Intelligent numerical control machining programming method and system for machining

Abstract

The invention discloses an intelligent numerical control machining programming method and system for machining, and relates to the technical field of numerical control machining. According to the method, three-axis MEMS vibration sensors are arranged on a main shaft assembly and a workpiece clamping device of numerical control machining equipment, three-axis acceleration time-domain signals in the machining process are collected, feature extraction is conducted in a data processor, and the feature extraction result is obtained; vibration data is acquired, and a resonance data set is constructed; and further based on a response amplification concept in structural dynamics, comparing the closeness degree of the inherent frequency of the structure and the external excitation frequency, and establishing a path resonance risk factor Rf. The factor can quantitatively characterize the resonance sensitivity degree of a processing path segment under the action of structural excitation, and identifies a vibration easily-excited path segment in real time, so that the processing unstable state is effectively avoided on the premise of not influencing the overall process planning, and the prediction capability of path-level dynamic instability and the processing reliability control level are improved.

Description

Technical Field

[0001] This invention relates to the field of CNC machining technology, specifically to an intelligent CNC machining programming method and system for machining. Background Technology

[0002] With the rapid development of intelligent manufacturing and high-end equipment industries, the machining field has placed higher demands on the high-precision and high-stability machining of complex structural parts. This invention relates to the field of mechanical manufacturing, and more particularly to intelligent programming methods in CNC machining technology. More specifically, it belongs to an intelligent CNC machining programming method applied to the machining of complex structural parts, which integrates sensor recognition and adaptive path optimization. This intelligent programming method is mainly aimed at machining tasks of parts with deep cavities, narrow channels, or irregular contours, such as medical devices, aerospace components, and precision molds. During the machining process, unpredictable problems such as structural resonance and chip accumulation are prone to occur, affecting machining stability and tool life.

[0003] Currently, most existing CNC programming systems employ static path planning strategies, lacking the ability to identify and respond in real-time to dynamic behaviors during machining, such as structural vibration, chip clogging, and heat accumulation. This is particularly problematic during the cutting of complex deep-cavity components for medical devices. Due to the large cavity diameter and narrow chip removal channels, conventional CAM paths cannot identify potential risks such as chip retention or cooling dead zones, leading to prolonged chip retention within the cavity. This heat accumulation can cause abnormal phenomena such as tool adhesion, carbonization, or breakage, severely impacting product accuracy and equipment stability. Existing technologies generally lack vibration recognition mechanisms and active path intervention strategies, hindering dynamic optimization and stability assessment of machining paths.

[0004] The aforementioned problems mainly stem from two aspects: First, traditional CNC programming logic focuses on geometric calculations, neglecting the dynamic characteristics of the structure, such as structural resonance response and excitation frequency interference, and their impact on machining quality. Second, it lacks the ability to quantitatively model cooling efficiency and chip behavior in the machining environment, making it impossible to accurately identify and predict the risk of chip retention. Because structural vibration and poor chip removal are not identified and intervened in a timely manner by the system, local heat accumulation and tool microcrack propagation are easily induced during deep cavity cutting, significantly increasing the probability of machining interruption and product scrap. In severe cases, it can even cause equipment cracking, damage, or overheating alarms, affecting machining continuity and safety. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent CNC machining programming method and system for machining, which solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution, comprising the following steps: S1. By setting vibration sensors on CNC machining equipment, vibration data during the machining process is collected in real time, and the vibration data is transmitted to the data processor through the signal transmission module. Feature extraction is performed in the data processor to obtain the resonance dataset. S2. Calculate the path resonance risk factor Rf based on the vibration data set, set a resonance risk interval threshold and compare it with the path resonance risk factor Rf, and trigger the debris intervention mechanism based on the comparison result to calculate the cleaning trigger index Ri. S3. Calculate the comprehensive chip removal score Sc based on the resonance risk factor Rf and the cleaning trigger index Ri of all path segments, compare it with the preset stability threshold Scth, and execute the corresponding strategy based on the comparison result.

[0007] Preferably, S1 includes S11 and S12; S11. Vibration sensors are respectively installed on the spindle assembly and workpiece clamping device of the CNC machining equipment. The vibration sensors are triaxial MEMS vibration sensors. The triaxial MEMS vibration sensors are used to detect the structural micro-vibration signals and spindle excitation frequency signals in real time during the machining process. The collected structural micro-vibration signals and spindle excitation frequency signals are initially converted from analog to digital to generate vibration data containing triaxial time-domain vibration signals of X-axis, Y-axis and Z-axis, where X-axis represents the horizontal axis, Y-axis represents the vertical axis and Z-axis represents the vertical axis. S12. The vibration data is transmitted to the data processor through a high-speed signal transmission module. The signal transmission module includes a differential signal amplification circuit with an anti-interference filter chip and a CAN bus communication module. Before entering the data processor, the vibration data is denoised and formatted by the signal transmission module and transmitted to the data processor in the form of communication protocol frames.

[0008] Preferably, S1 further includes S13; S13. Extract features from the vibration data in the data processor to obtain the resonance dataset; The resonant dataset includes the structural resonant frequency F1, the principal shaft excitation frequency Fs, and the structural response amplification factor Gs. The feature extraction is achieved by digitally processing vibration data based on three-axis time-domain vibration signals including the X-axis, Y-axis and Z-axis to obtain three-axis acceleration time-domain data along the X-axis acceleration time-domain data AX(t), Y-axis acceleration time-domain data AY(t) and Z-axis acceleration time-domain data AZ(t). The triaxial acceleration time-domain data was input and processed by fast Fourier transform to obtain the corresponding spectral power density distribution function. Based on the identification of the main frequency peak with the maximum energy density in the triaxial spectrum, it was extracted as the structural resonant frequency F1 corresponding to the current path segment. Meanwhile, based on the spindle speed r corresponding to the machining path segment and the number of teeth z of the selected tool, the spindle speed r is multiplied by the number of teeth z and then divided by sixty to calculate the spindle excitation frequency Fs; Extract the maximum amplitude Apeak and the average amplitude Aavg from the triaxial acceleration time domain data, and use the ratio of the maximum amplitude to the average amplitude as the first term of the structural response intensity. Then divide the first term by the difference between the structural resonant frequency F1 and the principal shaft excitation frequency Fs to construct the structural response amplification factor Gs. The structural resonant frequency F1, the principal shaft excitation frequency Fs, and the structural response amplification factor Gs are encapsulated into a resonant dataset.

[0009] Preferably, S2 includes S21; S21. Calculate the path resonance risk factor Rf based on the resonance dataset to identify the resonance sensitivity of the path segment under structural excitation. The calculation method is to use the structural response amplification coefficient Gs as the numerator and the absolute value of the difference between the structural resonance frequency F1 and the tool excitation frequency Fs plus 1 as the denominator. The calculation formula is: Rf=Gs / (1+|F1-Fs|).

[0010] Preferably, S2 further includes S22; S22. Calculate the path resonance risk factor Rf for each processing path segment. Compare each path resonance risk factor Rf with a preset resonance risk interval threshold to determine the processing stability of the current path segment. The resonance risk interval threshold is 0.5-1. Based on the numerical range of the path resonance risk factor Rf, each path segment is classified into three levels. The specific comparison is as follows: Path segments with a path resonance risk factor Rf < 0.5 are classified as resonance risk safe zones S; Path segments within the range of 0.5 ≤ path resonance risk factor Rf < 1.0 are classified as medium-risk zones M; path segments within the range of path resonance risk factor Rf ≥ 1.0 are classified as abnormal-risk zones H. When the evaluation results meet any of the following conditions, it is determined that there is abnormal structural vibration in the processing path, and the chip intervention mechanism is immediately triggered. First, the cumulative length of consecutive path segments belonging to the abnormal risk zone H in the path exceeds 10% of the total path length; Secondly, the path resonance risk factor Rf ≥ 1.2 for any single path segment.

[0011] Preferably, S2 further includes S23; S23. After triggering the chip intervention mechanism, chip cleaning data is collected, including chip retention probability factor Cf, cooling coverage Rc and cutting energy density Ed; and all parameters in the chip cleaning data are processed by a unified normalization method to eliminate the dimensional influence of all parameters in the chip cleaning data. The cleaning trigger index Ri is calculated based on the debris cleaning data and output. The cleanup trigger index Ri is calculated and output using the following algorithm formula; ; In the formula, Edref represents the cutting energy density reference value.

[0012] Preferably, S23 further includes S231; S231. Determine the risk level of debris accumulation and removal in a path segment based on the cleaning trigger index Ri, and trigger a path cleaning strategy based on numerical partitioning. Specifically: When the cleanup trigger index Ri is less than 0.6, no intervention is required, and the original processing path is maintained; When the cleaning trigger index Ri is greater than or equal to 0.6 and less than 1.2, a disturbance cleaning segment is inserted at the end of the path segment, and potential debris attachment is removed by lateral rapid swing and variable speed interpolation. When the cleanup trigger index Ri is greater than or equal to 1.2, insert more than two disturbance cleanup segments into the path segment and add a reverse extraction path to optimize the debris removal effect.

[0013] Preferably, S3 includes S31; S31. The path resonance risk factor Rf is used as the coupling gain adjustment term to modulate and enhance the thermal accumulation and chip removal risk represented by the cleaning trigger index Ri, thereby reflecting the degree of synergistic interference between structural vibration and debris accumulation, and obtaining the comprehensive chip removal score Sc. The comprehensive chip removal score Sc for the path is calculated and output using the following algorithm formula; ; In the formula, n represents the total number of path segments, and Rf i Let Ri represent the resonance risk factor of the i-th path segment. i L represents the cleanup trigger index for the i-th path segment. i This represents the length of the i-th path segment.

[0014] Preferably, S3 further includes S32; S32. Compare the overall chip removal score Sc of the current path with the preset stability threshold Scth to determine the processing stability of the current path segment, and execute the corresponding strategy based on the comparison result; the specific comparison content is as follows: When the overall chip removal score Sc < the stability threshold Scth, it indicates that the path is stable and can be put into execution directly. When the overall chip removal score Sc ≥ the stability threshold Scth, the following intervention strategy shall be implemented; When the preset stability threshold Scth ≤ the comprehensive chip removal score Sc < stability threshold Scth × 1.36, it indicates a level 1 risk. At this time, a disturbance cleaning segment is inserted into the path segment corresponding to the peak value of the cleaning trigger index Ri. The disturbance cleaning segment adopts a local extraction method to break the residual chip retention structure; at the same time, the cooling spray method is changed to a surrounding spray. When the stability threshold Scth×1.36≤path comprehensive chip removal score Sc<stability threshold Scth×1.82, it indicates a level 2 risk. At this time, no less than two disturbance cleaning sections are inserted in the path segment of the abnormal risk zone H. The disturbance path adopts a spiral disturbance method combined with the pullback trajectory to optimize the heat release efficiency. The tool feed rate and rotation speed are dynamically adjusted to keep the cutting frequency away from the structural resonance zone. When the overall chip removal score Sc of the path is greater than the stability threshold Scth × 1.82, it indicates a level 3 risk, which prevents the current path from executing and generates a red warning.

[0015] A smart CNC machining programming system for machining includes a resonance data acquisition module, a resonance identification and path segmentation module, and a comprehensive evaluation and control module; The resonance data acquisition module collects vibration data in real time during the machining process by setting vibration sensors on the CNC machining equipment, and transmits the vibration data to the data processor through the signal transmission module. The data processor then performs feature extraction to obtain the resonance dataset. The resonance identification and path segmentation module calculates the path resonance risk factor Rf based on the resonance data set, sets a resonance risk interval threshold and compares it with the path resonance risk factor Rf, and triggers the debris intervention mechanism based on the comparison result to calculate the cleaning trigger index Ri. The comprehensive evaluation and control module calculates the comprehensive chip removal score Sc of the path based on the resonance risk factor Rf and the cleaning trigger index Ri of all path segments, compares it with the preset stability threshold Scth, and executes the corresponding strategy based on the comparison result.

[0016] This invention provides an intelligent CNC machining programming method and system for machining. It has the following beneficial effects: (1) This method involves arranging triaxial MEMS vibration sensors on the spindle assembly and workpiece clamping device of a CNC machining equipment to collect time-domain signals of triaxial acceleration in the X, Y, and Z axes during machining. Feature extraction is performed in a data processor to obtain three parameters: structural resonant frequency F1, spindle excitation frequency Fs, and structural response amplification coefficient Gs, thus constructing a resonance dataset. Furthermore, based on the concept of response amplification in structural dynamics, the proximity between the structure's natural frequency and the external excitation frequency is compared, and a path resonance risk factor Rf is established. This factor can quantify the resonance sensitivity of machining path segments under structural excitation, identify easily excitable path segments in real time, and thus effectively avoid machining instability without affecting the overall process planning, thereby improving the prediction capability of path-level dynamic instability and the level of machining reliability control.

[0017] (2) This method establishes a chip cleaning trigger index Ri by collecting the chip retention probability factor Cf from the FEM chip prediction model, cooling coverage Rc, and cutting energy density Ed, based on the identification of high-risk vibration path segments. This formula integrates three key factors: structural vibration-induced accumulation risk, cooling obstruction degree, and unit heat power density, achieving three-field collaborative modeling across structural geometry, heat source input, and cooling channels. The Ri value can dynamically assess whether a path segment is prone to chip accumulation, and trigger the disturbance cleaning segment and reverse extraction path intervention mechanism accordingly, promptly breaking the chip adhesion chain, thereby effectively reducing carbon deposition and chip removal obstruction in structures such as blind cavities and deep holes, ensuring the thermal conductivity of the tool area and the cooling efficiency of the machining area.

[0018] (3) This method introduces a comprehensive chip removal score Sc, which integrates the resonance risk factor Rfi, the cleaning trigger index Rii, and the path length Li of each path segment to form a comprehensive score index for the risk of the entire path. This score can be used as a quantitative criterion for processing stability and compared with the stability threshold Scth obtained by fitting historical cases, thereby triggering a multi-level dynamic control strategy. Specifically: when the comprehensive chip removal score Sc < Scth, the path is executed directly; when Sc is between Scth and Scth × 1.36, disturbance cleaning and cooling mode adjustment are performed; when Sc further rises to Scth × 1.82, spiral disturbance and parameter dynamic vibration isolation are performed; if Sc > Scth × 1.82, it is determined to be a level 3 risk path, and the system automatically prevents execution and marks a high failure warning. This mechanism can realize integrated closed-loop control of structural resonance and chip accumulation risk, improve the response sensitivity to potential processing risks in the programming stage and the automatic adaptation capability of path reconstruction. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the steps of an intelligent CNC machining programming method for machining according to the present invention.

[0020] Figure 2 This is a block diagram of an intelligent CNC machining programming system for machining according to the present invention.

[0021] Figure 3 This is a schematic diagram of the data flow. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1 Please see Figure 1 and Figure 3 This invention provides an intelligent CNC machining programming method for machining. To achieve the above objectives, this invention is implemented through the following technical solution, including the following steps: S1. By setting vibration sensors on CNC machining equipment, vibration data during the machining process is collected in real time, and the vibration data is transmitted to the data processor through the signal transmission module. Feature extraction is performed in the data processor to obtain the resonance dataset. S2. Calculate the path resonance risk factor Rf based on the vibration data set, set a resonance risk interval threshold and compare it with the path resonance risk factor Rf, and trigger the debris intervention mechanism based on the comparison result to calculate the cleaning trigger index Ri. S3. Calculate the comprehensive chip removal score Sc based on the resonance risk factor Rf and the cleaning trigger index Ri of all path segments, compare it with the preset stability threshold Scth, and execute the corresponding strategy based on the comparison result.

[0024] In this embodiment, the method arranges a triaxial MEMS vibration sensor on the spindle assembly and the workpiece clamping device, enabling real-time acquisition of micro-vibration data during the machining process. This setup avoids the error accumulation problem associated with traditional external derivation, obtaining more timely and spatially accurate structural dynamic response data. After anti-interference encoding with a high-speed signal transmission module, the data is transmitted to the data processor, effectively ensuring stable transmission of high-frequency vibration signals in complex electromagnetic environments. This setup achieves real-time perception and channel-level monitoring of the resonance state, improving the responsiveness and data reliability of dynamic state extraction. In step S2, based on the structural resonance frequency F1, spindle excitation frequency Fs, and structural response amplification factor Gs extracted from the vibration data, a path resonance risk factor Rf is constructed to determine whether the machining path segment is in the excitation critical region. The path resonance risk factor Rf uses |F1-Fs| as a resonance approximation factor because when the excitation frequency is close to the structure's natural frequency, the structure is prone to high-amplitude resonant vibration, leading to tool damage and path machining instability. This method allows for early identification of easily excitable path segments, achieving proactive risk identification. Further, a chip removal trigger index Ri is introduced, integrating three-dimensional parameters of structural geometry, heat input, and cooling obstruction to comprehensively assess the risk of chip accumulation in the cutting area. This determines whether to insert a disturbance removal path, achieving proactive early warning control of chip thermal accumulation problems. In step S3, the resonance risk factor Rf of each path segment, the removal trigger index Ri, and the path segment length Li are integrally coupled to construct a comprehensive chip removal score Sc, which is then compared with a preset stability threshold Scth. This score design takes into account both dynamic response intensity and structural thermal blockage risk, giving higher weight to high-risk long path segments and more accurately reflecting the overall stability level of the path execution. By setting a graded intervention strategy based on multiples of Scth, excessive intervention can be avoided in low-risk areas, and disturbance paths or machining schemes can be accurately located and inserted or switched in high-risk scenarios, achieving coordinated control of path chip removal performance and structural vibration risk. For example, in complex contour paths with deep cavities and thin sidewalls, if high-vibration areas are not identified in time and disturbance removal strategies are not adopted, problems such as repeated chip impact on the machining surface, severe tool build-up, and surface quality deterioration are likely to occur. This method identifies path segments with Rf > 1.2 in real time, automatically inserts reverse disturbance trajectories, and optimizes cooling angles, thereby improving the efficiency of local hot channel recovery and chip removal, and significantly reducing the probability of failure. Overall, this method shifts the programming system from "result judgment" to "risk prediction," and from "global mean control" to "precise intervention in path segments," substantially improving the stability, reliability, and automation and intelligence level of the machining process in complex structural areas.

[0025] Example 2 Please see Figure 1 Specifically: S1 includes S11 and S12; S11. Vibration sensors are respectively installed on the spindle assembly and workpiece clamping device of the CNC machining equipment. The vibration sensors are three-axis MEMS vibration sensors. The three-axis MEMS vibration sensors are used to detect the structural micro-vibration signals and spindle excitation frequency signals in real time during the machining process. The collected structural micro-vibration signals and spindle excitation frequency signals are initially converted from analog to digital to generate vibration data containing three-axis time-domain vibration signals of X-axis, Y-axis and Z-axis, where X-axis represents the horizontal axis, Y-axis represents the vertical axis and Z-axis represents the vertical axis. S12. The vibration data is transmitted to the data processor through a high-speed signal transmission module. The signal transmission module includes a differential signal amplification circuit with an anti-interference filter chip and a CAN bus communication module. Before entering the data processor, the vibration data is denoised and formatted by the signal transmission module and transmitted to the data processor in the form of communication protocol frames.

[0026] S1 also includes S13; S13. Extract features from the vibration data in the data processor to obtain the resonance dataset; The resonant dataset includes the structural resonant frequency F1, the principal shaft excitation frequency Fs, and the structural response amplification factor Gs; Feature extraction is achieved by digitally processing vibration data based on three-axis time-domain vibration signals including the X-axis, Y-axis and Z-axis to obtain three-axis acceleration time-domain data along the X-axis acceleration time-domain data AX(t), Y-axis acceleration time-domain data AY(t) and Z-axis acceleration time-domain data AZ(t). The triaxial acceleration time-domain data was input and processed by fast Fourier transform to obtain the corresponding spectral power density distribution function. Based on the identification of the main frequency peak with the maximum energy density in the triaxial spectrum, it was extracted as the structural resonant frequency F1 corresponding to the current path segment. Meanwhile, based on the spindle speed r corresponding to the machining path segment and the number of teeth z of the selected tool, the spindle speed r is multiplied by the number of teeth z and then divided by sixty to calculate the spindle excitation frequency Fs. This calculation method is because the number of spindle rotations per unit time and the number of tool teeth together determine the periodic impact frequency generated on the workpiece per unit time. Therefore, the spindle excitation frequency Fs accurately characterizes the input frequency characteristics of external excitation. The maximum amplitude Apeak and the average amplitude Aavg are extracted from the triaxial acceleration time-domain data. The ratio of the maximum amplitude to the average amplitude is used as the first term of the structural response intensity. The first term is then divided by the difference between the structural resonant frequency F1 and the principal axis excitation frequency Fs to construct the structural response amplification factor Gs. This processing method is used to comprehensively reflect whether the path segment is in a state close to the excitation and structural resonance, and whether the structural response shows abnormal amplification, thereby assessing its resonance sensitivity. The structural resonant frequency F1, the principal shaft excitation frequency Fs, and the structural response amplification factor Gs are encapsulated into a resonant dataset.

[0027] In this embodiment, triaxial MEMS vibration sensors are arranged on the spindle assembly and workpiece clamping device of the CNC machining equipment to collect structural micro-vibration signals and spindle excitation frequency signals in real time during the machining process. The triaxial MEMS vibration sensors acquire vibration data in the X, Y, and Z axes respectively, ensuring comprehensive coverage of the structural response. The vibration signals are first converted from analog to digital at the sensor end and then transmitted to the data processor via a high-speed signal transmission module consisting of a differential signal amplification circuit with an anti-interference filter chip and a CAN bus communication module. Noise filtering and communication protocol encapsulation are performed during this process to ensure that the vibration data maintains a high signal-to-noise ratio and frame synchronization even under strong interference. In the data processor, the vibration data is digitized and then the acceleration time-domain data AX(t) along the X-axis, AY(t) along the Y-axis, and AZ(t) along the Z-axis are extracted. The three-axis acceleration time-domain data are input into a Fast Fourier Transform (FFT) module to obtain the power spectral density distribution function in the corresponding frequency domain. The frequency corresponding to the maximum energy density point is identified and extracted as the structural resonant frequency F1, reflecting the inherent vibration characteristics of the structure corresponding to the path segment. Simultaneously, the spindle excitation frequency Fs is calculated based on the spindle speed r and the number of teeth z of the selected tool for the current path segment. This calculation logic is based on the physical principle that "tool rotation frequency × number of tool teeth = number of excitations to the workpiece per unit time," accurately characterizing the periodic excitation frequency output by the spindle during machining. The maximum acceleration amplitude Apeak and the average acceleration amplitude Aavg are further extracted from the three-axis vibration data, and a structural response amplification factor Gs is constructed. The structural response amplification factor Gs characterizes the resonant amplification trend of the structure when the excitation frequency Fs is close to the structural resonant frequency F1. A higher structural response amplification factor Gs value indicates a stronger response after structural excitation in that path segment, and a higher potential vibration risk. The structural resonant frequency F1, the spindle excitation frequency Fs, and the structural response amplification factor Gs are collectively encapsulated into a resonance dataset for subsequent dynamic stability analysis of the machining path. Through this combined design of three-axis data acquisition, feature frequency extraction, and resonance risk modeling, the system can accurately identify resonance-sensitive segments in the machining path, effectively avoiding resonance instability, surface damage, and premature tool wear caused by the overlap of excitation and structural natural frequencies. This enhances the intelligent sensing capabilities and path safety of CNC machining.

[0028] Example 3 Please see Figure 1 Specifically: S2 includes S21; S21. Calculate the path resonance risk factor Rf based on the resonance dataset to identify the resonance sensitivity of the path segment under structural excitation. The calculation method is to use the structural response amplification factor Gs as the numerator and the absolute value of the difference between the structural resonance frequency F1 and the tool excitation frequency Fs plus 1 as the denominator. The calculation formula is: Rf=Gs / (1+|F1-Fs|); where the structural response amplification factor Gs reflects the intensity of the structure's response to excitation; the structural resonance frequency F1 is the first natural frequency of the structure corresponding to the path segment; the tool excitation frequency Fs is the periodic excitation frequency of the CNC equipment spindle during machining in the path segment; a higher path resonance risk factor Rf value indicates that the path segment is in a state where the excitation frequency is close to the natural frequency and the structural resonance response is significant, indicating a greater risk to machining stability. This calculation formula originates from the concept of "response amplification factor" in structural dynamics, such as the classical resonance response analysis method in "Principles of Mechanical Vibration" and "Engineering Handbook of Modal Analysis". This technical solution has normalized and corrected it and fused the resonance approximation factor. In resonance theory, the amplified response of a structure can be expressed by the correlation between dynamic compliance and excitation frequency difference. At the same time, considering the gain effect of the structure on the excitation, the structural amplification response factor Gs should be introduced. In order to avoid the denominator being zero or too small, which would lead to a sudden change in value, "+1" is introduced as a stabilizing term. Therefore, the path resonance risk factor Rf expression method in this invention is formed. The larger the path resonance risk factor Rf is, the closer the structural resonance frequency is to the excitation frequency, and the stronger the response to the excitation. At this time, the structure is prone to enter a strongly coupled vibration state, which leads to problems such as deterioration of machining stability in the path segment, easy tool wear, and serious heat accumulation. |F1-Fs| is the frequency difference, which is the degree of deviation between the structure's natural frequency and the excitation frequency. It is used to measure whether the system is close to the resonance state. The unit is Hertz (Hz), and its absolute value represents the degree of proximity to resonance. In the denominator on the right side of the entire formula, “1+|F1-Fs|” is the resonance approach factor after dimensionality reduction, and its reciprocal increases as the resonance critical point approaches. The structural response amplification factor Gs is used to introduce an amplification tendency in molecules. Even if the frequency difference is not small, if the structural response amplification factor Gs is extremely large, significant vibration problems may occur. Therefore, the structural response amplification factor Gs is an important factor in amplification risk.

[0029] S2 also includes S22; S22. Calculate the path resonance risk factor Rf for each processing path segment. Compare the path resonance risk factor Rf of each path segment with a preset resonance risk interval threshold to determine the stability of the current path segment processing. The resonance risk interval threshold is 0.5-1. Based on the numerical range of the path resonance risk factor Rf, each path segment is classified into three levels. The specific comparison is as follows: Path segments with a path resonance risk factor Rf < 0.5 are classified as resonance risk safe zones S; Path segments within the range of 0.5 ≤ path resonance risk factor Rf < 1.0 are classified as medium-risk zones M; path segments within the range of path resonance risk factor Rf ≥ 1.0 are classified as abnormal-risk zones H. When the evaluation results meet any of the following conditions, it is determined that there is abnormal structural vibration in the processing path, and the chip intervention mechanism is immediately triggered. First, the cumulative length of consecutive path segments belonging to the abnormal risk zone H in the path exceeds 10% of the total path length; Secondly, the path resonance risk factor Rf ≥ 1.2 for any single path segment.

[0030] In this embodiment, a path resonance risk factor Rf is introduced in S21 as a quantitative indicator of resonance sensitivity. This is combined with the structural response amplification factor Gs and the difference between the excitation frequency and the structure's natural frequency for normalization. The aim is to accurately reveal the potential structural resonance risk of the path segment during machining. When the tool excitation frequency Fs approaches F1, it enters the resonance critical region. If the structural response amplification factor Gs is also high, it will cause the small excitation to be significantly amplified by the structure, resulting in severe tool vibration, significant cutting force fluctuations, and a decrease in the quality of the machined surface. Through the calculation formula, the resonance approach trend and structural amplification characteristics can be reasonably integrated and modeled physically, enabling the identification of potential risks even when the frequency difference is small and the structural response capability is strong, thus enhancing the model's robustness. S22 further classifies and evaluates the path resonance risk factor Rf and threshold intervals for each path segment, dividing them into a safe zone S, a medium-risk zone M, and a high-risk zone H. This is to achieve a hierarchical management and differentiated processing strategy for machining path segments. Setting Rf < 0.5 as the safety threshold is based on general statistical analysis of the vibration stability region in actual machining. Rf ≥ 1.0 is classified as a high-risk zone because in this range, the excitation frequency is significantly close to the structural resonant frequency, and the superimposed structural amplification effect easily triggers strong resonance. Setting two triggering conditions—a cumulative Rf exceeding 10% in consecutive H segments or a single segment Rf ≥ 1.2—effectively avoids over-response to short-term minor anomalies while ensuring rapid identification and control of high-risk hazards. Through this mechanism, the system can dynamically identify resonance hazards in the path segment and intervene in advance in a real physical sense, effectively reducing typical failure problems caused by vibration, such as tool chipping, thermal wear, and surface cracks, thereby significantly improving the stability of path execution and machining yield in complex machining environments.

[0031] Example 4 Please see Figure 1 Specifically: S2 also includes S23; S23. After triggering the chip removal intervention mechanism, chip cleaning data is collected, including chip retention probability factor Cf, cooling coverage Rc and cutting energy density Ed; and all parameters in the chip cleaning data are processed in a unified normalization manner to eliminate the dimensional influence of all parameters in the chip cleaning data. The cleaning trigger index Ri is calculated based on the debris cleaning data and output. The cleanup trigger index Ri is calculated and output using the following algorithm formula; ; In the formula, Edref represents the cutting energy density reference value; The cleaning trigger index Ri is used to quantitatively determine the risk of chip retention caused by resonant excitation in the machining path. Its construction is based on the interaction of three physical dimensions: cutting heat input, cooling limitation degree, and structural disturbance chip accumulation tendency. The formula is derived from the integration of classical heat flux analysis principle, fluid cooling flow field evaluation theory and FEM structural excitation chip accumulation modeling method, and finally constructed by combining multi-source coupled field simulation analysis. This formula starts from the structural thermal-fluid-structure coupling perspective, integrates the main influencing factors of chip accumulation, and establishes the triggering conditions of active intervention mechanism. Calculation logic: The three multiplicative terms that trigger the exponent Ri have a clear synergistic effect logic: The first term, Cf, provides the original probability weights for the tendency of the structure to induce debris accumulation, and is the dominant factor. The second term (1+Ed / Edref) represents the amplification effect of thermal power density on debris accumulation. After normalization, it can be adapted to the thermal limits of different materials and equipment. The third term (1-Rc) reflects the amplification mechanism of the impact of cooling obstruction on the chip removal path; the weaker the cooling, the larger the product result. Therefore, a higher cleaning trigger index Ri indicates that "debris is more easily accumulated, heat is more difficult to dissipate, and cleaning is more difficult," and path cleaning strategies should be implemented as soon as possible to intervene. This combination method ensures that the left and right sides of the formula have consistent dimensions, and that all parameters are dimensionless or processed through a unified normalization method; The specific data collection method for debris cleaning is as follows: By acquiring the structural geometric feature parameters of the region corresponding to the processing path segment, including the local cavity depth-to-diameter ratio, sidewall tilt angle, corner radius, and structural opening morphology, and combining them with the vibration response feature vector of the region during processing, including peak acceleration, spectral energy density, and resonant frequency distribution, the above structural morphology and vibration behavior are input parameters into the trained chip retention probability prediction model. The chip retention probability prediction model is constructed based on the finite element method (FEM) and historical processing data, and outputs the chip retention probability factor Cf of the region, which is used to quantify and predict the accumulation potential of chips in the deep cavity or blind cavity of the structure. Secondly, the injection parameters of the cooling system are collected, including coolant nozzle angle, nozzle pressure, injection flow rate and injection direction. After constructing a three-dimensional geometric model of the machining process, computational fluid dynamics (CFD) simulation is performed. Based on the simulation results, it is determined whether the tool and cutting point are fully covered by coolant. As an alternative or supplementary means, an infrared thermal imager can be used to monitor the heat distribution spectrum of the machining area during the machining process. The cooling coverage rate Rc is derived from the analysis of temperature distribution gradient and cooling dead angle. The cooling coverage rate Rc is used to quantify whether the effect of coolant is limited. A low value indicates that cooling is blocked or ineffective. Finally, based on the real-time acquired spindle power signal and machining time record, combined with the preset tool geometry parameters (tool diameter and cutting edge length) and machining path volume information, the cutting energy density Ed is derived using the following expression relationship: the spindle power consumed per unit time is divided by the cutting volume of the corresponding path segment to obtain the average thermal power density per unit volume. Cutting energy density Ed is used to evaluate the degree of thermal load on the material in the path segment and is the thermodynamic criterion for chip carbonization, adhesion, and removal obstruction.

[0032] S23 also includes S231; S231. Determine the risk level of debris accumulation and removal in a path segment based on the cleaning trigger index Ri, and trigger a path cleaning strategy based on numerical partitioning. Specifically: When the cleanup trigger index Ri is less than 0.6, no intervention is required, and the original processing path is maintained; When the cleaning trigger index Ri is greater than or equal to 0.6 and less than 1.2, a disturbance cleaning segment is inserted at the end of the path segment, and potential debris attachment is removed by lateral rapid swing and variable speed interpolation. When the cleaning trigger index Ri is greater than or equal to 1.2, insert more than two disturbance cleaning segments into the path segment and add a reverse extraction path to optimize the chip removal effect, thereby achieving dynamic reduction of the risk of chip accumulation in local areas and real-time reconstruction of the chip removal channel. The disturbance clearing section is a non-cutting segment that actively guides chip removal through tool path disturbance. Inserted into high-risk path segments, this auxiliary non-cutting path segment induces local disturbances by altering the tool path or feed parameters, breaking the chip retention state and thus reconstructing the chip removal channel and mitigating heat buildup. This path segment does not aim to remove material but rather uses lateral oscillation, lifting and retraction, and helical disturbances, combined with coolant flow, to create a disturbance zone, prompting chips to detach from the machined surface or cavity. The disturbance clearing section is triggered by a clearing trigger index Ri. When Ri exceeds a set threshold, a corresponding number of disturbance paths are automatically inserted based on the risk level to improve chip removal efficiency and machining thermal stability. This mechanism effectively supplements conventional path chip removal strategies and has a significant improvement effect in scenarios with structural resonance and chip coupling risks.

[0033] In this embodiment, the method introduces a chip removal triggering index Ri, which is built upon three factors: chip accumulation tendency Cf, heat input density Ed, and cooling resistance degree Rc. This addresses the problem that chip accumulation induced by structural vibration in complex cavity structures is difficult to identify and handle in a timely manner during the machining path. Such chip accumulation, especially in deep cavities, corners, and dead zones, not only causes cutting heat concentration and uncontrolled local temperature rise, but also leads to tool edge welding, carbonization, and even premature failure. Through the product logic design of the chip removal triggering index Ri formula, the risk of the three factors of "heat, cold, and chips" can be synergistically managed. The system features enhanced precision identification and a multi-level threshold range for the cleaning trigger index Ri. This enables intelligent judgment and strategy triggering of chip removal capabilities at the machining path level. For example, when the cleaning trigger index Ri exceeds 1.2, it indicates significant chip adhesion and limited cooling channels. Continuing conventional cutting at this point would significantly increase the risk of thermal failure. Therefore, it is necessary to break the original chip flow path by inserting a non-cutting disturbance section and a reverse extraction section to restore local chip removal capabilities. This physically reconstructs the cooling chip removal channels, improving chip removal efficiency. The trajectory intervention of the disturbance section, combined with coolant disturbance, forms a synergistic turbulence zone, which can quickly remove chips from structural dead corners. This mechanism solves the hidden danger of machining failure caused by the superposition of "heat-chip-vibration" in traditional paths. It achieves quantitative assessment and dynamic intervention of micro-scale chip removal risks, resulting in significant improvements in thermal stability control, tool life enhancement, and cavity cleanliness assurance.

[0034] Example 5 Please see Figure 1 Specifically: S3 includes S31; S31. The path resonance risk factor Rf is used as the coupling gain adjustment term to modulate and enhance the thermal accumulation and chip removal risk represented by the cleaning trigger index Ri, thereby reflecting the degree of synergistic interference between structural vibration and debris accumulation, and obtaining the comprehensive chip removal score Sc. The comprehensive chip removal score Sc is calculated and output using the following algorithm formula; ; In the formula, n represents the total number of path segments, and Rf i Let Ri represent the resonance risk factor of the i-th path segment. i L represents the cleanup trigger index for the i-th path segment. i This represents the length of the i-th path segment. The path length is automatically extracted by exporting the trajectory using industrial software. In this formula, the path resonance risk factor Rf is coupled with the cleanup triggering index Ri by multiplying them, and the path length L is introduced as a weighting factor. This makes the anomalies in high-risk segments and long path segments more prominent, thereby enhancing the sensitivity and discriminative power of the score. By accumulating the score results of n path segments, the cumulative processing risk value of the entire path can be obtained, reflecting the potential failure risk and thermal coupling anomaly trend of the entire path in the execution process. This scoring model is based on the principle of superposition of energy excitation and heat dissipation impedance in composite systems in physics, and couples the risk of resonance drive (reflecting dynamic response) with the factor of heat accumulation due to debris blockage (reflecting energy flow obstruction) in a modeling manner. The introduction of Li stems from considerations of the energy distribution coverage of path segments, making the score contribution of high-risk long path segments more significant. Compared to the weighted model, the multiplicative combination method can more accurately amplify the interference impact of high-risk sections and effectively guide the adaptive path reconstruction logic; Consistency of parameter dimensions: Rf is a dimensionless risk factor, Ri is also a dimensionless exponent, and Li is length. The product has the physical meaning of energy risk intensity, which is consistent with the logic of the scoring model.

[0035] S3 also includes S32; S32. Compare the overall chip removal score Sc of the current path with the preset stability threshold Scth to determine the processing stability of the current path segment, and execute the corresponding strategy based on the comparison result; the specific comparison content is as follows: When the overall chip removal score Sc < the stability threshold Scth, it indicates that the path is stable and can be put into execution directly. When the overall chip removal score Sc ≥ the stability threshold Scth, the following intervention strategy shall be implemented; When the preset stability threshold Scth ≤ the comprehensive chip removal score Sc < stability threshold Scth × 1.36, it indicates a level 1 risk. At this time, a disturbance cleaning segment is inserted into the path segment corresponding to the peak value of the cleaning trigger index Ri. The disturbance cleaning segment adopts a local extraction method to break the residual chip retention structure; at the same time, the cooling spray method is changed to a surrounding spray. When the stability threshold Scth×1.36≤path comprehensive chip removal score Sc<stability threshold Scth×1.82, it indicates a level 2 risk. At this time, no less than two disturbance cleaning sections are inserted in the path segment of the abnormal risk zone H. The disturbance path adopts a spiral disturbance method combined with the pullback trajectory to optimize the heat release efficiency. The tool feed rate and rotation speed are dynamically adjusted to keep the cutting frequency away from the structural resonance zone. When the overall chip removal score Sc of the path is greater than the stability threshold Scth × 1.82, it indicates a level 3 risk, which prevents the current path from executing and generates a red warning; Among them, by statistically analyzing the comprehensive chip removal score Sc of the paths that have experienced abnormalities such as tool breakage, overheating, and poor chip removal in the past processing, the average value of these failure cases is taken as a reference threshold. For example, if the comprehensive chip removal score Sc of the path with problems in a large number of cases is around 220, then Scth=220 can be set.

[0036] In this embodiment, the method couples the path resonance risk factor Rf with the cleaning trigger index Ri, and then introduces the path segment length Li to construct a comprehensive chip removal score Sc. This enables a comprehensive assessment of the machining stability of the entire path under the triple risks of "vibration-heat accumulation-chip blockage". The multiplicative coupling not only enhances the sensitivity to identify high-risk segments, but also avoids the shortcomings of traditional weighted averaging in masking local anomalies, ensuring rapid response in areas of concentrated risk. Furthermore, by comparing the comprehensive chip removal score Sc with the stability threshold Scth, the automatic triggering of the hierarchical control strategy is achieved. For example, when the comprehensive chip removal score Sc exceeds the stability threshold Scth × 1.82, it indicates that the path segment may have a serious risk of resonance and chip blockage coupling. If execution is still carried out, it will easily lead to tool breakage, workpiece thermal damage, or chip removal failure. Therefore, execution is actively prohibited at this time, and a red warning signal is generated, which can proactively avoid problems before they occur. This mechanism is particularly suitable for structures such as deep cavities, thin walls, and long cantilever structures, which are prone to micro-resonance and chip retention coupling during machining. Without a coordinated intervention mechanism, the probability of failure will be greatly increased. The core design of the scoring model lies in "using energy excitation and structural resistance coupling as criteria", combined with path length weight, which enhances the model's adaptability to physical reality, thereby effectively improving the robustness of path planning and the stability of the processing.

[0037] Example 6 Please see Figure 1 and Figure 2 A smart CNC machining programming system for machining includes a resonance data acquisition module, a resonance identification and path segmentation module, and a comprehensive evaluation and control module; The resonance data acquisition module collects vibration data in real time during the machining process by setting vibration sensors on CNC machining equipment, and transmits the vibration data to the data processor through the signal transmission module. The data processor then performs feature extraction to obtain the resonance dataset. The resonance identification and path segmentation module calculates the path resonance risk factor Rf based on the resonance data set, sets a resonance risk interval threshold and compares it with the path resonance risk factor Rf, and triggers the debris intervention mechanism based on the comparison result to calculate the cleaning trigger index Ri. The comprehensive evaluation and control module calculates the comprehensive chip removal score Sc of the path based on the resonance risk factor Rf and the cleaning trigger index Ri of all path segments, compares it with the preset stability threshold Scth, and executes the corresponding strategy based on the comparison result.

[0038] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. An intelligent numerical control machining programming method for machining, characterized in that: The method comprises the following steps: S1, collecting vibration data in the machining process in real time by setting vibration sensors on the numerical control machining equipment, and transmitting the vibration data to the data processor through a signal transmission module, and extracting features in the data processor to obtain a resonance data set; S2, calculating the path resonance risk factor Rf based on the vibration data set, comparing the resonance risk interval threshold value with the path resonance risk factor Rf, triggering the chip intervention mechanism based on the comparison result, and calculating the cleaning trigger index Ri; S3, calculating the path comprehensive chip removal score Sc according to the resonance risk factor Rf and the cleaning trigger index Ri of all path segments, comparing with the preset stability threshold value Scth, and executing the corresponding strategy based on the comparison result.

2. The intelligent numerical control machining programming method for machining according to claim 1, characterized in that: The S1 comprises S11 and S12; S11, vibration sensors are arranged on the spindle assembly and the workpiece clamping device of the numerical control machining equipment, the vibration sensors are three-axis MEMS vibration sensors, the three-axis MEMS vibration sensors are used to detect structural micro-vibration signals and spindle excitation frequency signals in the machining process in real time, and the collected structural micro-vibration signals and spindle excitation frequency signals are subjected to preliminary analog-digital conversion to generate vibration data containing three-axis time-domain vibration signals of X-axis, Y-axis and Z-axis, wherein the X-axis represents the horizontal axis, the Y-axis represents the vertical axis, and the Z represents the vertical axis; S12, transmitting the vibration data to the data processor through a high-speed signal transmission module, the signal transmission module comprises a differential signal amplification circuit with an anti-interference filter chip and a CAN bus communication module, wherein the vibration data is subjected to denoising processing and format coding by the signal transmission module before entering the data processor, and is transmitted to the data processor in the form of a communication protocol frame.

3. The intelligent numerical control machining programming method for machining according to claim 2, characterized in that: The S1 further comprises S13; S13, extracting features of the vibration data in the data processor to obtain a resonance data set; The resonance data set comprises a structural resonance frequency F1, a spindle excitation frequency Fs and a structural response amplification coefficient Gs; The feature extraction is carried out by digital processing of the vibration data containing three-axis time-domain vibration signals of X-axis, Y-axis and Z-axis to obtain three-axis acceleration time-domain data in the directions of X-axis acceleration time-domain data AX(t), Y-axis acceleration time-domain data AY(t) and Z-axis acceleration time-domain data AZ(t); And inputting the three-axis acceleration time-domain data into a fast Fourier transform processor to obtain corresponding frequency spectrum power density distribution functions, and identifying the main frequency peak with the maximum energy density in the three-axis frequency spectrum to extract the structural resonance frequency F1 corresponding to the current path segment; At the same time, the spindle excitation frequency Fs is calculated by multiplying the spindle speed r by the number of teeth z of the selected tool and then dividing the product by sixty; The maximum amplitude Apeak and the average amplitude Aavg in the three-axis acceleration time-domain data are extracted, and the ratio of the maximum amplitude to the average amplitude is taken as the first item of the structural response strength, and the first item is divided by the difference between the structural resonance frequency F1 and the spindle excitation frequency Fs to construct the structural response amplification coefficient Gs. The structural resonance frequency F1, the main shaft excitation frequency Fs and the structural response amplification coefficient Gs are packaged into a resonance data set.

4. The intelligent numerical control machining programming method for machining according to claim 3, characterized in that: The S2 includes S21; The S21 calculates an output path resonance risk factor Rf based on the resonance data set, and identifies the resonance sensitivity of the path segment under the structural excitation, and the calculation formula is: Rf=Gs / (1+|F1-Fs|), wherein Gs is the numerator, and the value obtained by adding 1 to the absolute value of the difference between the structural resonance frequency F1 and the tool excitation frequency Fs is the denominator.

5. The intelligent numerical control machining programming method for machining according to claim 4, characterized in that: The S2 further includes S22; The S22 calculates a path resonance risk factor Rf for each of the entire machining path segments, compares and judges each path resonance risk factor Rf with a preset resonance risk interval threshold, judges the stability of the current path segment machining, the resonance risk interval threshold is 0.5-1, and each path segment is classified into three levels according to the numerical range of the path resonance risk factor Rf; the specific comparison content is as follows: The path segment with a path resonance risk factor Rf less than 0.5 is divided into a resonance risk safe zone S; The path segment in the interval of 0.5≤path resonance risk factor Rf<1.0 is divided into a medium risk zone M; The path segment with a path resonance risk factor Rf greater than or equal to 1.0 is divided into an abnormal risk zone H; When the evaluation result meets any of the following conditions, it is determined that the machining path has a structural vibration abnormality, and the chip interference mechanism is triggered immediately; First, the length of the continuous path segment belonging to the abnormal risk zone H in the path accumulates more than 10% of the total path length; Second, the path resonance risk factor Rf of any single path segment is greater than or equal to 1.

2.

6. The intelligent numerical control machining programming method for machining according to claim 5, characterized in that: The S2 further includes S23; The S23 collects chip cleaning data after triggering the chip interference mechanism, the chip cleaning data includes a chip retention probability factor Cf, a cooling coverage rate Rc and a cutting energy density Ed, and all parameters in the chip cleaning data are processed in a unified normalization manner to eliminate the dimension influence of all parameters in the chip cleaning data; The cleaning trigger index Ri is calculated based on the chip cleaning data; The cleaning trigger index Ri is calculated by the following algorithm formula: ； In the formula, Edref represents the reference value of the cutting energy density.

7. The intelligent numerical control machining programming method for machining according to claim 6, characterized in that: The S23 further includes S231; The S231 determines the chip accumulation and chip removal risk level of the path segment based on the cleaning trigger index Ri, and triggers the path cleaning strategy based on the numerical partition, Specifically: When the cleaning trigger index Ri is less than 0.6, no intervention is needed, and the original machining path is maintained; When the cleaning trigger index Ri is greater than or equal to 0.6 and less than 1.2, a disturbance cleaning segment is inserted at the end of the path segment, and transverse rapid swinging and variable interpolation are used to break the potential chip adhesion; When the cleaning trigger index Ri is greater than or equal to 1.2, more than two disturbance cleaning segments are inserted in the path segment, and a reverse pumping path is additionally added to optimize the chip removal effect.

8. The intelligent numerical control machining programming method for machining according to claim 7, characterized in that: The S3 includes S31; S31, taking the path resonance risk factor Rf as a coupling gain adjustment term to modulate and enhance the heat accumulation and chip removal risk represented by the cleaning trigger index Ri, thereby reflecting the degree of synergistic interference between structural vibration and chip accumulation, to obtain a path comprehensive chip removal score Sc; The path comprehensive chip removal score Sc is calculated and output by the following algorithm formula: ； where n represents the total number of path segments, Rf i represents the resonance risk factor for the i-th path segment, Ri i represents the cleaning trigger index for the i-th path segment, L i represents the length of the i-th path segment.

9. The intelligent numerical control machining programming method for machining according to claim 8, characterized in that: The S3 further includes S32; S32, comparing the path comprehensive chip removal score Sc with the preset stability threshold Scth to judge the stability of the current path segment machining, and executing corresponding strategies based on the comparison result; the specific comparison content is as follows: When the path comprehensive chip removal score Sc is less than the stability threshold Scth, it indicates that the path is stable, and it is directly put into execution; When the path comprehensive chip removal score Sc is greater than or equal to the stability threshold Scth, the following intervention strategies are executed; When the preset stability threshold Scth is less than or equal to the path comprehensive chip removal score Sc and less than the stability threshold Scth multiplied by 1.36, it indicates a first-level risk, at this time a disturbance cleaning segment is inserted in the path segment corresponding to the peak value of the cleaning trigger index Ri, the disturbance cleaning segment adopts a local pulling mode to break the chip retention structure; at the same time, the spray cooling mode is changed to a surrounding spray mode; When the stability threshold Scth multiplied by 1.36 is less than or equal to the path comprehensive chip removal score Sc and less than the stability threshold Scth multiplied by 1.82, it indicates a second-level risk, at this time not less than two disturbance cleaning segments are inserted in the abnormal risk area H path segment, the disturbance path adopts a spiral disturbance mode combined with a backtracking trajectory to optimize the heat release efficiency; the tool feed speed and rotational speed are dynamically adjusted to make the cutting frequency far away from the structural resonance area; When the path comprehensive chip removal score Sc is greater than the stability threshold Scth multiplied by 1.82, it indicates a third-level risk, and the current path is prevented from being executed, and a red warning is generated.

10. An intelligent numerical control machining programming system for machining, applied to the intelligent numerical control machining programming method of any one of claims 1-9, characterized in that: It comprises a resonance data acquisition module, a resonance identification and path segmentation module, and a comprehensive evaluation control module; The resonance data acquisition module acquires vibration data in real time during the machining process by setting a vibration sensor on the numerical control machining equipment, transmits the vibration data to the data processor through a signal transmission module, and extracts features in the data processor to obtain a resonance data set; The resonance identification and path segmentation module calculates the path resonance risk factor Rf based on the vibration data set, compares the resonance risk interval threshold with the path resonance risk factor Rf, and triggers the chip intervention mechanism based on the comparison result to calculate the cleaning trigger index Ri; The comprehensive evaluation control module calculates the path comprehensive chip removal score Sc according to the resonance risk factor Rf and the cleaning trigger index Ri of all path segments, compares it with the preset stability threshold Scth, and executes corresponding strategies based on the comparison result.

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