Intelligent numerical control machining programming method and system for machining

Abstract

The application discloses a kind of intelligent numerical control machining programming method and system for machining, it is related to numerical control machining technical field, this method is by being arranged three-axis MEMS vibration sensor on numerical control machining equipment main shaft assembly and workpiece clamping device, three-axis acceleration time domain signal is collected in processing process, and feature extraction is carried out in data processor, obtains vibration data, constructs resonance dataset.Further based on the concept of response amplification in structural dynamics, the closeness of the natural frequency of the structure and the external excitation frequency is compared, and the path resonance risk factor Rf is established.Through this factor, the resonance sensitivity of the machining path segment under the action of structural excitation can be quantitatively characterized, and the vibration prone path segment can be identified in real time, thereby effectively avoiding the unstable state of machining without affecting the overall process planning, and improving the prediction ability of path-level dynamic instability and the level of machining reliability control.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of numerical control machining, in particular to an intelligent numerical control machining programming method and system for machining. BACKGROUND

[0002] With the rapid development of intelligent manufacturing and high-end equipment industry, the field of mechanical machining puts forward higher requirements for high-precision and high-stability machining of complex structure parts. The application relates to the field of mechanical manufacturing, in particular to an intelligent programming method in the technical field of numerical control machining, and more particularly to an intelligent numerical control machining programming method applied to the machining process of complex structure parts, which integrates sensing identification and adaptive path optimization. The intelligent programming method is mainly aimed at machining tasks of parts with deep cavity structure, narrow channel or special-shaped profile, such as medical devices, aviation components and precision molds, and problems such as structural resonance and chip accumulation are difficult to predict in the machining process, which affects the machining stability and tool life.

[0003] At present, in the existing numerical control programming system, a static path planning strategy is mostly adopted, and the real-time identification and response capability for dynamic behaviors in the machining process, such as structural vibration, chip jamming and heat accumulation, is lacking. Especially in the cutting process of medical device type complex deep cavity structure parts, due to the large depth-diameter ratio of the cavity and the narrow chip removal channel, the conventional CAM path cannot identify potential risks such as chip retention and cooling dead angle, which leads to long-term retention of chips in the cavity, and further accumulation of heat, causing abnormal phenomena such as tool adhesion, carbonization or fracture, which seriously affects the product precision and equipment stability. The existing technology generally lacks vibration identification mechanism and active path intervention strategy, and cannot realize dynamic optimization and stability evaluation of the machining path.

[0004] The above problems are mainly caused by two aspects: one is that the traditional numerical control programming logic is mainly based on geometric calculation, ignoring the dynamic characteristics of the structure, such as structural resonance response and excitation frequency interference, which affect the machining quality; the other is the lack of quantitative modeling capability for the cooling efficiency and chip behavior in the machining environment, which cannot accurately identify and predict the chip retention risk. Due to the fact that structural vibration and poor chip removal are not identified and intervened in time, local heat accumulation and tool micro-crack propagation are easily induced during deep cavity cutting, which significantly increases the probability of machining interruption and product rejection, and in severe cases, even causes equipment vibration and damage or overheat alarm, affecting the machining continuity and safety. SUMMARY

[0005] In view of the deficiencies of the prior art, the application provides an intelligent numerical control machining programming method and system for machining, which solves the problems mentioned in the background art.

[0006] To achieve the above purpose, the technical scheme is as follows:

[0007] S1, collecting vibration data in the machining process in real time by setting a vibration sensor on a numerical control machining equipment, and transmitting the vibration data to a data processor through a signal transmission module, and performing feature extraction in the data processor to obtain a resonance data set;

[0008] S2, calculating a 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, and triggering a chip intervention mechanism based on the comparison result, and calculating a cleaning trigger index Ri;

[0009] S3, calculating a path comprehensive chip removal score Sc according to the resonance risk factor Rf and the cleaning trigger index Ri of all path segments, comparing the path comprehensive chip removal score Sc with a preset stability threshold Scth, and executing a corresponding strategy based on the comparison result.

[0010] Preferably, the S1 comprises S11 and S12.

[0011] S11, vibration sensors are respectively arranged on a spindle assembly and a 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-to-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 a horizontal axis, the Y-axis represents a vertical axis, and the Z represents a vertical axis.

[0012] 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.

[0013] Preferably, the S1 further comprises S13.

[0014] S13, performing feature extraction on the vibration data in the data processor to obtain a resonance data set;

[0015] The resonance data set comprises a structural resonance frequency F1, a spindle excitation frequency Fs and a structural response amplification coefficient Gs.

[0016] The feature extraction is performed by digital processing based on 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).

[0017] and inputting the triaxial acceleration time domain data into fast Fourier transform processing to obtain corresponding spectral power density distribution functions, and identifying the main frequency peak with the maximum energy density in the triaxial spectrum as the structural resonance frequency F1 corresponding to the current path segment;

[0018] Meanwhile, according to 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;

[0019] The maximum amplitude Apeak and the average amplitude Aavg in the triaxial acceleration time domain data are extracted, and the ratio of the maximum amplitude to the average value is taken as the first item of the structural response intensity, and then 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;

[0020] The structural resonance frequency F1, the spindle excitation frequency Fs and the structural response amplification coefficient Gs are packaged as a resonance data set.

[0021] Preferably, the S2 comprises S21;

[0022] S21, based on the resonance data set, calculates and outputs a path resonance risk factor Rf to identify the resonance sensitivity of the path segment under structural excitation, and the calculation method is to take the structural response amplification coefficient Gs as 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 as the denominator, and the calculation formula is: Rf=Gs / (1+|F1-Fs|).

[0023] Preferably, the S2 further comprises S22;

[0024] S22, the path resonance risk factor Rf is calculated for all machining path segments respectively, each path resonance risk factor Rf is compared with a preset resonance risk interval threshold value to judge the stability of the current path segment processing, the resonance risk interval threshold value 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:

[0025] When the path resonance risk factor Rf is less than 0.5, the path segment is divided into a resonance risk safe area S;

[0026] When the path resonance risk factor Rf is in the interval of 0.5 to 1.0, the path segment is divided into a medium risk area M; when the path resonance risk factor Rf is greater than or equal to 1.0, the path segment is divided into an abnormal risk area H;

[0027] 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.

[0028] First, the length of the continuous path segment belonging to the abnormal risk area H in the path accumulates more than 10% of the total length of the path;

[0029] Second, the path resonance risk factor Rf of any single path segment is greater than or equal to 1.2.

[0030] Preferably, the S2 further comprises S23;

[0031] S23, after triggering the residual chip intervention mechanism, collecting residual chip cleaning data, the residual chip cleaning data comprising residual chip retention probability factor Cf, cooling coverage rate Rc and cutting energy density Ed; and all parameters in the residual chip cleaning data are processed in a unified normalization manner to eliminate the dimension influence of all parameters in the residual chip cleaning data;

[0032] In the calculation based on the residual chip cleaning data, the cleaning trigger index Ri is outputted;

[0033] The cleaning trigger index Ri is calculated and outputted by the following algorithm formula;

[0034] ;

[0035] In the formula, Edref represents the reference value of the cutting energy density.

[0036] Preferably, the S23 further comprises S231;

[0037] S231, based on the cleaning trigger index Ri, the residual chip accumulation and chip removal risk level of the path segment is determined, and the numerical partition is used to trigger the path cleaning strategy,

[0038] Specifically,

[0039] When the cleaning trigger index Ri is less than 0.6, no intervention is needed, and the original machining path is maintained;

[0040] 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 swing and variable interpolation are used to break the potential residual chip adhesion;

[0041] When the cleaning trigger index Ri is greater than or equal to 1.2, more than 2 disturbance cleaning segments are inserted in the path segment, and a reverse pumping path is additionally added to optimize the residual chip removal effect.

[0042] Preferably, the S3 comprises S31;

[0043] S31, taking the path resonance risk factor Rf as a coupling gain adjustment term, modulating and enhancing 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;

[0044] The path comprehensive chip removal score Sc is calculated and output by the following algorithm formula:

[0045] ;

[0046] In the formula, n represents the total number of path segments, Rf i represents the resonance risk factor of the i-th path segment, Ri i represents the cleaning trigger index of the i-th path segment, L i represents the length of the i-th path segment.

[0047] Preferably, the S3 further comprises S32;

[0048] S32, comparing the path comprehensive chip removal score Sc with a 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:

[0049] When the path comprehensive chip removal score Sc is less than the stability threshold Scth, it indicates that the path is stable, and is directly put into execution;

[0050] When the path comprehensive chip removal score Sc is greater than or equal to the stability threshold Scth, the following intervention strategies are executed;

[0051] When the preset stability threshold Scth is less than the path comprehensive chip removal score Sc and the stability threshold Scth is less than the path comprehensive chip removal score Sc multiplied by 1.36, it indicates a first-level 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 pulling mode to break the chip retention structure; at the same time, the cold spraying mode is changed to a surrounding spraying mode;

[0052] When the stability threshold Scth multiplied by 1.36 is less than the path comprehensive chip removal score Sc and the stability threshold Scth multiplied by 1.82 is less than the path comprehensive chip removal score Sc, it indicates a second-level risk, at this time not less than two disturbance cleaning segments are inserted into the abnormal risk area H path segment, the disturbance path adopts a spiral disturbance mode to cooperate with the 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;

[0053] 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.

[0054] The application discloses an intelligent numerical control machining programming system for machining, which comprises a resonance data acquisition module, a resonance identification and path segmentation module and a comprehensive evaluation control module.

[0055] The resonance data acquisition module acquires vibration data in a machining process in real time through vibration sensors arranged on a numerical control machining device, and transmits the vibration data to a data processor through a signal transmission module, and extracts features in the data processor to obtain a resonance data set.

[0056] The resonance identification and path segmentation module calculates a path resonance risk factor Rf based on the vibration data set, compares the path resonance risk factor Rf with a resonance risk interval threshold value, judges based on the comparison result, triggers a chip interference mechanism, and calculates a cleaning trigger index Ri.

[0057] The comprehensive evaluation control module calculates a path comprehensive chip removal score Sc based on the resonance risk factor Rf and the cleaning trigger index Ri of all path segments, compares the path comprehensive chip removal score Sc with a preset stability threshold value Scth, and executes a corresponding strategy based on the comparison result.

[0058] The application provides an intelligent numerical control machining programming method and system for machining.

[0059] (1) The method arranges three-axis MEMS vibration sensors on a numerical control machining device spindle assembly and a workpiece clamping device, acquires three-axis acceleration time domain signals in X-axis, Y-axis and Z-axis directions in a machining process, extracts features in a data processor to obtain three parameters of structural resonance frequency F1, spindle excitation frequency Fs and structural response amplification coefficient Gs, and constructs a resonance data set. Further, based on the response amplification concept in structural dynamics, the closeness of the structural natural frequency and the external excitation frequency is compared, and a path resonance risk factor Rf is established. The factor can quantitatively represent the resonance sensitivity of the machining path segment under the action of structural excitation, identify vibration prone path segments in real time, effectively avoid unstable machining states without affecting the overall process planning, and improve the prediction ability of path level dynamic instability and the machining reliability control level.

[0060] (2) The method establishes a residual chip cleaning trigger index Ri by collecting residual chip retention probability factor Cf from the FEM residual chip prediction model, cooling coverage Rc and cutting energy density Ed on the basis of identifying high-risk vibration path segments. The formula integrates three key factors of structural vibration-induced aggregation risk, cooling obstruction degree and unit heat power density, realizing three-field collaborative modeling across structure geometry, heat source input and cooling channel. The Ri value can dynamically evaluate whether the path segment is prone to residual chip accumulation, and accordingly trigger the disturbance cleaning segment and reverse pumping path intervention mechanism, timely breaking the residual chip attachment chain, thereby effectively reducing carbonization deposition and residual chip obstruction in structures such as blind cavities and deep holes, and ensuring the heat passage smoothness of the tool area and the cooling efficiency of the machining area.

[0061] (3) The method introduces path comprehensive chip removal score Sc, multiplies the resonance risk factor Rfi, cleaning trigger index Rii and path length Li of each path segment to form a comprehensive score index for the whole path risk. The score value can be used as a quantitative criterion for machining stability, and compared with the stability threshold Scth obtained by fitting historical cases, thereby triggering a multi-level dynamic control strategy. Specifically, when the path comprehensive chip removal score Sc is less than Scth, the path is directly executed; when Sc is between Scth and Scth x 1.36, disturbance cleaning and cooling mode adjustment are executed; when Sc further rises to Scth x 1.82, spiral disturbance and parameter dynamic vibration avoidance are executed; if Sc is greater than Scth x 1.82, it is judged as a three-level 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 residual chip accumulation risk, and improve the response sensitivity to potential machining risks and the automatic adaptation ability of path reconstruction in the programming stage. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 A machining intelligent numerical control machining programming method step schematic diagram.

[0063] Figure 2 A machining intelligent numerical control machining programming system block diagram.

[0064] Figure 3 A data flow schematic diagram. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0066] Embodiment 1

[0067] Referring to Figure 1 and Figure 3 The application provides an intelligent numerical control machining programming method for machining, in order to achieve the above purposes, the application is implemented through the following technical solutions: comprising the following steps:

[0068] S1, by setting a vibration sensor on the numerical control machining equipment, real-time acquisition of vibration data in the machining process, and transmitting the vibration data to the data processor through the signal transmission module, and performing feature extraction in the data processor to obtain the resonance data set;

[0069] S2, based on the vibration data set, the path resonance risk factor Rf is calculated, and the resonance risk interval threshold value is compared with the path resonance risk factor Rf to judge, and based on the comparison result, the residual chip intervention mechanism is triggered, and the cleaning trigger index Ri is calculated;

[0070] S3, according to the resonance risk factor Rf and the cleaning trigger index Ri of all path segments, the path comprehensive chip removal score Sc is calculated, and compared with the preset stability threshold Scth, and based on the comparison result, the corresponding strategy is executed.

[0071] In this embodiment, the method can collect micro-vibration data in the machining process in real time by arranging a three-axis MEMS vibration sensor on the spindle assembly and the workpiece clamping device. This arrangement can avoid the error accumulation problem of traditional external derivation, obtain more timely and spatially consistent structural dynamic response data, and effectively ensure the stable transmission of high-frequency vibration signals in a complex electromagnetic environment after anti-interference coding by a high-speed signal transmission module. This arrangement realizes instant perception and channel-level monitoring of the resonance state, and improves the responsiveness and data reliability of dynamic state extraction. In step S2, based on the structural resonance frequency F1, the spindle excitation frequency Fs and the structural response amplification coefficient 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. In the design of the path resonance risk factor Rf, |F1-Fs| is used as the resonance approximation factor because when the excitation frequency is close to the structural natural frequency, the structure is prone to high-amplitude resonance vibration, which can cause tool damage and path machining instability. In this way, the easy-to-excite path segment can be identified in advance to realize risk pre-identification. Further, a residual chip cleaning trigger index Ri is introduced to integrate the structural geometry, heat input and cooling obstruction three-dimensional parameters to comprehensively evaluate the residual chip accumulation risk in the cutting area, and then determine whether to insert a disturbance cleaning path to realize active early warning control of residual chip heat accumulation. In step S3, the resonance risk factor Rf, the cleaning trigger index Ri and the path segment length Li of each path segment are integrated and coupled to construct a path comprehensive chip removal score Sc, which is compared with a preset stability threshold Scth. This score design takes into account the dynamic response strength and structural heat blocking risk, so that the high-risk long path segment has a larger score weight, and the stability level of the overall path execution can be more truly reflected. By setting a hierarchical intervention strategy based on the Scth multiple, it can not only avoid excessive intervention in the low-risk area, but also accurately locate and insert a disturbance path or switch a machining scheme in a high-risk scenario to realize the coordinated control of path chip removal performance and structural excitation risk. For example, in a complex profile path with a deep cavity and a thin side wall structure, if the high-vibration area is not identified in time and a disturbance chip removal strategy is not adopted, it is easy to cause problems such as repeated impact of residual chips on the machining surface, serious tool chip formation, and deterioration of surface quality. By identifying the path segment with Rf>1.2 in real time, the method can automatically insert a reverse disturbance trajectory and optimize the cooling angle to realize local heat channel recovery and improve the efficiency of residual chip removal, thereby significantly reducing the probability of failure. Overall, the method changes the programming system from “result determination” to “risk prediction”, from “global average control” to “precise intervention of path segments”, and substantially improves the stability, reliability and automatic programming intelligence level of the machining process in complex structure regions.

[0072] Embodiment 2

[0073] See Figure 1 , specifically: S1 includes S11 and S12;

[0074] S11, vibration sensors are arranged on the spindle assembly and the workpiece clamping device of the numerical control machining equipment respectively, the vibration sensor is a three-axis MEMS vibration sensor, the three-axis MEMS vibration sensor is used for detecting the structural micro-vibration signal and the spindle excitation frequency signal in the machining process in real time, and the collected structural micro-vibration signal and the spindle excitation frequency signal are subjected to preliminary analog-digital conversion to generate vibration data of three-axis time-domain vibration signals containing 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;

[0075] 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, 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.

[0076] S1 also includes S13;

[0077] S13, the vibration data is subjected to feature extraction in the data processor to obtain a resonance data set;

[0078] The resonance data set includes a structural resonance frequency F1, a spindle excitation frequency Fs and a structural response amplification coefficient Gs;

[0079] The feature extraction is carried out by digital processing of the vibration data containing the three-axis time-domain vibration signals of the X-axis, the Y-axis and the Z-axis to obtain three-axis acceleration time-domain data in the X-axis acceleration time-domain data AX(t), the Y-axis acceleration time-domain data AY(t) and the Z-axis acceleration time-domain data AZ(t) directions;

[0080] And the three-axis acceleration time-domain data is input for fast Fourier transform processing to obtain corresponding spectral power density distribution functions, and based on the main frequency peak value with the maximum energy density identified in the three-axis spectrum, the structural resonance frequency F1 corresponding to the current path segment is extracted;

[0081] At the same time, according to 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, and the calculation method is because the number of rotations of the spindle in unit time and the number of teeth of the tool together determine the periodic impact frequency on the workpiece in unit time, so the spindle excitation frequency Fs accurately represents the input frequency characteristics of the external excitation;

[0082] The maximum amplitude Apeak and the average amplitude Aavg in the triaxial acceleration time domain data are extracted, and the ratio of the maximum amplitude to the average value 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 principal axis excitation frequency Fs to construct the structure response amplification coefficient Gs; this processing mode is used to comprehensively reflect whether the path section is in a state close to excitation and structural resonance, and whether the structural response appears abnormal amplification, so as to evaluate the resonance sensitivity;

[0083] The structural resonance frequency F1, the principal axis excitation frequency Fs and the structure response amplification coefficient Gs are packaged as a resonance data set.

[0084] In this embodiment, three-axis MEMS vibration sensors are arranged on the spindle assembly and workpiece clamping device of the numerical control machining equipment to collect structural micro-vibration signals and spindle excitation frequency signals in real time during the machining process. The three-axis MEMS vibration sensor obtains vibration data in the X-axis, Y-axis and Z-axis directions respectively to ensure comprehensive coverage of the structural response. The vibration signals are first converted into digital signals at the sensor end, and then transmitted to the data processor through a high-speed signal transmission module composed of a differential signal amplification circuit with an anti-interference filter chip and a CAN bus communication module. Noise filtering and communication protocol packaging are completed in the process to ensure that the vibration data still has high signal-to-noise ratio and frame synchronization in a strong interference environment. In the data processor, the vibration data is digitized and processed to extract acceleration time domain data AX(t) along the X-axis, acceleration time domain data AY(t) along the Y-axis and acceleration time domain data AZ(t) along the Z-axis. The three-axis acceleration time domain data are input into a fast Fourier transform module to obtain the power spectral density distribution function in the corresponding frequency domain, and the frequency corresponding to the maximum energy density point is identified as the structural resonance frequency F1, which reflects the structural natural vibration characteristics corresponding to the path segment. At the same time, the spindle excitation frequency Fs is calculated according to the spindle speed r corresponding to the current path segment and the number of teeth z of the selected tool. This calculation logic is based on the physical principle of "tool rotation frequency x tool tooth number = excitation frequency of workpiece per unit time", which can accurately represent the periodic excitation frequency output by the spindle during machining. Further, the maximum acceleration amplitude Apeak and the average acceleration amplitude Aavg in the three-axis vibration data are extracted, and the structural response amplification coefficient Gs is constructed. The structural response amplification coefficient Gs is used to represent the resonance amplification trend of the structure when the excitation frequency Fs is close to the structural resonance frequency F1. The higher the value of the structural response amplification coefficient Gs, the stronger the response of the structure after excitation and the higher the potential vibration risk in the path segment. The structural resonance frequency F1, the spindle excitation frequency Fs and the structural response amplification coefficient Gs are packaged together as a resonance data set for dynamic stability analysis of subsequent path segments. Through the combined design of three-axis data acquisition-feature frequency extraction-resonance risk modeling, the system can accurately identify the resonance sensitive segment in the machining path, effectively avoid resonance instability, machining surface damage and tool premature wear caused by the coincidence of excitation and structural natural frequency, and thus improve the intelligent perception ability and path safety of numerical control machining.

[0085] Embodiment 3

[0086] See Figure 1 , specifically: S2 includes S21;

[0087] S21, calculate an output path resonance risk factor Rf based on the resonance data set, identify the resonance sensitivity of the path segment under structural excitation, and the calculation method is to take the structure response amplification coefficient Gs as the numerator, and take the absolute value of the difference between the structure resonance frequency F1 and the tool excitation frequency Fs plus 1 as the denominator, and the calculation formula is: Rf=Gs / (1+|F1-Fs|); wherein the structure response amplification coefficient Gs reflects the response intensity of the structure to the excitation; the structure resonance frequency F1 is the first order natural frequency of the structure corresponding to the path segment; the tool excitation frequency Fs is the periodic excitation frequency of the numerical control equipment spindle when machining the path segment; the higher the value of the path resonance risk factor Rf represents that the path segment is in a state close to the excitation frequency and the natural frequency and the structure resonance response is significant, indicating that the machining stability risk is larger;

[0088] The calculation formula is derived from the concept of "response amplification coefficient" in structural dynamics, such as the classical resonance response analysis method in "Mechanical Vibration Principle" and "Modal Analysis Engineering Handbook", and the present technical solution has normalized correction and resonance approximation coefficient fusion processing; in the resonance theory, the amplification response of the structure can be expressed by the dynamic flexibility and the difference between the excitation frequency; at the same time, considering the gain effect of the structure to the excitation, the structure amplification response factor Gs should be introduced, in order to avoid the numerator being zero or too small leading to numerical mutation, and "+1" is introduced as a stabilizing term, therefore, the expression of the path resonance risk factor Rf in the present invention is formed;

[0089] The larger the path resonance risk factor Rf is, the closer the structure resonance frequency is to the excitation frequency, and the more intense the response to the excitation is, at this time the structure is easy to enter the strong coupling vibration state, leading to the deterioration of the machining stability of the path segment, the tool damage, the serious heat accumulation and other problems;

[0090] |F1-Fs| is the frequency difference, which is the deviation between the natural frequency of the structure itself and the excitation frequency, and is used to measure whether the system is close to the resonance state, and the unit is hertz Hz, and the absolute value represents the degree of approaching resonance;

[0091] The "1+|F1-Fs|" in the denominator on the right side of the whole formula is the resonance approaching factor after dimension reduction, and the reciprocal of which increases with the approach to the resonance criticality;

[0092] The structure response amplification coefficient Gs is used in the numerator to introduce the amplification trend, even if the frequency difference is not small, but the structure response amplification coefficient Gs is very large, vibration problems may also occur, therefore the structure response amplification coefficient Gs is an important factor of amplification risk.

[0093] S2 also includes S22;

[0094] S22, all processing path segments respectively calculate path resonance risk factor Rf, compare each path resonance risk factor Rf with the preset resonance risk interval threshold to judge the stability of the current path segment processing, 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:

[0095] When the path resonance risk factor Rf < 0.5, the path segment is divided into the resonance risk safety zone S;

[0096] When 0.5≤path resonance risk factor Rf <1.0 interval, the path segment is divided into the medium risk zone M; When the path resonance risk factor Rf≥1.0, the path segment is divided into the abnormal risk zone H;

[0097] Among them, when the evaluation result meets any one of the following conditions, it is judged that the processing path exists structural vibration abnormality, and the residual chip intervention mechanism is triggered immediately;

[0098] 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;

[0099] Second, the path resonance risk factor Rf of any single path segment is greater than or equal to 1.2.

[0100] 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.

[0101] Example 4

[0102] Please see Figure 1 Specifically: S2 also includes S23;

[0103] 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.

[0104] The cleaning trigger index Ri is calculated based on the debris cleaning data and output.

[0105] The cleanup trigger index Ri is calculated and output using the following algorithm formula;

[0106] ;

[0107] In the formula, Edref represents the cutting energy density reference value;

[0108] The cleaning trigger index Ri is used to quantitatively distinguish the risk of residual chip retention caused by resonance excitation in the machining path, which is constructed based on the cross action of three physical dimensions: cutting heat input, cooling restriction degree and structural disturbance residual chip accumulation tendency. The formula is derived from the fusion of classical heat flux analysis principle, fluid cooling flow field evaluation theory and FEM structural excitation residual chip accumulation modeling method, combined with multi-source coupled field simulation analysis to finally construct. From the perspective of structural thermal-fluid-solid coupling, the formula comprehensively considers the main influencing factors of residual chip accumulation and establishes the trigger condition of active intervention mechanism;

[0109] The calculation logic of the three items of the cleaning trigger index Ri is as follows:

[0110] The first term Cf provides the original probability weight of the structural induced residual chip accumulation tendency, which is the dominant factor;

[0111] The second term (1+Ed / Edref) represents the amplification effect of heat power density on residual chip accumulation, which is normalized to adapt to different material and equipment thermal limits;

[0112] The third term (1-Rc) reflects the amplification mechanism of cooling resistance on the chip removal path. The weaker the cooling, the larger the product result;

[0113] Therefore, the higher the cleaning trigger index Ri, the easier the residual chip accumulation and the more difficult the heat diffusion and cleaning, and the path cleaning strategy should be taken as soon as possible;

[0114] This combination ensures that the formula has consistent dimensions, and all parameters are dimensionless or processed through uniform normalization;

[0115] The specific collection method of residual chip cleaning data is as follows:

[0116] By obtaining the structural geometric feature parameters of the region corresponding to the machining path segment, including the local cavity depth-diameter ratio, side wall inclination angle, corner radius and structural body opening morphology characteristics, and combining the corresponding vibration response characteristic vectors in the machining process, including acceleration peak value, spectral energy density and resonance frequency distribution, the above structure morphology and vibration behavior are input into the trained residual chip retention probability prediction model as input parameters. The residual chip retention probability prediction model is constructed based on the finite element method FEM and historical machining data, and outputs the residual chip retention probability factor Cf of the region, which is used to quantitatively predict the accumulation potential of residual chip in the structural deep cavity or blind cavity;

[0117] Secondly, the jet parameters of the cooling system are collected, including the cooling liquid nozzle angle, nozzle pressure, jet flow rate and jet direction, and a three-dimensional geometric model of the machining process is constructed for computational fluid dynamics (CFD) simulation. Based on the simulation results, it is determined whether the tool and cutting point are fully covered by the cooling liquid. As an alternative or supplementary means, the infrared thermal imager can be used to monitor the thermal distribution map of the machining area during the machining process. The cooling coverage rate Rc is derived from the temperature distribution gradient and the cooling dead angle analysis. The cooling coverage rate Rc is used to quantify whether the cooling effect of the cooling liquid is limited. A low value indicates that the cooling is blocked or ineffective.

[0118] Finally, based on the real-time collected spindle power signal and machining time record, combined with the preset tool geometric parameters tool diameter and edge length and machining path segment volume information, the following expression relationship is used to derive the cutting energy density Ed: the spindle power consumed per unit time is divided by the cutting volume of the corresponding path segment to obtain the average heat power density per unit volume. The cutting energy density Ed: is used to evaluate the thermal load degree of the material in the path segment, which is the basis for the thermodynamic criterion of chip carbonization, adhesion and discharge resistance.

[0119] S23 also includes S231;

[0120] S231, based on the cleaning trigger index Ri, the residual chip accumulation and chip removal risk level of the path segment is determined, and the numerical partition triggers the path cleaning strategy,

[0121] Specifically:

[0122] When the cleaning trigger index Ri is less than 0.6, no intervention is needed, and the original machining path is maintained;

[0123] 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 swing and variable speed interpolation are used to break the potential residual chip adhesion;

[0124] When the cleaning trigger index Ri is greater than or equal to 1.2, more than 2 disturbance cleaning segments are inserted in the path segment, and a reverse pumping path is additionally added to optimize the residual chip removal effect, so as to realize the dynamic reduction of local area residual chip accumulation risk and the real-time reconstruction of chip removal channel;

[0125] Wherein: the disturbance cleaning section is a non-cutting section that actively guides chip removal through tool path disturbance, a non-cutting auxiliary path section inserted in the high-risk path section, which induces local disturbance to break the residual chip retention state by changing the tool running track or feed parameters, so as to realize the reconstruction of the chip removal channel and the relief of heat accumulation; the path section is not for the purpose of material removal, but through lateral swing, lifting and retracting, spiral disturbance and the like, a disturbance zone is formed in cooperation with the cooling liquid flow to make the residual chip separate from the machining surface or the inside of the cavity; the triggering basis of the disturbance cleaning section is the cleaning triggering index Ri, when the cleaning triggering index Ri exceeds the set threshold, the corresponding number of disturbance paths are automatically inserted according to the risk level, which is used to improve the chip removal efficiency and the thermal stability in the machining process. The mechanism effectively supplements the conventional path chip removal strategy and has a significant improvement effect in the structure resonance and residual chip coupling risk scenario.

[0126] In the embodiment, the method introduces the residual chip cleaning triggering index Ri, which is constructed on the basis of the residual chip accumulation tendency Cf, the heat input density Ed and the cooling obstruction degree Rc, in order to solve the problem that the residual chip accumulation caused by structure vibration in a complex cavity structure cannot be timely identified and processed in the machining path. Such residual chip accumulation, especially in deep cavities, corners and dead angles, not only causes cutting heat concentration and local temperature rise out of control, but also causes tool edge adhesion, carbonization and even premature failure. Through the product logic design of the cleaning triggering index Ri formula, the accurate identification of the risk synergistic amplification of the three factors of "heat, cold and residual chip" can be realized. In addition, the multi-level threshold interval of the cleaning triggering index Ri is set to realize intelligent judgment and strategy triggering of the chip removal capacity at the machining path level. For example, when the cleaning triggering index Ri exceeds 1.2, it represents that the residual chip adhesion is significant and the cooling channel is limited. At this time, if the conventional cutting continues, the thermal failure risk will be greatly increased, so it is necessary to insert the non-cutting disturbance section + reverse pumping section to break the original residual chip flow path, restore the local chip removal capacity, and thus physically reconstruct the cooling residual chip channel and improve the chip removal efficiency. The trajectory intervention of the disturbance section cooperates with the cooling liquid disturbance to form a synergistic disturbance area, which can quickly detach the structure dead angle residual chip. This mechanism solves the hidden danger of machining failure caused by the superposition of "heat-chip-vibration" in the traditional path, realizes the quantitative evaluation and dynamic intervention of the micro-scale chip removal risk, and has a significant improvement in thermal stability control, tool life improvement and cavity cleanliness guarantee.

[0127] Embodiment 5

[0128] Please refer to Figure 1 , specifically: S3 includes S31;

[0129] S31, taking the path resonance risk factor Rf as a coupling gain adjustment term, modulating and enhancing the heat accumulation and chip removal risk represented by the cleaning trigger index Ri, thereby reflecting the degree of synergistic interference of structural vibration and chip accumulation, to obtain a path comprehensive chip removal score Sc;

[0130] The path comprehensive chip removal score Sc is calculated and output by the following algorithm formula;

[0131] ;

[0132] In the formula, n represents the total number of path segments, Rf i represents the resonance risk factor of the i-th path segment, Ri i represents the cleaning trigger index of the i-th path segment, L i represents the length of the i-th path segment, which is obtained by trajectory export and automatic extraction of path segment length by industrial software;

[0133] In the formula, the formula is obtained by multiplying and coupling the path resonance risk factor Rf and the cleaning trigger index Ri, and introducing the path length L as a weight factor, so that the abnormality on the high-risk segment and the long path segment is more prominent, thereby enhancing the sensitivity and discriminability of the score. By accumulating the score results of n path segments, the cumulative machining risk value of the entire path can be obtained, reflecting the potential failure risk and heat coupling abnormal trend of the path as a whole in the execution process;

[0134] The scoring model is based on the principle of superposition of energy excitation and heat dissipation impedance of a complex system in physics, coupling the resonance driving risk (reflecting dynamic response) and the chip jamming heat accumulation factor (reflecting energy flow obstruction) to model;

[0135] The introduction of Li is derived from the consideration of path segment energy distribution coverage, so that the scoring contribution of high-risk long path segments is more significant;

[0136] The multiplication combination method can more accurately amplify the interference influence of high-risk sections compared to the weighted model, effectively guiding the adaptive path reconstruction logic;

[0137] Parameter dimension consistency: Rf is a dimensionless risk factor, Ri is also a dimensionless index, Li is length, the product has the physical meaning of energy risk strength, and conforms to the logic of the scoring model.

[0138] S3 also includes S32;

[0139] 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:

[0140] When the path comprehensive chip removal score Sc is less than the stability threshold Scth, it indicates that the path is stable, and the path is directly executed;

[0141] When the path comprehensive chip removal score Sc is greater than or equal to the stability threshold Scth, the following intervention strategy is executed;

[0142] When the preset stability threshold Scth is less than the path comprehensive chip removal score Sc and the path comprehensive chip removal score Sc is less than the stability threshold Scth multiplied by 1.36, it indicates a first-level risk. At this time, a disturbance cleaning section is inserted into the path section corresponding to the peak of the cleaning trigger index Ri. The disturbance cleaning section adopts a local pulling mode to break the residual chip retention structure. At the same time, the cold spraying mode is changed to a surrounding spraying mode.

[0143] When the stability threshold Scth multiplied by 1.36 is less than the path comprehensive chip removal score Sc and the path comprehensive chip removal score Sc is less than the stability threshold Scth multiplied by 1.82, it indicates a second-level risk. At this time, no less than two disturbance cleaning sections are inserted into the path section of the abnormal risk area H. The disturbance path adopts a spiral disturbance mode to cooperate with the backtracking trajectory to optimize the heat release efficiency. The tool feed speed and rotation speed are dynamically adjusted to make the cutting frequency far away from the structural resonance area.

[0144] 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.

[0145] Among them, the path comprehensive chip removal score Sc of the abnormality such as tool breakage, overheating, and poor chip removal in the past machining is counted, and the average value of these failure cases is taken as the reference threshold. For example, if the path comprehensive chip removal score Sc value of the problem path in a large number of cases is about 220, the Scth can be set to 220.

[0146] In this embodiment, the method is coupled by multiplying the path resonance risk factor Rf and the cleaning trigger index Ri, and then introducing the path segment length Li to construct the path comprehensive chip removal score Sc, which can realize the comprehensive evaluation of the machining stability of the entire path under the triple risk of "vibration-heat accumulation-chip jamming". The multiplication coupling not only enhances the sensitive identification ability of high-risk segments, but also avoids the defects of traditional weighted average that masks local abnormalities, ensuring rapid response in risk concentration areas. Further, by comparing the path comprehensive chip removal score Sc with the stability threshold Scth, the automatic triggering of the hierarchical control strategy is realized. For example, when the path comprehensive chip removal score Sc exceeds the stability threshold Scth x 1.82, it represents that the path segment may have a serious resonance and chip jamming coupling risk, and if it is still executed, it will easily lead to tool breakage, workpiece heat damage or chip removal failure, so at this time, active prohibition is executed and a red warning signal is generated, which can actively avoid problems before they occur. This mechanism is particularly suitable for structures such as deep cavities, thin walls, and long cantilevers, which are prone to micro-resonance and chip retention coupling during machining, and if there is a lack of coordinated intervention mechanism, the failure probability will be greatly increased. The core of the scoring model is to "use energy excitation and structure resistance coupling as the criterion", combined with the path length weight, which strengthens the adaptability of the model in the physical reality, thereby effectively improving the robustness of path planning and the stability of the machining process.

[0147] Embodiment 6

[0148] Referring to Figure 1 and Figure 2 An intelligent numerical control machining programming system for machining, comprising a resonance data acquisition module, a resonance identification and path segmentation module, and a comprehensive evaluation control module;

[0149] 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, and transmits the vibration data to the data processor through the signal transmission module, and performs feature extraction in the data processor to obtain the resonance data set;

[0150] 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, and calculates the cleaning trigger index Ri;

[0151] 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 the corresponding strategy based on the comparison result.

[0152] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and changes can be made by those skilled in the art without departing from the spirit and principles of the present application.

Claims

1. A programming method for intelligent numerical control machining, characterized in that: Includes 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. 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. 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. 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, trigger the debris intervention mechanism based on the comparison result, and calculate the cleaning trigger index Ri. S2 also 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; S3. Calculate the comprehensive chip removal score Sc for all path segments based on the resonance risk factor Rf and the cleaning trigger index Ri. Simultaneously, compare the score with the preset stability threshold Scth and execute the corresponding strategy based on the comparison results.

2. The intelligent CNC machining programming method for machining according to claim 1, characterized in that: 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.

3. The intelligent CNC machining programming method for machining according to claim 1, characterized in that: 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|).

4. The intelligent CNC machining programming method for machining according to claim 3, characterized in that: 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 with 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.

5. The intelligent CNC machining programming method for machining according to claim 1, characterized in that: 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.

6. The intelligent CNC machining programming method for machining according to claim 5, characterized in that: 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.

7. The intelligent CNC machining programming method for machining according to claim 6, characterized in that: 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.

8. An intelligent CNC machining programming system for machining, applied to the intelligent CNC machining programming method for machining as described in any one of claims 1-7, characterized in that: It 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.

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