A method for optimizing bolt ball tapping process parameters based on spindle load analysis

Abstract

This invention relates to the field of metal processing technology, specifically to a method for optimizing bolt ball tapping process parameters based on spindle load analysis. The method includes: collecting time-series current data of the machining spindle and feed state data of the feed axis; calculating instantaneous cutting specific energy and high-frequency chatter energy, and calculating the thermoplastic viscosity factor in conjunction with the feed depth; when the thermoplastic viscosity factor exceeds a preset safety threshold, calculating the target critical cutting angular velocity for cutting off the bonded material in conjunction with the material strain rate sensitivity coefficient; configuring a process parameter set including reverse rotation and micro-vibration based on the target critical cutting angular velocity and feed depth, and distributing the process parameter set to the corresponding actuator. This invention solves the problem of chip melting and sticking to the tool due to speed reduction upon encountering resistance. By controlling the spindle to reverse rapidly, the softened bonded chips are cut off, avoiding deadlock during machining and ensuring the continuous operation of automated production lines.

Description

Technical Field

[0001] This invention relates to the field of metal processing technology, and specifically to a method for optimizing bolt ball tapping process parameters based on spindle load analysis. Background Technology

[0002] As a core load-bearing and connecting component of large-span space frame structures, the quality and continuity of the deep hole tapping process directly determine the assembly safety of the entire space frame and the overall production capacity of the automated production line. Because the material of the bolt ball is usually quite tough and the space for deep hole machining is relatively enclosed, in actual production, especially during heavy-load tapping, the cutting fluid often cannot effectively enter the bottom of the hole, and the chips accumulate in the narrow space, resulting in poor heat dissipation. The temperature of the cutting zone rises sharply in a very short time, causing the metal chips to soften and become sticky, resulting in thermoplastic viscosity.

[0003] To prevent machining accidents, existing technologies typically employ spindle load-based protection mechanisms. When the spindle resistance increases due to thermoplastic viscosity and exceeds a set safety limit, the spindle speed is immediately reduced or the machine is stopped and an alarm is triggered to protect the machine tool and cutting tool. However, when dealing with deep hole tapping scenarios involving bolt balls, existing technologies overlook the fact that once the spindle speed is reduced, the cutting tool does not leave the cutting zone. Instead, it prolongs the frictional heat exchange time between the cutting tool and the chips. This not only fails to effectively cool the chips but can also cause the semi-molten chips to solidify, ultimately leading to spindle lock-up and tool breakage, thus reducing overall machining efficiency and yield. Summary of the Invention

[0004] To address the problem that existing technologies cannot accurately identify the viscous state in the initial stage of processing, and thus accurately optimize process parameters, this invention provides a method for optimizing bolt ball tapping process parameters based on spindle load analysis. This method includes: A physical feature acquisition device is configured to collect and preprocess the timing current data of the machining spindle and the feed state data of the feed axis to obtain the physical torque of the machining spindle, as well as the feed depth and instantaneous angular velocity of the feed axis. Based on the physical torque, high-frequency chatter energy is calculated. Based on the physical torque and the instantaneous angular velocity, instantaneous cutting specific energy is calculated. Based on the instantaneous cutting specific energy, the high-frequency chatter energy, and the feed depth, a thermoplastic viscosity factor is calculated. A material strain rate sensitivity coefficient is obtained, and a preset safety benchmark threshold is established. When the thermoplastic viscosity factor meets the abnormal triggering conditions set based on the safety benchmark threshold, the target critical cutoff angular velocity is calculated in conjunction with the material strain rate sensitivity coefficient. Based on the target critical cutoff angular velocity and the feed depth, a process parameter set is configured, and based on the process parameter set, the motion states of the machining spindle and the feed axis are adjusted.

[0005] This invention effectively avoids the risk of thermoplastic sticking and spindle lock-up caused by cutting fluid failure during tapping by making adjustments at the initial stage of anomalies. It can cut off the sticky material by using a sudden change in kinetic energy before the softened chips cause the tool to lock up, thus ensuring the continuous processing capability of automated production lines.

[0006] Further, the preprocessing includes: obtaining the torque constant of the machining spindle, using the torque constant as a mapping coefficient to perform mapping calculation on the timing current data to obtain the physical torque; performing position and velocity analysis on the feed state data to obtain the feed depth and the instantaneous angular velocity; and performing timing alignment on the physical torque, the feed depth, and the instantaneous angular velocity based on a unified timestamp.

[0007] This invention provides an accurate data foundation for subsequent operations by aligning the physical torque, feed depth, and instantaneous angular velocity with unified timestamps, thus preventing misjudgments due to misaligned basic data.

[0008] Furthermore, the high-frequency flutter energy is calculated based on the physical torque, specifically including: performing frequency domain conversion on the physical torque to obtain a high-frequency band signal, and performing feature extraction on the high-frequency band signal to obtain the high-frequency flutter energy.

[0009] This invention, by stripping and extracting features from high-frequency band signals, can accurately pinpoint the weak oscillation characteristics between the cutting tool and the hole wall inside a deep hole during the initial stage of thermoplastic viscosity, eliminating interference caused by uneven workpiece material hardness or fluctuations in cutting resistance.

[0010] Furthermore, the instantaneous cutting ratio satisfies the following relationship:

[0011] in, The instantaneous cutting specific energy; The physical torque; The instantaneous angular velocity; The cutting linear velocity is perpendicular to the cutting surface. This represents the cross-sectional area of ​​the side blade.

[0012] Furthermore, the thermoplastic viscosity factor satisfies the following relationship:

[0013] in, The thermoplastic viscosity factor; The high-frequency vibration energy; The instantaneous cutting specific energy; This is the depth compensation coefficient; The feed depth; It is an exponential function with the natural constant as its base; The volume of the physical chip removal groove of the machining spindle.

[0014] This invention constructs a thermoplastic viscosity factor by using high-frequency vibration energy, instantaneous cutting specific energy, and feed depth, and introduces a depth compensation coefficient to correct the influence of physical constraints on the chip removal space. This effectively restores the process of chips changing from normal brittle shear to abnormal thermoplastic viscosity, providing accurate data indicators for subsequent operations.

[0015] Furthermore, the safety benchmark threshold is obtained through a steady-state calibration experiment.

[0016] Furthermore, the abnormal triggering conditions include: the thermoplastic viscosity factor is greater than the safety benchmark threshold, and the duration is greater than a preset fault tolerance time window.

[0017] Furthermore, the target critical cutting angular velocity satisfies the following relationship:

[0018] in, The target critical cutting angular velocity; Based on the basic feed angular velocity; The strain rate sensitivity coefficient of the material; The thermoplastic viscosity factor; The security benchmark threshold; It is the natural logarithm function.

[0019] This invention calculates the target critical cutting angular velocity to ensure that the transient reverse impact force output by the machine tool can break the adhesive material, thus avoiding secondary mechanical damage caused by excessive output torque.

[0020] Furthermore, based on the target critical cut-off angular velocity and the feed depth, a process parameter set is configured, specifically including: configuring the target critical cut-off angular velocity as a transient reversal extreme speed parameter for controlling the machining spindle; performing dynamic mapping with the feed depth as a spatial constraint condition to calculate the micro-vibration feed parameter for controlling the feed axis; and encapsulating the transient reversal extreme speed parameter and the micro-vibration feed parameter into multi-dimensional data to obtain the process parameter set.

[0021] Furthermore, the method for optimizing bolt ball tapping process parameters based on spindle load analysis also includes: controlling the machining spindle to perform a reverse action at the target critical cutting angular velocity based on the transient reversal speed parameter; and controlling the feed axis to perform reciprocating vibration based on the micro-vibration feed parameter.

[0022] The present invention has the following technical effects: This invention reduces the natural load interference caused by the increase in feed depth by integrating multi-dimensional bottom-layer cutting motion parameters when the chips are in the early stage of softening, and improves the data fidelity of monitoring the micro-friction state of the bottom layer. This invention transforms the invisible process of metal softening and rheology inside deep holes due to heat into a comprehensive degradation index by nonlinearly amplifying and mapping the micro-friction energy. This enables real-time monitoring of the working conditions, improves the adaptive obstacle removal capability of the entire deep hole tapping process, and ensures the final yield rate. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for optimizing bolt ball tapping process parameters based on spindle load analysis, provided by an embodiment of the present invention. Figure 2 This is a comparison diagram of the effects of the prior art and the method of the present invention provided in the embodiments of the present invention. Detailed Implementation

[0024] This invention provides a method for optimizing bolt ball tapping process parameters based on spindle load analysis, referring to... Figure 1 This includes steps S1-S4: S1: Data Acquisition and Preprocessing.

[0025] Specifically, by configuring a physical feature acquisition device, the timing current data of the machining spindle and the feed status data of the feed axis are collected and preprocessed to obtain the physical torque of the machining spindle, as well as the feed depth and instantaneous angular velocity of the feed axis. Based on a unified timestamp, the collected data are time-aligned.

[0026] First, a physical feature acquisition device is configured with a acquisition frequency of 4000 Hz. In this embodiment, a high-frequency through-hole Hall current sensor is selected as the physical feature acquisition device on the machining spindle side. It is connected in series to the three-phase power output terminal of the servo driver of the machining spindle to acquire the stator active current component that drives the machining spindle to rotate in real time, and obtain discrete current sampling points in time sequence, which are defined as time sequence current data. At the same time, an absolute photoelectric encoder is used as the physical feature acquisition device on the feed axis side. It is coaxially connected to the tail of the motor of the feed axis to acquire the digital pulse signal reflecting the absolute mechanical position of the motor rotor during the movement of the feed axis in real time, which is defined as feed status data.

[0027] Next, the motor nameplate data in the servo driver's underlying firmware register of the machining spindle is read to obtain the torque constant of the machining spindle. This torque constant represents the mechanical torque that can be generated by a unit of active current. Therefore, this torque constant can be used as a mapping coefficient to perform mapping calculations on the collected time-series current data: the effective current value of each discrete current sampling point in the time-series current data is multiplied by this torque constant to obtain the physical torque of the output end of the machining spindle at the current moment.

[0028] Subsequently, position and velocity analyses are performed on the collected feed status data: the absolute pulse values ​​in the feed status data are extracted and divided by the single-turn resolution of the absolute photoelectric encoder to obtain the actual number of rotations of the feed shaft motor; this actual number of rotations is multiplied by the lead of the feed shaft to obtain the feed depth; the sampling time interval between two adjacent data sampling points in the feed status data is extracted, the pulse difference of the feed status data within this sampling time interval is calculated, this pulse difference is divided by the single-turn resolution of the absolute photoelectric encoder, and then multiplied by twice pi to obtain the radian change of the feed shaft motor; this radian change is divided by the sampling time interval to obtain the rotational rate of change of the feed shaft motor, i.e., the instantaneous angular velocity of the feed shaft.

[0029] Finally, the acquired physical torque, feed depth, and instantaneous angular velocity are time-aligned: In this embodiment, an industrial Ethernet communication network with distributed clock function is used for underlying data transmission. When the hardware acquisition action is triggered, the network underlying protocol is used to assign a microsecond-level time tag to the acquired data under the same global reference time, forming a unified timestamp. By comparing the timestamps of the acquired data, only physical torque, feed depth, and instantaneous angular velocity with the same timestamp are extracted, combined and latched into the same data row to obtain the time-aligned physical torque, feed depth, and instantaneous angular velocity.

[0030] It should be noted that the above-described specific implementation details are merely preferred embodiments for implementing the data acquisition and preprocessing of the present invention. In practical applications, the physical feature acquisition device on the machining spindle side is not limited to a high-frequency through-hole Hall current sensor, but can also be a precision shunt, or directly read the digital data fed back by the current loop observer inside the servo drive of the machining spindle; the physical feature acquisition device on the feed axis side is not limited to an absolute photoelectric encoder, but can also be an incremental encoder, a rotary transformer, or a linear grating ruler; the underlying communication network used to achieve timing alignment is not limited to an industrial Ethernet communication network with a distributed clock function. Any hardware topology or network protocol equivalent replacement that supports isochronous synchronous real-time communication or is based on a time-sensitive network architecture and can meet the microsecond-level phase synchronization of multi-source underlying dynamic data is included within the protection scope of the present invention.

[0031] S2: Instantaneous cutting specific energy calculation.

[0032] Specifically, the physical torque obtained in S1 is frequency domain converted to obtain a high-frequency band signal, and then the high-frequency band signal is feature extracted to obtain the high-frequency vibration energy; combined with the instantaneous angular velocity, vertical cutting line velocity, and side cutting edge cross-sectional area, the instantaneous cutting specific energy is calculated.

[0033] First, a sliding time window is set based on the rotational speed of the machining spindle. In order to ensure that the complete cutting dynamic features can be covered and extracted, the length of the sliding time window should cover at least 3 to 5 rotational cycles of the machining spindle. Under the rated working conditions of this embodiment, a sliding time window with a time length of 500 milliseconds is preferred. For each sampling moment, the physical torque within the sliding time window is extracted, and a fast Fourier transform is used to convert the physical torque in time sequence into a frequency domain spectrum to obtain the spectral amplitude corresponding to each frequency point. According to the principles of mechanical cutting dynamics, the normal mechanical working frequency, transmission gear meshing frequency, and their low-order harmonics of the machining spindle are concentrated in the low-to-mid frequency range. In order to remove the interference data that represents normal working, it is necessary to set a low-frequency cutoff threshold. In this embodiment, based on the comprehensive mechanical characteristics of the machining spindle, the machine tool is controlled to perform a trial cut before formal machining, and the spectrum distribution of the physical torque is output. The upper limit frequency of the low-frequency dominant frequency band with concentrated energy is extracted and defined as the low-frequency cutoff threshold. After the above operation, the low-frequency cutoff threshold obtained in this embodiment is 500 Hz. In the frequency domain spectrum, data points with frequencies below 500 Hz are removed; since the sampling frequency of the physical feature acquisition device is 4000 Hz, based on the Nyquist sampling theorem, the effective high-frequency upper limit of the frequency domain spectrum is determined to be 2000 Hz; all frequency points between 500 Hz and 2000 Hz are extracted and defined as high-frequency band signals. Extract the spectral amplitude of all frequency points in the high-frequency band signal, and sum them up by squaring each one to obtain the high-frequency amplitude sum of squares; divide the high-frequency amplitude sum of squares by the total number of frequency points contained in the high-frequency band signal to obtain the high-frequency mean square value; take the square root of the high-frequency mean square value to obtain the root mean square value of the high-frequency band signal, which is used as the high-frequency jitter energy.

[0034] Next, extract the pitch of the machining spindle, divide the instantaneous angular velocity obtained in S1 by twice the pi, multiply the calculation result by the pitch to obtain the vertical cutting linear velocity; obtain the nominal outer diameter of the machining spindle and the inner diameter of the bottom hole of the hole to be machined, calculate the difference between the two and divide by 2 to obtain the maximum cutting thickness on one side. Obtain the total axial physical length of the tapered guide section at the front end of the thread forming actuator of the machining spindle. Compare the feed depth with the total axial physical length. When the feed depth is less than or equal to the total axial physical length, it indicates that the process is in the progressive cutting stage. Divide the feed depth by the total axial physical length to obtain the depth ratio. Multiply the maximum cutting thickness on one side by this depth ratio to obtain the instantaneous bottom width of the current cutting section. Multiply the feed depth by the instantaneous bottom width and divide by 2 to obtain the side cutting cross-sectional area at the current moment. When the feed depth is greater than the total axial physical length, it indicates that the cutting is in the steady-state cutting stage. Multiply the maximum cutting thickness on one side by the total axial physical length and divide by 2 to get the cross-sectional area of ​​the side cutting edge at the current moment.

[0035] According to the *International Journal of Machine Tools and Manufacture*, dividing the cutting power by the material removal rate is a standard algorithm for converting macroscopic machine tool load into microscopic material removal energy consumption. Based on this, the instantaneous cutting specific energy is calculated using the following formula:

[0036] in, Instantaneous cutting specific energy, characterizing the mechanical energy required to process a unit volume of material; This refers to physical torque; It is the instantaneous angular velocity; The cutting linear velocity is perpendicular to the cutting surface. The cross-sectional area of ​​the side blade; The term represents the mechanical power output of the machining spindle at the current moment; The term represents the volume of material removed per unit time due to processing, i.e., the material removal volume ratio.

[0037] It can be seen that the obtained instantaneous cutting ratio eliminates the interference caused by changes in feed depth and cutting area during machining. Under steady-state conditions where the cutting fluid functions normally, the instantaneous cutting ratio remains at a relatively constant level; however, once poor heat dissipation leads to metal softening and thermoplastic viscosity, The friction will increase rapidly, and Even though the number of terms is not increased, the final calculated instantaneous cutting specific energy value will increase significantly.

[0038] S3: Viscous state assessment and target critical cutting angular velocity calculation.

[0039] Specifically, the thermoplastic viscosity factor is calculated using the feed depth obtained in S1, the high-frequency chatter energy obtained in S2, and the instantaneous cutting specific energy; the material strain rate sensitivity coefficient is obtained, and a preset safety benchmark threshold is set. When the thermoplastic viscosity factor meets the abnormal triggering conditions set based on the safety benchmark threshold, the target critical cutting angular velocity is calculated in combination with the material strain rate sensitivity coefficient.

[0040] In deep hole machining, chip removal encounters significant resistance due to the enclosed geometric space within the hole. To assess the extent of this resistance, it is necessary to calculate the depth compensation coefficient. , ,in, The cross-sectional area of ​​the side cutting edge represents the size of the cross-sectional area of ​​the physical space within the hole occupied by the mechanical actuator of the machining spindle; The cross-sectional area of ​​the cavity represents the size of the spiral flow channel available for the removal of cut metal. The total design depth represents the maximum penetration distance for the entire machining task.

[0041] Based on the principles outlined in *Mechanical Systems and Signal Processing*, in tool condition monitoring scenarios, the extracted absolute energy of high-frequency vibrations needs to be divided by the theoretical baseline work done under the current cutting parameters to facilitate subsequent cross-condition comparisons. Furthermore, considering the Jensen effect in granular mechanics, chips behave as granules within closed spiral grooves, and the frictional force propelling them along the sidewalls increases exponentially with depth. Based on this, the thermoplastic viscosity factor is calculated, with the specific relationship as follows:

[0042] in, It is the thermoplastic viscosity factor; It is high-frequency vibration energy; This refers to the instantaneous cutting specific energy. This is the depth compensation coefficient; For feed depth; It is an exponential function with the natural constant as its base; The volume of the physical chip removal groove for machining the spindle.

[0043] It can be seen that by calculating the thermoplastic viscosity factor, the invisible microscopic thermoplastic softening process inside deep holes is transformed into an accurate mathematical index. Under steady-state conditions where the cutting fluid is functioning normally, the thermoplastic viscosity factor remains stable and low. However, once the bottom of the hole shows a slight tendency to soften due to heat and produce viscous adhesion, the value of the thermoplastic viscosity factor increases rapidly, achieving highly sensitive detection of deep hole metal adhesion and seizing problems.

[0044] Subsequently, the physical and chemical properties parameter table of the material being processed is consulted to obtain its material strain rate sensitivity coefficient. This material strain rate sensitivity coefficient represents the sensitivity of the yield strength of the metal to changes in external working conditions when the metal is in a high-temperature softening state. Next, obtain the base rotational speed of the machining spindle, multiply this base rotational speed by twice pi and divide by 60 to obtain the base feed angular velocity of the machining spindle; obtain the total number of physical chip flutes configured at the output end of the machining spindle, and calculate the fault tolerance time window. , ,in, Pi This represents the total number of physical chip removal troughs. Based on the base feed angular velocity, the tolerance time window characterizes the time required for the machining spindle to rotate and sweep across a single chip groove at the base feed angular velocity.

[0045] A preset safety threshold is set. When the thermoplastic viscosity factor is greater than the safety threshold, the duration is recorded. If the duration is less than or equal to the fault tolerance time window, it is determined to be a transient false spike caused by interference, and the recorded duration is cleared to zero. If the duration is greater than the fault tolerance time window, it means that the duration of the abnormal state exceeds the limit sweeping cycle of a single chip flue. It can be determined from a mechanical perspective that the current abnormality is not caused by interference, but by the metal softening in the hole caused by the failure of the cutting fluid, and the semi-molten adhesive has formed a continuous wrapping along the circumferential direction. It is determined that the abnormality triggering condition is met. According to the Johnson-Cook constitutive model, the dynamic yield stress required for a metal to undergo plastic flow is linearly positively correlated with the natural logarithm of the strain rate. Based on this, when the abnormal triggering condition is determined to be met, the target critical shearing angular velocity is calculated, and the specific relationship is as follows:

[0046] in, The target critical cutoff angular velocity is a parameter used for subsequent physical obstacle removal. Based on the basic feed angular velocity; This is the material strain rate sensitivity coefficient; It is the thermoplastic viscosity factor; This is a safety baseline threshold. It is the natural logarithm function.

[0047] It can be seen that by constructing the relationship between the target critical cutting angular velocity, the lower limit of mechanical kinetic energy that must be reached to cause brittle fracture of the bonded material by instantaneously applying high-frequency shear force before the chip cools and the tool seizes up can be obtained, providing accurate data support for subsequent operations.

[0048] It should be noted that the safety benchmark threshold was obtained through a steady-state calibration experiment, the specific steps of which are as follows: First, turn on the cutting fluid supply pump to its maximum rated pressure and flow rate to ensure that the cutting fluid can be fully sprayed and penetrate deep into the hole, ensuring that the chip removal groove is unobstructed; at the same time, confirm that the mechanical actuator of the machining spindle is in a brand new, sharp and unworn condition, eliminating interference from mechanical wear and poor heat dissipation. Next, the machining spindle and feed axis are driven to perform one cutting operation. During the entire cycle of cutting pressure and rotation exit, the thermoplastic viscosity factor at each discrete time point is calculated according to the microsecond-level timestamps set in S1, and a steady-state sample dataset is constructed. Finally, the arithmetic mean and standard deviation of the steady-state sample dataset are calculated, according to the statistical principle of 3... The principle is to calculate the sum of the arithmetic mean and three times the standard deviation to obtain the safety baseline threshold.

[0049] S4: Parameter optimization and collaborative execution.

[0050] Specifically, based on the target critical cutting angular velocity and feed depth, a set of process parameters is configured, and the motion state of the machining spindle and feed axis is adjusted according to the set of process parameters.

[0051] Since forward rotation intensifies adhesion and encapsulation during thermoplastic viscosity, requiring instantaneous reverse shearing to break the adhesion, the numerical value of the target critical cutting angular velocity is retained, and its direction of motion in the underlying coordinate system is reversed, i.e., converted into a parameter opposite to the current forward cutting direction, as a transient reverse speed parameter for controlling the machining spindle to perform reverse action.

[0052] Next, the micro-vibration feed parameters used to control the feed axis are calculated, as follows: As the feed depth increases, more and more chips are squeezed at the bottom of the hole, and the escape space becomes smaller. If the large-amplitude reciprocating vibration is maintained, the downward reciprocating motion of the feed axis will squeeze the already softened chips into a denser, rigid knot, i.e., secondary compaction overload. Therefore, combining the penalty function model in adaptive control theory, and using the feed depth as a spatial constraint, dynamic mapping is performed to calculate the displacement amplitude. , ,in, To machine the spindle pitch, For depth compensation coefficient, The feed depth is shown below. As the feed depth increases, the displacement amplitude decreases continuously, and the vibration amplitude of the feed axis decreases, thus reducing the risk of secondary compaction overload. As the displacement amplitude continuously decreases, the physical stroke of a single vibration becomes shorter. To ensure that the mechanical kinetic energy used to cut the adhesive does not decay, it is necessary to calculate the vibration frequency by combining the energy conservation approximation and the principle of motion compensation. Amplification compensation is applied to the feed axis. ,in, The target critical cutting angular velocity, Pi This represents the total number of physical chip removal slots.

[0053] The obtained transient reversal speed parameters and micro-vibration feed parameters are extracted into a unified data frame. Using the underlying industrial bus protocol, a global synchronization timestamp for triggering execution is uniformly added to this data frame to complete multi-dimensional data encapsulation and obtain the process parameter set.

[0054] Based on the set of process parameters, the motion states of the machining spindle and feed axis are adjusted as follows: For the machining spindle, the transient reversal speed parameter is overridden with its original speed loop feedforward command, and the machining spindle is controlled to perform the reversal action at the target critical cutting angular velocity. By applying a shear torque with extremely high strain, the semi-molten adhesive is cut off. For the feed axis, based on displacement amplitude and vibration frequency Constructing micro-vibration trajectory equations ,in, For a moment, Absolute feed depth controls the feed axis to reciprocate and jitter around the absolute feed depth as a spatial reference point.

[0055] It can be seen that, through multi-axis coordinated action, the parameters of the unidirectional continuous cutting process are optimized into the parameters of the spiral cutting and spatial oscillation composite process within an extremely short microsecond cycle. The heated and softened chips are physically crushed and discharged, and then the original parameters can be restored to continue processing, thus realizing adaptive parameter optimization for deep hole tapping.

[0056] Figure 2 This is a comparison diagram of the effects of the prior art and the method of the present invention provided in the embodiments of the present invention. It can be seen that during the tapping process of bolt ball, the yield rate shows a deteriorating trend with the increase of feed depth. In the normal processing range with shallow feed depth, the yield rates of the prior art and the method of the present invention are basically the same. However, after entering the thermoplastic viscosity and secondary compaction triggering range, due to the smaller chip removal space, the yield rate of the prior art deteriorates sharply, while the yield rate of the method of the present invention only decreases slightly. The comparison shows that the method of the present invention has a high robustness and adaptability under the harsh working conditions of chip removal obstruction in deep holes.

Claims

1. A method for optimizing bolt ball tapping process parameters based on spindle load analysis, characterized in that, include: A physical feature acquisition device is configured to collect the timing current data of the machining spindle and the feed state data of the feed axis and perform preprocessing to obtain the physical torque of the machining spindle, as well as the feed depth and instantaneous angular velocity of the feed axis. Based on the physical torque, the high-frequency vibration energy is calculated. Based on the physical torque and the instantaneous angular velocity, calculate the instantaneous cutting specific energy; based on the instantaneous cutting specific energy, the high-frequency chatter energy, and the feed depth, calculate the thermoplastic viscosity factor; Obtain the material strain rate sensitivity coefficient, preset a safety benchmark threshold, and when the thermoplastic viscosity factor meets the abnormal triggering condition set based on the safety benchmark threshold, calculate the target critical shearing angular velocity in combination with the material strain rate sensitivity coefficient. Based on the target critical cutting angular velocity and the feed depth, a set of process parameters is configured, and based on the set of process parameters, the motion states of the machining spindle and the feed axis are adjusted.

2. The method for optimizing bolt ball tapping process parameters based on spindle load analysis according to claim 1, characterized in that, The preprocessing includes: obtaining the torque constant of the machining spindle; using the torque constant as a mapping coefficient to perform mapping calculation on the timing current data to obtain the physical torque; performing position and velocity analysis on the feed state data to obtain the feed depth and the instantaneous angular velocity; and performing timing alignment on the physical torque, the feed depth, and the instantaneous angular velocity based on a unified timestamp.

3. The method for optimizing bolt ball tapping process parameters based on spindle load analysis according to claim 1, characterized in that, The calculation of high-frequency vibration energy based on the physical torque specifically includes: performing frequency domain transformation on the physical torque to obtain a high-frequency band signal, and extracting features from the high-frequency band signal to obtain the high-frequency vibration energy.

4. The method for optimizing bolt ball tapping process parameters based on spindle load analysis according to claim 1, characterized in that, The instantaneous cutting specific energy satisfies the following relationship: in, The instantaneous cutting specific energy; The physical torque; The instantaneous angular velocity; The cutting linear velocity is perpendicular to the cutting surface. This is the cross-sectional area of ​​the side blade.

5. The method for optimizing bolt ball tapping process parameters based on spindle load analysis according to claim 1, characterized in that, The thermoplastic viscosity factor satisfies the following relationship: in, The thermoplastic viscosity factor; The high-frequency vibration energy; The instantaneous cutting specific energy; This is the depth compensation coefficient; The feed depth; It is an exponential function with the natural constant as its base; The volume of the physical chip removal groove of the machining spindle.

6. The method for optimizing bolt ball tapping process parameters based on spindle load analysis according to claim 1, characterized in that, The safety benchmark threshold was obtained through a steady-state calibration experiment.

7. The method for optimizing bolt ball tapping process parameters based on spindle load analysis according to claim 1, characterized in that, The abnormal triggering conditions include: the thermoplastic viscosity factor is greater than the safety benchmark threshold, and the duration is greater than the preset fault tolerance time window.

8. The method for optimizing bolt ball tapping process parameters based on spindle load analysis according to claim 1, characterized in that, The target critical cutoff angular velocity satisfies the following relationship: in, The target critical cutting angular velocity; Based on the feed angular velocity; The strain rate sensitivity coefficient of the material; The thermoplastic viscosity factor; The security benchmark threshold; It is the natural logarithm function.

9. The method for optimizing bolt ball tapping process parameters based on spindle load analysis according to claim 1, characterized in that, Based on the target critical cut-off angular velocity and the feed depth, a process parameter set is configured, specifically including: configuring the target critical cut-off angular velocity as a transient reversal extreme speed parameter for controlling the machining spindle; performing dynamic mapping with the feed depth as a spatial constraint condition to calculate the micro-vibration feed parameter for controlling the feed axis; and encapsulating the transient reversal extreme speed parameter and the micro-vibration feed parameter into multi-dimensional data to obtain the process parameter set.

10. The method for optimizing bolt ball tapping process parameters based on spindle load analysis according to claim 9, characterized in that, The method further includes: controlling the machining spindle to perform a reverse action at the target critical cutting angular velocity based on the transient reversal extreme speed parameter; and controlling the feed axis to perform reciprocating jitter based on the micro-vibration feed parameter.

Images