Automatic clamping method for precision boring and milling machine suitable for heavy truck transmission system box

Abstract

The application relates to the technical field of machine tool clamping control, in particular to an automatic clamping method suitable for a precision boring and milling machine of a heavy truck transmission system box, which comprises the following steps: acquiring clamping force of a clamping jaw, cutting force of a tool and vibration data of a clamping mechanism in a feeding machining process, calculating a transient impact factor of the tool in the feeding machining, calculating an abnormal vibration coefficient of the clamping mechanism in the feeding machining, further calculating a comprehensive influence coefficient of a clamping force state in the feeding machining, obtaining an abnormal value of the clamping force data of the clamping jaw in the feeding machining based on overall deviation characteristics of the clamping force data and a clamping force change trend, analyzing correlation between the comprehensive influence coefficient and the clamping force state, acquiring a dynamic correlation coefficient, regulating a proportional parameter of a PID controller and realizing automatic clamping of the boring and milling machine in combination with a PLC controller. The application can compensate for defects of unstable clamping force control of a milling machine and improve machining precision of parts.

Description

Technical Field

[0001] This application relates to the field of machine tool clamping control technology, specifically to an automated clamping method for precision boring and milling machines suitable for heavy truck transmission system housings. Background Technology

[0002] As a core component of new energy heavy-duty trucks, the transmission system of heavy-duty trucks directly affects the vehicle's power transmission efficiency, reliability, and service life through the machining quality of its parts. During machining, the challenge of automated clamping control on precision boring and milling machines lies in addressing the cumulative fatigue loosening of the fixture caused by continuous multiple feed operations, as well as the long-term trend of clamping force attenuation. The clamping force fluctuates due to various factors, including dynamic changes in cutting force, the state of the workpiece-fixture contact surface, and abnormal vibrations generated during machining. Excessive clamping force can cause workpiece deformation or surface damage, while insufficient clamping force can cause workpiece displacement or vibration during machining, and may even lead to tool damage or safety accidents.

[0003] Traditional clamping systems have low automation levels, with clamp selection, layout, and clamping force settings heavily reliant on experience. Their control processes fail to adequately consider long-term changes in cutting forces and vibration interference from the clamping mechanism, resulting in unstable clamping force control and consequently affecting part machining accuracy. The automated clamping machining device for CNC lathes (publication number CN220575340U) uses a reciprocating motor-driven rack and pinion transmission mechanism. Through gear meshing, the racks on both sides and the clamping blocks move synchronously inward, achieving automatic workpiece clamping and fixation. Anti-slip pads are also used to prevent loosening during machining, improving clamping efficiency and automation. However, the dynamic changes in clamping force during machining can easily lead to unstable clamping force control. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides an automated clamping method for precision boring and milling machines suitable for heavy-duty truck transmission system housings, thereby resolving the existing issues.

[0005] The automated clamping method for precision boring and milling machines applicable to heavy-duty truck transmission system housings in this application adopts the following technical solution: One embodiment of this application provides an automated clamping method for a precision boring and milling machine suitable for heavy-duty truck transmission system housings, including the following steps: Acquire data on the clamping force of the grippers, the cutting force of the tool, and the vibration of the clamping mechanism during the feeding process; By utilizing the degree of change and average fluctuation level of cutting force data during feed machining, the transient impact factor of the tool during feed machining is obtained. The abnormal vibration coefficient of the clamping mechanism during feed machining is obtained by the difference and frequency deviation between the vibration data of the clamping mechanism. Then, the comprehensive influence coefficient of the clamping force state during feed machining is calculated. Based on the overall deviation characteristics of the clamping force data and the clamping force change trend, the abnormal values ​​of the clamping force data of the jaws during feed machining are obtained. The correlation between the abnormal values ​​and the comprehensive influence coefficient of the clamping force state is analyzed, and the dynamic correlation coefficient between disturbance and clamping force abnormality during feed machining is obtained. The proportional parameters of the PID controller are adjusted by the dynamic correlation coefficient, and the automatic clamping of the boring and milling machine is achieved in combination with the PLC controller.

[0006] Preferably, the process of obtaining the transient impact factor of the tool during the feed machining includes: Obtain the first-order difference sequence of the cutting force data of the tool during the current feed, calculate the square root amplitude of the first-order difference sequence, and then calculate the transient impact factor of the tool during the current feed.

[0007] Preferably, the product of the maximum absolute value of the data in the first-order difference sequence and the square root amplitude is used as the transient impact factor of the tool during the current feed machining.

[0008] Preferably, the abnormal vibration coefficient of the clamping mechanism during the feed process is obtained specifically as follows: In the formula, Let be the abnormal vibration coefficient of the clamping mechanism during the i-th feed. , These represent the effective values ​​of the vibration data corresponding to the j-th and (j-1)-th time periods, respectively. is the offset coefficient of the clamping mechanism vibration data during the i-th feed, and N is the total number of time periods.

[0009] Preferably, the offset coefficient of the clamping mechanism vibration data during the i-th feed process specifically includes: dividing all vibration data of the clamping mechanism during the current feed process into N equal-length time periods, obtaining the spectrum diagram corresponding to each vibration data period through frequency domain transformation, extracting the spectral centroid of each spectrum diagram, taking the frequency of the spectral centroid as the dominant frequency of the corresponding spectrum diagram, calculating the variation coefficient of the dominant frequency of all spectrum diagrams, and using it as the offset coefficient of the clamping mechanism vibration data during the feed process.

[0010] Preferably, the calculation of the comprehensive influence coefficient of the clamping force state during the feed process is as follows: ;in , To preset the first and second weighting coefficients, is the comprehensive influence coefficient of the clamping force state during the i-th feed machining. Let be the abnormal vibration coefficient of the clamping mechanism during the i-th feed. Let be the transient impact factor of the tool during the i-th feed.

[0011] Preferably, the acquisition of abnormal values ​​of the gripper clamping force data during the feed process specifically includes: The average of the absolute values ​​of the differences between all clamping force data of a single jaw and the set clamping force during the current feed machining, as well as the trend statistics of the clamping force data of a single jaw, are calculated. The product of the absolute values ​​of the average and the trend statistics is taken as the outlier of the clamping force data of the jaw during the current feed machining.

[0012] Preferably, the dynamic correlation coefficient between disturbance and clamping force abnormality during the feed process is obtained by: statistically analyzing the average value of the abnormal values ​​corresponding to all clamping force data of all jaws during any feed process, calculating the correlation coefficient between the comprehensive influence coefficient obtained in the any one and several consecutive feed processes before that average value, and using it as the dynamic correlation coefficient between disturbance and clamping force abnormality.

[0013] Preferably, the step of adjusting the proportional parameter of the PID controller through the dynamic correlation coefficient specifically involves: In the formula, This is the proportional parameter for the next adjustment cycle of the PID controller. This is the normalized mean of all dynamic correlation coefficients within the current adjustment period. These are the preset maximum and minimum values ​​for the proportional parameter, respectively.

[0014] Preferably, the duration of the adjustment cycle is a preset number of feed processing cycles.

[0015] This application has at least the following beneficial effects: This application, through in-depth analysis of the transient impact characteristics of cutting force during part machining, as well as the energy intensity fluctuations and main frequency shifts of clamping mechanism vibration, can accurately assess the comprehensive impact of clamping force, thereby reducing the interference of cutting force changes and clamping mechanism vibration during long-term operation; Furthermore, this application considers the deviation of clamping force data from the set value and the trend drift characteristics, and analyzes the dynamic correlation between the comprehensive influence coefficient and the abnormal value of clamping force. It accurately quantifies the clamping force instability risk in part processing, so as to optimize and adjust the relevant parameters of the PID controller, thereby realizing automated clamping. This helps to make up for the defects of unstable clamping force control and improve the machining accuracy of parts. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the steps of the automated clamping method for a precision boring and milling machine applicable to the housing of a heavy-duty truck transmission system, as provided in this application. Detailed Implementation

[0017] The following description, in conjunction with the accompanying drawings, details the specific scheme of the automated clamping method for the precision boring and milling machine applicable to the gearbox of heavy-duty truck transmission systems provided in this application.

[0018] This application provides an embodiment of an automated clamping method for a precision boring and milling machine suitable for heavy-duty truck transmission system housings. For details, please refer to [link to specific documentation]. Figure 1 This includes the following steps: S1: Acquire the clamping force of the grippers, the cutting force of the tool, and the vibration data of the clamping mechanism during the machining process.

[0019] The clamping system in this embodiment adopts a modular design, integrating a servo control module and a data acquisition and transmission module. The control module consists of an industrial computer, a PLC controller, and a human-machine interface. The industrial computer is responsible for running algorithms and data processing, comprehensively analyzing the real-time acquired data; the PLC controller executes the instructions issued by the industrial computer; and the human-machine interface provides operators with an intuitive monitoring and operation platform. In the data acquisition and transmission module, high-precision force sensors are used to acquire clamping force data at each gripper point, as well as cutting force data of the tool. Simultaneously, vibration sensors are used to collect vibration data of the clamping mechanism during machining in real time. In this embodiment, the acquisition frequency for all data is set to 1kHz. The sensor signals are converted from analog to digital and transmitted to the control system via a CAN bus. It should be noted that to avoid inconsistencies in dimensions during subsequent analysis, this embodiment uses a minimax method for data normalization preprocessing. The specific normalization process is existing technology and will not be elaborated in this embodiment. Furthermore, in practical applications, implementers can choose other existing normalization methods to normalize the data; this embodiment does not impose any special restrictions on this.

[0020] S2: By utilizing the degree of change and average fluctuation level of cutting force data during feed machining, the transient impact factor of the tool during feed machining is obtained. The abnormal vibration coefficient of the clamping mechanism during feed machining is obtained by the difference and frequency deviation between the vibration data of the clamping mechanism. Then, the comprehensive influence coefficient of the clamping force state during feed machining is calculated. Based on the overall deviation characteristics of the clamping force data and the clamping force change trend, the abnormal value of the clamping force data of a single jaw during feed machining is obtained. The correlation between the abnormal value and the comprehensive influence coefficient of the clamping force state is analyzed, and the dynamic correlation coefficient between disturbance and clamping force abnormality during feed machining is obtained.

[0021] Precise workpiece positioning and reliable clamping are crucial for ensuring the machining quality of transmission system components. During machining, dynamic changes in cutting forces and vibration impacts can easily cause fluctuations in clamping force. Excessive or insufficient clamping force will affect the workpiece's machining accuracy and surface quality, requiring the system to automatically adjust process parameters according to actual machining conditions to stabilize the machining process. The following analysis addresses this issue.

[0022] Specifically, during intermittent cutting, entry and exit cuts, or when encountering hard points in the material, the cutting force can experience sudden and significant changes or peak values ​​within a short period. This instantaneous overload is transmitted through the workpiece clamping system, causing slight elastic deformation or slippage of the clamping elements, resulting in a redistribution and violent fluctuation of the clamping force. Therefore, the stronger the transient impact of the cutting force, the greater its impact on the stability of the clamping force.

[0023] Therefore, in this embodiment, taking the i-th feed machining process as an example, firstly, the first-order difference sequence of the cutting force data of the tool during the current feed machining is obtained. Each value in the obtained first-order difference sequence reflects the instantaneous change amplitude of the cutting force data. Then, the root square amplitude of the difference sequence is calculated, which reflects the average fluctuation level of the cutting force during this machining process. The product of the maximum absolute value of the data in the first-order difference sequence and the root square amplitude is taken as the transient impact factor of the tool. The transient impact factor of the tool during the i-th feed machining is denoted as... The result The larger the value, the more pronounced the transient impact characteristics of the cutting force during that machining operation. It should be noted that, to avoid inconsistencies in dimensions, this embodiment uses a minimax method to normalize the data into dimensionless data during calculations. The specific normalization process is existing technology and will not be elaborated upon in this embodiment.

[0024] Furthermore, abnormal vibration characteristics of the clamping mechanism itself often precede or accompany the instability of the clamping force. Specifically, when the contact state between the fixture and the workpiece changes, such as the appearance of gaps, loosening, or collisions, the vibration data will exhibit significant energy intensity fluctuations in the time domain and a shift in the dominant frequency component in the frequency domain.

[0025] Therefore, for all vibration data of the clamping mechanism during the current feed machining, this embodiment divides the vibration data into N equally long time periods according to the time sequence, where the value of N ranges from [10, 15], and is 10 in this embodiment. The effective value of each vibration data segment is statistically analyzed. The effective value reflects the energy intensity and stability of the vibration data. The specific calculation process of the effective value of the vibration data is existing technology and will not be elaborated in this embodiment. When the effective value becomes abnormally large, it indicates that the clamping mechanism is in a more abnormal state. Further, a fast discrete Fourier transform is used to obtain the spectrum of each vibration data segment, and then the spectral centroid of all spectrum segments is obtained. The spectral centroid corresponds to the frequency where the vibration energy is mainly concentrated, and the frequency where the spectral centroid is located is taken as the dominant frequency of the corresponding spectrum segment. If the dominant frequency of each segment of vibration data shows a significant shift, it indicates that the stability of the fixture is worse. The coefficient of variation of all dominant frequencies is then calculated and used as the offset coefficient of the clamping mechanism vibration data during the feed machining. The offset coefficient of the clamping mechanism vibration data during the i-th feed machining is denoted as... , This reflects the dominant frequency shift characteristics of the vibration data during this feeding process. Therefore, the abnormal vibration coefficient of the clamping mechanism during this feeding process is calculated. In this embodiment, the specific formula is as follows: In the formula, Let be the abnormal vibration coefficient of the clamping mechanism during the i-th feed. , These represent the effective values ​​of the vibration data corresponding to the j-th and (j-1)-th time periods, respectively. Let N be the offset coefficient of the clamping mechanism vibration data during the i-th feed, and N be the total number of time periods. This reflects the abnormal vibration characteristics of the clamping mechanism itself during this feeding process.

[0026] It is understandable that the above offset coefficient The larger the value, the higher the dispersion of the dominant frequency of the vibration data throughout the processing, and the worse the frequency stability. Then, by comparing the differences between the effective values ​​of each vibration data segment, the larger the calculated cumulative sum and mean, the more drastic the fluctuation of kinetic energy in the time domain. The final calculated... It is a comprehensive assessment of the frequency stability and energy fluctuation of the vibration data of the clamping mechanism. The larger the value, the more significant the impact on the clamping force.

[0027] As analyzed above, the unstable fluctuations of clamping force in actual machining are significantly affected by the transient impact of cutting force and the abnormal vibration of the clamping mechanism itself. Therefore, the overall risk of clamping force instability is assessed by combining the effects of both factors. The comprehensive influence coefficient of the clamping force state during the i-th feed is calculated using the following formula: ;in , To preset the first and second weighting coefficients, in this embodiment, both are set to 0.5, resulting in... This reflects the combined influence of changes in cutting force and abnormal vibrations of the clamping mechanism on the clamping force during this machining process. It should be noted that after normalization processing in S1, and All values ​​are dimensionless.

[0028] As can be seen from the above process, regarding the influence of clamping force, the transient impact of cutting force is a disturbance directly applied externally; the abnormal vibration of the clamping mechanism reflects the deterioration of the clamping mechanism's own state. The two are not isolated; strong cutting impact can easily exacerbate the abnormal vibration of the fixture. Therefore, by adding the two together, the overall influence characteristics of clamping force instability are comprehensively evaluated from both the external disturbance and the internal system response aspects.

[0029] Furthermore, when there is a risk of clamping force instability, the clamping force data collected during the machining process will typically deviate continuously or intermittently from the set value under stable conditions, and exhibit a slow upward or downward trend over time. Therefore, taking the clamping force data collected by a single jaw during the i-th feed as an example, firstly, the absolute value of the difference between all collected clamping force data and the set clamping force is calculated, and then the mean of all such absolute values ​​is calculated. The mean of the absolute values ​​of the differences between all clamping force data of a single jaw and the set clamping force during the i-th machining process is denoted as... The result This reflects the average degree to which the overall clamping force deviates from the set value. Then, the Menkendall test algorithm is used to obtain the trend statistics of the clamping force data during this machining operation. The trend statistics of the clamping force data of a single jaw during the i-th machining operation are denoted as... The result The absolute value reflects the trend of the clamping force data. Therefore, and The absolute value product is taken as the outlier of the clamping force data of a single jaw during the i-th feed. The larger the outlier, the more obvious the deviation of the clamping force data from the set value and the more obvious the trend drift characteristics.

[0030] Clamping force stability is crucial for ensuring the machining quality of precision parts. As the risk of fixture instability increases, the comprehensive influence coefficient and abnormal clamping force values ​​typically show a synchronous upward trend during consecutive feeds. If the abnormal characteristics of the clamping force consistently intensify with the increase of the comprehensive influence coefficient, it indicates a significant risk of workpiece loosening or even scrapping, necessitating timely automatic adjustment of the clamping force. To quantify the dynamic strength of this correlation, we first obtain the average value of the abnormal values ​​corresponding to the clamping force data of all jaws during the i-th feed. Then, we calculate the Spearman correlation coefficient between the comprehensive influence coefficient obtained during the i-th and M consecutive feeds and the average value of the abnormal clamping force data, using this coefficient as the dynamic correlation coefficient between disturbance and clamping force anomaly. The dynamic correlation coefficient between disturbance and clamping force anomaly during the i-th feed is denoted as... Where M takes values ​​in the range [16, 20], and in this embodiment, it is taken as 20. The resulting... The larger the value, the higher the risk of clamping force instability during part processing. It should be noted that when the variance of the sequence corresponding to the comprehensive influence coefficient or the abnormal average value of the clamping force is close to 0, or when the calculated Spearman correlation coefficient is less than or equal to 0, it is determined that there is currently no positive disturbance intervention, and the dynamic correlation coefficient is directly set to 0. This is to prevent the denominator from being zero during the calculation of the Spearman coefficient, and to avoid the situation where negative correlation leads to incorrect control of the PID proportional parameters.

[0031] S3: The proportional parameters of the PID controller are adjusted by the dynamic correlation coefficient, and the PLC controller is combined to realize the automatic clamping of the boring and milling machine.

[0032] The above process in this embodiment allows for the analysis of the transient impact characteristics of cutting forces during part machining, as well as the energy intensity fluctuations and dominant frequency shifts of the clamping mechanism vibration, thereby enabling a precise assessment of the comprehensive impact of the clamping force. Furthermore, considering the deviation of the clamping force data from the set value and its trend drift characteristics, and analyzing the dynamic correlation between the comprehensive influence coefficient and abnormal clamping force values, the unstable clamping force risk in part machining is precisely quantified. In this embodiment, the clamping force is controlled in real time using a PLC controller.

[0033] Specifically, the output torque of the gripper is controlled by a PID controller, and the PID controller is set to... The scaling parameter ranges from [0.1, 0.7]. The dynamic correlation coefficient directly affects the adjustment response rate of the clamping force. A higher coefficient indicates a higher risk of loosening of the machined part, meaning that timely automatic adjustment of the clamping force is necessary. The larger the ratio parameter is set, the better; conversely, the smaller the ratio parameter is set, the better. A smaller proportional parameter value helps avoid overshoot. In this embodiment, eight feed cycles are used as one adjustment period. Preferably, the proportional parameter of the PID controller in the next adjustment period is... The calculation formula is: Where S represents the normalized mean of all dynamic correlation coefficients in the current adjustment period, and the maximum-minimum method is used for processing. To prevent the maximum and minimum normalized denominators from being zero when all dynamic correlation coefficients are equal, which would make calculation impossible, when the difference between the maximum and minimum values ​​of all dynamic correlation coefficients in the current adjustment period is less than a preset minimum value, the normalized mean S is directly taken as the S value of the previous adjustment period; if it is the first adjustment period, S is taken as 0. In this embodiment, the minimum value is taken as 0.01. These are the preset maximum and minimum values ​​of the proportional parameter, respectively; in this embodiment, these values ​​are 0.7 and 0.1. The obtained... As the proportional term parameter for the next adjustment cycle, in this embodiment, it is set to... , (Integral parameters) The initial preset values ​​for the (differential parameters) are 0.4, 0.1, and 0.1. These preset values ​​are used during the first M feed operations after the machine tool starts. Controlling the clamping force through a PLC controller to achieve automated clamping helps compensate for the instability of clamping force control and improves the machining accuracy of parts. It should be noted that the machine tool starts with M consecutive feed processes before the i-th cycle as a data accumulation period, using an initial Kp for control; from the i-th cycle onwards, a sliding window is used to calculate the Kp for each cycle, updating it after each preset number of feed processes.

[0034] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An automated clamping method for precision boring and milling machines applicable to the housing of heavy-duty truck transmission systems, characterized in that, Includes the following steps: Acquire data on the clamping force of the grippers, the cutting force of the tool, and the vibration of the clamping mechanism during the feeding process; By utilizing the degree of change and average fluctuation level of cutting force data during feed machining, the transient impact factor of the tool during feed machining is obtained. The abnormal vibration coefficient of the clamping mechanism during feed machining is obtained by the difference and frequency deviation between the vibration data of the clamping mechanism. Then, the comprehensive influence coefficient of the clamping force state during feed machining is calculated. Based on the overall deviation characteristics of the clamping force data and the clamping force change trend, the abnormal values ​​of the clamping force data of the jaws during feed machining are obtained. The correlation between the abnormal values ​​and the comprehensive influence coefficient of the clamping force state is analyzed, and the dynamic correlation coefficient between disturbance and clamping force abnormality during feed machining is obtained. The proportional parameters of the PID controller are adjusted by the dynamic correlation coefficient, and the PLC controller is used to realize the automated clamping of the boring and milling machine. The process of obtaining the transient impact factor of the tool during the feed machining includes: Obtain the first-order difference sequence of the cutting force data of the tool during the current feed, calculate the square root magnitude of the first-order difference sequence, and then calculate the transient impact factor of the tool during the current feed. The abnormal vibration coefficient of the clamping mechanism during the feed process is obtained as follows: In the formula, Let be the abnormal vibration coefficient of the clamping mechanism during the i-th feed. , These represent the effective values ​​of the vibration data corresponding to the j-th and (j-1)-th time periods, respectively. is the offset coefficient of the clamping mechanism vibration data during the i-th feed process, and N is the total number of time periods; The calculation of the comprehensive influence coefficient of the clamping force state during the feed process is as follows: ;in , To preset the first and second weighting coefficients, is the comprehensive influence coefficient of the clamping force state during the i-th feed machining. Let be the abnormal vibration coefficient of the clamping mechanism during the i-th feed. The transient impact factor of the tool during the i-th feed is denoted as . The acquisition of abnormal values ​​in the clamping force data of the grippers during the feed process specifically includes: The average of the absolute values ​​of the differences between all clamping force data of a single jaw and the set clamping force during the current feed machining, and the trend statistics of the clamping force data of a single jaw, are calculated. The product of the absolute value of the average and the trend statistics is taken as the outlier of the clamping force data of the jaw during the current feed machining. The dynamic correlation coefficient between disturbance and clamping force abnormality during the feed process is obtained by: statistically analyzing the average value of the abnormal values ​​corresponding to the clamping force data of all jaws during any feed process, calculating the correlation coefficient between the comprehensive influence coefficient obtained in the any one and several consecutive feed processes before that average value, and using it as the dynamic correlation coefficient between disturbance and clamping force abnormality.

2. The automated clamping method for precision boring and milling machines applicable to heavy-duty truck transmission system housings as described in claim 1, characterized in that, The product of the maximum absolute value of the data in the first-order difference sequence and the square root amplitude is used as the transient impact factor of the tool during the current feed.

3. The automated clamping method for precision boring and milling machines applicable to heavy-duty truck transmission system housings as described in claim 1, characterized in that, The offset coefficient of the clamping mechanism vibration data during the i-th feed process specifically includes: dividing all vibration data of the clamping mechanism during the current feed process into N equal-length time periods, obtaining the spectrum diagram corresponding to each vibration data period through frequency domain transformation, extracting the spectral centroid of each spectrum diagram, taking the frequency of the spectral centroid as the dominant frequency of the corresponding spectrum diagram, calculating the variation coefficient of the dominant frequency of all spectrum diagrams, and using it as the offset coefficient of the clamping mechanism vibration data during the feed process.

4. The automated clamping method for precision boring and milling machines applicable to heavy-duty truck transmission system housings as described in claim 1, characterized in that, The regulation of the proportional parameter of the PID controller through the dynamic correlation coefficient is specifically as follows: In the formula, This is the proportional parameter for the next adjustment cycle of the PID controller. This is the normalized mean of all dynamic correlation coefficients within the current adjustment period. These are the preset maximum and minimum values ​​for the proportional parameter, respectively.

5. The automated clamping method for precision boring and milling machines applicable to heavy-duty truck transmission system housings as described in claim 4, characterized in that, The duration of the adjustment cycle is a preset number of feed processing cycles.

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