Woodworking machine noise sound power level measurement method based on sound intensity method

Abstract

This invention discloses a method for measuring the sound power level of woodworking machine tool noise based on the sound intensity method, belonging to the field of machine tool noise measurement technology. Specifically, it includes: determining the housing surface covered by the projection of the tool's rotation plane as the main sound radiation area, and the main bearing seat mounting surface as the secondary sound radiation area; acquiring sound pressure, vibration acceleration signals, and dynamic air pressure signals in the main sound radiation area; resampling the dynamic air pressure signal in the rotational angular domain, extracting the reference air pulse waveform synchronized with the tool tooth sweep, and establishing an airflow-induced sound pressure model based on this and the phase relationship; calculating the surface normal vibration velocity based on the vibration acceleration, and establishing a structural radiation sound pressure model based on the sound wave equation; decomposing the sound pressure signal to obtain estimates of two types of components, and obtaining the net sound intensity of the main sound radiation area after parameter optimization; extracting the vibration frequency band energy of the secondary sound radiation area, calculating the equivalent sound intensity based on the sound radiation efficiency model, and spatially integrating to output the total sound power level. This invention provides an effective method for the accurate measurement and control of woodworking machine tool noise.

Description

Technical Field

[0001] This invention relates to the field of machine tool noise measurement technology, and specifically to a method for measuring the sound power level of woodworking machine tool noise based on the sound intensity method. Background Technology

[0002] Woodworking machine tools are core equipment in the wood processing industry, generating significant noise during sawing, planing, and milling operations. Excessive noise not only affects the occupational health of operators, and long-term exposure can lead to hearing damage and nervous system disorders, but also requires compliance with national standards such as noise limits for woodworking machine tools. Therefore, accurately measuring the noise power level of woodworking machine tools is fundamental to achieving noise control and compliance assessment.

[0003] Currently, the sound intensity method has become the mainstream method for measuring noise in woodworking machine tools because it does not require a special acoustic environment and can directly measure the radiated power of the sound source. Existing technologies typically collect sound pressure signals by placing sound intensity probes on the machine tool surface and calculating the sound power level by combining the geometric parameters of the measurement area. Some methods introduce vibration sensors to assist in analyzing the location of the noise source, and this method has been applied in general mechanical noise measurement.

[0004] However, the noise of woodworking machine tools has significant complex characteristics. In addition to the airflow-induced noise generated by the interaction between the high-speed rotation of the cutting tool and the wood and air, structural vibrations such as the spindle bearing housing also radiate structural noise. These two types of noise are superimposed in the measured signal, and the existing sound intensity method is difficult to effectively separate these two types of noise, resulting in the measurement results being greatly affected by cross-interference. At the same time, the division of noise radiation areas relies heavily on empirical judgment and lacks a precise positioning method based on vibration energy distribution, which may miss secondary but not negligible radiation sources. Summary of the Invention

[0005] The purpose of this invention is to provide a method for measuring the sound power level of woodworking machine tool noise based on the sound intensity method, and to solve the following technical problems:

[0006] The noise of woodworking machine tools consists of airflow-induced noise and structural noise superimposed. Existing sound intensity methods are difficult to effectively separate them, leading to cross-interference. Furthermore, the division of noise radiation areas often relies on experience-based judgment, which may overlook secondary but not negligible radiation sources.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A method for measuring the sound power level of woodworking machine tool noise based on the sound intensity method includes the following steps:

[0009] S1. When the woodworking machine tool is in operation, the area of ​​the housing surface covered by the projection of the tool rotation plane is taken as the main sound radiation area, and the area of ​​the main bearing seat mounting surface is taken as the secondary sound radiation area.

[0010] S2. Collect the sound pressure signal sequence and three-dimensional vibration acceleration signal sequence of the surface of the main sound radiation area, as well as the dynamic air pressure signal sequence of the near field area of ​​the tool rotation.

[0011] S3. Perform signal resampling processing based on the rotational angular domain on the dynamic air pressure signal sequence to extract the reference air pulse dynamic component waveform synchronized with the periodic sweeping action of the tool teeth;

[0012] S4. Based on the phase correspondence between the reference air pulse dynamic component waveform and the sound pressure signal sequence, establish a mathematical expression model for the airflow-induced sound pressure component.

[0013] S5. Calculate the surface normal vibration velocity sequence based on the three-dimensional vibration acceleration signal sequence, and establish a mathematical expression model of the structural radiated sound pressure component in combination with the physical equation of sound wave propagation.

[0014] S6. Decompose the sound pressure signal sequence into estimated values ​​of airflow-induced sound pressure components and estimated values ​​of structure-radiated sound pressure components, and obtain the net surface sound intensity value of the main sound radiation region through the parameter optimization solution process.

[0015] S7. Extract the frequency band energy features of the vibration acceleration signal sequence in the infrasound radiation zone, and calculate the equivalent radiated sound intensity value by combining the sound radiation efficiency theoretical model; perform spatial integration calculation on the net surface sound intensity value of the main sound radiation zone and the equivalent radiated sound intensity value of the infrasound radiation zone, and output the total sound power level.

[0016] As a further aspect of the present invention: in S1, the process of using the area of ​​the shell surface covered by the projection of the tool rotation plane as the main acoustic radiation area is as follows:

[0017] Under no-load operation and at least three typical load operation conditions of woodworking machine tools, a contact vibration sensor is used to scan and measure the surface of the housing by step distance based on the minimum characteristic dimension of the mechanical structure.

[0018] The vibration acceleration signal collected at each measurement point is converted into a vibration velocity signal by time-domain integration. The spatial distribution map of the root mean square value of vibration velocity at each measurement point is calculated and stored in the form of a two-dimensional coordinate matrix.

[0019] The region growing algorithm in image processing is used to identify continuous high-energy regions in the spectrum whose vibration energy exceeds a set multiple of the average energy level of the eight surrounding points. The vertical distance between the geometric center point of the identified high-energy region and the tool rotation axis is calculated, and the three high-energy regions with the smallest vertical distance are selected as candidate regions for the main sound radiation area.

[0020] The spectral characteristics of the vibration acceleration signal in the candidate region are extracted, and the correlation coefficient between its energy distribution and the fundamental frequency of the spindle rotation speed is calculated. The candidate region with the largest correlation coefficient is finally confirmed as the main acoustic radiation region, and the boundary coordinate information of the main acoustic radiation region is recorded.

[0021] As a further aspect of the present invention: in step S3, the process of performing signal resampling processing based on the rotational angular domain on the dynamic pressure signal sequence is as follows:

[0022] The pulse signal per revolution and the zero-phase reference signal output by the spindle rotary encoder are acquired. The time-amplitude sequence of the dynamic air pressure signal is resampled at equal angular intervals, with the zero-phase reference signal as the angular zero point.

[0023] The complete rotation cycle is divided into a corresponding number of angle intervals based on the actual number of teeth installed on the tool. A phase acquisition window of fixed width is set in each angle interval, and the window width is proportional to the sweep angle of a single tooth of the tool.

[0024] Dynamic air pressure data segments within the same phase window in a continuous rotation cycle are extracted. Time axis normalization is performed on the data segments to eliminate the influence of rotation speed fluctuations. A curve registration algorithm is used to align the waveform feature points of the data segments in multiple cycles. The arithmetic mean waveform of the aligned data segments is calculated as the reference air pressure dynamic component waveform template. A cubic spline interpolation algorithm is applied to reconstruct the continuous waveform of the air pressure dynamic component in the complete rotation cycle.

[0025] As a further aspect of the present invention: in step S4, the process of establishing a mathematical expression model for the airflow-induced sound pressure component is as follows:

[0026] Establish a three-dimensional spatial coordinate transformation relationship between the position of the dynamic barometric pressure sensor and the position of the acoustic intensity probe, and define a position-dependent acoustic transfer function expression, which includes the acoustic propagation path length attenuation factor and the air medium absorption coefficient.

[0027] Discrete convolution operation is performed between the reference air pulsation component waveform and the sound wave transfer function expression, and an airflow velocity field correction model is introduced. The airflow velocity field correction model describes the turbulence interference effect on the sound wave propagation path based on computational fluid dynamics theory.

[0028] The airflow velocity field correction model is applied to the convolution operation result for compensation calculation, and the time-domain waveform estimation sequence of the airflow-induced sound pressure component is output. The coherence function value of the estimated sequence and the measured sound pressure signal in the aerodynamic characteristic frequency band is verified. When the coherence function value is lower than the set threshold, the sound wave transfer function expression parameters are recalibrated.

[0029] As a further aspect of the present invention: In step S5, the process of establishing a mathematical expression model for the structural radiated sound pressure components based on the physical equations of sound wave propagation is as follows:

[0030] Digital filtering is applied to the three-dimensional vibration acceleration signal sequence to eliminate high-frequency noise; a time-domain numerical integration algorithm is applied to transform the vibration acceleration signal sequence into a normal vibration velocity signal sequence; a differential relationship expression between surface vibration velocity and radiated sound pressure is established based on the acoustic Helmholtz equation; and the surface acoustic impedance parameter matrix in the differential relationship expression is solved using the boundary element discretization method.

[0031] The normal vibration velocity signal sequence is multiplied with the surface acoustic impedance parameter matrix to output the theoretical calculation sequence of the structure's radiated sound pressure component. The amplitude difference between the theoretical calculation sequence and the measured sound pressure signal at the structure's resonant frequency is compared. When the amplitude difference exceeds the allowable range, the boundary element mesh density is re-divided.

[0032] As a further aspect of the present invention: In step S6, the process of obtaining the net surface acoustic intensity value of the main acoustic radiation region through parameter optimization is as follows:

[0033] The measured sound pressure signal sequence is represented as a linear superposition model of the estimated sequence of airflow-induced sound pressure component and the estimated sequence of structure-radiated sound pressure component; an objective function for optimizing the parameters of the superposition model is constructed, which is the integral value of the mean square error between the measured sound pressure signal sequence and the model estimation sequence;

[0034] Initialize the acoustic transfer function coefficient vector and the surface acoustic impedance parameter matrix, and iteratively optimize the coefficient vector and parameter matrix using the conjugate gradient descent algorithm; monitor the change in the descent rate of the objective function value during the iteration process, and terminate the optimization process when the descent rate is lower than the convergence threshold for multiple consecutive iterations.

[0035] Extract the estimated sequence of structural radiated sound pressure components from the final iteration result, calculate the cross-correlation function between the estimated sequence of structural radiated sound pressure components and the normal vibration velocity sequence, perform numerical integration within the main lobe interval of the cross-correlation function to obtain the instantaneous acoustic energy flux density, and output the net surface acoustic intensity value sequence by moving average processing of the instantaneous acoustic energy flux density on the time axis.

[0036] As a further aspect of the present invention: the convergence determination in the parameter optimization solution process specifically includes:

[0037] Record the change in the objective function value of each iteration of the gradient descent algorithm, calculate the average change in the objective function value of multiple consecutive iterations, and start the convergence confirmation procedure when the average change is lower than the first judgment threshold.

[0038] If the convergence condition is not met even after the number of iterations exceeds the maximum allowed number of iterations, the parameter search space is expanded, and the optimization algorithm is reinitialized within the expanded parameter search space.

[0039] Establish a database of historical optimized parameter combinations and their corresponding objective function values. When convergence fails multiple times in a row, retrieve the parameter combination with the smallest objective function value from the database, output this parameter combination as the final solution, and generate an algorithm anomaly report. Based on the anomaly report, automatically adjust the gain parameters of the signal acquisition system and restart the measurement process.

[0040] As a further aspect of the present invention: in step S7, the specific process of calculating the equivalent radiated sound intensity value using the acoustic radiation efficiency theoretical model is as follows:

[0041] Vibration acceleration signal sequences from multiple measurement points uniformly distributed on the surface of the infrasound radiation zone are collected. A windowed Fourier transform is performed on the signal sequence of each measurement point to obtain the vibration acceleration spectrum. The frequency domain numerical integration algorithm is then applied to convert the vibration acceleration spectrum into the vibration velocity spectrum.

[0042] Determine the effective frequency band of sound radiation, covering the main noise radiation frequency band of the machine tool, calculate the energy integral value of the vibration velocity spectrum within the effective frequency band of sound radiation, and establish a calculation model for the radiation efficiency coefficient based on the theory of flat plate sound radiation.

[0043] The surface of the infrasound radiation zone is divided into several sub-regions, and the radiation efficiency coefficient of each sub-region is calculated. The arithmetic mean of the radiation efficiency coefficients of all sub-regions is taken as the overall radiation efficiency coefficient. The vibration energy integral value, the overall radiation efficiency coefficient, and the air characteristic impedance parameter are substituted into the plane wave sound intensity calculation formula to output the equivalent radiated sound intensity value of the infrasound radiation zone.

[0044] As a further aspect of the present invention: In step S7, the process of performing spatial integration on the net surface sound intensity value of the main sound radiation region and the equivalent radiated sound intensity value of the secondary sound radiation region to output the total sound power level is as follows:

[0045] A three-dimensional geometric mesh model of the surface of the main sound radiation zone is constructed, with the mesh element size smaller than the shortest measurement wavelength. The coordinates of the sound intensity probe measurement points are mapped to the node positions of the mesh model. The thin plate spline interpolation algorithm is used to calculate the sound intensity distribution function in the area where no probe is placed.

[0046] An equal-area grid division strategy is adopted for the surface of the infrasound radiation zone. The normal sound intensity component at the center point of each grid unit is calculated. The unit sound power contribution value is calculated based on the grid unit area and the normal sound intensity component.

[0047] An algebraic summation operation is performed on the sound power contribution values ​​of all grid cells in the main sound radiation zone, and on the sound power contribution values ​​of all grid cells in the secondary sound radiation zone. The summation of the sound power values ​​of the two zones is used to obtain the total sound power value, which is then converted into a decibel scale value with reference to the reference sound power.

[0048] The beneficial effects of this invention are:

[0049] This invention, based on vibration energy spatial distribution analysis and a region growing algorithm, accurately delineates the shell surface covered by the tool's rotation plane projection as the primary sound radiation zone and the main bearing housing mounting surface as the secondary sound radiation zone. This replaces the experience-based region division method, avoiding the omission of secondary but not negligible radiation sources and laying the foundation for subsequent accurate measurements. By performing rotational angular domain resampling processing on the dynamic air pressure signal, the reference air pulse dynamic component waveform synchronized with the periodic sweeping motion of the tool teeth is extracted. A mathematical expression model of the airflow-induced sound pressure component is established based on the phase correspondence relationship. Simultaneously, the surface normal vibration velocity sequence is calculated based on the three-dimensional vibration acceleration signal, and the structural radiated sound pressure is established by combining the physical equations of sound wave propagation. The mathematical expression model of the components is used, and the measured sound pressure signal is decomposed into two types of components through parameter optimization to obtain the net surface sound intensity value of the main sound radiation area. This effectively separates airflow-induced noise and structural vibration noise, and solves the problem of cross-interference caused by the superposition of the two types of noise. By extracting the frequency band energy characteristics of the vibration acceleration signal in the infrasound radiation area and calculating the equivalent radiated sound intensity value in combination with the sound radiation efficiency theoretical model, the total sound power level is finally output by performing spatial integration on the net surface sound intensity value of the main sound radiation area and the equivalent radiated sound intensity value of the infrasound radiation area. This comprehensively quantifies the noise contribution of each radiation source, significantly improves the accuracy and reliability of the sound power level measurement of woodworking machine tool noise, and provides a more scientific basis for noise control and product optimization. Attached Figure Description

[0050] The invention will now be further described with reference to the accompanying drawings.

[0051] Figure 1 This is a flowchart illustrating the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figure 1 As shown, this invention is a method for measuring the sound power level of woodworking machine tool noise based on the sound intensity method, including the following steps:

[0054] S1. Precisely locate the noise radiation area. During the operation of the woodworking machine tool, the main sound radiation area is determined by analyzing the spatial distribution of vibration energy. Under no-load and various typical load conditions, contact vibration sensors scan the shell surface at step distances according to the minimum characteristic size of the mechanical structure. The vibration acceleration signal of each measurement point is converted into a vibration velocity signal by time-domain integration. The spatial distribution spectrum of the root mean square value of vibration velocity is calculated. A region growing algorithm is used to identify continuous high-energy areas. Combining the perpendicular distance between the geometric center point and the tool rotation axis and the correlation coefficient between the spectral characteristics and the fundamental frequency of the spindle speed, the shell surface area covered by the projection of the tool rotation plane is finally determined as the main sound radiation area. At the same time, through the analysis of bearing vibration transmission characteristics, the mounting surface area of ​​the main bearing seat is determined as the secondary sound radiation area.

[0055] S2 is multi-dimensional signal synchronous acquisition. During the sound intensity measurement stage, three types of signals are acquired simultaneously: the sound pressure signal sequence of the dual-microphone sound intensity probes arranged on the surface of the main sound radiation area, which is used to directly reflect the noise sound pressure; the three-dimensional vibration acceleration signal sequence of the surface of the main sound radiation area, which is used to analyze the structural vibration characteristics; and the dynamic air pressure signal sequence of the near-field area of ​​the rotating tool, which is used to capture the airflow disturbance characteristics.

[0056] S3. Extraction of the reference pneumatic pulse dynamic component. The dynamic pressure signal sequence undergoes signal resampling processing based on the rotational angular domain. Using the pulses per revolution of the spindle rotary encoder and the zero-phase reference signal, the time-amplitude sequence is resampled at equal angular intervals. The complete rotation cycle is divided into corresponding angular intervals based on the actual number of teeth on the tool. A phase acquisition window proportional to the single-tooth sweep angle is set in each interval. Dynamic pressure data segments from the same window within the continuous cycle are extracted, normalized along the time axis to eliminate the influence of rotational speed fluctuations, and waveform feature points are aligned using a curve registration algorithm. The arithmetic mean waveform is calculated as the reference pneumatic pulse dynamic component waveform template, and cubic spline interpolation is used to reconstruct the continuous waveform of the complete cycle.

[0057] S4. Construct a model for airflow-induced sound pressure components. Establish a three-dimensional spatial coordinate transformation relationship between the dynamic air pressure sensor and the sound intensity probe, and define a sound transfer function that includes the propagation path attenuation factor and the air absorption coefficient. Perform discrete convolution operations between the reference aerodynamic component waveform and this function, and introduce an airflow velocity field correction model based on computational fluid dynamics to compensate for turbulence interference. calibrate the sound transfer function parameters by verifying the coherence function values ​​of the estimated sequence and the measured sound pressure signal within the aerodynamic characteristic frequency band.

[0058] S5. Establish a structural radiated sound pressure component model. Digitally filter the three-dimensional vibration acceleration signal sequence to eliminate high-frequency noise, and convert it into a normal vibration velocity signal sequence through time-domain numerical integration. Establish the differential relationship between surface vibration velocity and radiated sound pressure based on the acoustic Helmholtz equation, and solve the surface acoustic impedance parameter matrix using the boundary element discretization method. Calculate the theoretically calculated sequence by operating the vibration velocity sequence and impedance matrix, compare its amplitude difference with the measured sound pressure signal at the structural resonant frequency, and adjust the boundary element mesh density to optimize the model.

[0059] S6. Solve for the net surface acoustic intensity value in the main acoustic radiation region. Represent the measured sound pressure signal sequence as a linear superposition of the estimated airflow-induced sound pressure component sequence and the estimated structural radiated sound pressure component sequence. Construct a parameter optimization function with the mean square error integral as the objective. Initialize the sound wave transfer function coefficients and surface acoustic impedance parameters, and iteratively optimize using the conjugate gradient descent algorithm. Monitor the rate of descent of the objective function value; terminate the iteration when the convergence condition is met. Extract the estimated structural radiated sound pressure component sequence, and output the net surface acoustic intensity value sequence by integrating with the cross-correlation function of the normal vibration velocity sequence and performing moving average processing.

[0060] S7. Calculate the total sound power level. Perform a windowed Fourier transform on the vibration acceleration signal sequence in the infrasound radiation zone to obtain the vibration acceleration spectrum. After frequency domain integration, convert it into a vibration velocity spectrum to determine the effective frequency band of sound radiation and calculate the energy integral value. Combined with the radiation efficiency coefficient model established by the planar sound radiation theory, divide the surface of the infrasound radiation zone into sub-regions to calculate the average radiation efficiency coefficient. Substitute it into the plane wave sound intensity calculation formula to obtain the equivalent radiated sound intensity value. Construct a three-dimensional geometric mesh model of the main sound radiation zone, interpolate to calculate the sound intensity distribution in the area where no probes are placed, and sum the sound power contribution values ​​of each mesh unit in the main and infrasound radiation zones respectively. Convert the sum to a decibel scale value with reference to the reference sound power and output the total sound power level.

[0061] In step S1, the process of using the area of ​​the shell surface covered by the projection of the tool rotation plane as the main acoustic radiation area is as follows:

[0062] Measurements were conducted under no-load operating conditions and at least three typical load operating conditions of the machine tool. The no-load operating condition reflects the basic vibration characteristics of the machine tool itself, while the different load operating conditions correspond to the vibration differences when processing parameters such as wood hardness and feed speed change. By fusing data from multiple operating conditions, the random influence of a single operating condition can be reduced.

[0063] During the measurement process, a contact vibration sensor is used to scan the shell surface. The scanning step distance is based on the minimum characteristic dimension of the mechanical structure to ensure that subtle vibration changes on the shell surface can be captured, avoiding the omission of critical vibration areas due to excessively large sampling intervals. The vibration acceleration signal collected by the sensor needs to be processed by time-domain integration to convert it into a vibration velocity signal. This is because vibration velocity is more directly related to vibration energy and can more accurately reflect the vibration intensity on the shell surface.

[0064] Based on the vibration velocity signals at each measurement point, the root mean square value of the vibration velocity is calculated, and a spatial distribution map is generated. The map is stored in the form of a two-dimensional coordinate matrix, which intuitively presents the distribution pattern of vibration energy on the shell surface. Subsequently, a region growing algorithm from image processing is introduced to identify continuous high-energy regions from the map. The algorithm uses points whose vibration energy exceeds the average energy level of eight neighboring points by a set multiple as seeds, and gradually expands to form continuous regions, ensuring that the identified regions have spatial coherence and eliminating isolated high-energy noise points.

[0065] For the identified high-energy regions, calculate the perpendicular distance between their geometric center point and the tool rotation axis, and select the three regions with the smallest distance as candidate regions for the main sound radiation zone. This is because the vibration energy generated by the tool rotation is usually transmitted along the structure at short distances, and the closer the region is to the rotation axis, the more likely it is to be the main region for direct vibration radiation.

[0066] Finally, the spectral characteristics of the vibration acceleration signals in the candidate regions were extracted, and the correlation coefficient between their energy distribution and the fundamental frequency of the spindle speed was calculated. The vibration energy in the main acoustic radiation region should be closely related to the tool rotation frequency. The candidate region with the largest correlation coefficient is most significantly affected by the direct influence of tool rotation, and therefore was finally identified as the main acoustic radiation region. Simultaneously, the boundary coordinate information of this region was recorded to provide a precise positioning basis for the probe placement in subsequent acoustic intensity measurements.

[0067] In step S3, the process of performing signal resampling processing based on the rotational angular domain on the dynamic pressure signal sequence is as follows:

[0068] First, two key signals from the spindle rotary encoder output need to be acquired: the pulse signal per revolution and the zero-phase reference signal. The pulse signal per revolution marks the moment when the spindle completes one revolution, while the zero-phase reference signal serves as the starting point for angle measurement, establishing a complete angular coordinate system with this as the zero point. Based on this coordinate system, the original time-amplitude sequence of the dynamic pneumatic signal is converted into an angle-amplitude sequence, with resampling at equal angular intervals during the conversion. Angular intervals are chosen instead of time intervals because the tool rotation speed may fluctuate slightly, and the rotation angle within the same time period may differ. Pneumatic pulses have a stronger correlation with the tool rotation angle, and angular sampling more stably reflects the periodic characteristics of the tooth sweeping action.

[0069] Next, the complete rotation cycle is divided into angular intervals based on the actual number of teeth installed on the tool. For example, if the tool has 5 teeth, the 360-degree rotation cycle is divided into 5 equal angular intervals, each corresponding to the sweep range of a single tooth. A phase acquisition window of fixed width is set within each angular interval, with the window width proportional to the sweep angle of a single tooth. This ensures that the window can completely cover the critical phase range of pneumatic pulsations generated during the sweep of a single tooth, avoiding the omission of valid signals.

[0070] Subsequently, dynamic pressure data segments within the same phase acquisition window are extracted from multiple consecutive rotation cycles. Since the spindle speed may fluctuate, the length of the data segments within the same window on the time axis may differ across cycles. Therefore, time axis normalization is performed on these data segments to uniformly map them to the same time length, eliminating the influence of speed fluctuations on waveform morphology. Then, a curve registration algorithm is used to align waveform feature points of the data segments across multiple cycles, such as the positions of peaks and troughs, ensuring that the waveforms across different cycles maintain consistency in characteristics. After alignment, the arithmetic mean waveform of these data segments is calculated and used as the reference gastrointestinal dynamic component waveform template for that phase window.

[0071] Finally, a cubic spline interpolation algorithm is applied to connect the template waveforms of each phase window, reconstructing a continuous waveform of the pneumatic pulsation component within the complete rotation cycle. The interpolation process fills the waveform gaps between different windows, resulting in a smooth and continuous angular-domain pneumatic pulsation signal, providing accurate reference input for subsequent establishment of an airflow-induced sound pressure model.

[0072] In step S4, the process of establishing the mathematical expression model for the airflow-induced sound pressure component is as follows:

[0073] Establish a three-dimensional spatial coordinate transformation relationship between the position of the dynamic barometric pressure sensor and the position of the acoustic intensity probe, and define a position-dependent acoustic transfer function expression, which includes the acoustic propagation path length attenuation factor and the air medium absorption coefficient.

[0074] Discrete convolution operation is performed between the reference air pulsation component waveform and the sound wave transfer function expression, and an airflow velocity field correction model is introduced. The airflow velocity field correction model describes the turbulence interference effect on the sound wave propagation path based on computational fluid dynamics theory.

[0075] The airflow velocity field correction model is applied to the convolution operation result for compensation calculation, and the time-domain waveform estimation sequence of the airflow-induced sound pressure component is output. The coherence function value of the estimated sequence and the measured sound pressure signal in the aerodynamic characteristic frequency band is verified. When the coherence function value is lower than the set threshold, the sound wave transfer function expression parameters are recalibrated.

[0076] In S5, the process of establishing a mathematical expression model for the structural radiated sound pressure components based on the physical equations of sound wave propagation is as follows:

[0077] The first step is to establish a three-dimensional spatial coordinate transformation relationship between the dynamic pressure sensor and the sound intensity probe. The dynamic pressure sensor is installed in the near field of the rotating tool to collect raw pneumatic pulse signals, while the sound intensity probe is positioned on the surface of the main sound radiation area; the two have a spatial difference. Through coordinate transformation, the relative distance and orientation of the two positions in three-dimensional space can be determined, providing a basis for subsequent calculation of the sound wave propagation path.

[0078] Based on the coordinate transformation results, a position-dependent sound wave transfer function expression is defined. This function needs to include two key parameters: first, the sound wave propagation path length attenuation factor, as sound wave energy decreases with increasing propagation distance in air; the farther the distance, the smaller the sound pressure amplitude; second, the air medium absorption coefficient, as air absorption of sound waves is frequency-dependent, with higher frequency sound waves experiencing more significant absorption losses, a characteristic that needs to be reflected in the function. The core function of the transfer function is to describe the amplitude and phase changes of the pneumatic pulse signal as it propagates from the barometer location to the sound intensity probe location.

[0079] Next, the baseline pneumatic motion component waveform obtained in S3 is discretized and convolved with the acoustic transfer function expression. Convolution simulates the process by which the pneumatic motion signal is modified by the transfer function during propagation, yielding a preliminary estimate of the airflow-induced sound pressure component. However, in actual propagation, the tool rotation causes the surrounding air to form a complex airflow velocity field. Turbulence in the airflow interferes with sound wave propagation, causing a shift in the propagation path and phase. Therefore, an airflow velocity field correction model needs to be introduced. This model is based on computational fluid dynamics theory. It numerically simulates the airflow field distribution caused by tool rotation, quantifies the interference effects of turbulence on sound wave propagation such as refraction and scattering, and then applies compensation calculations to the convolution result to correct the deviation caused by turbulence.

[0080] After compensation calculations, the time-domain waveform estimation sequence of the airflow-induced sound pressure component is output. To verify the accuracy of the model, the coherence function values ​​of this estimation sequence and the measured sound pressure signal within the aerodynamic characteristic frequency band need to be calculated. The aerodynamic characteristic frequency band refers to the frequency range dominated by airflow disturbances. The higher the coherence function value, the stronger the correlation between the estimated sequence and the measured signal, and the more accurate the model. If the coherence function value is lower than a set threshold, it indicates that the parameters of the sound wave transfer function (such as attenuation factor and absorption coefficient) may be deviated. These parameters need to be recalibrated until the coherence function value meets the requirements, ensuring that the model can accurately reflect the actual characteristics of airflow-induced sound pressure.

[0081] In step S6, the process of obtaining the net surface acoustic intensity value of the main acoustic radiation region through parameter optimization is as follows:

[0082] The measured sound pressure signal sequence is represented as a linear superposition model of the estimated sequence of airflow-induced sound pressure component and the estimated sequence of structure-radiated sound pressure component. This is because the measured sound pressure signal of the woodworking machine tool is essentially the result of the combined effect of two types of noise, and the linear superposition model can intuitively reflect the combined relationship between the two, providing a basic framework for subsequent separation. Based on this, an objective function for optimizing the superposition model parameters is constructed, which is defined as the integral value of the mean square error between the measured sound pressure signal sequence and the model estimation sequence. The mean square error integral is chosen as the objective because it can comprehensively measure the deviation between the model estimation and the actual measurement in the entire signal sequence, ensuring that the optimization result fits the measured data in the entire time domain.

[0083] Next, parameter initialization and iterative optimization are performed. The parameters requiring initialization include the acoustic transfer function coefficient vector and the surface acoustic impedance parameter matrix. The former affects the calculation accuracy of the airflow-induced sound pressure component, while the latter directly relates to the model accuracy of the structure-radiated sound pressure component. After initialization, the conjugate gradient descent algorithm is used to iteratively optimize these parameters. This algorithm gradually reduces the objective function value by continuously adjusting the coefficient vector and parameter matrix, making the model estimation sequence closer to the measured sound pressure signal. During optimization, the rate of decrease of the objective function value needs to be continuously monitored. When the rate of decrease falls below the convergence threshold for several consecutive iterations, it indicates that parameter adjustments no longer significantly improve the objective function, and the model is close to its optimal state. At this point, the optimization process is terminated to avoid unnecessary iterations and wasted computational resources.

[0084] After optimization, the estimated sequence of structural radiated sound pressure components is extracted from the final iterative results. Since net surface acoustic intensity primarily reflects the noise energy radiated by structural vibration, this component requires special attention. By calculating the cross-correlation function between the estimated sequence of structural radiated sound pressure components and the normal vibration velocity sequence, the temporal correlation between the two can be measured. A higher cross-correlation function value indicates a stronger synchronicity between sound pressure changes and vibration velocity changes, better reflecting the true characteristics of structural vibration-radiated noise. Numerical integration within the main lobe interval of the cross-correlation function yields the instantaneous acoustic energy flux density, which directly reflects the rate of sound energy transfer per unit area at a given moment. Finally, a moving average of the instantaneous acoustic energy flux density is applied over time to smooth out interference from instantaneous fluctuations, ultimately outputting a stable sequence of net surface acoustic intensity values, providing accurate foundational data for subsequent total sound power level calculations.

[0085] The convergence determination in the parameter optimization solution process is specifically as follows:

[0086] Record the change in the objective function value for each iteration of the gradient descent algorithm, i.e., the difference between the objective function values ​​in two adjacent iterations. Calculate the average change in the objective function value across multiple consecutive iterations. When this average value falls below a first judgment threshold, it indicates that the descent of the objective function value has entered a plateau phase, and parameter adjustments have limited effect on improving model accuracy. At this point, initiate a convergence confirmation procedure to further verify whether the objective function value is stable within a small range, confirming whether the optimization has truly converged and avoiding premature termination due to accidental fluctuations.

[0087] If the convergence condition is not met even after exceeding the maximum allowed number of iterations, it indicates that the current parameter search space may be too narrow and fails to cover the optimal parameter combination. In this case, the parameter search space needs to be expanded, and the optimization algorithm should be reinitialized within a wider range to explore potential optimal solutions with greater flexibility. While expanding the search space, the algorithm will adjust parameters such as the step size to balance search efficiency and accuracy, reducing the probability of missing the optimal solution.

[0088] To address persistent convergence failures, a database mapping historical optimized parameter combinations to their corresponding objective function values ​​is required. This database stores parameter combinations and their corresponding objective function values ​​under different past operating conditions. When convergence failures occur repeatedly, the system automatically searches the database and selects the parameter combination with the smallest objective function value. While this combination may not be the theoretically optimal solution under the current operating conditions, it performs best in historical data and can be output as a temporary feasible solution. Simultaneously, the system generates an algorithm anomaly report, detailing the operating conditions and parameter characteristics of the convergence failures. Based on the report, the system automatically adjusts the gain parameters of the signal acquisition system, such as amplifying weak signals or reducing the risk of saturation in strong signals. The measurement process is then restarted to improve signal quality from the data source, providing a more reliable input basis for the next optimization.

[0089] This multi-layered convergence determination mechanism ensures efficient convergence of the optimization process under normal operating conditions, while maintaining the availability of results through adaptive adjustment under abnormal conditions, providing stable algorithmic support for the accurate calculation of net surface acoustic intensity values.

[0090] In step S7, the specific process for calculating the equivalent radiated sound intensity value using the acoustic radiation efficiency theoretical model is as follows:

[0091] Vibration acceleration signals from the surface of the infrasound radiation zone were collected. Multiple measurement points were evenly distributed on the surface of the infrasound radiation zone to ensure a comprehensive reflection of the vibration distribution characteristics of the area. A windowed Fourier transform was performed on the vibration acceleration signal sequence collected at each measurement point. Windowing reduces spectral leakage caused by signal truncation, making the obtained vibration acceleration spectrum closer to the true frequency component distribution. Subsequently, a frequency domain numerical integration algorithm was applied to transform the vibration acceleration spectrum into a vibration velocity spectrum. This is because the correlation between sound radiation intensity and structural vibration velocity is stronger, and the velocity spectrum can more directly reflect the potential for vibration energy to be converted into sound energy.

[0092] Next, the effective frequency band of acoustic radiation is determined. This range needs to cover the radiation frequency band of the main noise during machine tool operation, typically including the spindle rotation fundamental frequency, tool tooth frequency, and their harmonics, ensuring that no frequency components that significantly contribute to the total noise are omitted. Within the effective frequency band, the energy integral value of the vibration velocity spectrum is calculated. This integral value comprehensively reflects the total vibration energy in the key frequency band of the infrasound radiation region. Simultaneously, a radiation efficiency coefficient calculation model is established based on the flat plate acoustic radiation theory. The flat plate theory is applicable to the acoustic radiation analysis of approximately planar structures such as the infrasound radiation region (e.g., bearing housing mounting surface), and can quantify the efficiency of vibration energy conversion into sound energy. A higher radiation efficiency coefficient indicates stronger radiated sound energy for the same vibration energy.

[0093] To improve calculation accuracy, the surface of the infrasound radiation zone is divided into several sub-regions. Since the material properties and vibration modes of different parts of the surface may vary, sub-region division can more accurately reflect changes in local radiation efficiency. After calculating the radiation efficiency coefficient of each sub-region, the arithmetic mean of all sub-region coefficients is taken as the overall radiation efficiency coefficient, balancing the impact of local differences on the overall result. Finally, the vibration energy integral value, the overall radiation efficiency coefficient, and the air characteristic impedance parameter are substituted into the plane wave sound intensity calculation formula to obtain the equivalent radiated sound intensity value of the infrasound radiation zone. This value comprehensively reflects the noise intensity radiated through structural vibration in the infrasound radiation zone.

[0094] In step S7, the process of performing spatial integration calculations on the net surface sound intensity value of the main sound radiation region and the equivalent radiated sound intensity value of the secondary sound radiation region to output the total sound power level is as follows:

[0095] A three-dimensional geometric mesh model of the surface of the main sound radiation zone is constructed. The mesh element size is set to be smaller than the shortest wavelength involved in the measurement to ensure that the mesh can capture the spatial variation details of high-frequency sound intensity. Since high-frequency sound waves have short wavelengths, an excessively large mesh may miss local sound intensity peaks. The coordinates of the sound intensity probe's measurement points are accurately mapped to the node positions of the mesh model. For areas where no probes are deployed, a thin-plate spline interpolation algorithm is used to calculate the sound intensity distribution function. Thin-plate spline interpolation can generate a smooth and continuous spatial distribution surface based on the sound intensity data of known points, maintaining the accuracy of the original measurement data while reasonably inferring the sound intensity characteristics of unmeasured areas, avoiding the loss of spatial information due to insufficient sampling points.

[0096] For the infrasound radiation region, an equal-area grid division strategy is adopted. Equal-area division ensures that the area of ​​each grid cell is basically the same, guaranteeing a unified benchmark for calculating the sound power contribution of each cell and reducing errors caused by uneven cell size. The normal sound intensity component at the center point of each grid cell is calculated. The normal component directly reflects the direction of sound energy propagation perpendicular to the surface and is a key parameter for calculating sound power. Based on the grid cell area and the normal sound intensity component, the sound power contribution value of each cell can be obtained; the larger the cell area and the higher the normal sound intensity, the greater its contribution to the total sound power.

[0097] After meshing the two regions, algebraic summation is performed on the sound power contribution values ​​of all grid cells in both the primary and secondary sound radiation zones to obtain the total sound power contribution for each zone. The summation of the two regions yields the total sound power value of the woodworking machine tool, reflecting the total noise energy radiated by the machine tool through the primary and secondary sound radiation zones. Finally, the total sound power value is converted to a decibel-scaled value with reference to a baseline sound power. The decibel scale is a commonly used logarithmic scale in acoustic measurements, better reflecting the human ear's perception of sound intensity and facilitating comparison with noise limit standards. The final output decibel value is the total noise sound power level of the machine tool. This spatial integration process transforms dispersed local sound intensity measurements into a comprehensive indicator reflecting overall noise emissions, providing a complete and accurate quantitative basis for machine tool noise assessment and control.

[0098] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for measuring the sound power level of woodworking machine tool noise based on the sound intensity method, characterized in that, Includes the following steps: S1. When the woodworking machine tool is in operation, the area of ​​the housing surface covered by the projection of the tool rotation plane is taken as the main sound radiation area, and the area of ​​the main bearing seat mounting surface is taken as the secondary sound radiation area. S2. Collect the sound pressure signal sequence and three-dimensional vibration acceleration signal sequence of the surface of the main sound radiation area, as well as the dynamic air pressure signal sequence of the near field area of ​​the tool rotation. S3. Perform signal resampling processing based on the rotational angular domain on the dynamic air pressure signal sequence to extract the reference air pulse dynamic component waveform synchronized with the periodic sweeping action of the tool teeth; S4. Based on the phase correspondence between the reference air pulse dynamic component waveform and the sound pressure signal sequence, establish a mathematical expression model for the airflow-induced sound pressure component. S5. Calculate the surface normal vibration velocity sequence based on the three-dimensional vibration acceleration signal sequence, and establish a mathematical expression model of the structural radiated sound pressure component in combination with the physical equation of sound wave propagation. S6. Decompose the sound pressure signal sequence into estimated values ​​of airflow-induced sound pressure components and estimated values ​​of structure-radiated sound pressure components, and obtain the net surface sound intensity value of the main sound radiation region through the parameter optimization solution process. S7. Extract the frequency band energy features from the vibration acceleration signal sequence in the infrasound radiation region, and calculate the equivalent radiated sound intensity value by combining the sound radiation efficiency theoretical model. Spatial integration is performed on the net surface acoustic intensity value of the main sound radiation zone and the equivalent radiated sound intensity value of the secondary sound radiation zone to output the total sound power level. In step S4, the process of establishing the mathematical expression model for the airflow-induced sound pressure component is as follows: Establish a three-dimensional spatial coordinate transformation relationship between the position of the dynamic barometric pressure sensor and the position of the acoustic intensity probe, and define a position-dependent acoustic transfer function expression, which includes the acoustic propagation path length attenuation factor and the air medium absorption coefficient. Discrete convolution operation is performed between the reference air pulsation component waveform and the sound wave transfer function expression, and an airflow velocity field correction model is introduced. The airflow velocity field correction model describes the turbulence interference effect on the sound wave propagation path based on computational fluid dynamics theory. The airflow velocity field correction model is applied to the convolution operation result to compensate for the operation, and the time-domain waveform estimation sequence of the airflow-induced sound pressure component is output. The coherence function value of the estimated sequence and the measured sound pressure signal in the aerodynamic characteristic frequency band is verified. When the coherence function value is lower than the set threshold, the sound wave transfer function expression parameters are recalibrated. In S5, the process of establishing a mathematical expression model for the structural radiated sound pressure components based on the physical equations of sound wave propagation is as follows: Digital filtering is applied to the three-dimensional vibration acceleration signal sequence to eliminate high-frequency noise; a time-domain numerical integration algorithm is applied to transform the vibration acceleration signal sequence into a normal vibration velocity signal sequence; a differential relationship expression between surface vibration velocity and radiated sound pressure is established based on the acoustic Helmholtz equation; and the surface acoustic impedance parameter matrix in the differential relationship expression is solved using the boundary element discretization method. The normal vibration velocity signal sequence is multiplied with the surface acoustic impedance parameter matrix to output the theoretical calculation sequence of the structure's radiated sound pressure component. The amplitude difference between the theoretical calculation sequence and the measured sound pressure signal at the structure's resonant frequency is compared. When the amplitude difference exceeds the allowable range, the boundary element mesh density is re-divided.

2. The method for measuring the sound power level of woodworking machine tool noise based on the sound intensity method according to claim 1, characterized in that, In step S1, the process of using the area of ​​the shell surface covered by the projection of the tool rotation plane as the main acoustic radiation area is as follows: Under no-load operation and at least three typical load operation conditions of woodworking machine tools, a contact vibration sensor is used to scan and measure the surface of the housing by step distance based on the minimum characteristic dimension of the mechanical structure. The vibration acceleration signal collected at each measurement point is converted into a vibration velocity signal by time-domain integration. The spatial distribution map of the root mean square value of vibration velocity at each measurement point is calculated and stored in the form of a two-dimensional coordinate matrix. The region growing algorithm in image processing is used to identify continuous high-energy regions in the spectrum whose vibration energy exceeds a set multiple of the average energy level of the eight surrounding points. The vertical distance between the geometric center point of the identified high-energy region and the tool rotation axis is calculated, and the three high-energy regions with the smallest vertical distance are selected as candidate regions for the main sound radiation area. The spectral characteristics of the vibration acceleration signal in the candidate region are extracted, and the correlation coefficient between its energy distribution and the fundamental frequency of the spindle rotation speed is calculated. The candidate region with the largest correlation coefficient is finally confirmed as the main acoustic radiation region, and the boundary coordinate information of the main acoustic radiation region is recorded.

3. The method for measuring the sound power level of woodworking machine tool noise based on the sound intensity method according to claim 1, characterized in that, In step S3, the process of performing signal resampling processing based on the rotational angular domain on the dynamic pressure signal sequence is as follows: The pulse signal per revolution and the zero-phase reference signal output by the spindle rotary encoder are acquired. The time-amplitude sequence of the dynamic air pressure signal is resampled at equal angular intervals, with the zero-phase reference signal as the angular zero point. The complete rotation cycle is divided into a corresponding number of angle intervals based on the actual number of teeth installed on the tool. A phase acquisition window of fixed width is set in each angle interval, and the window width is proportional to the sweep angle of a single tooth of the tool. Dynamic air pressure data segments within the same phase window in a continuous rotation cycle are extracted. Time axis normalization is performed on the data segments to eliminate the influence of rotation speed fluctuations. A curve registration algorithm is used to align the waveform feature points of the data segments in multiple cycles. The arithmetic mean waveform of the aligned data segments is calculated as the reference air pressure dynamic component waveform template. A cubic spline interpolation algorithm is applied to reconstruct the continuous waveform of the air pressure dynamic component in the complete rotation cycle.

4. The method for measuring the sound power level of woodworking machine tool noise based on the sound intensity method according to claim 1, characterized in that, In step S6, the process of obtaining the net surface acoustic intensity value of the main acoustic radiation region through parameter optimization is as follows: The measured sound pressure signal sequence is represented as a linear superposition model of the estimated sequence of airflow-induced sound pressure component and the estimated sequence of structure-radiated sound pressure component; an objective function for optimizing the parameters of the superposition model is constructed, which is the integral value of the mean square error between the measured sound pressure signal sequence and the model estimation sequence; Initialize the acoustic transfer function coefficient vector and the surface acoustic impedance parameter matrix, and iteratively optimize the coefficient vector and parameter matrix using the conjugate gradient descent algorithm; Monitor the rate of decrease of the objective function value during the iteration process, and terminate the optimization process when the rate of decrease is lower than the convergence threshold for multiple consecutive iterations. Extract the estimated sequence of structural radiated sound pressure components from the final iteration result, calculate the cross-correlation function between the estimated sequence of structural radiated sound pressure components and the normal vibration velocity sequence, perform numerical integration within the main lobe interval of the cross-correlation function to obtain the instantaneous acoustic energy flux density, and output the net surface acoustic intensity value sequence by moving average processing of the instantaneous acoustic energy flux density on the time axis.

5. The method for measuring the sound power level of woodworking machine tool noise based on the sound intensity method according to claim 4, characterized in that, The convergence determination in the parameter optimization solution process is specifically as follows: Record the change in the objective function value of each iteration of the gradient descent algorithm, calculate the average change in the objective function value of multiple consecutive iterations, and start the convergence confirmation procedure when the average change is lower than the first judgment threshold. If the convergence condition is not met even after the number of iterations exceeds the maximum allowed number of iterations, the parameter search space is expanded, and the optimization algorithm is reinitialized within the expanded parameter search space. Establish a database of historical optimized parameter combinations and their corresponding objective function values. When convergence fails multiple times in a row, retrieve the parameter combination with the smallest objective function value from the database, output this parameter combination as the final solution, and generate an algorithm anomaly report. Based on the anomaly report, automatically adjust the gain parameters of the signal acquisition system and restart the measurement process.

6. The method for measuring the sound power level of woodworking machine tool noise based on the sound intensity method according to claim 1, characterized in that, In step S7, the specific process for calculating the equivalent radiated sound intensity value using the acoustic radiation efficiency theoretical model is as follows: Vibration acceleration signal sequences from multiple measurement points uniformly distributed on the surface of the infrasound radiation zone are collected. A windowed Fourier transform is performed on the signal sequence of each measurement point to obtain the vibration acceleration spectrum. The frequency domain numerical integration algorithm is then applied to convert the vibration acceleration spectrum into the vibration velocity spectrum. Determine the effective frequency band of sound radiation, covering the main noise radiation frequency band of the machine tool, calculate the energy integral value of the vibration velocity spectrum within the effective frequency band of sound radiation, and establish a calculation model for the radiation efficiency coefficient based on the theory of flat plate sound radiation. The surface of the infrasound radiation zone is divided into several sub-regions, and the radiation efficiency coefficient of each sub-region is calculated. The arithmetic mean of the radiation efficiency coefficients of all sub-regions is taken as the overall radiation efficiency coefficient. The vibration energy integral value, the overall radiation efficiency coefficient, and the air characteristic impedance parameter are substituted into the plane wave sound intensity calculation formula to output the equivalent radiated sound intensity value of the infrasound radiation zone.

7. The method for measuring the sound power level of woodworking machine tool noise based on the sound intensity method according to claim 1, characterized in that, In step S7, the process of performing spatial integration calculations on the net surface sound intensity value of the main sound radiation region and the equivalent radiated sound intensity value of the secondary sound radiation region to output the total sound power level is as follows: A three-dimensional geometric mesh model of the surface of the main sound radiation zone is constructed, with the mesh element size smaller than the shortest measurement wavelength. The coordinates of the sound intensity probe measurement points are mapped to the node positions of the mesh model. The thin plate spline interpolation algorithm is used to calculate the sound intensity distribution function in the area where no probe is placed. An equal-area grid division strategy is adopted for the surface of the infrasound radiation zone. The normal sound intensity component at the center point of each grid unit is calculated. The unit sound power contribution value is calculated based on the grid unit area and the normal sound intensity component. An algebraic summation operation is performed on the sound power contribution values ​​of all grid cells in the main sound radiation zone, and on the sound power contribution values ​​of all grid cells in the secondary sound radiation zone. The summation of the sound power values ​​of the two zones is used to obtain the total sound power value, which is then converted into a decibel scale value with reference to the reference sound power.

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