Milling acoustic sensing enhancement method and system based on thin film covering gradient acoustic metamaterial, terminal and medium
By covering the surface of a gradient acoustic metamaterial with a thin film and performing a nonlinear design, combined with a microelectromechanical microphone sensor, the problem of poor acoustic signal acquisition quality was solved, and high signal-to-noise ratio and characteristic signal enhancement were achieved in tool condition monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENZHEN POLYTECHNIC
- Filing Date
- 2026-01-14
- Publication Date
- 2026-04-21
AI Technical Summary
In existing technologies, gradient acoustic metamaterials suffer from poor acoustic signal acquisition quality in tool condition monitoring, mainly due to limitations in surface roughness and frequency selectivity.
By employing thin-film coated gradient acoustic metamaterials and through innovative nonlinear gradient design and surface engineering, surface roughness is reduced and frequency matching is achieved. A microelectromechanical microphone sensor is integrated to enhance and acquire acoustic signals in real time.
Significantly improves the signal-to-noise ratio and enhances the characteristic signal, achieving high-quality acquisition of acoustic signals. The signal-to-noise ratio is improved by 6.54dB, the amplitude of the characteristic signal is enhanced by 35 times, and the frequency selectivity is enhanced by 11-12 times, avoiding information loss caused by post-processing.
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Figure CN121898596A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of tool condition monitoring technology, and in particular to a method, system, terminal and medium for enhancing acoustic sensing in milling based on thin-film covered gradient acoustic metamaterials. Background Technology
[0002] In tool condition monitoring systems in the mechanical manufacturing field, acoustic sensor-based monitoring technology is widely used due to its advantages such as low implementation cost, high flexibility, and low system interference. The acoustic signals generated during the cutting process contain rich state information, such as characteristic signals like periodic impacts, fractional harmonics, and higher-order harmonics. Effective detection of these signals is crucial for fault analysis and diagnosis. Traditional tool condition monitoring mainly employs the following sensing technologies: force sensors: high accuracy but expensive and requiring invasive installation; vibration sensors: a balance between cost, accuracy, and reliability; acoustic emission sensors: highly non-invasive but limited by cost and flexibility; low cost, high flexibility, and minimal system interference, but insufficient sensitivity in high-noise environments. Existing acoustic signal processing techniques include wavelet analysis, empirical mode decomposition (EMD), short-time Fourier transform (STFT), and stochastic resonance (SR), but these post-processing techniques have significant limitations. Gradient acoustic metamaterials (GAMs), as part of the existing technology, exhibit excellent acoustic wave manipulation capabilities, such as acoustic wave compression and pressure amplification effects, but suffer from serious defects in practical applications, resulting in poor acoustic signal acquisition quality.
[0003] Therefore, existing technologies still need to be improved and developed. Summary of the Invention
[0004] The main purpose of this application is to provide a method, system, terminal and medium for enhancing acoustic sensing in milling based on thin-film covered gradient acoustic metamaterials, aiming to solve the problem of poor acoustic signal acquisition quality in the prior art when monitoring the tool status during the cutting process due to the limitations of metamaterial surface roughness and frequency selectivity.
[0005] The first aspect of this application provides a method for enhancing acoustic sensing in milling processes based on thin-film-coated gradient acoustic metamaterials. The method includes the following steps: Acquire frequency position information of thin-film-covered gradient acoustic metamaterials; Obtain the machining parameters for milling, and obtain the characteristic frequency information of tool wear during the milling process based on the machining parameters; The target location of the metamaterial is determined based on the frequency location information and the characteristic frequency information. Acoustic enhancement information of metamaterial milling is acquired at the target location.
[0006] Optionally, in one embodiment of this application, the frequency position information is gap-frequency correspondence data; The acquisition of frequency position information of the thin-film covered gradient acoustic metamaterial specifically includes: Determine the structural parameters of the thin-film-coated gradient acoustic metamaterial, wherein the gaps between the structural parameters satisfy a nonlinear functional relationship; Simulations and experiments were conducted on the thin-film-covered gradient acoustic metamaterial with the aforementioned structural parameters to obtain data on the relationship between gap and frequency.
[0007] Optionally, in one embodiment of this application, the step of simulating and experimenting on the thin-film-covered gradient acoustic metamaterial of the structural parameters to obtain the data on the relationship between gap and frequency specifically includes: A three-dimensional model of the thin film-covered gradient acoustic metamaterial is constructed according to the structural parameters. A broadband sweep frequency acoustic wave is applied to the three-dimensional model to obtain the first gap frequency data of the three-dimensional model. Construct a solid model of the thin film-covered gradient acoustic metamaterial according to the structural parameters, apply the broadband swept frequency acoustic wave to the solid model, and obtain the second gap frequency data of the solid model; The gap-frequency correspondence data of the thin film-covered gradient acoustic metamaterial is determined based on the first gap frequency data and the second gap frequency data.
[0008] Optionally, in one embodiment of this application, the machining parameters include spindle speed and number of tool teeth, and the characteristic frequency information includes sideband frequency; The step of obtaining the characteristic frequency information of tool wear during milling based on the machining parameters specifically includes: Based on the spindle speed and the number of teeth of the tool, the spindle rotation frequency and cutting frequency are obtained; Based on the spindle rotation frequency and the cutting frequency, the edge band frequency of tool wear during milling is obtained.
[0009] Optionally, in one embodiment of this application, obtaining the edge band frequency of tool wear during milling based on the spindle rotation frequency and the cutting frequency specifically includes: The higher harmonics are obtained based on the cutting frequency, and the carrier frequency is determined based on the higher harmonics. The sideband frequency of tool wear during milling is calculated based on the carrier frequency and the spindle rotation frequency.
[0010] Optionally, in one embodiment of this application, the gap-frequency correspondence data includes each gap and the center frequency corresponding to each gap, and the target position is the target gap; Determining the target location of the metamaterial based on the frequency location information and the characteristic frequency information specifically includes: Calculate the difference between the sideband frequency and the center frequency corresponding to each gap; The smallest difference among all the stated differences is taken as the target difference. The gap corresponding to the target difference is taken as the target gap.
[0011] Optionally, in one embodiment of this application, the acoustic enhancement information includes an acoustic signal waveform diagram; The acquisition of acoustic enhancement information from metamaterial milling at the target location specifically includes: The microphone sensor is installed in the target gap of the metamaterial; During the milling process, the microphone sensor collects the acoustic piezoelectric signal; The acoustic piezoelectric signal is digitized to obtain the acoustic signal waveform of metamaterial milling.
[0012] A second aspect of this application also provides a milling acoustic sensing enhancement system based on a thin-film-coated gradient acoustic metamaterial, wherein the milling acoustic sensing enhancement system based on a thin-film-coated gradient acoustic metamaterial is applied to the milling acoustic sensing enhancement method based on a thin-film-coated gradient acoustic metamaterial as described in any of the above-described solutions; the milling acoustic sensing enhancement system based on a thin-film-coated gradient acoustic metamaterial includes: The characteristic frequency pre-calculation module is used to obtain the frequency position information of the thin film-covered gradient acoustic metamaterial; The machining feature frequency extraction module is used to obtain the machining parameters of milling and to obtain the feature frequency information of tool wear during milling based on the machining parameters. The optimal gap selection module is used to determine the target position of the metamaterial based on the frequency position information and the characteristic frequency information. The directional enhancement and signal acquisition module is used to acquire acoustic enhancement information of metamaterial milling according to the target location.
[0013] A third aspect of this application also provides a terminal, wherein the terminal includes: a memory, a processor, and a milling acoustic sensing enhancement program based on a thin-film-coated gradient acoustic metamaterial stored in the memory and executable on the processor. When the milling acoustic sensing enhancement program based on a thin-film-coated gradient acoustic metamaterial is executed by the processor, it implements the steps of the milling acoustic sensing enhancement method based on a thin-film-coated gradient acoustic metamaterial as described above.
[0014] A fourth aspect of this application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a milling acoustic sensing enhancement program based on a thin-film-coated gradient acoustic metamaterial, and when the milling acoustic sensing enhancement program based on a thin-film-coated gradient acoustic metamaterial is executed by a processor, it implements the steps of the milling acoustic sensing enhancement method based on a thin-film-coated gradient acoustic metamaterial as described above.
[0015] Beneficial Effects: This application provides a method, system, terminal, and medium for enhancing acoustic sensing in milling based on thin-film covered gradient acoustic metamaterials. This application calibrates the bandpass amplification center frequency of each gap unit of the FGAM through simulation and experiments, completing the spectral characteristic calibration. Based on process parameters such as spindle speed before machining, the main cutting characteristic frequency is calculated. The cutting characteristic frequency is matched with the center frequency of each gap in the FGAM, and the gap with the best match is selected as the signal acquisition channel. A MEMS microphone is integrated into the selected gap, and the real-time enhanced acquisition of the target signal is achieved by utilizing the physical amplification effect of the metamaterial. Through surface engineering innovation, nonlinear gradient design, and front-end physical enhancement, this application achieves improved signal-to-noise ratio, enhanced characteristic signals, and optimized real-time performance in tool wear condition monitoring, thereby ensuring the quality of acoustic signal acquisition. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a schematic diagram of the thermal viscosity effect between gradient acoustic metamaterials. Figure 2 This is a flowchart of a preferred embodiment of the method for enhancing acoustic sensing during milling based on thin-film coated gradient acoustic metamaterials according to this application; Figure 3 This is a schematic diagram of the thin-film-covered gradient acoustic metamaterial in a preferred embodiment of the milling acoustic sensing enhancement method based on thin-film-covered gradient acoustic metamaterials in this application. Figure 4 This is a schematic diagram of the specific implementation steps of the entire execution process in a preferred embodiment of the method for enhancing acoustic sensing during milling based on thin-film covered gradient acoustic metamaterials in this application. Figure 5 This is a schematic diagram showing the comparison results of different methods for detecting cutting acoustic signals in a preferred embodiment of the method for enhancing acoustic sensing in milling based on thin-film covered gradient acoustic metamaterials in this application; Figure 6This is a structural diagram of a preferred embodiment of the acoustic sensing enhancement system for milling based on thin-film coated gradient acoustic metamaterials according to this application; Figure 7 This is a structural diagram of a preferred embodiment of the terminal of this application.
[0018] Explanation of reference numerals in the attached figures: 100. Feature frequency pre-calculation module; 200. Processing feature frequency extraction module; 300. Optimal gap selection module; 400. Directional enhancement and signal acquisition module. Detailed Implementation
[0019] To make the objectives, technical solutions, and effects of this application clearer and more explicit, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are only possible technical implementations of this application and not all possible implementations. Based on the embodiments in this application, those skilled in the art can obtain other embodiments without creative effort, and these embodiments are also within the protection scope of this application.
[0020] First, let's introduce the terms used in the embodiments of this application: Fgam, film-covered gradient acoustic metamaterial. Gams, gradient acoustic metamaterials. PMMA, polymethyl methacrylate (acrylic / plexiglass); MEMS, micro-electro-mechanical microphone sensor. pla, polylactic acid (PLA, a commonly used material in 3D printing). SNR stands for signal-to-noise ratio. am, amplitude modulation. EMD stands for Empirical Mode Decomposition. STFT, Short-time Fourier Transform. sr, stochastic resonance.
[0021] Among related technologies, gradient acoustic metamaterials (GAMs), as part of the existing technology, exhibit excellent sound wave manipulation capabilities, such as sound wave compression and pressure amplification effects, but they have serious drawbacks in practical applications: Firstly, see... Figure 1 First, there are several limitations: First, significant thermal viscous dissipation occurs in the subwavelength channel, resulting in experimental pressure gain being far lower than theoretical values. Second, surface roughness affects the material; the roughness of the 3D-printed metamaterials leads to acoustic wave scattering and energy loss. Third, frequency selectivity is limited; traditional GAMs have a limited frequency response range, making them unsuitable for complex industrial environments. Fourth, structural design limitations exist; linear gradient design leads to wave vector mismatch and modal field discontinuities.
[0022] In this application, through innovative surface engineering, a polymethyl methacrylate (PMMA) film is coated onto the surface of a 3D-printed gradient acoustic metamaterial, reducing the surface roughness from 20.335 μm to 0.410 μm, a reduction of 49 times. This application also employs a nonlinear gradient design, utilizing a nonlinear width distribution function. W i ( x )= 0.0008x ²+ 8 This application addresses the wave vector mismatch problem at the air-solid interface. It employs front-end physical enhancement by integrating FGAM with a microelectromechanical microphone sensor (MEMS) to achieve in-situ enhancement of acoustic features during the signal acquisition stage.
[0023] Compared with related technologies, this application has the following significant advantages: Improved signal-to-noise ratio: Experiments show that the SNR is improved by 6.54dB and the characteristic signal amplitude is enhanced by 35 times; Reduced thermoviscous loss: Improved surface roughness makes the sound pressure gain closer to the theoretical value; Enhanced frequency selectivity: Accurate space-frequency mapping is achieved, and specific frequency components are enhanced by 11-12 times; Avoidance of post-processing: The characteristic signal is directly enhanced in the physical sensing stage, avoiding information loss caused by the algorithm.
[0024] The technical solutions of this application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.
[0025] The preferred embodiment of this application describes a method for enhancing acoustic sensing during milling based on thin-film-coated gradient acoustic metamaterials, such as... Figure 2 As shown, the method for enhancing acoustic sensing during milling based on thin-film-coated gradient acoustic metamaterials includes the following steps: In step S101, the frequency position information of the thin film-covered gradient acoustic metamaterial is obtained.
[0026] It is worth noting that the purpose of this application is to amplify the characteristic signals related to tool wear in the milling sound signal, and the modulation characteristics of the acoustic signal in the milling process. The cutting sound signal can be characterized as a multi-frequency composite signal, including: tool rotation frequency (spindle rotation frequency), cutting frequency (tool-workpiece contact frequency), and various harmonic components. Tool wear leads to process instability, generating periodic impact modulation synchronized with spindle rotation, producing amplitude modulation (AM) sidebands around the cutting frequency and its harmonics in the frequency domain. Analysis of the distribution of these sidebands can achieve quantitative characterization of the wear state.
[0027] In this application, the amplification characteristic center frequencies of each gap in the FGAM were first calculated using simulation and experimental methods. Before machining began, the cutting characteristic frequency and its higher harmonics were calculated by determining the actual machining parameters (spindle speed). This characteristic frequency was then compared with the characteristic center frequencies of each gap in the metamaterial to determine the optimal gap for acquiring the cutting acoustic signal. Finally, a MEMS miniature microphone sensor was coupled into the specific gap to achieve real-time amplification and acquisition of the cutting sound signal.
[0028] In one possible implementation, the frequency location information is gap-frequency correspondence data. The structural parameters of the thin-film-covered gradient acoustic metamaterial are determined, wherein the gaps of the structural parameters satisfy a nonlinear functional relationship; simulations and experiments are performed on the thin-film-covered gradient acoustic metamaterial with the structural parameters to obtain the gap-frequency correspondence data.
[0029] In one possible implementation, a three-dimensional model of the thin-film-covered gradient acoustic metamaterial is constructed according to the structural parameters; a broadband sweep frequency acoustic wave is applied to the three-dimensional model to obtain first gap frequency data of the three-dimensional model; a solid model of the thin-film-covered gradient acoustic metamaterial is constructed according to the structural parameters; the broadband sweep frequency acoustic wave is applied to the solid model to obtain second gap frequency data of the solid model; and the gap-frequency correspondence data of the thin-film-covered gradient acoustic metamaterial is determined based on the first gap frequency data and the second gap frequency data.
[0030] Specifically, the structural design involves an array of 20 rectangular plates. The key is that the width of these plates is not fixed, but follows a nonlinear function Wi(x) = ax² + b. This nonlinear design aims to solve the "wave vector mismatch" problem when sound waves propagate at the air-solid interface. Simply put, it allows sound waves to enter the metamaterial structure more smoothly and efficiently from the air, rather than being largely reflected. PLA is chosen as the substrate material, covered with a PMMA film. The key function of the PMMA film is to reduce surface roughness; the ultra-smooth surface greatly reduces the "thermoviscous loss" caused by friction between the sound wave and the wall, resulting in less sound energy loss and amplification closer to the theoretical value. This allows for the determination of parameters such as the total height, length, plate thickness, and plate spacing. Through meticulous nonlinear geometric design and surface engineering, this application creates a structure that can selectively amplify sound waves within a specific frequency band (1000-2500 Hz). Each gap is equivalent to an independent resonant cavity with its own resonant frequency, enabling a physical structure to produce different responses to different frequencies.
[0031] Furthermore, such as Figure 3 (a) and Figure 3 As shown in (b), in the thin-film covered gradient acoustic metamaterial, the structural parameters are: total height H = 80 mm, total length L = 230 mm; plate thickness d = 4 mm, plate spacing g = 8 mm; initial plate width 16 mm, using the nonlinear distribution function Wi(x) = ax² + b; a and b represent design coefficients, where a = 0.0008 and b = 8. x represents the distance from the origin in the x-axis direction; period constant p = 12 mm, and final width 160 mm (20th plate).
[0032] The material properties are as follows: Substrate material: Polylactic acid (PLA), density ρ = 1250 kg / m³, bulk modulus K = 2 GPa. Covering film: Acrylic film (PMMA), thickness 0.05 mm (two orders of magnitude smaller than the substrate). Air medium: density ρ = 1.2 kg / m³, bulk modulus K = 1.4 × 10⁻⁶. 5 Pa.
[0033] The key innovation of this application lies in the surface treatment technology (surface optimization). An acrylic film (PMMA) is used to cover the acoustic metamaterial substrate. The film is 0.05 mm thick and has a surface roughness Ra = 0.410 μm, which is 49 times higher than that of the substrate. It has high reflection efficiency, which can promote efficient wave reflection and reduce sound wave transmission loss.
[0034] Specifically, during the simulation process (theoretical prediction), professional multiphysics simulation software is used to completely build the previously designed 3D model of the FXAM (including the PLA substrate, PMMA film, and air domain) within the software. A broadband acoustic wave (e.g., a sweeping signal from 100Hz to 3000Hz) is applied to the entrance of the model. This simulates the propagation, reflection, and resonance of the acoustic wave in the complex structure. By analyzing the sound pressure response inside each gap, a "sound pressure-frequency" curve can be plotted. For each gap, the frequency corresponding to the peak point on this curve is the resonance / amplification center frequency of that gap. During the experiment (physical verification), the 3D-printed FXAM is manufactured. An acoustic experimental platform is built, and a loudspeaker plays the same broadband sweeping acoustic wave as in the simulation at the entrance. A high-precision microphone (or laser vibrometer) is used to measure the sound pressure response inside each gap one by one. Similarly, the frequency point with the strongest sound pressure response in each gap is recorded. Then, the frequencies obtained from the simulation are compared with those obtained from the experiment. If the two match closely, it proves the accuracy of the simulation model and finally confirms a reliable gap-frequency correspondence table, accurately obtaining the bandpass amplification center frequency corresponding to each gap (gap i).
[0035] In step S102, the machining parameters for milling are obtained, and the characteristic frequency information of tool wear during the milling process is obtained based on the machining parameters.
[0036] In one possible implementation, the machining parameters include spindle speed and the number of tool teeth, and the characteristic frequency information includes sideband frequency. Based on the spindle speed and the number of tool teeth, the spindle rotation frequency and the cutting frequency are obtained; based on the spindle rotation frequency and the cutting frequency, the sideband frequency of tool wear during milling is obtained.
[0037] Specifically, the actual machining parameters are input, mainly the spindle speed (rpm) and the number of tool teeth (z). The spindle rotation frequency f_spindle and the main cutting frequency f_cutting are then calculated. The spindle rotation frequency (f_spindle) is the number of revolutions the spindle makes per second, which is the basis of all frequencies. If the given spindle speed is n rpm, then its frequency f_spindle = n / 60 Hz. The cutting frequency (i.e., the tool-workpiece contact frequency f_cutting) is the number of times the tool teeth contact the workpiece per second, f_cutting = z × f_spindle, where z is the number of tool teeth.
[0038] In one possible implementation, higher harmonics are obtained based on the cutting frequency, and a carrier frequency is determined based on the higher harmonics; the sideband frequency of tool wear during milling is calculated based on the carrier frequency and the spindle rotation frequency.
[0039] Specifically, higher harmonics are integer multiples of the cutting frequency, such as 2×f_cutting, 3×f_cutting, and 4×f_cutting. They arise because when the tool wears or the cutting force becomes unstable, the originally smooth cutting force waveform is distorted, producing non-fundamental frequency vibration components. These components are precisely integer multiples of the fundamental frequency. Understandably, the amplitude and occurrence of harmonics are key indicators for judging the tool wear condition; a sharp tool produces weak harmonics, while a worn tool produces significant harmonics.
[0040] In the process of identifying wear characteristics through sideband frequency identification, amplitude modulation (AM) sidebands are generated when a high-frequency signal (carrier, such as f_cutting and its harmonics) is modulated by a low-frequency signal (modulation wave, such as f_spindle). New frequency components, i.e., sidebands, are generated on both sides of the carrier frequency. The calculation logic is: sideband frequency f_sideband = f_carrier ± k × f_modulation; f_carrier (carrier frequency) is f_cutting and its higher harmonics. f_modulation (modulation frequency) here is the spindle rotation frequency f_spindle, because the impact generated by tool wear occurs synchronously with each spindle revolution. K is the order of the sideband; typically, the sideband is most prominent and has the strongest energy when k=1. This ultimately yields a list of target frequencies, including f_cutting, its harmonics, and, most importantly, the AM sideband frequencies.
[0041] In step S103, the target position of the metamaterial is determined based on the frequency position information and the characteristic frequency information.
[0042] In one possible implementation, the gap-frequency correspondence data includes each gap and the center frequency corresponding to each gap, and the target position is the target gap. The difference between the sideband frequency and the center frequency corresponding to each gap is calculated; the smallest difference among all the differences is taken as the target difference; and the gap corresponding to the target difference is taken as the target gap.
[0043] Specifically, the difference is calculated by comparing the target frequency f_target with the center frequency f_gap_i of each gap and calculating the absolute difference |f_target - f_gap_i|. Then, all 20 gaps are iterated through to find the gap i that minimizes the difference |f_target - f_gap_i|. This gap with the smallest difference is the optimal gap. Figure 4 As shown. This application binds the processing conditions with hardware characteristics through simple numerical comparison, ensuring that no matter how the processing parameters change, the strongest acoustic enhancement capability can be automatically and accurately focused on the most critical wear characteristic signal, thereby achieving high signal-to-noise ratio and high sensitivity real-time monitoring.
[0044] In step S104, acoustic enhancement information of metamaterial milling is acquired according to the target position.
[0045] In one possible implementation, the acoustic enhancement information includes an acoustic signal waveform. A microphone sensor is installed in the target gap of the metamaterial; during the milling process, the acoustic piezoelectric signal collected by the microphone sensor is received; the acoustic piezoelectric signal is digitized to obtain an acoustic signal waveform of the metamaterial milling process.
[0046] Specifically, embed or tightly couple the MEMS microphone sensor to the geometric center of a selected gap (such as gap number 7), which is usually the location where the sound pressure is most concentrated and strongest; ensure that the sensor is securely mounted to avoid loosening in the processing vibration environment, thereby ensuring the stability of the acoustic coupling; connect the microphone's signal output line to the subsequent data acquisition system (such as the DT9837B system).
[0047] When milling begins, sound waves of various frequencies (including characteristic signals and a large amount of irrelevant noise) enter the fgam structure. Each gap in the fgam acts like a mechanical bandpass filter, allowing only sound waves with frequencies close to the resonant frequency at the center of the gap to enter efficiently. The selected optimal gap, due to its resonant frequency closely matching the target characteristic frequency (e.g., 1450 Hz), allows sound waves of that frequency to enter. The special structure of the metamaterial (e.g., nonlinear gradient design) concentrates the energy of these specific frequency sound waves at the center of the gap, causing strong local resonance. This resonance effect significantly enhances the sound pressure at that frequency. Thus, the originally weak characteristic signal of 0.159 Pa is physically amplified to 5.610 Pa within the gap. For sound waves with frequencies far from the resonant frequency of the gap (i.e., most noise), they are either reflected by the metamaterial structure (thanks to the high reflectivity of the PMMA film) or attenuated, unable to effectively enter the gap, thus effectively suppressing out-of-band frequency components. The MEMS microphone located at the center of the gap is now in an enhanced target frequency sound field and a suppressed noise field, and the signal it picks up is the characteristic signal with a high signal-to-noise ratio. For example... Figure 5 As shown, the microphone converts the amplified sound pressure signal into an electrical signal, which is then digitized by the data acquisition card to obtain a clear, clean acoustic signal waveform containing rich wear information for subsequent analysis and wear condition identification.
[0048] In this application, the FGAM structure is a 3D-printed PLA substrate covered with a PMMA thin film. The sensor is a MEMS microphone (sensitivity: -42dBV / Pa; gain: 66dB; bandwidth: 100Hz-8kHz). Data acquisition uses a DT9837B system (24-bit resolution, 105.4kHz sampling rate).
[0049] This application utilizes a surface thin film to cover metamaterials, reducing surface roughness and bringing the sound pressure gain closer to the theoretical value, effectively reducing thermal viscous losses. Nonlinear gradient design solves the wave vector mismatch problem, improving acoustic coupling efficiency. A multi-gap layout, with different gaps corresponding to different resonant frequencies, achieves broadband signal coverage (1000-2500Hz). Employing MEMS integration, a miniature sensor is embedded in the center of the gap to ensure optimal sound pressure measurement location. This significantly improves the signal-to-noise ratio; the FGAM enhancement system achieves a 6.54dB SNR improvement compared to traditional acoustic detection methods. It enhances characteristic signals, increasing the sound pressure amplitude of characteristic cutting frequency components from 0.159Pa to 5.610Pa, achieving a 35-fold gain. It achieves 11-12 times amplification of specific frequency components while effectively suppressing out-of-band frequency components. Front-end physical enhancement avoids post-processing algorithm delays, enabling real-time monitoring.
[0050] like Figure 4As shown below, the specific implementation of this application will be described in conjunction with a specific application scenario.
[0051] Step K1, Characteristic Frequency Pre-calculation and Metamaterial Design: First, through simulation and experimentation, the bandpass amplification center frequency of each gap element in the acoustic metamaterial was pre-determined. The core of this step is to complete the "spectral characteristic calibration" of the metamaterial.
[0052] Step K2, Machining Feature Frequency Extraction: Before the actual machining begins, calculate the main cutting feature frequency and its main higher harmonics under the working condition based on the actual spindle speed and other process parameters.
[0053] Step K3, Spectrum Matching and Optimal Gap Selection: The calculated cutting characteristic frequency is matched with the center frequencies of each gap obtained in Step K1. Through comparison, the gap whose center frequency best matches the cutting characteristic frequency is selected as the signal acquisition channel. This step ensures that the target signal is amplified to the maximum extent, while irrelevant noise is suppressed.
[0054] Step K4, Directional Enhancement and Signal Acquisition: Finally, a MEMS miniature microphone sensor is integrated into the selected specific gap. This sensor acts as a "selective ear," achieving real-time, enhanced acquisition of the target cutting sound signal through the physical amplification effect of the metamaterial.
[0055] This application employs surface engineering technology to integrate a PMMA thin film onto the surface of a 3D-printed acoustic metamaterial for the first time, reducing the surface roughness from 20.335 μm to 0.410 μm and minimizing thermal viscous losses. A nonlinear gradient design is employed, proposing a nonlinear width distribution function Wi(x) = 0.0008x²+8 to address the wave vector mismatch problem. A front-end physical enhancement mechanism is used to physically enhance acoustic features during signal acquisition, avoiding information loss during post-processing. In tool wear monitoring applications, FGAM technology is specifically applied to tool condition monitoring during milling, achieving a 6.54 dB improvement in SNR.
[0056] Next, referring to the accompanying drawings, the acoustic sensing enhancement system for milling based on thin-film covered gradient acoustic metamaterials proposed in the embodiments of this application is described, and applied to the acoustic sensing enhancement method for milling based on thin-film covered gradient acoustic metamaterials as described in any of the above schemes.
[0057] Figure 6 This is a structural diagram of a milling acoustic sensing enhancement system based on a thin-film covered gradient acoustic metamaterial, according to an embodiment of this application.
[0058] like Figure 6As shown, the acoustic sensing enhancement system for milling based on thin-film covered gradient acoustic metamaterials includes: a characteristic frequency pre-calculation module 100, a machining characteristic frequency extraction module 200, an optimal gap selection module 300, and a directional enhancement and signal acquisition module 400.
[0059] Specifically, the characteristic frequency pre-calculation module 100 is used to obtain the frequency position information of the thin film-covered gradient acoustic metamaterial; The machining feature frequency extraction module 200 is used to acquire the machining parameters of milling and obtain the feature frequency information of tool wear during milling based on the machining parameters. The optimal gap selection module 300 is used to determine the target position of the metamaterial based on the frequency position information and the characteristic frequency information. The directional enhancement and signal acquisition module 400 is used to acquire acoustic enhancement information of metamaterial milling according to the target position.
[0060] Figure 7 A structural diagram of a terminal provided in an embodiment of this application. The terminal may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.
[0061] When the processor 502 executes the program, it implements the method for enhancing acoustic sensing in milling based on thin-film covered gradient acoustic metamaterials provided in the above embodiments.
[0062] Furthermore, the terminal also includes: Communication interface 503 is used for communication between memory 501 and processor 502.
[0063] The memory 501 is used to store computer programs that can run on the processor 502.
[0064] Memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.
[0065] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EIS) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.
[0066] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.
[0067] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.
[0068] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for enhancing acoustic sensing during milling based on thin-film covered gradient acoustic metamaterials.
[0069] One embodiment of this application provides a computer program product, including a computer program that, when executed by a processor, implements the features described in this application. Figure 2 The corresponding embodiments provide an acoustic sensing enhancement method for milling based on thin-film covered gradient acoustic metamaterials.
[0070] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0071] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.
[0072] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.
[0073] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable storage medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable storage medium could be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.
[0074] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0075] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
[0076] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
[0077] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.
[0078] It should be understood that the application of this application is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.
[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.
Claims
1. A method for enhancing acoustic sensing during milling based on thin-film coated gradient acoustic metamaterials, characterized in that, The method for enhancing acoustic sensing during milling based on thin-film covered gradient acoustic metamaterials includes: Acquire frequency position information of thin-film-covered gradient acoustic metamaterials; Obtain the machining parameters for milling, and obtain the characteristic frequency information of tool wear during the milling process based on the machining parameters; The target location of the metamaterial is determined based on the frequency location information and the characteristic frequency information. Acoustic enhancement information of metamaterial milling is acquired at the target location.
2. The method for enhancing acoustic sensing during milling based on thin-film-coated gradient acoustic metamaterials according to claim 1, characterized in that, The frequency location information is data relating the gap to the frequency. The acquisition of frequency position information of the thin-film covered gradient acoustic metamaterial specifically includes: Determine the structural parameters of the thin-film-coated gradient acoustic metamaterial, wherein the gaps between the structural parameters satisfy a nonlinear functional relationship; Simulations and experiments were conducted on the thin-film-covered gradient acoustic metamaterial with the aforementioned structural parameters to obtain data on the relationship between gap and frequency.
3. The method for enhancing acoustic sensing during milling based on thin-film-coated gradient acoustic metamaterials according to claim 2, characterized in that, The simulation and experimentation of the thin-film-covered gradient acoustic metamaterial with the structural parameters to obtain data on the relationship between gap and frequency specifically includes: A three-dimensional model of the thin film-covered gradient acoustic metamaterial is constructed according to the structural parameters. A broadband sweep frequency acoustic wave is applied to the three-dimensional model to obtain the first gap frequency data of the three-dimensional model. Construct a solid model of the thin film-covered gradient acoustic metamaterial according to the structural parameters, apply the broadband swept frequency acoustic wave to the solid model, and obtain the second gap frequency data of the solid model; The gap-frequency correspondence data of the thin film-covered gradient acoustic metamaterial is determined based on the first gap frequency data and the second gap frequency data.
4. The method for enhancing acoustic sensing during milling based on thin-film-coated gradient acoustic metamaterials according to claim 3, characterized in that, The machining parameters include spindle speed and number of tool teeth, and the characteristic frequency information includes sideband frequency; The step of obtaining the characteristic frequency information of tool wear during milling based on the machining parameters specifically includes: Based on the spindle speed and the number of teeth of the tool, the spindle rotation frequency and cutting frequency are obtained; Based on the spindle rotation frequency and the cutting frequency, the edge band frequency of tool wear during milling is obtained.
5. The method for enhancing acoustic sensing during milling based on thin-film covered gradient acoustic metamaterials according to claim 4, characterized in that, The step of obtaining the edge band frequency of tool wear during milling based on the spindle rotation frequency and the cutting frequency specifically includes: The higher harmonics are obtained based on the cutting frequency, and the carrier frequency is determined based on the higher harmonics. The sideband frequency of tool wear during milling is calculated based on the carrier frequency and the spindle rotation frequency.
6. The method for enhancing acoustic sensing during milling based on thin-film covered gradient acoustic metamaterials according to claim 4, characterized in that, The gap-frequency correspondence data includes each gap and the center frequency corresponding to each gap, and the target position is the target gap; Determining the target location of the metamaterial based on the frequency location information and the characteristic frequency information specifically includes: Calculate the difference between the sideband frequency and the center frequency corresponding to each gap; The smallest difference among all the stated differences is taken as the target difference. The gap corresponding to the target difference is taken as the target gap.
7. The method for enhancing acoustic sensing during milling based on thin-film-coated gradient acoustic metamaterials according to claim 6, characterized in that, The acoustic enhancement information includes an acoustic signal waveform diagram; The acquisition of acoustic enhancement information from metamaterial milling at the target location specifically includes: The microphone sensor is installed in the target gap of the metamaterial; During the milling process, the microphone sensor collects the acoustic pressure signal; The acoustic piezoelectric signal is digitized to obtain the acoustic signal waveform of metamaterial milling.
8. A milling acoustic sensing enhancement system based on a thin-film coated gradient acoustic metamaterial, characterized in that, The milling acoustic sensing enhancement system based on thin-film covered gradient acoustic metamaterial is used to implement the milling acoustic sensing enhancement method based on thin-film covered gradient acoustic metamaterial as described in any one of claims 1-7. The milling acoustic sensing enhancement system based on thin-film covered gradient acoustic metamaterial includes: The characteristic frequency pre-calculation module is used to obtain the frequency position information of the thin film-covered gradient acoustic metamaterial; The machining feature frequency extraction module is used to obtain the machining parameters of milling and to obtain the feature frequency information of tool wear during milling based on the machining parameters. The optimal gap selection module is used to determine the target position of the metamaterial based on the frequency position information and the characteristic frequency information. The directional enhancement and signal acquisition module is used to acquire acoustic enhancement information of metamaterial milling according to the target location.
9. A terminal, characterized in that, The terminal includes: a memory, a processor, and a milling acoustic sensing enhancement program based on a thin-film-coated gradient acoustic metamaterial stored in the memory and executable on the processor. When the milling acoustic sensing enhancement program based on a thin-film-coated gradient acoustic metamaterial is executed by the processor, it implements the steps of the milling acoustic sensing enhancement method based on a thin-film-coated gradient acoustic metamaterial as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a milling acoustic sensing enhancement program based on a thin-film-coated gradient acoustic metamaterial, which, when executed by a processor, implements the steps of the milling acoustic sensing enhancement method based on a thin-film-coated gradient acoustic metamaterial as described in any one of claims 1-7.