Method and system for online monitoring and suppression of chatter in a process of grinding an internal thread

Abstract

The application discloses an online monitoring and inhibiting method and system for chatter in an internal thread grinding process, and the method comprises the following steps: signal monitoring, signal processing and feature identification, chatter inhibition decision, instruction execution and parameter adjustment; the system comprises a monitoring unit for collecting and preliminarily processing grinding process signals; a processing and identification unit connected with the monitoring unit and used for performing fast Fourier transform and feature energy calculation on the signals; a decision unit connected with the processing and identification unit and used for threshold comparison and random fine adjustment instruction generation; and an execution unit in communication connection with the decision unit and a machine tool numerical control system and used for forwarding and ensuring that the adjustment instruction is executed. The application can realize real-time and accurate diagnosis of the budding state of chatter, and based on the variable parameter cutting chatter inhibition principle, the regenerative cycle mechanism of chatter is actively destroyed through automatic and intelligent small random parameter disturbance, the chatter is inhibited before the chatter is fully developed, and the machining process stability and quality are ensured.

Description

Technical Field

[0001] This invention relates to the field of precision machinery manufacturing technology, and in particular to an online monitoring and suppression method and system for chatter during internal thread grinding. Background Technology

[0002] Internal thread grinding is a critical process in the machining of key components for high-end equipment, such as the tenon grooves of aero-engine turbine disks, the internal threads of precision hydraulic valve bodies, and the nuts of precision ball screw pairs. Due to the small diameter and long overhang of the grinding wheel, and the narrow space between the workpiece thread profile and the wheel, the overall rigidity of the contact area between the grinding wheel and the workpiece is poor, making it highly susceptible to regenerative chatter during grinding. This phenomenon manifests as severe relative vibration, leaving obvious chatter marks on the workpiece surface, significantly reducing the surface roughness, profile accuracy, and fatigue strength of the thread. Simultaneously, chatter accelerates abnormal wear and shedding of abrasive grains from the grinding wheel, reducing grinding efficiency and potentially damaging precision components such as machine tool spindle bearings.

[0003] Traditional methods rely on operators' experience to detect flutter by listening to sounds, but by this time the flutter has often progressed to a severe stage, resulting in delayed intervention. Another approach is to install passive dampers, but these have limited damping bandwidth, increase system complexity, have poor adaptability to changing operating conditions, and do not involve effective online active suppression strategies. Summary of the Invention

[0004] To address the aforementioned existing technical problems, this invention provides an online monitoring and suppression method and system for chatter during internal thread grinding, which can monitor in real time, provide early warnings, and automatically suppress chatter.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: an online monitoring and suppression method for chatter during internal thread grinding, comprising the following steps:

[0006] S1. Signal monitoring: Physical signals reflecting the dynamic characteristics of the grinding process are collected in real time by sensors installed in the grinding area;

[0007] S2. Signal processing and feature recognition: The signal acquired in step S1 is preprocessed and subjected to spectrum analysis. The signal energy within the preset interest frequency band is calculated, and the energy peak and its corresponding frequency are used as the flutter feature energy value.

[0008] S3. Vibration Suppression Decision: Compare the flutter characteristic energy value obtained in step S2 with a preset energy threshold; when the flutter characteristic energy value continuously exceeds the energy threshold, it is determined that flutter has occurred, and a processing parameter adjustment instruction is generated, wherein the processing parameter adjustment instruction includes a random micro-variable superimposed on the original set parameter baseline value;

[0009] S4. Command Execution and Parameter Adjustment: Send the machining parameter adjustment command generated in step S3 to the machine tool CNC system, adjust the spindle speed or feed rate parameters in real time, and restore the original setting parameters after the chatter characteristic energy value falls below the threshold and remains stable.

[0010] In a further step of the present invention, the sensor in step S1 is an acoustic emission sensor.

[0011] In a further step of the present invention, the preset focus frequency band in step S2 is a frequency band higher than the spindle rotation frequency and related to the structural mode of the machine tool-workpiece-grinding wheel system, ranging from 200Hz to 2000Hz.

[0012] In a further step of the present invention, in step S3, the random slight variation causes the adjusted processing parameter value to fluctuate randomly within the range of ±0.5% to ±5% of the reference value.

[0013] Furthermore, in this invention, the period of fluctuation of the processing parameter adjustment command control parameter is 0.1 seconds to 1 second.

[0014] In a further step of this invention, in step S2, spectrum analysis and energy calculation are performed on the filtered signal. A short-time Fourier transform (STFT) or a fast Fourier transform (FFT) is performed with a time window of 0.05 seconds and an overlap of 50%. The total signal energy E_t within the frequency band Fb = [f_mod - 50Hz, f_mod + 50Hz] is calculated. Here, f_mod is the structural modal frequency of the machine tool-workpiece-grinding wheel system in the grinding direction, which is pre-identified through a hammer impact experiment.

[0015] In a further step of this invention, in step S2, the threshold setting and flutter determination are as follows: during the no-load or trial cutting stabilization phase before each processing begins, the baseline value E_0 of the Fb band energy E_t and its standard deviation σ are recorded; the flutter determination threshold E_th is set to E_th = E_0 + 3σ; during real-time monitoring, when E_t is greater than E_th for three consecutive calculation windows, i.e., within 0.1 seconds, the software determines that flutter has occurred and triggers the vibration suppression decision module.

[0016] Furthermore, the present invention also provides an online monitoring and suppression system for chatter in the internal thread grinding process for implementing any of the above methods, comprising: a monitoring unit, including the sensor and signal conditioning circuit, for acquiring and initially processing grinding process signals; a processing and identification unit, connected to the monitoring unit, including a digital signal processor, for performing fast Fourier transform and characteristic energy calculation of the signals; a decision unit, connected to the processing and identification unit, including a logic judgment module and a random instruction generation module, for threshold comparison and generating random fine-tuning instructions; and an execution unit, communicatively connected to the decision unit and the machine tool CNC system, for forwarding and ensuring that the adjustment instructions are executed.

[0017] The beneficial effects of adopting the above technical solution are: Proactive prevention: Early identification and automatic intervention in the nascent stage of chatter, preventing its deterioration. Intelligent adaptation: The vibration suppression strategy adjusts the speed or feed based on real-time signal analysis, providing strong targeting. Quality assurance: Effectively avoids chatter marks, achieving excellent surface roughness and geometric accuracy. Equipment protection: Reduces impact damage to the grinding wheel and spindle bearings caused by chatter. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the steps of the present invention;

[0019] Figure 2 This is a flowchart of the flutter identification and suppression algorithm of the present invention;

[0020] Figure 3 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0021] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0022] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0023] One embodiment of the present invention: as follows Figure 1 and Figure 2 As shown, an online monitoring and suppression method for chatter during internal thread grinding includes the following steps:

[0024] S1. Signal monitoring: Physical signals reflecting the dynamic characteristics of the grinding process are collected in real time by sensors installed in the grinding area;

[0025] S2. Signal processing and feature recognition: The signal acquired in step S1 is preprocessed and subjected to spectrum analysis. The signal energy in the preset interest frequency band is calculated, and the energy peak and its corresponding frequency are used as the flutter feature energy value.

[0026] S3. Vibration Suppression Decision: Compare the flutter characteristic energy value obtained in step S2 with a preset energy threshold; when the flutter characteristic energy value continuously exceeds the energy threshold, it is determined that flutter has occurred, and a processing parameter adjustment instruction is generated, wherein the processing parameter adjustment instruction includes a random micro-variable superimposed on the original set parameter baseline value;

[0027] S4. Command Execution and Parameter Adjustment: Send the machining parameter adjustment command generated in step S3 to the machine tool CNC system to adjust the spindle speed or feed rate parameters in real time, and restore the original setting parameters after the chatter characteristic energy value falls below the threshold and remains stable.

[0028] This invention enables real-time and accurate diagnosis of chatter in its early stages. Based on the principle of variable-parameter cutting vibration suppression, it actively disrupts the chatter regeneration cycle mechanism through automated and intelligent micro-random parameter perturbations, thereby suppressing chatter before it fully develops and ensuring the stability and quality of the machining process. Its core lies in constructing a closed-loop control circuit of "perception-decision-execution." First, a high-sensitivity sensor collects stress wave signals from the grinding zone in real time, which are extremely sensitive to micro-chatter. Second, the collected signals undergo bandpass filtering and fast Fourier transform to convert the time-domain signal into a frequency-domain signal, focusing on energy changes in the frequency band near the system's structural modal frequency. By comparing the energy of the characteristic frequency band calculated in real time with the energy threshold obtained from historical stable machining data, early chatter identification is achieved. Once chatter is identified, the system immediately makes a decision without interrupting the machining process, sending an adjustment command for the spindle speed or axial feed rate to the CNC system. The key to this command is that the adjustment amount is small and random, for example, causing the spindle speed to fluctuate randomly within ±2% of the original set value at a period of 0.2 seconds. These continuous, unpredictable micro-disturbances alter the fixed phase relationship between subsequent cutting ripples and those of the previous revolution, thus disrupting the regenerative effect. This prevents vibrational energy from periodically superimposing and instead causes them to cancel each other out, forcing chatter to decay rapidly. Once the system stabilizes, the parameters automatically return to their original settings.

[0029] Based on the above embodiments of the present invention, such as Figure 3 As shown, in step S1, the sensor is an acoustic emission sensor. The core function of the acoustic emission sensor is to convert the mechanical vibrations (i.e., acoustic emission signals) generated by changes in the microstructure of the material (such as crack propagation, plastic deformation, fracture, etc.) into detectable and analyzable electrical signals, thereby realizing real-time, non-destructive monitoring of the structural health status.

[0030] Based on the above embodiments of the present invention, such as Figure 2As shown, in step S2, the preset focus frequency band is a frequency band higher than the spindle rotation frequency and related to the structural mode of the machine tool-workpiece-grinding wheel system, ranging from 200Hz to 2000Hz.

[0031] Based on the above embodiments of the present invention, such as Figure 2 As shown, in step S3, the random slight variation causes the adjusted processing parameter value to fluctuate randomly within the range of ±0.5% to ±5% of the reference value.

[0032] Based on the above embodiments of the present invention, such as Figure 2 As shown, the period of fluctuation of the control parameter of the processing parameter adjustment command is preferably 0.1 seconds to 1 second.

[0033] like Figure 3 As shown, based on any of the above embodiments, the present invention also provides an online monitoring and suppression system for chatter during internal thread grinding, comprising:

[0034] The monitoring unit, including the aforementioned sensor and signal conditioning circuit, is used to collect and preliminarily process grinding process signals;

[0035] The processing and identification unit, connected to the monitoring unit, includes a digital signal processor for performing fast Fourier transform and feature energy calculation of the signal;

[0036] The decision unit, connected to the processing and identification unit, includes a logic judgment module and a random instruction generation module, used for threshold comparison and generating random fine-tuning instructions;

[0037] The execution unit is communicatively connected to the decision-making unit and the machine tool CNC system, and is used to forward and ensure that adjustment instructions are executed.

[0038] The specific installation and operation process of the system is as follows: Figure 2 and Figure 3 As shown:

[0039] (1) Hardware Configuration and Installation: An acoustic emission sensor with a resonant frequency range of 100kHz-1MHz is installed on the spindle box of the grinding machine tool, near the rear end of the grinding wheel spindle, to capture stress waves generated by micro-fractures and friction in the grinding zone. A magnetic clamp or special adhesive is used to ensure good coupling between the sensor and the machine tool housing. The sensor signal is connected via a coaxial cable to a signal conditioner installed in the electrical cabinet, which contains a preamplifier with a gain of 40-60dB and a bandpass filter of 100kHz-400kHz. The conditioned analog signal is converted to digital signal by a data acquisition card, with a sampling frequency set to 2MHz. The data acquisition card is connected to an industrial control computer (IPC) via a USB interface. The IPC establishes a communication connection with the machine tool's CNC system via Ethernet.

[0040] (2) Signal processing and flutter feature extraction: including

[0041] Data preprocessing: Dedicated software in the IPC performs software bandpass filtering on the acquired acoustic emission signal digital sequence to retain the frequency bands most relevant to grinding and initial flutter, and to suppress mechanical noise.

[0042] Spectrum analysis and energy calculation: For the filtered signal, perform Short-Time Fourier Transform (STFT) or Fast Fourier Transform (FFT) with a time window of 0.05 seconds and a 50% overlap. Calculate the total signal energy E_t within the frequency band Fb = [f_mod - 50Hz, f_mod + 50Hz]. Here, f_mod is the structural modal frequency of the machine tool-workpiece-grinding wheel system in the grinding direction, pre-identified through hammer impact experiments.

[0043] Threshold setting and chatter detection: During the no-load or trial-cut stabilization phase before each processing cycle, the baseline value E_0 and its standard deviation σ of the Fb band energy E_t are recorded. The chatter detection threshold E_th is set to E_th = E_0 + 3σ. In real-time monitoring, when E_t is greater than E_th for three consecutive calculation windows (i.e., within 0.1 seconds), the software determines that chatter has occurred and triggers the vibration suppression decision module.

[0044] (3) is vibration suppression decision and command generation.

[0045] Once chatter is detected, the decision module immediately generates a spindle speed adjustment command. Assume the currently set spindle speed is S_set = 3000 rpm.

[0046] Generate a random number rand that is uniformly distributed in the interval [-1, 1].

[0047] Calculate the random disturbance amplitude factor Δ = 0.02 rand (i.e., the fluctuation range of ±2%).

[0048] Generate the command rotation speed for the current cycle: S_cmd = S_set (1 + Δ). For example, if rand = 0.35, then S_cmd = 3000. (1 + 0.007) = 3021 rpm.

[0049] The hold period T_set for the rotational speed S_cmd in this command is 0.2 seconds. After 0.2 seconds, the decision module generates a new random number and calculates a new S_cmd. This process is repeated until the chattering is eliminated.

[0050] (4) Instruction execution and recovery

[0051] The IPC, via Ethernet and following the API provided by the CNC system, writes the new spindle speed command S_cmd into the CNC system's R parameters in real time or directly sets the spindle setpoint. The CNC system drives the spindle motor to smoothly adjust to the target speed. Simultaneously, the software continuously monitors E_t. When the E_t value is below 90% of the threshold E_th for 10 consecutive calculation windows (0.5 seconds), chatter is considered eliminated. Subsequently, the system stops sending random adjustment commands and restores the spindle speed setpoint to the original S_set = 3000 rpm, and machining continues.

[0052] Regarding specific types and adjustment ranges of machining parameters: In the online monitoring and suppression method for chatter during internal thread grinding, steps S3 and S4 of this invention involve machining parameters that are key process variables capable of altering energy input and cutting regeneration effects during the grinding process. In one or more embodiments, the machining parameters include, but are not limited to, at least one of the following: spindle speed, axial feed rate, and depth of cut (radial depth of cut).

[0053] The following is an explanation using specific parameter examples:

[0054] 1. Spindle speed adjustment: Machining object: M20×1.5 internal thread, material is hardened bearing steel GCr15 (HRC60). Original setting parameter baseline value: Spindle speed S_set = 4500 rpm. Monitoring and decision-making: When chatter is determined to occur in step S3, the decision unit generates a random fine-tuning command. Parameter adjustment execution: According to the present invention, the range of random micro-variation is ±0.5% to ±5% of the baseline value. In this embodiment, the system randomly generates a variation within the range of [-3%, +3%]. For example, if the variation generated within a 0.2-second period is +1.8%, then the real-time adjustment command speed is S_cmd = 4500 rpm. (1 + 1.8%) = 4581 rpm. The next cycle (e.g., after 0.15 seconds) may generate a change of -2.5%, then S_cmd = 4500. (1 - 2.5%) = 4387.5 rpm. By rapidly, randomly, and with small fluctuations in the spindle speed, the phase conditions for chatter regeneration are disrupted.

[0055] 2. Axial Feed Rate Adjustment: Machining object: M36×4 internal thread, material is high-strength alloy steel 42CrMo. Original parameter baseline: Axial feed rate F_set = 120 mm / min. Monitoring and Decision: Determine chatter occurrence. Parameter Adjustment Execution: Select a variation range of ±0.5% to ±2% of the baseline value. If the system generates a variation of -1.2% within a 0.5-second cycle, the real-time feed rate is adjusted to F_cmd = 120. (1 - 1.2%) = 118.56 mm / min. Vibration suppression is achieved by slightly changing the material removal rate per unit time, thus disrupting the periodicity of cutting force fluctuations.

[0056] 3. Example of adjusting grinding depth (radial depth of cut) (applicable to machine tools whose CNC systems support real-time adjustment of this parameter):

[0057] Machining object: M52×2 internal thread, material is carburized steel 20CrMnTi. Initial parameter baseline: Single grinding depth a_p = 0.02 mm. Monitoring and decision-making: Determine chatter occurrence. Parameter adjustment execution: Select a variation range of ±0.5% to ±4% of the baseline value. If the system generates a variation of +2.5% within a certain cycle, the real-time grinding depth is adjusted to a_p_cmd = 0.02 mm. (1 + 2.5%) = 0.0205 mm. This minute variation must be ensured to be within the tolerance zone allowed by the process.

[0058] In a preferred embodiment of the present invention, the spindle speed is the preferred parameter to be adjusted because it has the most significant direct impact on the regenerative chatter mechanism and the CNC system has a fast dynamic response speed to it.

[0059] To verify the vibration suppression effect of this invention, a comparative experiment was designed and implemented using internal thread grinding as a scenario. The experimental conditions are as follows:

[0060] Machine tool: High-precision CNC internal thread grinding machine. Workpiece: M30×2 internal thread, material is 20CrMnTi, hardness HRC58-62 after carburizing and quenching. Grinding wheel: CBN shaped grinding wheel, diameter D=25mm. Cooling: Water-based emulsion, fully cooled. Inspection equipment: Surface roughness meter (measures Ra and Rz of the tooth flank surface), coordinate measuring machine (measures the taper of the thread pitch diameter), industrial endoscope (observes surface vibration patterns), accelerometer (monitors the intensity of spindle vibration).

[0061] Experimental Groups:

[0062] Control group: Grinding was performed with fixed parameters, and the online monitoring and suppression system of this invention was not activated. The process parameters were: spindle speed 4000 rpm and axial feed 100 mm / min.

[0063] Experimental group: The online monitoring and suppression system of the present invention was activated, with the same baseline parameters as the control group. When the system detected fluttering teeth eruption, it automatically applied random fine-tuning within ±2% of the spindle speed.

[0064] The experimental results are compared and analyzed in the table below:

[0065]

[0066] The above experimental data strongly demonstrates that the method and system provided by this invention, through online monitoring and the application of minute random parameter perturbations, can effectively suppress chatter: eliminating chatter in its infancy and preventing it from developing into severe and harmful vibrations. It significantly improves machining quality: the surface roughness Ra value obtained in the experimental group was reduced by 66.7%, and chatter marks were completely eliminated, ensuring surface quality and geometric accuracy. It effectively protects machining equipment and tools: the spindle vibration intensity in the experimental group was reduced by 73.3%, and the grinding wheel wear rate was reduced by 62.5%, proving that this method can significantly reduce impact damage caused by chatter and extend the service life of key components and consumables.

Claims

1. A method for online monitoring and suppression of chatter during internal thread grinding, characterized in that, Includes the following steps: S1. Signal monitoring: Physical signals reflecting the dynamic characteristics of the grinding process are collected in real time by sensors installed in the grinding area; S2. Signal processing and feature recognition: The signal acquired in step S1 is preprocessed and subjected to spectrum analysis. The signal energy in the preset interest frequency band is calculated, and the energy peak and its corresponding frequency are used as the flutter feature energy value. S3. Vibration Suppression Decision: Compare the flutter characteristic energy value obtained in step S2 with a preset energy threshold; when the flutter characteristic energy value continuously exceeds the energy threshold, it is determined that flutter has occurred, and a processing parameter adjustment instruction is generated, wherein the processing parameter adjustment instruction includes a random micro-variable superimposed on the original set parameter baseline value; S4. Command Execution and Parameter Adjustment: Send the machining parameter adjustment command generated in step S3 to the machine tool CNC system to adjust the spindle speed or feed rate parameters in real time, and restore the original setting parameters after the chatter characteristic energy value falls below the threshold and remains stable.

2. The online monitoring and suppression method for chatter during internal thread grinding according to claim 1, characterized in that, In step S1, the sensor is an acoustic emission sensor.

3. The online monitoring and suppression method for chatter during internal thread grinding according to claim 1 or 2, characterized in that, In step S2, the preset focus frequency band is a frequency band higher than the spindle rotation frequency and related to the structural mode of the machine tool-workpiece-grinding wheel system, ranging from 200Hz to 2000Hz.

4. The online monitoring and suppression method for chatter during internal thread grinding according to claim 1 or 2, characterized in that, In step S3, the random slight variation causes the adjusted processing parameter value to fluctuate randomly within the range of ±0.5% to ±5% of the reference value.

5. The online monitoring and suppression method for chatter during internal thread grinding according to claim 4, characterized in that, The period of fluctuation of the control parameters in the processing parameter adjustment command is 0.1 seconds to 1 second.

6. The online monitoring and suppression method for chatter during internal thread grinding according to claim 1, characterized in that, In step S2, spectrum analysis and energy calculation: For the filtered signal, perform Short Time Fourier Transform (STFT) or Fast Fourier Transform (FFT) with a time window of 0.05 seconds and an overlap of 50%, and calculate the total signal energy E_t within the frequency band Fb = [f_mod - 50Hz, f_mod + 50Hz]. Where f_mod is the structural modal frequency of the machine tool-workpiece-grinding wheel system in the grinding direction, which was identified in advance through a hammering experiment.

7. A method for online monitoring and suppression of chatter during internal thread grinding according to claim 1 or 6, characterized in that, In step S2, threshold setting and chatter judgment: During the no-load or trial cutting stabilization phase before each processing begins, the baseline value E_0 of the Fb band energy E_t and its standard deviation σ are recorded; the chatter judgment threshold E_th is set as E_th = E_0 + 3σ; during real-time monitoring, when E_t is greater than E_th for three consecutive calculation windows, i.e., within 0.1 seconds, the software determines that chatter has occurred and triggers the vibration suppression decision module.

8. An online monitoring and suppression system for chatter during internal thread grinding in accordance with any one of claims 1-7, characterized in that, include: The monitoring unit, including the aforementioned sensor and signal conditioning circuit, is used to collect and preliminarily process grinding process signals; The processing and identification unit, connected to the monitoring unit, includes a digital signal processor for performing fast Fourier transform and feature energy calculation of the signal; The decision unit, connected to the processing and identification unit, includes a logic judgment module and a random instruction generation module, used for threshold comparison and generating random fine-tuning instructions; The execution unit is communicatively connected to the decision-making unit and the machine tool CNC system, and is used to forward and ensure that adjustment instructions are executed.

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