture of the stirring head is adjusted according to the compensation inclination angle.

[0114] Among them, the preset second range refers to the allowable wear degree range, and posture compensation is required beyond this range; the compensation inclination angle refers to the inclination angle of the stirring head that needs to be adjusted to compensate for the influence of wear; the posture of the stirring head refers to the spatial position and angular relationship of the stirring head relative to the workpiece surface.

[0115] Specifically, the server establishes a mathematical model of the wear profile based on the circumferential distribution characteristics of the wear area. Based on this model, the server calculates the compensation angle that maximizes the pressure distribution at the contact surface between the mixing head and the workpiece. This calculation takes into account the influence of the mixing head's geometric characteristics (such as thread angle) and process parameters (such as rotational speed and feed rate). After determining the compensation angle, the server decomposes it into motion commands for each axis of the machine tool and executes posture adjustments through the control system to achieve dynamic compensation for wear.

[0116] After step S105, the following steps may be performed or not performed, which is not limited here:

[0117] S205 , determining a power consumption range according to the effective power consumption of the stirring head, where the power consumption range includes a normal power consumption range, an overload power consumption range, and a critical power consumption range.

[0118] Among them, the effective power consumption of the stirring head refers to the effective electric power actually consumed by the stirring head during the welding process; the power consumption range refers to the different power consumption level ranges divided according to process requirements; the normal power consumption range refers to the power consumption range of the stirring head under ideal working conditions, usually 60%-80% of the rated power; the overload power consumption range refers to the power consumption range exceeding the safe working load, usually higher than 90% of the rated power; the critical power consumption range refers to the transition area between the normal power consumption range and the overload power consumption range, usually 80%-90% of the rated power; the rated power refers to the design power value of the equipment under standard working conditions.

[0119] Specifically, the server obtains the rated power of the mixing head and then sets the boundary values ​​for three power consumption ranges. For example, for a mixing head with a rated power of 10kW, the normal power consumption range is 6-8kW, the critical power consumption range is 8-9kW, and the overload power consumption range is above 9kW. The server compares the real-time monitored effective power consumption of the mixing head with these boundary values ​​to determine the current power consumption range.

[0120] S206. When the power consumption range is a critical power consumption range, determine the degree of eccentricity of the stirring head according to the sound field distortion factor.

[0121] The eccentricity of the mixing head refers to the degree of deviation between the actual rotation axis of the mixing head and the ideal rotation axis; the deviation degree refers to the relative value of the center distance, usually expressed as a percentage.

[0122] Specifically, the server obtains the current sound field distortion factor and establishes a mapping relationship between the sound field distortion factor and the degree of eccentricity. For example, for every 0.1 increase in the sound field distortion factor, the corresponding eccentricity increases by 5%. The server uses this mapping relationship to calculate the actual eccentricity of the mixing head and compares it with historical data to analyze the changing trend of the eccentricity state. The server also considers the impact of power consumption on the eccentricity calculation and makes necessary corrections.

[0123] S207: When the eccentricity of the stirring head exceeds a preset first range, automatic correction of the stirring head trajectory is triggered.

[0124] Among them, the eccentricity of the stirring head refers to the offset of the center of rotation of the stirring head relative to the ideal position; the preset first range refers to the maximum allowable eccentricity range, which is usually 1%-3% of the stirring head diameter; automatic trajectory correction refers to the process of automatically adjusting the motion trajectory of the stirring head; the motion trajectory refers to the spatial path formed during the movement of the stirring head; correction refers to the control process of adjusting the actual trajectory to the target trajectory.

[0125] Specifically, the server calculates the required compensation based on the direction and magnitude of the currently detected stir head eccentricity. This compensation is converted into position adjustment instructions in the machine tool coordinate system and decomposed into incremental motions for each axis. The server controls the motors on each axis to synchronously execute these motion instructions, achieving dynamic correction of the stir head trajectory. During the correction process, the acoustic field distortion factor is continuously monitored until the eccentricity returns to within the allowable range.

[0126] S208 , drawing a variation trend curve of the effective power consumption of the stirring head, and determining the slope of the variation trend curve as the power consumption variation rate.

[0127] Among them, the change trend curve refers to a graph that describes the change pattern of the effective power consumption of the stirring head over time; the slope refers to the tangent value of the angle between the tangent line of the change trend curve at a certain point and the horizontal line; the power consumption change rate refers to the change in power consumption per unit time, indicating how fast the power consumption increases or decreases.

[0128] Specifically, the server arranges the collected active power consumption data for the mixing heads in chronological order and selects an appropriate time window (e.g., 1 second) for data smoothing. The server then uses methods such as polynomial fitting or spline interpolation to generate a continuous trend curve for the active power consumption of the mixing heads. The server calculates the local slope of the trend curve at each sampling point to obtain the rate of change of power consumption. Furthermore, the server calculates the average rate of change within a specific time window for trend analysis.

[0129] S209: When the effective power consumption of the stirring head exceeds the upper limit of the normal power consumption range and the power consumption change rate is greater than the preset first threshold, predict the remaining time for the effective power consumption of the stirring head to reach the lower limit of the overload power consumption range.

[0130] Among them, the upper limit value of the normal power consumption range refers to the maximum power consumption value of the normal power consumption range; the power consumption change rate refers to the speed of change of power consumption per unit time; the preset first threshold value refers to the maximum allowable power consumption change rate; the lower limit value of the overload power consumption range refers to the minimum power consumption value of the overload power consumption range; the remaining time refers to the estimated time required for the effective power consumption of the stirring head to reach the lower limit value of the overload power consumption range at the current moment.

[0131] Specifically, the server builds a mathematical model of power consumption variation based on the effective power consumption of the agitator head, the rate of change of power consumption, and historical data. This model predicts power consumption trends and calculates the time required to increase from the current power consumption level to the lower limit of the overload power consumption range, i.e., the remaining duration. This prediction process considers the nonlinear characteristics of power consumption variation and employs an adaptive prediction algorithm to improve accuracy.

[0132] S210. Within the remaining time, reduce the rotation speed of the stirring head according to the preset step size until the power consumption change rate is less than the preset second threshold, and the preset second threshold is less than the preset first threshold.

[0133] Among them, the preset step size refers to the fixed increment of each speed adjustment, usually 50-100rpm; the stirring head speed refers to the number of rotations of the stirring head per minute; the preset second threshold refers to the target power consumption change rate of the speed reduction control.

[0134] Specifically, the server evenly divides the remaining time into several speed reduction cycles, each of which reduces the speed by a preset step size. After each speed reduction, the server monitors the response to the power consumption change rate. If the power consumption change rate is still greater than the preset second threshold, the server proceeds to the next speed reduction; if the power consumption change rate drops below the preset second threshold, the server maintains the current speed. This entire process is ensured to complete within the remaining time to prevent power consumption from entering the overload range.

[0135] The following describes the server in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of a server in an embodiment of the present application.

[0136] It should be noted that Figure 3 The structure of the server shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0137] like Figure 3As shown, the server includes a CPU 301, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage unit 308 into a random access memory (RAM) 303, such as executing the methods described in the above embodiments. RAM 303 also stores various programs and data required for system operation. CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An I / O interface 305 is also connected to bus 304.

[0138] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, push button switches, and the like; an output section 307 including a liquid crystal display (LCD), an audio output device, indicator lights, and the like; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read from the removable media can be installed in the storage section 308 as needed.

[0139] In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present invention includes a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from the removable medium 311. When the computer program is executed by the CPU 301, the various functions defined in the present invention are performed.

[0140] It should be noted that specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0141] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings.

[0142] Specifically, the server of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the stirring head power consumption detection method provided in the above embodiment is implemented.

[0143] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the server described in the above embodiments, or may exist independently and not incorporated into the server. The storage medium carries one or more computer programs, and when executed by a processor of the server, the server implements the method for detecting power consumption of a stirring head provided in the above embodiments.

[0144] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0145] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0146] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for detecting power consumption of a stirring head, characterized in that: Applied to a server, the method includes: collecting sound field vibration signals during welding by means of sound field sensors uniformly arranged in the circumferential direction of a stirring head, the sound field vibration signals being used to represent the sound field energy distribution at the contact surface between the stirring head and the workpiece; performing wavelet transform on the sound field vibration signals to obtain main frequency band energy and phase information corresponding to the sound field vibration signals, the main frequency band energy being used to represent the intensity of the sound field vibration signal, and the phase information being used to represent the spatial position of the sound field vibration signal in the circumferential direction of the stirring head; calculating a sound field distortion factor based on the main frequency band energy, the sound field distortion factor being used to represent the uniformity of the sound field energy distribution; calculating the phase difference between adjacent sound field sensors according to the phase information, establishing a phase difference matrix, and calculating a sound field rotation angular velocity based on the phase difference matrix, the phase difference being used to represent the sound field vibration position offset, and the sound field rotation angular velocity being used to characterize the energy transfer rate of the stirring head during the stirring process; inputting the sound field distortion factor and the sound field rotation angular velocity into a power consumption calculation model to obtain the effective power consumption of the stirring head; The power consumption calculation model is: P=k×ω×(1-θ); wherein k is used to represent the characteristic coefficient corresponding to the geometric dimensions of the stirring head, ω is used to represent the angular velocity of the sound field rotation, and θ represents the sound field distortion factor.

2. The method according to claim 1, characterized in that The method of performing wavelet transform on the sound field vibration signal to obtain the main frequency band energy and phase information corresponding to the sound field vibration signal specifically includes: performing wavelet decomposition on the sound field vibration signal to obtain wavelet coefficients corresponding to multiple frequency bands, reconstructing the wavelet coefficients to obtain a reconstructed signal for each frequency band; determining the main frequency band range based on the energy distribution characteristics of the reconstructed signal, calculating the energy value within the main frequency band range to obtain the main frequency band energy; performing Hilbert transform on the reconstructed signal within the main frequency band range to obtain an analytical signal; and calculating the phase angle based on the real part and imaginary part of the analytical signal to obtain the phase information.

3. The method according to claim 1, characterized in that The sound field distortion factor is calculated based on the main frequency band energy, and the sound field distortion factor is used to indicate the uniformity of the sound field energy distribution, specifically including: determining the maximum main frequency band energy and the minimum main frequency band energy in the main frequency band energy; dividing the difference between the maximum main frequency band energy and the minimum main frequency band energy by the maximum main frequency band energy to obtain the sound field distortion factor.

4. The method according to claim 1, wherein After the step of inputting the sound field distortion factor and the sound field rotation angular velocity into the power consumption calculation model to obtain the effective power consumption of the stirring head, the method further includes: determining the power consumption interval according to the effective power consumption of the stirring head, the power consumption interval including the normal power consumption interval, the overload power consumption interval and the critical power consumption interval; when the power consumption interval is the critical power consumption interval, determining the degree of eccentricity of the stirring head according to the sound field distortion factor; when the degree of eccentricity of the stirring head exceeds the preset first range, triggering automatic correction of the stirring head trajectory.

5. The method according to claim 4, characterized in that After the step of inputting the sound field distortion factor and the sound field rotation angular velocity into the power consumption calculation model to obtain the effective power consumption of the stirring head, the method further includes: drawing a change trend curve of the effective power consumption of the stirring head, and determining the slope of the change trend curve as the power consumption change rate; when the effective power consumption of the stirring head exceeds the upper limit value of the normal power consumption range and the power consumption change rate is greater than the preset first threshold, predicting the remaining time for the effective power consumption of the stirring head to reach the lower limit value of the overload power consumption range; within the remaining time, reducing the stirring head rotation speed according to a preset step size until the power consumption change rate is less than a preset second threshold, and the preset second threshold is less than the preset first threshold.

6. The method according to claim 1, characterized in that After the step of collecting the sound field vibration signal during the welding process by the sound field sensors evenly arranged in the circumferential direction of the stirring head, the method further includes: establishing a sound field energy distribution thermogram based on the sound field vibration signal, the sound field energy distribution thermogram being used to represent the energy distribution density of the contact surface between the stirring head and the workpiece; calculating the energy density gradient in the circumferential direction of the stirring head based on the sound field energy distribution thermogram; when the energy density gradient is greater than a preset third threshold, calculating the wear position and wear degree of the stirring head based on the direction and magnitude of the energy density gradient; when the wear degree exceeds a preset second range, calculating the compensation inclination angle corresponding to the wear position, and adjusting the posture of the stirring head according to the compensation inclination angle.

7. A server, characterized in that: The server includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the server to execute the method according to any one of claims 1 to 6.

8. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a server, the server is caused to perform the method according to any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program product is run on a server, the server is caused to perform the method according to any one of claims 1 to 6.

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