Multi-parameter ABS material abnormal sound risk prediction method based on stick-slip amplitude

Abstract

The invention relates to the field of material abnormal sound risk prediction, and discloses a stick-slip amplitude-based multi-parameter ABS material abnormal sound risk prediction method, which comprises the following steps: step 1, carrying out an ABS material friction test, and synchronously measuring the stick-slip amplitude in the friction process through a force sensor; collecting noise through a noise collection module and calculating a peak decibel value; and 2, establishing a quantitative correlation model of the stick-slip amplitude and the noise decibel value, and judging the abnormal sound risk level of the material. And the noise level is quantized, the relevance with friction force mechanical parameters is achieved, and real-time monitoring can be achieved.

Description

Technical Field

[0001] This invention relates to the field of material noise risk prediction, specifically to a multi-parameter ABS material noise risk prediction method based on stick-slip amplitude. Background Technology

[0002] ABS resin is widely used in the automotive, electronics, and home appliance industries due to its excellent mechanical and processing properties. However, ABS material is prone to stick-slip during friction, resulting in harsh friction noises (such as squak and creak), which seriously affects the user experience.

[0003] Currently, the assessment of friction noise mainly relies on the following three methods: 1) Friction coefficient test: by measuring the dynamic friction coefficient (μ d ) and static friction coefficient (μ s The difference (Δμ) between the two values ​​indirectly assesses the risk of stick-slip, but cannot directly quantify the noise level; 2) Noise decibel test: The sound level meter records the noise peak value, but it lacks correlation with tribological parameters and is difficult to analyze the noise generation mechanism; 3) Scanning electron microscopy (SEM) surface morphology analysis: Observe the friction scratches to judge the degree of stick-slip, but it is time-consuming and cannot be monitored in real time. Summary of the Invention

[0004] The present invention aims to provide a multi-parameter method for predicting the abnormal noise risk of ABS materials based on stick-slip amplitude, which quantifies the noise level, has a correlation with tribological parameters, and can monitor in real time.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a multi-parameter method for predicting abnormal noise risk in ABS materials based on stick-slip amplitude, comprising the following steps:

[0006] Step 1: Conduct friction tests on ABS material, simultaneously measuring the stick-slip amplitude during the friction process using a force sensor; collect noise data using a noise acquisition module and calculate the peak decibel value.

[0007] Step 2: Establish a quantitative correlation model between stick-slip amplitude and noise decibel value to determine the material noise risk level.

[0008] The beneficial effects of this plan are:

[0009] To address the shortcomings of existing technologies, the purpose of this invention is to establish a rapid and quantitative method for evaluating the frictional noise of ABS materials. A multi-factor coupled nonlinear FSI (Friction-induced squeak index) algorithm is proposed. Based on the interaction of load, velocity, ΔF, and noise spectrum characteristics in experimental data, a more accurate abnormal noise risk prediction model is established, enabling simultaneous measurement and correlation analysis of multiple parameters to guide material modification and product design optimization. This method can quantify noise levels and demonstrates correlation with tribological parameters.

[0010] Specifically, a high-precision force sensor (sampling rate ≥ 1 kHz) is used to record the friction force curve, and the stick-slip amplitude (defined as the average difference between the peak and trough of the friction force) is extracted.

[0011]

[0012] Material friction noise was collected using a directional microphone array (frequency response 20Hz-20kHz), and the peak decibel value of the characteristic frequency band (1-5kHz, high-frequency components determine the sharpness of the noise) was extracted by FFT analysis.

[0013] A nonlinear model coupling multiple factors, including ΔF and noise decibel values, was established based on experimental data, and risk levels were classified.

[0014] Furthermore, in step one, the force sensor records the friction force curve, and the stick-slip amplitude is calculated based on the arithmetic mean of the differences between the continuous peaks and troughs in the friction force curve.

[0015] Furthermore, the sampling frequency of the friction force curve is ≥1kHz.

[0016] Furthermore, the formula for calculating the material noise risk (FSI) is as follows:

[0017]

[0018] α+β+γ=1;

[0019] △F - Mean value of the difference between the peak and trough of the friction force;

[0020] F0 - Amplitude reference value;

[0021] dB0 - dB reference value;

[0022] P0 - Load baseline value;

[0023] V0 - Speed ​​reference value;

[0024] α - Amplitude weighting coefficient;

[0025] β-noise weighting coefficient;

[0026] γ-Working condition correction factor:

[0027] P - Normal pressure at the friction interface;

[0028] v-Sliding speed:

[0029] E 1-5kHz / E total : Energy percentage of characteristic frequency bands.

[0030] Furthermore, the energy proportion of the characteristic frequency band is the proportion of noise energy in the 1-5kHz frequency band to the total energy.

[0031] Furthermore, the energy percentage of the characteristic frequency bands was obtained through FFT analysis.

[0032] Furthermore, F0 = 0.1N, corresponding to the critical ΔF value for "acceptable noise", dB0 = 70, P0 = 40N, V0 = 6mm / s, α = 0.4, β = 0.3, λ = 0.3; when FSI ≤ 2, the risk level is 1; when 2 < FSI ≤ 4, the risk level is 2; when 4 < FSI ≤ 6, the risk level is 3; when FSI > 6, the risk level is 4.

[0033] Furthermore, the noise acquisition module includes a directional microphone array located 3±0.5cm from the friction interface.

[0034] Furthermore, in step one, the adjustable range of the frictional load is 2-80N.

[0035] Furthermore, in step one, the adjustable range of the moving speed during friction is 1-12 mm / s. Attached Figure Description

[0036] Figure 1 The following is a logical relationship diagram for determining the risk level of abnormal noise in an embodiment;

[0037] Figure 2 The test results are shown in the example diagram;

[0038] Figure 3 This is a schematic diagram of the test equipment used in an embodiment. Detailed Implementation

[0039] The following detailed description illustrates the specific implementation method:

[0040] The reference numerals in the accompanying drawings include: platform 1, linear motor 21, linear servo motor 22, spring plate 3, fixed pulley 4, friction block 51, friction plate 52, microphone 53, support part 6, steel wire rope 7, force sensor 8.

[0041] Example

[0042] To address the shortcomings of existing technologies, the purpose of this invention is to establish a rapid and quantitative method for evaluating the friction noise of ABS materials. It proposes a multi-factor coupled nonlinear FSI (Friction-induced squeak index) algorithm. Based on the interaction of load, speed, ΔF, and noise spectrum characteristics in experimental data, a more accurate abnormal noise risk prediction model is established, enabling simultaneous measurement and correlation analysis of multiple parameters to guide material modification and product design optimization.

[0043] A multi-parameter method for predicting abnormal noise risk in ABS materials based on stick-slip amplitude includes the following steps:

[0044] Step 1: Conduct a friction test on the ABS material. The testing equipment is as follows: Figure 3 As shown,

[0045] The testing equipment includes platform 1, on which a bracket (not shown in the figure) is mounted. The bracket is equipped with a friction pair, a noise acquisition module, and a normal force loading module. Friction acquisition modules are located on both sides of the bracket.

[0046] The normal force loading module includes a linear motor 21, which is bolted to the bracket. A spring plate 3 is bolted to the lower end of the output shaft of the linear motor 21.

[0047] The friction pair includes a friction block 51, a friction plate 52, and a drive unit. The friction block 51 and the lower end of the spring plate 3 are bolted together. The drive unit is typically a drive component connected to the friction plate 52. The drive component is a linear servo motor 22, which drives the friction plate 52 to reciprocate linearly, thus achieving friction between the friction block 51 and the friction plate 52. The drive unit is existing technology and will not be described in detail. Two friction materials used for testing are respectively bonded and fixed to the friction material friction block 51 and the friction plate 52.

[0048] The friction force acquisition module includes a support part 6 and a steel wire rope 7. The front side of the support part 6 has an L-shaped groove. A force sensor 8 is bolted to the vertical section of the groove sidewall. A fixed pulley 4 is rotatably connected at the corner of the groove. One end of the steel wire rope 7 is connected to the force sensor 8, and the other end passes around the fixed pulley 4 and is bolted to the side of the friction block 51.

[0049] The noise acquisition module includes a microphone array evenly distributed around the friction pair. The microphone array includes several microphones 53 arranged at equal intervals. In this embodiment, the microphones 53 are arranged in a matrix. Figure 1 The diagram shows only one microphone 53, which is used to collect noise. The microphone 53 is located 3±0.5cm away from the friction interface of the friction pair.

[0050] The adjustable range of friction load is 2-80N; the adjustable range of moving speed during friction is 1-12mm / s; the friction force curve is recorded by a force sensor, the sampling frequency of the friction force curve is ≥1kHz, and the stick-slip amplitude is calculated based on the arithmetic mean of the difference between continuous peaks and troughs in the friction force curve, thereby synchronizing the stick-slip amplitude during the friction process; the noise is collected by a noise acquisition module and the peak decibel value is calculated.

[0051] Step 2: Establish a quantitative correlation model between stick-slip amplitude and noise decibel value to determine the material's abnormal noise risk level. The determination logic is as follows: Figure 2 As shown;

[0052] The formula for calculating the material noise risk (FSI) is:

[0053]

[0054] α+β+γ=1;

[0055] △F - Mean value of the difference between the peak and trough of the friction force;

[0056] F0 - Amplitude reference value;

[0057] dB0 - dB reference value;

[0058] P0 - Load baseline value;

[0059] V0 - Speed ​​reference value;

[0060] α - Amplitude weighting coefficient;

[0061] β-noise weighting coefficient;

[0062] γ-Working condition correction factor:

[0063] P - Normal pressure at the friction interface;

[0064] v-Sliding speed:

[0065] E 1-5kHz / E total : Energy percentage of characteristic frequency bands; the energy percentage of characteristic frequency bands is the proportion of noise energy in the 1-5kHz frequency band to the total energy; the energy percentage of characteristic frequency bands is obtained through FFT analysis;

[0066] In this embodiment, F0 = 0.1N, dB0 = 70, P0 = 40N, V0 = 6mm / s, α = 0.4, β = 0.3, λ = 0.3; when FSI ≤ 2, the risk level is 1; when 2 < FSI ≤ 4, the risk level is 2; when 4 < FSI ≤ 6, the risk level is 3; when FSI > 6, the risk level is 4.

[0067] The noise acquisition module includes a directional microphone array located 3±0.5cm from the friction interface.

[0068] Explanation of the formula for calculating Material Noise Risk (FSI):

[0069] 1. Stick-slip amplitude (normalized term: ΔF / F0)

[0070] 1.1 Physical Meaning: The stick-slip amplitude is the arithmetic mean of the differences between consecutive peaks and troughs in the friction force curve, directly reflecting the severity of the stick-slip phenomenon (unit: N). F max,i F min,i ΔF represents the maximum and minimum frictional forces (in N) during the i-th stick-slip cycle (requiring more than 5 test cycles). The larger ΔF is, the more significant the energy accumulation and release during friction, and the sharper the noise. Figure 2 Based on the test results conducted according to this embodiment, the ΔF of ordinary ABS can reach 0.3N, while the ΔF of modified ABS (such as ABS with added polyethylene wax) is <0.02N.

[0071] 1.2 Normalization Processing

[0072] The baseline value F0 = 0.1N corresponds to the critical ΔF value of "acceptable noise" in the test (ΔF < 0.02N is low risk in the table below);

[0073] The square term (ΔF / F0)^2 reflects the nonlinear relationship between ΔF and noise energy (experimental data show that the noise decibel value increases sharply when ΔF > 0.15N);

[0074] The test results of friction coefficient and noise of ABS materials with different lubricating modifiers are shown in the table below:

[0075]

[0076] 2. Peak noise level in dB

[0077] Physical meaning: The peak decibel value (unit: dB) of frictional noise collected by a microphone characterizes the objective intensity of the noise. In Table 1, the peak noise level of ordinary ABS reaches 82 dB, while that of modified ABS can be reduced to 58 dB.

[0078] Normalization: Baseline value dB0 = 70dB, corresponding to the industry tolerance threshold for automotive interior noise (refer to SAE J1441 standard); 1.5 term (dB / dB0) 1.5: reflects the sensitivity of the human ear to high-frequency noise (>3kHz) (psychoacoustic correction). Secondly, it has been found that, at the same decibel value, noise with a higher proportion of high frequencies is more subjectively harsh.

[0079] 3. Operating condition coupling term (P*v / P0v0)

[0080] Physical meaning: Load P(N): Normal pressure at the friction interface, affecting the actual contact area; Velocity v(mm / s): Sliding speed, affecting frictional heat and molecular chain relaxation (determined by the inherent properties of ABS material); P*v product: Characterizes the mechanical power input to the friction system, positively correlated with noise energy;

[0081] Normalization: Baseline operating condition P0 = 40N, v0 = 6mm / s, corresponding to typical parameters of vehicle bumpy operating condition; Load P0 = 5N, speed v0 = 1mm / s, corresponding to typical parameters of vehicle torsional operating condition.

[0082] 4. Frequency band energy percentage (E 1-5kHz / E total )

[0083] Physical meaning: E 1-5kHz / E total : The proportion of noise energy in the 1-5kHz frequency band to the total energy (obtained through FFT analysis); the higher the proportion of high-frequency energy, the sharper the noise spectrum; correction effect: when the proportion of high-frequency energy is high, (1-E 1-5kHz / E total The value is reduced, the weight of the operating condition item is decreased, and the high-frequency influence is avoided by recalculating it repeatedly; the modulation effect of the spectrum characteristics on the subjective perception of noise is reflected (refer to the sharpness index in the ISO 532-1 standard).

[0084] 5. Weighting coefficients (α, β, λ)

[0085] Calibration criteria: α = 0.4: assigns the highest weight to ΔF, as it directly determines the stick-slip vibration energy (ΔF is the main cause of noise); β = 0.3: balances the objective measurement of noise in decibels with the subjective perception of the human ear; λ = 0.3: operating condition correction term to ensure the robustness of the model under varying loads / velocities.

[0086] Constraint: α+β+λ=1, ensuring that the RPN output value range is controllable (usually 0-10).

[0087] The relationships between the parameters are shown in the table below:

[0088] parameter Experimental correlation Model role △F △F is positively correlated with noise intensity Dominant stick-slip energy contribution dB Decibel level quantifies noise level Objective noise intensity characterization P*v Load / speed affects stick-slip Dynamic correction of operating conditions <![CDATA[E 1-5kHz ]]> High-frequency components determine noise sharpness Spectral characteristic modulation

[0089] 6. Example calculation (ordinary ABS, P = 40 N, v = 6 mm / s):

[0090] Test results show: ΔF = 0.28 N, dB = 78, E 1-5kHz / E total =0.82

[0091] FSI = 0.4 * (0.28 / 0.1) 2 +0.3*(78 / 70) 1.5+0.3*1*(1-0.82)=3.14+0.42+0.05

[0092] =7.61 → Grade 4 (Poor)

[0093] This result is consistent with the "high noise" phenomenon in ordinary ABS, verifying the rationality of the model.

[0094] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A multi-parameter method for predicting abnormal noise risk in ABS materials based on stick-slip amplitude, characterized in that, Includes the following steps: Step 1: Conduct friction tests on ABS material, and simultaneously measure the stick-slip amplitude during the friction process using a force sensor; The noise is collected by the noise acquisition module and the peak decibel value is calculated. Step 2: Establish a quantitative correlation model between stick-slip amplitude and noise decibel value to determine the material noise risk level.

2. The method for predicting abnormal noise risk in ABS material based on stick-slip amplitude according to claim 1, characterized in that: In step one, the force sensor records the friction force curve, and the stick-slip amplitude is calculated based on the arithmetic mean of the differences between the continuous peaks and troughs in the friction force curve.

3. The method for predicting abnormal noise risk in ABS material based on stick-slip amplitude according to claim 2, characterized in that: The sampling frequency of the friction force curve is ≥1kHz.

4. The method for predicting abnormal noise risk in ABS material based on stick-slip amplitude according to claim 1, characterized in that: The formula for calculating the material noise risk (FSI) is: α+β+γ=1; △F - Mean value of the difference between the peak and trough of the friction force; F0 - Amplitude reference value; dB0 - dB reference value; P0 - Load baseline value; V0 - Speed ​​reference value; α - Amplitude weighting coefficient; β-noise weighting coefficient; γ-Working condition correction factor: P - Normal pressure at the friction interface; v-Sliding speed: E 1-5kHz / E total : Energy percentage of characteristic frequency bands.

5. The method for predicting abnormal noise risk in ABS material based on stick-slip amplitude according to claim 4, characterized in that: The energy proportion of the characteristic frequency band is the proportion of noise energy in the 1-5kHz frequency band to the total energy.

6. The method for predicting abnormal noise risk in ABS material based on stick-slip amplitude according to claim 5, characterized in that: The energy percentage of the characteristic frequency bands was obtained through FFT analysis.

7. The method for predicting abnormal noise risk in ABS material based on stick-slip amplitude according to claim 4, characterized in that: F0 = 0.1N, dB0 = 70, P0 = 40N, V0 = 6mm / s, α = 0.4, β = 0.3, λ = 0.3; when FSI ≤ 2, the risk level is 1; when 2 < FSI ≤ 4, the risk level is 2; when 4 < FSI ≤ 6, the risk level is 3; when FSI > 6, the risk level is 4.

8. The method for predicting abnormal noise risk in ABS material based on stick-slip amplitude according to claim 1, characterized in that: The noise acquisition module includes a directional microphone array located 3±0.5cm from the friction interface.

9. The method for predicting abnormal noise risk in ABS material based on stick-slip amplitude according to claim 1, characterized in that: In step one, the adjustable range of the friction load is 2-80N.

10. The method for predicting abnormal noise risk of ABS material based on stick-slip amplitude according to claim 1, characterized in that: In step one, the moving speed during friction is adjustable from 1 to 12 mm / s.

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