A method and system for chip control in a machining center

Abstract

The present application belongs to the technical field of metal cutting machining control, and particularly relates to a chip removal control method and system for a machining center, which method comprises: collecting real-time vibration signals of the machining center and preprocessing to obtain pure vibration signals; performing periodic stationarity analysis on the pure vibration signals to obtain periodic stationarity purity of each frequency band signal and determine an optimal characteristic frequency band; extracting features capable of representing chip accumulation evolution trend based on the optimal characteristic frequency band and performing real-time grading on the chip accumulation state; and executing a matching active avoidance or graded response control strategy according to the chip accumulation state grade. The present application can accurately distinguish random chip accumulation impact from normal machining load fluctuation, significantly improve the robustness and sensitivity of monitoring, and realize intelligent control upgrade from passive response to active avoidance.

Description

Technical Field

[0001] This invention relates to the field of metal cutting control technology. More specifically, this invention relates to a chip removal control method and system for machining centers. Background Technology

[0002] In CNC machining centers, metal cutting, especially the machining of deep holes or complex cavities, generates a large amount of chips. If these chips are not removed promptly and effectively, they will accumulate, entangle, and even be used for secondary cutting by the tool. This chip accumulation not only severely scratches the machined surface of the workpiece, leading to excessive surface roughness and reduced machining accuracy, but it can also cause excessive tool wear or sudden chipping. In high-precision machining, even a small chip accumulation can cause workpiece scrap, resulting in significant economic losses. In automated production lines, severe chip accumulation can force production interruptions, affecting overall production efficiency. When chip accumulation reaches a certain level, it can even trigger safety accidents, threatening the safety of operators and equipment. Therefore, achieving early warning and effective control of chip accumulation is crucial for ensuring machining quality, improving production efficiency, and ensuring production safety.

[0003] To achieve early warning of chip accumulation, existing technologies have proposed using vibration signals for analysis. However, most of these methods rely on the analysis of static and apparent characteristics of the signal morphology, such as kurtosis, sparsity, and waveform factors. This approach is essentially blind source processing, meaning that without understanding the signal generation mechanism, feature extraction and pattern recognition are performed solely based on the external manifestation of the signal. Analysis is limited to amplitude or energy levels, making it impossible to effectively distinguish between vibrations from normal cutting and random impacts caused by chip accumulation. Under complex working conditions, such as when the tool enters / exits the workpiece, when the material is uneven, or when process parameters change, the vibration signal will exhibit changes similar to those of chip accumulation, leading to a high false alarm rate in existing technologies. Summary of the Invention

[0004] To address the technical problem that the existing technology cannot effectively distinguish between vibration signals from normal cutting and random impact signals from chip accumulation, resulting in insufficient monitoring sensitivity and robustness, the present invention provides solutions in the following aspects.

[0005] In a first aspect, the present invention provides a chip removal control method for a machining center, comprising:

[0006] Real-time vibration signals from the machining center are acquired and preprocessed to obtain clean vibration signals. These clean vibration signals are then converted to the frequency domain, which is divided into several pre-selected frequency bands of equal width. The cyclic spectral density function of each pre-selected frequency band is calculated, and the periodic stationary purity index of each pre-selected frequency band is determined based on the cyclic spectral density function. The optimal characteristic frequency band is determined based on the periodic stationary purity index. Time-domain characteristic signals are extracted from the optimal characteristic frequency band, and the chip accumulation evolution trend index at the current moment is determined based on the characteristic signals. The chip accumulation state is classified according to the changes in the chip accumulation evolution trend index. Based on the real-time determined chip accumulation state level, a control strategy matching that level is executed.

[0007] This invention analyzes the periodicity of each pre-selected frequency band, starting from the inherent rhythm and dynamic characteristics of the signal. It can accurately capture the damage caused by chip accumulation to the normal cutting cycle, thus identifying early anomalies with high sensitivity. Compared with traditional analysis methods that rely on signal amplitude or energy form, it has stronger anti-interference ability, can effectively distinguish between normal machining load fluctuations and true random anomalies, and significantly reduces the false alarm rate. Through graded response and active avoidance control, this invention realizes the upgrade from passive cleaning to active prevention, comprehensively improving machining quality, efficiency and safety.

[0008] Preferably, the preprocessing is noise reduction processing.

[0009] Preferably, the periodic stable purity index satisfies the expression: In the formula, For the first The periodic stability purity index of each pre-selected frequency band; For the first Cyclic spectral density function of a preselected frequency band signal; This is the normal frequency; The cycle frequency; For the first related to normal cutting A set of characteristic cyclic frequencies, which consists of the tooth pass frequency and its harmonics; This represents the total number of selected characteristic cycle frequencies.

[0010] In this invention, the periodic stability purity index reflects the energy proportion of periodic stability components. When random impacts are introduced into chip accumulation, non-periodic energy will increase significantly, resulting in a decrease in the periodic stability purity index. The periodic stability purity index can reflect the purity of the signal periodicity, providing a reliable data basis for identifying chip accumulation.

[0011] Preferably, determining the optimal feature frequency band based on the periodic stability purity index includes: selecting the pre-selected frequency band with the smallest periodic stability purity index as the optimal feature frequency band.

[0012] The lower the periodicity purity index, the more sensitive the frequency band is to chip accumulation. This invention selects the frequency band with the smallest periodicity purity index as the optimal feature frequency band, which can adaptively lock the signal frequency band that best reflects the early chip accumulation characteristics, providing the optimal signal input for subsequent refined feature extraction and state judgment.

[0013] Preferably, the step of extracting the time-domain feature signal from the optimal feature frequency band includes: performing wavelet packet decomposition on the pure vibration signal according to the center frequency and bandwidth of the optimal feature frequency band, and reconstructing the time-domain signal corresponding to the optimal feature frequency band as the feature signal.

[0014] Preferably, the chip accumulation evolution trend index satisfies the expression: In the formula, This is the sequence number of the current time. This serves as an indicator of the current chip accumulation evolution trend. The adaptive smoothing factor at the current moment; This is an indicator of the chip accumulation evolution trend at the previous moment.

[0015] This invention transforms a series of instantaneous vibration information into a continuously changing chip accumulation evolution trend index that can stably reflect the accumulation process and severity. This effectively smooths out short-term fluctuations in the signal, enabling it to better reflect the long-term development trend of chip accumulation and providing a reliable decision-making basis for subsequent state classification.

[0016] Preferably, the adaptive smoothing factor satisfies the expression, including: In the formula, This is the sequence number of the current time. The adaptive smoothing factor at the current moment; The characteristic signal amplitude at the current moment; The baseline of the average amplitude of the feature signal learned during the normal cutting phase without chip buildup; The standard deviation of the characteristic signal during the normal cutting stage; It is a natural exponential function; and These are the minimum and maximum value boundaries of the smoothing factor; This is the sensitivity coefficient.

[0017] This invention achieves adaptive adjustment of the smoothing factor by calculating the normalization degree of the current signal point's deviation from the normal baseline. When the signal fluctuates within the normal range, the adaptive smoothing factor tends to the minimum value, and the system exhibits strong memory. When the signal deviates significantly from the baseline, the adaptive smoothing factor tends to the maximum value, and the system exhibits strong sensitivity. This allows the system to switch between stability and response speed, ensuring accurate tracking of the chip accumulation evolution process.

[0018] Preferably, the step of classifying the chip accumulation state includes: responding to the chip accumulation evolution trend index at the current moment being less than an alarm threshold. When the chip accumulation evolution trend index at the current moment is greater than or equal to the alarm threshold, it is determined to be a normal cutting state. When the growth rate of the chip accumulation evolution trend index at the current moment is less than the growth rate threshold, it is determined to be in a trend growth state; in response, when the chip accumulation evolution trend index at the current moment is greater than or equal to the alarm threshold... When the growth rate of the chip accumulation evolution trend index at the current moment is greater than or equal to the growth rate threshold, it is judged to be in a state of rapid deterioration; in response to the chip accumulation evolution trend index at the current moment being greater than or equal to... and continue When the time exceeds milliseconds, it is considered a severe accumulation state, where, greater than The severity coefficient.

[0019] Preferably, the step of executing a control strategy matching the real-time determined chip accumulation state level includes: executing a silent monitoring strategy in response to normal cutting conditions; executing a low-intensity early warning chip removal strategy in response to a trend-increasing state; executing a real-time machining parameter intervention strategy in response to a rapidly deteriorating state; and executing a protective shutdown and powerful chip removal strategy in response to a severe accumulation state.

[0020] Secondly, the present invention provides a chip removal control system for a machining center, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the aforementioned chip removal control method for a machining center is implemented.

[0021] By adopting the above technical solution, a chip removal control method for machining centers is generated into a computer program and stored in a memory for loading and execution by a processor. This allows for the creation of terminal devices based on the memory and processor, facilitating their use.

[0022] The beneficial effects of this invention are as follows: By constructing a periodically stable purity index, this invention, starting from the inherent rhythm and dynamic characteristics of the signal, can accurately capture the damage caused by chip accumulation to the normal cutting cycle, thereby highly sensitively identifying early anomalies. Compared with traditional analysis methods that rely on signal amplitude or energy form, it has stronger anti-interference capabilities. This invention introduces a real-time machining parameter intervention strategy that matches the accumulation state level, upgrading the system from a passive response mode of cleaning up after a problem occurs to an active avoidance mode that prevents the problem from worsening at the source of the process. This effectively ensures the continuity and stability of the machining process, avoids downtime caused by severe accumulation, avoids unnecessary forceful chip removal, reduces the consumption of coolant and compressed air, and achieves comprehensive optimization of production quality, efficiency, and cost. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a chip removal control method for a machining center according to the present invention;

[0024] Figure 2 A schematic diagram of a pure vibration signal;

[0025] Figure 3 This is a schematic diagram for obtaining the optimal feature frequency band.

[0026] Figure 4 This is a schematic diagram illustrating the changes in the trend indicators of chip accumulation evolution. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0029] This invention discloses a chip removal control method for machining centers, referring to... Figure 1 This includes steps S1-S4:

[0030] S1. Collect real-time vibration signals from the machining center, preprocess the real-time vibration signals to obtain clean vibration signals.

[0031] It should be noted that during precision machining in a machining center, any minute abnormality, such as abnormal scraping of a single chip against the tool, will leave a trace in the overall vibration of the machine tool. However, these critical weak signals are often masked by the strong background noise generated by the spindle rotation, coolant pump, etc. Therefore, this invention collects the real-time vibration signal of the machining center and performs noise reduction processing on the vibration signal.

[0032] Specifically, an acceleration sensor is installed near the spindle bearing housing or tool holder of the machining center to collect real-time vibration signals of the machining center.

[0033] In this embodiment, the sampling frequency is set to ,in The maximum spindle speed that the machining center can achieve, in rpm. For the maximum number of teeth that can be used, for example when rpm At that time, sampling frequency Hz. In other embodiments, the implementer may adjust the sampling frequency according to the parameters of the actual machining center, but should ensure that the sampling frequency is not less than 10 times the maximum tooth pass frequency of the tool in order to fully capture the vibration characteristics during the cutting process.

[0034] Furthermore, the acquired raw vibration signal is subjected to noise reduction processing to obtain a clean vibration signal. In this embodiment, median filtering is used to reduce the noise of the raw vibration signal to eliminate interference from environmental and electrical noise. It should be noted that in the deep hole drilling process, median filtering is particularly suitable for processing pulse noise, effectively preserving the transient impact characteristics generated by chip accumulation, while suppressing periodic interference generated by coolant pumps, etc. In other embodiments, implementers can also choose the noise reduction algorithm according to the actual implementation situation, such as wavelet threshold denoising. For example, Figure 2 This is a schematic diagram of a pure vibration signal.

[0035] S2. Convert the pure vibration signal to the frequency domain, divide the frequency domain into several pre-selected frequency bands of equal width, calculate the cyclic spectral density function of each pre-selected frequency band, determine the periodic stationary purity index of each pre-selected frequency band based on the cyclic spectral density function, and determine the optimal characteristic frequency band based on the periodic stationary purity index.

[0036] It should be noted that the rotational motion of the cutting tool in a CNC machining center causes the vibration signal generated during normal cutting to exhibit a statistically time-dependent periodic variation. This periodic rhythm is strictly synchronized with process parameters such as spindle speed and the number of tool teeth. However, the random collisions and scraping caused by early chip accumulation constitute a non-stationary, non-periodic impact interference. This interference disrupts the periodic stability of the original signal. Therefore, this invention analyzes the purity of the periodic stability of signals in each frequency band to inversely identify the intensity of non-periodic interference, thereby identifying the most sensitive characteristic frequency band.

[0037] Specifically, the spindle speed is obtained in real time from the CNC system of the machining center. and the number of teeth of the currently used cutting tool The fundamental frequency component related to the normal cutting process, i.e., the tooth pass frequency, is calculated. and its harmonic frequencies ,in It is a positive integer.

[0038] Furthermore, the preprocessed pure vibration signal undergoes time-frequency domain transformation, and the entire frequency domain is divided into... A preselected frequency band of equal width, in which = , Sampling frequency, The frequency of the tooth pass. This is the floor sign. In this invention, the bandwidth is set to the tooth pass frequency. This ensures sufficient bandwidth resolution near each harmonic frequency, thereby accurately capturing changes in periodic components.

[0039] Calculate the cyclic spectral density function for the signal within each preselected frequency band.

[0040] It should be noted that the cyclic spectral density function (CSD) is an existing technique in signal processing used to analyze the frequency domain characteristics of periodically stationary signals. In this embodiment, the time-domain smoothed periodogram method is used to calculate the CSD. Specifically, the cyclic autocorrelation function of the signal is first calculated, and then a Fourier transform is performed on the cyclic autocorrelation function to obtain the CSD. During the calculation, a Hanning window is used to window the signal to reduce spectral leakage. The CSD can represent the signal energy along the conventional frequency axis. and cycle frequency axis Distribution on.

[0041] Furthermore, based on the cyclic spectral density function, the periodic stationary purity index of each pre-selected frequency band is calculated:

[0042]

[0043] In the formula, For the first The periodic stability purity index of each pre-selected frequency band; For the first Cyclic spectral density function of a preselected frequency band signal; This is a standard frequency; The cycle frequency; For the first related to normal cutting A set of characteristic cyclic frequencies, whose collection consists of the tooth frequency and its harmonics, i.e. ; This represents the total number of selected feature cycle frequencies. To balance detection reliability with real-time computation efficiency, The value range is usually between 2 and 5, with an empirical value of 3. Implementers can set it according to the actual implementation situation. The value of .

[0044] It should be noted that, in order to quantify the relative intensity of regular and random components in a signal, this invention constructs a periodic stationary purity index. The numerator of the periodic stationary purity index is calculated by integration at the characteristic cycle frequency of the signal energy related to regular normal cutting. The sum of the numerator and denominator represents the energy of the periodic stationary components in the signal; the denominator is the sum of the energies of the periodic stationary components and the non-periodic stationary components, representing the overall energy of the signal. Therefore, the periodic stationary purity index reflects the proportion of the energy of the periodic stationary components to the total energy. During normal cutting, the signal is highly periodic, with most of the energy concentrated at the characteristic cycle frequency, and the numerator is approximately equal to the denominator. The value approaches When chips begin to accumulate, the non-periodic collisions between the tool and the chips generate random impacts. These impacts disrupt the periodic vibrations produced by normal cutting, affecting various positions in the cycle spectrum, especially... The axis introduces a large amount of aperiodic energy, causing the energy of the aperiodic stationary component in the denominator to increase, while the numerator remains essentially unchanged or even decreases, thus leading to... The value decreases significantly. Therefore, the smaller the periodic stability purity index, the more severe the influence of random shocks on the preselected frequency band, and the more sensitive it is to chip accumulation.

[0045] Furthermore, the pre-selected frequency band with the smallest periodic stability purity index is taken as the optimal characteristic frequency band, and the center frequency of this optimal characteristic frequency band is taken as the optimal center frequency, and its bandwidth is taken as the optimal bandwidth. For example, Figure 3 This is a schematic diagram for obtaining the optimal feature frequency band.

[0046] S3. Extract the time-domain feature signal from the optimal feature frequency band, and determine the chip accumulation evolution trend index at the current moment based on the feature signal.

[0047] Specifically, based on the optimal center frequency and optimal bandwidth, a customized wavelet packet decomposition is performed on the pure vibration signal, and the time-domain signal corresponding to the optimal characteristic frequency band is reconstructed as the characteristic signal.

[0048] The number of wavelet packet decomposition layers is determined based on the optimal center frequency and the sampling frequency.

[0049]

[0050] In the formula, The number of wavelet packet decomposition levels. For the optimal center frequency, Sampling frequency, This is a floor function. This invention selects the db4 wavelet from the Daubechies wavelet family as the basis function, and decomposes the signal into its constituent parts through wavelet packet decomposition. Each frequency band, and then based on the optimal center frequency and optimal bandwidth The corresponding frequency band coefficients are selected for reconstruction to obtain the time-domain signal corresponding to the optimal characteristic frequency band. It should be noted that the db4 wavelet in the Daubechies wavelet system is selected as the basis function in this embodiment because the db4 wavelet has good time-frequency localization characteristics and is suitable for analyzing transient impact signals in the cutting process. In other embodiments, implementers can select the basis function of wavelet packet decomposition according to the actual implementation situation.

[0051] Furthermore, based on the statistical properties of the characteristic signals, a smoothing factor is determined for calculating the evolutionary trend index:

[0052]

[0053] In the formula, This is the sequence number of the current time. The adaptive smoothing factor at the current moment; The characteristic signal amplitude at the current moment; The baseline of the average amplitude of the feature signal learned during the normal cutting phase without chip buildup; The standard deviation of the characteristic signal during the normal cutting stage; It is a natural exponential function.

[0054] In the formula, and The minimum and maximum values ​​of the smoothing factor are empirically set at 0.05 and 0.95, respectively. In other embodiments, the implementer can adjust these values ​​according to the actual processing conditions. and For example, in high-precision machining, to improve system stability and reduce false alarm rate, one can... Increase to 0.1, Reduce to 0.9; in rough machining or conditions prone to chip accumulation, to improve detection sensitivity and avoid false negatives, the following parameters can be used: Reduced to 0.01, Increase to 0.99. It should be noted that... It should not be set too low, generally not lower than 0.01, otherwise the system will be too sensitive in a stable state and easily affected by random noise. It should not be set too high, generally not higher than 0.99, otherwise the system will have too weak memory in the event of a sudden change and will be unable to effectively track the development trend of the anomaly.

[0055] In the formula, This is the sensitivity coefficient, used to adjust the system's threshold for distinguishing between normal fluctuations and abnormal signals. According to statistical rules of thumb, approximately 99.7% of normal data will fall within the mean ± 3 standard deviations. Therefore, this invention will... Set to 3; implementers can also set this according to the actual implementation situation. A higher setting raises the threshold for the system to identify anomalies, resulting in a lower false alarm rate but potentially missing early chip accumulation. The smaller the setting, the higher the system's sensitivity to anomalies, enabling earlier detection of initial signs of chip accumulation; however, the false alarm rate increases accordingly. In practical applications, implementers should weigh the factors based on machining process requirements, false alarm tolerance, and the risk of missed alarms. For example, in high-precision machining, the setting can be appropriately increased. The value is increased to reduce the false alarm rate, and can be appropriately reduced in rough machining or conditions prone to chip accumulation. To improve detection sensitivity.

[0056] In the formula, The degree of deviation of the characteristic signal from the normal baseline at the current moment is calculated. This invention maps the degree of signal deviation to the value range of a smoothing factor through an exponential decay function, achieving adaptive adjustment of the smoothing factor. When the system is in the normal cutting stage without chip accumulation, the signal fluctuation is small, and the deviation is minimal. Smaller, when the deviation is less than the sensitivity coefficient. At that time, the exponent term Approaching 1, making Approaching ,at this time Approaching the minimum value The system exhibits strong memory and can effectively filter random noise; conversely, in the early stages of chip accumulation, the signal undergoes abrupt changes, with the signal point deviating significantly from the baseline. When the deviation exceeds... At that time, the exponential term approaches , making Approaching ,at this time Approaching the maximum value The system can quickly improve its sensitivity and promptly capture abnormal development trends, providing a sufficient time window for subsequent proactive intervention.

[0057] Furthermore, based on the characteristic signal and the adaptive smoothing factor, the chip accumulation evolution trend index is calculated:

[0058]

[0059] In the formula, This is the sequence number of the current time. This serves as an indicator of the current chip accumulation evolution trend. The adaptive smoothing factor at the current moment; This is an indicator of the chip accumulation evolution trend at the previous moment. This invention tracks the deviation of the characteristic signal amplitude from the normal baseline using an exponentially weighted moving average with an adaptive smoothing factor, thereby transforming a series of instantaneous vibration information into a continuously changing chip accumulation evolution trend indicator that stably reflects the accumulation process and severity. For example, Figure 4 This is a schematic diagram illustrating the changes in the trend indicators of chip accumulation evolution.

[0060] S4. Classify the chip accumulation state according to the changes in the chip accumulation evolution trend index, and execute a control strategy that matches the real-time determined chip accumulation state level.

[0061] Specifically, set a dynamically adaptive alarm threshold that matches the operating conditions. The method for obtaining the data is as follows: at the beginning of each new working condition, vibration signals are collected under a non-accumulation state, and the mean value of the chip accumulation evolution trend index at that stage is calculated. and standard deviation and set alarm thresholds Set to: .

[0062] Furthermore, calculate the growth rate of the chip accumulation evolution trend index at the current moment. : In the formula, This serves as an indicator of the current chip accumulation evolution trend. This is an indicator of the chip accumulation evolution trend at the previous moment. This represents the sampling time interval.

[0063] Set growth rate threshold By analyzing historical chip buildup events, the growth rate under normal cutting conditions is calculated. mean and standard deviation Then set ,in For safety reasons, the implementers can adjust the parameters according to the actual processing conditions. For example, in high-precision machining, to avoid misjudgment, one can... Increased to 0.8, making the growth rate threshold... Increase; in rough machining, to improve detection sensitivity, one can... Reduce to 0.3 to make the growth rate threshold Decrease.

[0064] The chip accumulation evolution trend indicator in response to the current moment Less than the alarm threshold When the cutting condition is normal, it is determined to be in a normal cutting state; this is in response to the chip accumulation evolution trend index at the current moment. Greater than or equal to the alarm threshold And the growth rate of the chip accumulation evolution trend indicator at the current moment. Less than the growth rate threshold When the trend is increasing, it is determined to be in a state of increasing trend; the chip accumulation evolution trend indicator at the current moment is used as a response. Greater than or equal to the alarm threshold And the growth rate of the chip accumulation evolution trend indicator at the current moment. Greater than or equal to the growth rate threshold At that time, it is determined to be a state of rapid deterioration; the chip accumulation evolution trend index at the current moment. Greater than or equal to and continue A time frame exceeding milliseconds is considered a severe accumulation state. greater than The severity coefficient is set by the implementers based on the actual implementation situation, for example... .

[0065] It should be noted that the present invention is set The millisecond duration threshold is designed to avoid misjudgments caused by brief fluctuations, ensuring that only persistent and severe anomalies trigger the highest level of response. In actual machining, when the system detects a chip accumulation trend, immediately implementing forceful chip removal measures might disrupt normal machining and affect surface quality. Through a tiered response strategy, the system can intervene with low intensity in the early stages of chip accumulation, eliminating potential problems without interrupting machining. Only when the accumulation severely threatens machining quality will forceful measures such as machine shutdown be implemented, maximizing the continuity and stability of the machining process.

[0066] Specifically, in response to normal cutting conditions, a silent monitoring strategy is implemented, and the system does not intervene in any way;

[0067] In response to the increasing trend, a low-intensity early warning chip removal strategy is implemented. A command is sent to the PLC to trigger a short, targeted pulse jet, or the coolant flow rate is increased slightly and then restored. This eliminates early problems at the lowest cost. In actual deep hole drilling, the short pulse jet can effectively remove the chips that have just begun to accumulate without interfering with the normal drilling process.

[0068] In response to a rapidly deteriorating situation, a real-time machining parameter intervention strategy is implemented, prioritizing the sending of instructions to the CNC controller to proactively prevent the problem from worsening.

[0069] In response to severe chip buildup, a protective shutdown and powerful chip removal strategy are implemented. A "feed hold" command is sent to the CNC to pause machining, while a command is sent to the PLC to trigger the highest power powerful chip removal program and issue an alarm signal.

[0070] Optionally, in the event of a rapidly deteriorating state, the processing parameter intervention strategy includes at least one of the following:

[0071] 1. Dynamic feed rate adjustment: Instantly reduces the current feed rate. This reduces the amount of chips generated per unit time.

[0072] 2. Spindle speed perturbation: Performing adjustments around the current spindle speed. Within range, frequency is The periodic fluctuation of Hz is used to assist in the breakage of long chips by utilizing the resonance variation.

[0073] 3. Instantaneous toolpath adjustment: During cavity machining, control the tool to momentarily retract slightly along the axial direction. After mm, it recovers, using the gap to allow coolant to flow in and flush away chips.

[0074] It should be noted that during complex cavity milling, when chip accumulation deteriorates rapidly, the spindle speed perturbation strategy can effectively utilize the resonance effect to break long chips and prevent them from wrapping around the tool. Meanwhile, the instantaneous adjustment of the tool path can create an opportunity for coolant to enter the tool-workpiece interface without interrupting the machining process, effectively flushing away the accumulated chips.

[0075] This invention also discloses a chip removal control system for a machining center, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a chip removal control method for a machining center according to the present invention.

[0076] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. A method for chip evacuation control of a machining center, characterized by, The method comprises: Collecting and processing the real-time vibration signal of the machining center to obtain a pure vibration signal; Converting the pure vibration signal to the frequency domain, dividing the frequency domain into a plurality of equal-width preselected frequency bands, calculating the cyclic spectral density function of each preselected frequency band, and determining the periodic stationary purity index of each preselected frequency band according to the cyclic spectral density function; determining the optimal feature frequency band according to the periodic stationary purity index, including: taking the preselected frequency band with the smallest periodic stationary purity index as the optimal feature frequency band; Extracting a time-domain feature signal from the optimal feature frequency band, and determining the chip accumulation evolution trend index at the current time according to the feature signal; Classifying the chip accumulation state according to the change of the chip accumulation evolution trend index; and executing a control strategy matched with the classified chip accumulation state according to the real-time determination. The cyclostationarity purity index satisfies the expression: ; where is the cyclostationarity purity index of the th preselected frequency band; is the cyclostationarity purity index of the th preselected frequency band signal; is the normal frequency; is the cyclic frequency; is the th characteristic cyclic frequency associated with normal cutting, the set of which is composed of the tooth pass frequency and its harmonics; is the total number of characteristic cyclic frequencies used.

2. A method for chip evacuation control for a machining center according to claim 1, characterized in that, The preprocessing is noise reduction processing.

3. A method for chip evacuation control for a machining center according to claim 1, characterized in that, The feature signal in the time domain is extracted from the optimal feature frequency band, including: Performing wavelet packet decomposition on the pure vibration signal according to the center frequency and bandwidth of the optimal feature frequency band, and reconstructing the time-domain signal corresponding to the optimal feature frequency band as the feature signal.

4. A method for chip evacuation control for a machining center according to claim 1, characterized in that, The chip accumulation evolution trend index satisfies the expression: ； In the formula, is the serial number of the current time, is the chip accumulation evolution trend index of the current time; is the adaptive smoothing factor of the current time; is the chip accumulation evolution trend index of the previous time.

5. A method of chip evacuation control for a machining center according to claim 4, characterized in that, The adaptive smoothing factor satisfies the expression, including: ； In the formula, is the sequence number of the current time, is the adaptive smoothing factor of the current time; is the characteristic signal amplitude of the current time; is the average amplitude baseline of the characteristic signal learned in the normal cutting phase without chip accumulation; is the standard deviation of the characteristic signal in the normal cutting phase; is the natural exponential function; and are the minimum and maximum value boundaries of the smoothing factor; is the sensitivity coefficient.

6. A method of chip evacuation control for a machining center according to claim 1, wherein, The chip accumulation state is classified, including: in response to the trend index of the accumulation of the cutting chips at the current time being less than the alarm threshold , the current time being less than the alarm threshold , and the growth rate of the trend index of the accumulation of the cutting chips at the current time being less than the growth rate threshold , the current time being less than the alarm threshold , and the growth rate of the trend index of the accumulation of the cutting chips at the current time being greater than or equal to the growth rate threshold , the current time being greater than or equal to the alarm threshold , and the growth rate of the trend index of the accumulation of the cutting chips at the current time being greater than or equal to the growth rate threshold , the current time being greater than or equal to the alarm threshold 7. A method of chip evacuation control for a machining center according to claim 6, characterized in that, The control strategy matched with the classified chip accumulation state is executed according to the real-time determination of the chip accumulation state, including: In response to the normal cutting state, a silent monitoring strategy is executed; in response to the trend growth state, a low-intensity early warning type chip removal strategy is executed; in response to the sharp deterioration state, a real-time machining parameter intervention strategy is executed; and in response to the serious accumulation state, a protective shutdown and strong chip removal strategy is executed.

8. A chip evacuation control system for a machining center, characterized by, The method comprises: A processor and a memory, the memory storing computer program instructions, when the computer program instructions are executed by the processor, realizing a chip removal control method for a machining center according to any one of claims 1-7.

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