Seamless steel tube end face chamfering machine

Abstract

The invention relates to the technical field of metal machining, and discloses a seamless steel pipe end face chamfering machine which comprises a rack, a milling cutter feeding mechanism, a feeding and discharging mechanism and a milling cutter feeding speed optimizing system. The system is based on real-time sensing and fusion of multi-source information, and a process stability coefficient, a product quality coefficient, thickness-section roundness deviation suitability and a tool health state coefficient are output through a process stability evaluation module, a product quality evaluation module, a suitability evaluation module and a tool health state evaluation module respectively; and the milling cutter feeding speed optimization module synthesizes the coefficients and the cutting torque to adaptively generate the milling cutter feeding speed of next chamfering. According to the method, the feeding parameters can be dynamically adjusted according to the machining state, the geometrical characteristics of the workpiece and the health degree of the cutter, the defects of a traditional fixed speed strategy are overcome, and the production efficiency and the equipment operation stability are remarkably improved on the premise that the machining quality and safety are guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of metal processing technology, and in particular relates to a seamless steel pipe end face chamfering machine. Background Technology

[0002] In the end-use applications of seamless steel pipes, such as hydraulic systems and precision machinery, high-quality chamfering of their end faces is crucial for eliminating burrs, facilitating welding and assembly, and improving system reliability. Currently, the end face chamfering of large batches of steel pipes is generally completed using automated special-purpose machines, but there is a significant technical drawback: when programming the same batch of steel pipes, the equipment generally uses a fixed milling cutter feed speed.

[0003] This "one-size-fits-all" parameter strategy ignores the inherent dynamic fluctuations within the machining system and the microscopic differences in the workpiece itself. Specifically: First, the tool condition continuously changes, with significant differences in cutting performance between new and worn tools, and tool temperature and wear fluctuating in real time during machining; second, the workpiece material exhibits microscopic inhomogeneity, with unavoidable fluctuations in tolerance zones for wall thickness uniformity and cross-sectional roundness even within the same batch of steel pipes; third, the machining process is unstable, with spindle vibration and cutting noise fluctuating in real time due to the combined effects of the above factors.

[0004] Using a fixed feed rate to handle these fluctuations is extremely rigid. The direct consequence is that, to ensure safety under the worst operating conditions, operators are forced to conservatively choose lower feed rates, severely limiting equipment productivity. Simultaneously, the equipment's efficiency is not fully utilized under favorable processing conditions. More seriously, when the tool wears down or encounters workpieces with slightly larger geometric deviations, the fixed speed cannot adaptively adjust, easily leading to unstable machining quality (such as deteriorated surface roughness and increased burrs), and even causing tool breakage, equipment overload, and other malfunctions.

[0005] Therefore, there is an urgent need in this field for an intelligent optimization system that can sense and adapt to fluctuations in these multi-dimensional parameters, so as to maximize the potential of equipment and achieve global optimization of production efficiency while ensuring processing quality and equipment safety. Summary of the Invention

[0006] The purpose of this invention is to provide a seamless steel pipe end face chamfering machine to solve the above-mentioned problems.

[0007] This invention is implemented as follows: a seamless steel pipe end face chamfering machine includes a feed frame and a milling cutter rotatably connected to one end of the feed frame. A second motor is fixed inside the feed frame, and the rotating end of the second motor is connected to the milling cutter. The machine also includes: a milling cutter feeding mechanism connected to the feed frame for driving the feed frame to move horizontally; a loading and unloading mechanism connected to the milling cutter feeding mechanism for loading and unloading seamless steel pipes; and a milling cutter feed speed optimization system connected to the control end of the milling cutter feeding mechanism for optimizing the milling cutter feed speed, including: a process stability assessment module that outputs a process stability coefficient based on the spindle vibration acceleration and cutting noise sound pressure level of the current processing through a process stability model; and a product quality assessment module that, based on the current processing... The product quality model outputs a product quality coefficient based on the surface roughness and burr height of the workpiece. The adaptability assessment module, based on the process stability coefficient and product quality coefficient, outputs a thickness-section roundness deviation adaptability for the next steel pipe machining operation based on the wall thickness uniformity deviation and cross-sectional roundness deviation. The tool health status assessment module, based on the average value of the milling cutter temperature and acoustic emission signal amplitude during the current machining operation, as well as the tool wear after the current machining operation, outputs a tool health status coefficient based on the tool evaluation model. The milling cutter feed rate optimization module, based on the thickness-section roundness deviation adaptability, the tool health status coefficient, and the average value of the cutting torque during the current machining operation, outputs the milling cutter feed rate for the next chamfering operation.

[0008] A further technical solution involves the following steps for optimizing the milling cutter feed rate for the next chamfering operation based on the thickness-section roundness deviation adaptability, tool health coefficient, and the average value of the current machining cutting torque:

[0009] The average value of the cutting torque in this machining operation is normalized by the maximum-minimum process to obtain the cutting torque index;

[0010] Thickness-section roundness deviation adaptability, tool health coefficient, and cutting torque exponent are incorporated into the formula. Get the milling cutter feed rate for the next chamfering operation. ,in, This refers to the feed rate of the milling cutter for this chamfering operation. For thickness-section roundness deviation adaptation, For tool health status coefficient, The cutting torque index is... For tool health weighting coefficient, The value range is 0-1.

[0011] A further technical solution, the specific steps for outputting the tool health status coefficient through a tool evaluation model based on the average value of the milling cutter temperature and acoustic emission signal amplitude during the current machining process, and the tool wear after the current machining process, are as follows: The average value of the milling cutter temperature and acoustic emission signal amplitude during the current machining process, as well as the tool wear after the current machining process, are all subjected to maximum-minimum normalization processing to obtain the milling cutter temperature index, tool wear index, and acoustic emission signal amplitude index; the larger value among the milling cutter temperature index, tool wear index, and acoustic emission signal amplitude index is taken as the tool health status coefficient. .

[0012] A further technical solution, specifically the steps for outputting the thickness-section roundness deviation adaptability of the next-processed steel pipe based on the process stability coefficient and product quality coefficient using a adaptability evaluation model, are as follows: The wall thickness uniformity deviation and section roundness deviation of the next-processed steel pipe are respectively ratioed to the product tolerance band width, and the tanh function is used for smoothing and limiting to obtain the wall thickness uniformity deviation index and the section roundness deviation index; the process stability coefficient, product quality coefficient, wall thickness uniformity deviation index, and section roundness deviation index are then imported into the formula. Obtain the thickness-section roundness deviation adaptability ,in, This is the wall thickness uniformity deviation index. The standard deviation of the wall thickness uniformity deviation. The cross-sectional roundness deviation index. This represents the standard deviation of the cross-sectional roundness deviation.

[0013] A further technical solution involves the following steps for outputting a process stability coefficient based on the spindle vibration acceleration and cutting noise sound pressure level during the current machining process, using a process stability model: The spindle vibration acceleration and cutting noise sound pressure level are logarithmically transformed and then normalized to obtain the spindle vibration acceleration index and the cutting noise sound pressure level index; these two parameters characterizing the mechanical vibration and acoustic state are fused together to output a comprehensive process stability coefficient, which decreases as any one or both parameters deteriorate simultaneously; the spindle vibration acceleration index and the cutting noise sound pressure level index are then balanced and fused to generate a process stability coefficient with a value range of 0-1. The value of the process stability coefficient is negatively correlated with both the spindle vibration acceleration index and the cutting noise sound pressure level index.

[0014] A further technical solution, the specific steps for outputting a product quality coefficient based on the surface roughness and burr height of this processing through a product quality model, are as follows: The surface roughness and burr height of this processing are respectively compared with the upper limit value of the product specification, and a min function is used to limit the upper limit to 1 to obtain the surface roughness index and the burr height index; the surface roughness index and the burr height index are then balanced and fused to generate a product quality coefficient with a value range of 0-1. The product quality coefficient is negatively correlated with both the surface roughness index and the burr height index.

[0015] A further technical solution is provided, wherein the milling cutter feed mechanism includes a guide frame two and a lead screw rotatably connected inside the guide frame two. The feed frame is slidably connected inside the guide frame two and is slidably threadedly connected to the lead screw. A motor three is fixedly mounted at one end of the guide frame two, and the rotating end of the motor three is connected to the lead screw.

[0016] A further technical solution includes a cylindrical housing, with the other end of the guide frame fixed to one end of the cylindrical housing. A rotating workstation column is rotatably connected inside the cylindrical housing. Three workstation slots are evenly arranged on the side wall of the rotating workstation column. A motor is fixed to one end of the cylindrical housing and connected to one end of the rotating workstation column. A material unloading notch is provided on the rear side of the cylindrical housing, and a positioning and clamping assembly for positioning and clamping seamless steel pipes is provided on the front side of the cylindrical housing.

[0017] A further technical solution is that the positioning and clamping assembly has a guide frame fixed to the front side of the cylindrical shell, and a positioning clamping block is slidably connected inside the guide frame along its length direction. A telescopic cylinder is fixed to one end of the guide frame away from the cylindrical shell, and the telescopic end of the telescopic cylinder is connected to the positioning clamping block.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0019] 1. By comprehensively considering process stability, product quality, tool health, and workpiece geometry, the comprehensiveness and accuracy of decision-making can be improved;

[0020] 2. It has the ability to make forward adjustments, predict the processing difficulty based on the geometric deviation of the next workpiece, adjust the parameters in advance, and enhance the system's adaptability and quality consistency;

[0021] 3. The milling cutter feed rate optimization system evaluates the stability of the machining process, product quality, adaptability, and tool health status in real time, and dynamically adjusts the milling cutter feed rate to adapt to changes in tool condition, fluctuations in workpiece characteristics, and dynamic changes in the machining process. It has the advantage of being able to adaptively adjust the milling cutter feed rate according to the dynamic parameters of the machining process, thereby improving production efficiency while ensuring machining quality and equipment safety. Attached Figure Description

[0022] Figure 1 A flowchart of the milling cutter feed rate optimization system provided by the present invention;

[0023] Figure 2 A schematic diagram of the structure of a seamless steel pipe end face chamfering machine provided by the present invention;

[0024] Figure 3 Provided by the present invention Figure 2 Schematic diagram of the rear angle structure of the central cylindrical shell;

[0025] Figure 4 Provided by the present invention Figure 2 Schematic diagram of the structure of the rotating workstation column;

[0026] Figure 5 Provided by the present invention Figure 2 A schematic diagram of the feed mechanism for the middle milling cutter.

[0027] In the attached diagram: 1. Cylindrical housing; 2. Rotary station column; 3. Motor 1; 4. Station slot; 5. Feed hopper; 6. Guide frame 1; 7. Positioning clamp; 8. Telescopic cylinder; 9. Unloading notch; 10. Guide frame 2; 11. Feed frame; 12. Milling cutter; 13. Motor 2; 14. Lead screw; 15. Motor 3. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0029] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0030] like Figure 1 and Figure 4 As shown, an embodiment of the present invention provides a seamless steel pipe end face chamfering machine, including a feed frame 11 and a milling cutter 12 rotatably connected to one end of the feed frame 11. A second motor 13 is fixed inside the feed frame 11, and the rotating end of the second motor 13 is connected to the milling cutter 12. The machine also includes: a milling cutter feeding mechanism connected to the feed frame 11 for driving the feed frame 11 to move horizontally; a loading and unloading mechanism connected to the milling cutter feeding mechanism for loading and unloading seamless steel pipes; and a milling cutter feed speed optimization system connected to the control end of the milling cutter feeding mechanism for optimizing the milling cutter feed speed, including:

[0031] The process stability assessment module, based on the spindle vibration acceleration and cutting noise sound pressure level of this machining process, outputs the process stability coefficient through the process stability model.

[0032] The process stability assessment module is a unit that assesses the stability of the machining process based on the spindle vibration acceleration and cutting noise sound pressure level of the current machining operation. It can be implemented by combining the spectral feature extraction of vibration signals with the time-domain statistical analysis of noise signals. For example, vibration data can be collected by an accelerometer and the spectral energy distribution can be calculated, and noise signals can be acquired by a microphone and their time-domain fluctuation characteristics can be analyzed. Its main purpose is to achieve a quantitative assessment of the dynamic stability of the cutting process.

[0033] The product quality assessment module outputs product quality coefficients based on the surface roughness and burr height of this processing through the product quality model.

[0034] The product quality assessment module is a unit that evaluates product quality based on the surface roughness and burr height of the processed product. It can be implemented using non-contact optical measurement and image processing technologies, such as using a laser profilometer to measure surface roughness and using edge detection algorithms to identify burr height. Its main purpose is to achieve an objective evaluation of the quality of the processed surface.

[0035] The adaptability assessment module, based on the process stability coefficient and product quality coefficient, outputs the thickness-section roundness deviation adaptability of the next processed steel pipe wall thickness uniformity deviation and cross-sectional roundness deviation through the adaptability assessment model.

[0036] The adaptability assessment module is a unit that predicts the processing adaptability based on the process stability coefficient, product quality coefficient, and the deviation of the uniformity of the wall thickness and the roundness of the cross section of the steel pipe to be processed in the next process. It can be implemented by statistical modeling methods based on historical processing data, such as establishing a correlation model between the geometric characteristics of the workpiece and the processing results based on regression analysis. Its main purpose is to achieve a forward-looking assessment of subsequent processing conditions.

[0037] The tool health status assessment module, based on the average value of the milling cutter temperature and acoustic emission signal amplitude during this machining process, as well as the tool wear after this machining process, outputs the tool health status coefficient through the tool assessment model;

[0038] The tool health status assessment module is a unit that assesses the tool status based on the temperature of the milling cutter 12 during the current machining, the average amplitude of the acoustic emission signal, and the tool wear after the current machining. It can be implemented using multi-source sensor fusion technology, such as combining infrared thermometer to measure temperature, wavelet transform analysis of acoustic emission signal amplitude characteristics, and contact wear measurement device. Its main purpose is to achieve an accurate judgment of the real-time health status of the tool.

[0039] The milling cutter feed rate optimization module, based on the thickness-section roundness deviation adaptability, tool health coefficient, and the average value of the cutting torque in the current machining operation, outputs the milling cutter feed rate for the next chamfering operation.

[0040] The milling cutter feed rate optimization module is a unit that dynamically adjusts the feed rate based on the thickness-section roundness deviation adaptability, tool health coefficient, and the average cutting torque of the current machining operation. It can be implemented using adaptive control algorithms, such as a fuzzy logic controller based on a rule base to generate speed adjustment commands. Its main purpose is to optimize the feed rate to balance machining efficiency and quality. Therefore, this embodiment, through the real-time monitoring and dynamic optimization mechanism of the aforementioned multi-dimensional parameters, can adaptively adjust the milling cutter's feed rate, thereby improving production efficiency while ensuring machining safety and quality.

[0041] In an embodiment of the present invention, a seamless steel pipe end face chamfering machine includes a feed frame 11, one end of which is rotatably connected to a milling cutter 12. A second motor 13 is fixed inside the feed frame 11, and the rotating end of the second motor 13 is connected to the milling cutter 12. The milling cutter feeding mechanism is connected to the feed frame 11 to drive the feed frame 11 to move horizontally. The loading and unloading mechanism is connected to the milling cutter feeding mechanism to realize the automated loading and unloading of seamless steel pipes. The milling cutter feed speed optimization system is connected to the control end of the milling cutter feeding mechanism for dynamically adjusting the feed speed of the milling cutter 12. This optimization system achieves adaptive speed optimization through multi-dimensional parameter monitoring and a closed-loop feedback mechanism. Specifically, the process stability assessment module, based on real-time data collected during the current machining process, including spindle vibration acceleration and cutting noise sound pressure level, inputs the data into the process stability model after logarithmic transformation and normalization, and outputs a process stability coefficient characterizing the dynamic stability of the machining process. The product quality assessment module, based on the surface roughness and burr height detected after the current machining process, inputs the data into the product quality model after being processed as a ratio to the upper limit of the specification and then limited, and outputs a product quality coefficient reflecting the end face quality level. The adaptability assessment module, combining the process stability coefficient and the product quality coefficient, compares the wall thickness uniformity deviation and cross-sectional roundness deviation of the steel pipe to be processed in the next machining cycle. The system performs value processing and smoothing, and then outputs the thickness-section roundness deviation adaptability through the adaptability evaluation model to predict the adaptability of workpiece geometry to machining. The tool health status evaluation module is based on the average value of the end mill 12 temperature, the acoustic emission signal amplitude during the current machining, and the tool wear measured after machining. After normalization, it takes the larger value of the three to output the tool health status coefficient to quantify the degree of real-time performance degradation of the tool. The end mill feed rate optimization module integrates the thickness-section roundness deviation adaptability, the tool health status coefficient, and the average value of the cutting torque in the current machining. After normalization, it calculates and outputs the end mill 12 feed rate for the next chamfering through the optimization model, thus forming a complete closed loop from parameter perception to speed decision. Furthermore, the specific implementation of this optimization system can use an industrial control computer as the core processing unit. The process stability assessment module collects spindle vibration and noise data in real time through accelerometers and sound level meters. The product quality assessment module obtains end face quality parameters through surface roughness meters and optical measuring devices. The adaptability assessment module predicts geometric deviations based on the workpiece inspection report provided by the production management system. The tool health status assessment module integrates an infrared thermometer, an acoustic emission sensor, and an online wear monitoring device. The milling cutter feed speed optimization module dynamically generates speed commands based on a preset algorithm model and transmits them to the servo drive system of the milling cutter feed mechanism.Therefore, by integrating and analyzing multiple parameters such as machining process status, product quality, workpiece characteristics, and tool health, this technical solution effectively overcomes the rigidity of the fixed feed rate strategy. Under the condition of dynamic fluctuations within the machining system and microscopic differences in the workpiece, it can adjust the feed rate of the milling cutter in real time to match the current machining conditions, avoiding quality deterioration and equipment overload risks caused by increased tool wear or workpiece geometric deviations. At the same time, it reduces overly conservative speed settings for safety, and significantly improves production efficiency and equipment operating potential while ensuring stable and qualified end face chamfer quality and safe equipment operation.

[0042] like Figure 1 As shown, in a preferred embodiment of the present invention, the specific steps for the milling cutter feed rate for the next chamfering operation, based on the thickness-section roundness deviation adaptability, tool health coefficient, and the average value of the current machining cutting torque, are as follows:

[0043] The average value of the cutting torque in this machining operation is normalized by the maximum-minimum process to obtain the cutting torque index;

[0044] Max-min normalization refers to mapping the average value of the cutting torque to the standardized range of [0,1] through a linear transformation. This can be achieved by using the floating-point arithmetic unit built into the industrial control computer to dynamically calculate the real-time data from the torque sensor. The purpose is to eliminate the dimensional differences of the original data and transform the cutting load fluctuation into an exponential representation that can be directly compared with other dimensionless parameters.

[0045] Thickness-section roundness deviation adaptability, tool health coefficient, and cutting torque exponent are incorporated into the formula. Get the milling cutter feed rate for the next chamfering operation. ,in, This refers to the feed rate of the milling cutter for this chamfering operation. For thickness-section roundness deviation adaptation, For tool health status coefficient, The cutting torque index is... For tool health weighting coefficient, Value range: 0-1. Tool health weight coefficient. It refers to configurable parameters used to adjust the priority of tool status and cutting load in speed optimization. It can be preset as a fixed threshold in the CNC system based on the hardness of the machining material and the tool type, or dynamically adjusted through the human-machine interface. It can also be assigned values ​​through preset strategies or dynamic algorithms. The purpose is to flexibly allocate the weight ratio of tool protection and efficiency improvement according to the actual working conditions.

[0046] Specifically, the solution in this application first normalizes the average value of the cutting torque, converting the real-time load data into a standardized exponent. Subsequently, the thickness-section roundness deviation adaptability, tool health coefficient, and cutting torque index are input into the optimization formula, and the next feed rate is dynamically generated through product and weighted combination. In this formula, the thickness-section roundness deviation adaptability is directly multiplied by the current feed rate, and the speed reference is dynamically modulated based on the quantitative characterization of the workpiece's geometric deviation on its machining adaptability; the tool health coefficient is... The form participates in the weighted calculation, transforming the tool wear state into a speed inhibition factor to ensure... A smaller value (healthier tool) allows for higher speeds; the cutting torque index is... The form incorporates weighting terms to automatically suppress speed increases when the load rises; tool health weighting coefficient. It provides a scenario-adaptive priority adjustment mechanism. Overall, the parameters form a closed-loop feedback through normalization preprocessing and coupling formulas, enabling the feed rate to accurately respond to dynamic fluctuations in the machining system.

[0047] As a specific implementation method, the solution of this application is implemented as follows: The control unit adopts a microcontroller, whose built-in analog-to-digital converter acquires the output signal of the torque sensor in real time, and obtains the signal after normalization processing. Simultaneously, it receives data from the process stability assessment module and the tool health status assessment module. and The numerical input is used; the microcontroller executes the optimization algorithm, substituting the parameters into the formula for calculation. It controls the driver of motor 315 through a digital output interface to achieve real-time adjustment of the moving speed of feed frame 11.

[0048] Through the above scheme, this application can accurately quantify the comprehensive influence of thickness-section roundness deviation adaptability, tool health status coefficient and cutting torque on feed rate, so that the speed adjustment is timely and accurate, effectively maintains the stability of surface roughness and burr height when tool wear intensifies or workpiece geometric deviation fluctuates, and avoids the risk of equipment overload caused by sudden load changes.

[0049] like Figure 1 As shown in the preferred embodiment of the present invention, the specific steps for outputting the tool health status coefficient through the tool evaluation model based on the average value of the milling cutter temperature and acoustic emission signal amplitude during the current machining process, and the tool wear after the current machining process, are as follows:

[0050] The average values ​​of the milling cutter temperature and acoustic emission signal amplitude during this machining process, as well as the tool wear after this machining process, are all subjected to maximum-minimum normalization to obtain the milling cutter temperature index, tool wear index, and acoustic emission signal amplitude index.

[0051] Max-min normalization refers to a method of mapping raw data to a preset interval through linear transformation. It can be achieved using a standardized formula based on the dynamic range of historical data. The purpose is to eliminate the dimensional differences between milling cutter temperature, acoustic emission signal amplitude, and tool wear, making different physical quantities comparable on a unified scale. Specifically, the milling cutter temperature index is a normalized quantitative value that characterizes the relative severity of temperature. It can be calculated by the ratio of real-time temperature data to the equipment's safe temperature threshold range, aiming to accurately reflect the impact of temperature rise on the risk of tool thermal deformation. Similarly, the tool wear index and acoustic emission signal amplitude index characterize the relative level of tool geometric wear and the risk of micro-chipping during cutting, respectively.

[0052] The larger value among the milling cutter temperature index, tool wear index, and acoustic emission signal amplitude index is taken as the tool health status coefficient. , The value ranges from 0 to 1, with smaller values ​​indicating a healthier tool condition.

[0053] Taking the larger value as the tool health status coefficient can be understood as an evaluation mechanism based on the "weakest link effect." This can be implemented using numerical comparison circuits or software logic judgments. The purpose is to sensitively capture the weakest link in the tool condition. Since the tool health status is dominated by the worst single indicator, this mechanism ensures that the evaluation results can reflect potential failure risks in a timely manner.

[0054] Specifically, the solution in this application normalizes the milling cutter temperature, the average amplitude of the acoustic emission signal, and the tool wear after machining, transforming these three into dimensionless exponential parameters, thereby establishing a unified evaluation benchmark across physical quantities. Based on this, by selecting the maximum value among the three exponents as the tool health status coefficient, the most severe condition defects can be effectively identified and amplified, avoiding the masking of local deterioration due to other good indicators. This coefficient is directly input into the milling cutter feed rate optimization module, driving the feed rate to dynamically adjust according to the actual tool health status, forming a closed-loop control logic of "state perception - quantitative evaluation - parameter response," ensuring that the machining potential of the equipment is fully released when the tool is in good condition, and that the feed load is reduced in time to avoid the risk of chipping when the condition deteriorates.

[0055] As a specific implementation method, the solution of this application is implemented as follows: During the chamfering process, the temperature signal of the milling cutter body is collected in real time by an embedded thermocouple sensor, and the high-frequency vibration signal of the cutting area is monitored by a piezoelectric acoustic emission sensor and the average amplitude is calculated. After the processing is completed, the cutting edge of the tool is imaged by an industrial camera and the wear is quantified by an edge detection algorithm. The above three types of data are respectively subjected to maximum-minimum normalization operation with the historical extreme value range stored in the equipment operation database to generate the corresponding temperature index, wear index and acoustic emission signal amplitude index. Then, the maximum value of the three is selected by the comparison module built into the microcontroller as the tool health status coefficient. This coefficient is transmitted to the milling cutter feed rate optimization module in real time through the communication interface to dynamically correct the feed parameters of the next chamfering process.

[0056] Through the above technical solution, this application can achieve accurate quantitative characterization of tool health status, so that the tool health status coefficient can truly reflect the actual trend of tool status change, thereby providing a reliable decision basis for milling cutter feed rate optimization, effectively avoiding feed rate adjustment lag or over-adjustment caused by status assessment distortion, significantly improving the stability and adaptability of chamfering process, and extending tool service life while ensuring the processing quality of seamless steel pipe end face.

[0057] like Figure 1 As shown in the preferred embodiment of the present invention, the specific steps for outputting the thickness-section roundness deviation adaptability through the adaptability evaluation model based on the wall thickness uniformity deviation and cross-sectional roundness deviation of the next processed steel pipe under the process stability coefficient and product quality coefficient are as follows:

[0058] The wall thickness uniformity deviation and cross-sectional roundness deviation of the steel pipe to be processed in the next process are respectively compared with the product tolerance zone width, and the tanh function is used for smoothing and limiting to obtain the wall thickness uniformity deviation index and cross-sectional roundness deviation index.

[0059] The wall thickness uniformity deviation index is the ratio of the wall thickness uniformity deviation of the next processed steel pipe to the product tolerance zone width. It can be calculated using linear proportions or nonlinear transformations. The purpose is to convert absolute deviations into relative deviations, making the evaluation closely related to the product specification allowable range. The cross-sectional roundness deviation index is the ratio of the cross-sectional roundness deviation of the next processed steel pipe to the product tolerance zone width. It can be implemented using the tanh function or other smoothing and limiting functions. The purpose is to impose nonlinear constraints on the relative deviations and suppress outlier interference.

[0060] The process stability coefficient, product quality coefficient, wall thickness uniformity deviation index, and cross-sectional roundness deviation index are imported into the formula. Obtain the thickness-section roundness deviation adaptability , The value ranges from 0 to 1, with larger values ​​indicating better adaptability. This is the wall thickness uniformity deviation index. The standard deviation of the wall thickness uniformity deviation. The cross-sectional roundness deviation index. This represents the standard deviation of the cross-sectional roundness deviation.

[0061] Thickness-section roundness deviation adaptability refers to the degree of matching between workpiece geometric deviation and machining state through a specific formula. It can use an exponential decay function to quantify the influence of geometric deviation on adaptability, with the aim of providing a quantitative basis for milling cutter feed speed optimization.

[0062] Specifically, the solution in this application compares the wall thickness uniformity deviation and cross-sectional roundness deviation of the next processed steel pipe with the product tolerance zone width, thus closely linking the deviation assessment to the allowable range of product specifications and avoiding assessment distortion caused by tolerance zone differences. A tanh function is used for smoothing and limiting, ensuring the wall thickness uniformity deviation index and cross-sectional roundness deviation index change continuously within a reasonable range, effectively suppressing outlier interference. Subsequently, the process stability coefficient, product quality coefficient, wall thickness uniformity deviation index, and cross-sectional roundness deviation index are imported into a specific formula, where... This approach integrates the dynamic stability of the current processing procedure with product quality performance, ensuring that the adaptability assessment takes into account the real-time processing status; and The method normalizes the wall thickness uniformity deviation index and cross-sectional roundness deviation index by combining them with the standard deviation. It uses an exponential decay function to quantify the influence of geometric deviation on adaptability. When the deviation is small relative to historical fluctuations, the adaptability is high, and vice versa. This allows the evaluation results to objectively reflect the matching degree between the workpiece geometry and the machining system. The final output is the thickness-cross-sectional roundness deviation adaptability, which is limited to the range of 0-1. The larger the value, the better the adaptability, providing a reliable quantitative basis for adjusting the milling cutter feed rate.

[0063] As a specific embodiment, the solution of this application is implemented as follows: When calculating the wall thickness uniformity deviation index, the actual measured wall thickness uniformity deviation value can be divided by the tolerance band width specified in the product specification to obtain the relative deviation value; then, the tanh function is applied to smooth the relative deviation value to ensure that the index varies within the range of [-1, 1]; the same processing method is used for the cross-sectional roundness deviation index. When calculating the thickness-cross-sectional roundness deviation compatibility, the process stability coefficient, product quality coefficient, wall thickness uniformity deviation index, and cross-sectional roundness deviation index can be input into the compatibility evaluation model, wherein the standard deviation of the wall thickness uniformity deviation and the standard deviation of the cross-sectional roundness deviation can be obtained based on historical processing data statistics, and finally the compatibility coefficient is calculated by formula.

[0064] Through the above technical solution, this application achieves accurate evaluation of the coupling effect of workpiece geometric deviation and machining state, making the quantification of thickness-section roundness deviation adaptability more accurate. Thus, in the process of milling cutter feed speed optimization, it can dynamically adapt to actual machining conditions, avoid over-reliance on conservative parameters or ignoring actual fluctuations, and effectively improve production efficiency and machining quality stability.

[0065] like Figure 1 As shown, in a preferred embodiment of the present invention, the specific steps for outputting the process stability coefficient based on the spindle vibration acceleration and cutting noise sound pressure level during the current machining process using a process stability model are as follows:

[0066] The spindle vibration acceleration and cutting noise sound pressure level of this machining process are logarithmically transformed and then normalized by maximum-minimum to obtain the spindle vibration acceleration index and the cutting noise sound pressure level index.

[0067] Logarithmic transformation is a data compression method that performs logarithmic operations on the original physical quantities. It can be achieved using natural logarithms or base-10 logarithmic transformations. Its purpose is to suppress the dominant influence of extreme values ​​in vibration and noise signals, highlighting relative trends and providing a data foundation that meets the requirements of linear analysis for subsequent processing. Maximum-minimum normalization refers to a conversion mechanism that maps indicators to standardized intervals based on dynamic boundaries determined by historical monitoring data. This can be achieved by dynamically adjusting the normalization parameters according to the maximum and minimum values ​​collected during the equipment's operating cycle. Its purpose is to eliminate the dimensional differences between spindle vibration acceleration and cutting noise sound pressure level, ensuring the comparability of different physical quantities on a unified scale.

[0068] The spindle vibration acceleration index and the cutting noise sound pressure level index are balanced and integrated to generate a process stability coefficient with a value ranging from 0 to 1. The value of the process stability coefficient is negatively correlated with both the spindle vibration acceleration index and the cutting noise sound pressure level index.

[0069] The specific method of balancing and integrating the spindle vibration acceleration index and the cutting noise sound pressure level index is as follows: The spindle vibration acceleration index and the cutting noise sound pressure level index are imported into the formula. Obtain the stability coefficient of the process , The value ranges from 0 to 1, with larger values ​​indicating a more stable processing procedure. The main shaft vibration acceleration index, The sound pressure level index is for cutting noise. This is the vibration acceleration weighting coefficient. The value range is 0-1.

[0070] In practical applications, the weighted fusion formula refers to a mathematical model that combines multi-source evaluation indicators through configurable weight coefficients. It can be implemented by using weight coefficients preset based on the working condition type or by adaptive weights learned in real time. The purpose is to dynamically adjust the contribution ratio of vibration and noise to stability assessment according to the processing conditions, so that the model can adapt to the stability quantification requirements under different dominant factors.

[0071] Specifically, the scheme in this application forms a complete data transformation chain by sequentially performing logarithmic transformation, normalization, and weighted fusion calculation: First, logarithmic transformation is performed on the spindle vibration acceleration and cutting noise sound pressure level to effectively compress their wide dynamic range and weaken their nonlinear distribution characteristics; then, maximum-minimum normalization is performed based on the actual fluctuation boundary of the sensor to uniformly map the processed data to the 0-1 interval, eliminating dimensional differences; finally, the standardized spindle vibration acceleration index and cutting noise sound pressure level index are input into the weighted fusion formula, through... and The conversion mechanism inversely correlates the index value with the stability contribution and dynamically balances the influence weights of the two indicators using the vibration acceleration weighting coefficient. This series of steps works together to transform heterogeneous raw monitoring data into a continuous, quantifiable single stability index, ensuring that the process stability coefficient can reflect the fluctuations in the machining state in real time and accurately, providing a reliable basis for optimizing the milling cutter feed rate.

[0072] As a specific implementation method, the solution of this application is implemented as follows: In the control system of the seamless steel pipe chamfering machine, the process stability assessment module collects the spindle vibration acceleration and cutting noise sound pressure level signals in real time through a piezoelectric vibration sensor and an industrial-grade microphone; after the signals are filtered and amplified by the pre-signal conditioning circuit, they are digitized by the analog-to-digital conversion interface of the embedded microcontroller; the microcontroller executes the logarithmic transformation algorithm to process the raw data, and performs maximum-minimum normalization based on the dynamic boundary determined by the equipment's historical operation database to generate the spindle vibration acceleration index and the cutting noise sound pressure level index; subsequently, according to the vibration acceleration weight coefficient preset for the current processing material type, the weighted fusion formula is called to calculate the process stability coefficient, and the result is transmitted to the milling cutter feed speed optimization module through the communication interface.

[0073] Through the above technical solution, the system can effectively convert the raw data of spindle vibration acceleration and cutting noise sound pressure level into a unified quantitative stability index, accurately capture the real-time fluctuation state of the machining process, and thus provide a reliable basis for the dynamic adjustment of the milling cutter feed speed, ensuring that the chamfering process can maintain stable machining quality and equipment operation safety under different working conditions.

[0074] like Figure 1As shown, in a preferred embodiment of the present invention, the specific steps for outputting the product quality coefficient based on the surface roughness and burr height of the current processing using the product quality model are as follows:

[0075] The surface roughness and burr height of this processing are compared with the upper limit of the product specification, and the upper limit is limited to 1 by the min function to obtain the surface roughness index and burr height index.

[0076] Ratio processing refers to dividing the measured value by the upper limit of the product specification. This can be implemented using an arithmetic unit executed by a microprocessor. The purpose is to convert the original measured value into a relative deviation ratio, providing a unified benchmark for the quality assessment of steel pipes of different specifications. The min function limiting refers to applying a minimum value function to limit the calculation result to an upper limit of 1. This can be implemented using conditional logic in software or a dedicated limiting circuit. The purpose is to ensure that the index value is strictly limited within the valid range, avoiding invalid calculations caused by out-of-specification measurements. The surface roughness index and burr height index are standardized parameters after ratio processing and limiting. They can be stored in the data buffer as intermediate variables, providing quantifiable input for quality assessment.

[0077] The surface roughness index and the burr height index are balanced and integrated to generate a product quality coefficient with a value ranging from 0 to 1. The product quality coefficient is negatively correlated with both the surface roughness index and the burr height index.

[0078] The specific method for balancing and integrating the surface roughness index and the burr height index is as follows: The surface roughness index and the burr height index are imported into the formula. , obtain, The value ranges from 0 to 1, with a larger value indicating better processing quality. It is the surface roughness index. The burr height index, This is the surface roughness weighting coefficient. The value range is 0-1.

[0079] The weighted calculation formula refers to the fusion of multiple quality indicators through linear combination. It can be implemented using floating-point operations executed by a digital signal processor. Its purpose is to dynamically adjust the contribution weight of each indicator according to process requirements, generating a comprehensive quality coefficient. The product quality coefficient is a continuous numerical value reflecting the overall machining quality level. It can be output to the milling cutter feed rate optimization module as an input parameter, aiming to provide accurate and operable quantitative basis for feed rate optimization. The surface roughness weight coefficient is a parameter that adjusts the degree of influence of roughness indicators. It can be set by the operator through a human-machine interface or dynamically adjusted by an adaptive algorithm, aiming to flexibly allocate the weight of roughness and burr height according to process requirements.

[0080] Specifically, the solution in this application first generates a surface roughness index and a burr height index based on the surface roughness and burr height measurements obtained during the processing, respectively, by performing ratio processing and amplitude limiting operations. Then, these indices are used as input parameters to a weighted calculation formula, which outputs the product quality coefficient. In this process, the ratio processing step converts the original measurements into a relative deviation ratio, establishing a unified benchmark for quality assessment of steel pipes of different specifications; the min function amplitude limiting step ensures that the index values ​​are within the valid range; and the weighted calculation step... and The reverse transformation converts the original index into a quality metric, and dynamically assigns index weights using a surface roughness weighting coefficient. Each step is executed sequentially, and information flows from measured values ​​through standardization to quality coefficients, forming a complete quality assessment chain. This effectively transforms key quality indicators into operable standardized parameters, thereby supporting the optimization system's dynamic response to actual processing quality fluctuations.

[0081] As a specific embodiment, the solution of this application is implemented as follows: surface roughness measurement uses a laser displacement sensor to acquire data, and burr height is determined by an industrial camera in conjunction with an image processing algorithm; ratio processing and amplitude limiting operation are implemented in a programmable logic controller; the calculation of the product quality coefficient is completed by the host computer software, wherein the surface roughness weight coefficient can be dynamically adjusted within the range of 0-1 according to the characteristics of the current processed material.

[0082] Through the above technical solutions, key quality indicators such as surface roughness and burr height are effectively transformed into standardized evaluation parameters. The optimization system can respond to actual processing quality fluctuations in real time, avoiding the problems of delayed or excessive feed rate adjustment, thereby improving processing efficiency and quality stability.

[0083] like Figures 2-5As shown, in a preferred embodiment of the present invention, the milling cutter feed mechanism includes a guide frame 2 10 and a lead screw 14 rotatably connected inside the guide frame 2 10. The feed frame 11 is slidably connected inside the guide frame 2 10 and is slidably threadedly connected to the lead screw 14. A motor 3 15 is fixedly mounted at one end of the guide frame 2 10, and the rotating end of the motor 3 15 is connected to the lead screw 14.

[0084] In this embodiment of the invention, guide frame 10 refers to a frame structure that provides rigid support and guidance. It can be implemented using cast iron or welded steel structure. Its purpose is to ensure that the feed frame 11 strictly follows a straight trajectory during horizontal movement and avoids path deviation. Lead screw 14 refers to a precision transmission element that converts rotational motion into linear displacement. It can be implemented using trapezoidal thread or ball screw. Its purpose is to achieve backlash-free motion transmission and enable precise response of feed speed fine-tuning to control signals. Feed frame 11 refers to a component that carries the milling cutter and realizes horizontal movement. It can be implemented using a structure that combines a slider and a guide rail. Its purpose is to form a threaded transmission connection with lead screw 14 to achieve a stable sliding guidance mechanism. Motor 15 refers to a motor device that provides adjustable speed driving force. It can be implemented using a servo motor or stepper motor. Its purpose is to dynamically adjust the speed according to the control signal so that the feed speed can adapt to the microscopic differences of different workpieces.

[0085] In actual operation, after receiving the feed speed command output by the milling cutter feed speed optimization module, the motor 15 adjusts its speed according to the command, and converts the rotational motion into the linear displacement of the feed frame 11 through the ball screw, thereby realizing the precise chamfering of the end face of the seamless steel pipe by the milling cutter 12.

[0086] like Figures 2-5 As shown, in a preferred embodiment of the present invention, the loading and unloading mechanism includes a cylindrical housing 1, with the other end of the guide frame 10 fixedly mounted on one end of the cylindrical housing 1. One end of the cylindrical housing 1 has a circular notch for allowing the milling cutter 12 to enter and exit the cylindrical housing 1. A rotating workstation column 2 is rotatably connected inside the cylindrical housing 1. Three workstation slots 4 are evenly arranged on the side wall of the rotating workstation column 2. The top of the cylindrical housing 1 is connected to and fixedly provided with a feed hopper 5. A motor 3 is fixedly mounted on one end of the cylindrical housing 1. The motor 3 is connected to the rotating workstation column 2. One end of the rotating column 2 is connected to the column housing 1. A material unloading notch 9 is provided on the rear side of the column housing 1. A positioning and clamping assembly for positioning and clamping seamless steel pipes is provided on the front side of the column housing 1. A notch for discharging chamfered waste is provided on the side wall of the column housing 1. A guide frame 6 is fixed on the front side of the column housing 1. A positioning clamping block 7 is slidably connected inside the guide frame 6 along its length. A telescopic cylinder 8 is fixed on the end of the guide frame 6 away from the column housing 1. The telescopic end of the telescopic cylinder 8 is connected to the positioning clamping block 7.

[0087] In this embodiment of the invention, the guide frame 6 specifically adopts a dovetail groove guide rail structure, and its guide rail surface is hardened to enhance wear resistance; the positioning clamp 7 is designed as a V-shaped clamp, and the clamping surface is covered with an anti-slip rubber layer to increase the coefficient of friction; the telescopic cylinder 8 is a standard pneumatic cylinder, which is rigidly connected to the positioning clamp 7 through a piston rod, and the air source pressure is controlled by a solenoid valve to achieve a rapid response of clamping force.

[0088] Seamless steel pipes are placed into the feed hopper 5. When the rotating station column 2 drives the station slot 4 to rotate to the opening at the top of the cylindrical shell 1, the seamless steel pipes in the feed hopper 5 enter the station slot 4. As the rotating station column 2 drives the station slot 4 and the seamless steel pipes in the station slot 4 to revolve, when the seamless steel pipes move to the position of the milling cutter 12, the telescopic cylinder 8 extends, and the positioning clamp 7 extends into one of the station slots 4. The station slot 4, together with the positioning clamp 7, positions and clamps the seamless steel pipes. The motor 13 drives the milling cutter 12 to rotate. The milling cutter feed mechanism drives the feed frame 11 and the milling cutter 12 to extend into the cylindrical shell 1 and mill the seamless steel pipes to make chamfers. The opening for discharging chamfered waste chips can be connected to a centrifugal chip removal fan for auxiliary chip removal. After the chamfering is completed, the milling cutter 12 moves in the reverse direction to reset, the telescopic cylinder 8 retracts, the positioning clamp 7 disengages from the work station slot 4, and the rotating work station column 2 drives the work station slot 4 to rotate to the unloading notch 9 of the cylindrical shell 1. The chamfered seamless steel pipe falls out of the unloading notch 9 and is unloaded.

[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A seamless steel pipe end face chamfering machine, comprising a feed frame and a milling cutter rotatably connected to one end of the feed frame, wherein a second motor is fixedly mounted inside the feed frame, and the rotating end of the second motor is connected to the milling cutter, characterized in that, Also includes: The milling cutter feed mechanism is connected to the feed carriage and is used to drive the feed carriage to move horizontally; The loading and unloading mechanism, connected to the milling cutter feed mechanism, is used for loading and unloading seamless steel pipes; A milling cutter feed rate optimization system, connected to the control end of the milling cutter feed mechanism, is used to optimize the milling cutter feed rate, including: The process stability assessment module, based on the spindle vibration acceleration and cutting noise sound pressure level of this machining process, outputs the process stability coefficient through the process stability model. The product quality assessment module outputs product quality coefficients based on the surface roughness and burr height of this processing through the product quality model. The adaptability assessment module, based on the process stability coefficient and product quality coefficient, outputs the thickness-section roundness deviation adaptability of the next processed steel pipe wall thickness uniformity deviation and cross-sectional roundness deviation through the adaptability assessment model. The tool health status assessment module, based on the average value of the milling cutter temperature and acoustic emission signal amplitude during this machining process, as well as the tool wear after this machining process, outputs the tool health status coefficient through the tool assessment model; The milling cutter feed rate optimization module, based on the thickness-section roundness deviation adaptability, tool health coefficient, and the average value of the cutting torque in the current machining operation, outputs the milling cutter feed rate for the next chamfering operation.

2. The seamless steel pipe end face chamfering machine according to claim 1, characterized in that, The specific steps for the optimization module to output the milling cutter feed rate for the next chamfering operation, based on the thickness-section roundness deviation adaptability, tool health coefficient, and the average value of the cutting torque in the current machining operation, are as follows: The average value of the cutting torque in this machining operation is normalized by the maximum-minimum process to obtain the cutting torque index; Thickness-section roundness deviation adaptability, tool health coefficient, and cutting torque exponent are incorporated into the formula. Get the milling cutter feed rate for the next chamfering operation. ,in, This refers to the feed rate of the milling cutter for this chamfering operation. For thickness-section roundness deviation adaptation, For tool health status coefficient, The cutting torque index is... For tool health weighting coefficient, The value range is 0-1.

3. The seamless steel pipe end face chamfering machine according to claim 2, characterized in that, The specific steps for outputting the tool health status coefficient through the tool evaluation model based on the average value of the milling cutter temperature and acoustic emission signal amplitude during this machining process, and the tool wear after this machining process, are as follows: The average values ​​of the milling cutter temperature and acoustic emission signal amplitude during this machining process, as well as the tool wear after this machining process, are all subjected to maximum-minimum normalization to obtain the milling cutter temperature index, tool wear index, and acoustic emission signal amplitude index. The larger value among the milling cutter temperature index, tool wear index, and acoustic emission signal amplitude index is taken as the tool health status coefficient. .

4. The seamless steel pipe end face chamfering machine according to claim 2, characterized in that, The specific steps for outputting the thickness-section roundness deviation adaptability of the steel pipe wall thickness uniformity deviation and cross-sectional roundness deviation based on the process stability coefficient and product quality coefficient under the adaptability evaluation model are as follows: The wall thickness uniformity deviation and cross-sectional roundness deviation of the steel pipe to be processed in the next process are respectively compared with the product tolerance zone width, and the tanh function is used for smoothing and limiting to obtain the wall thickness uniformity deviation index and cross-sectional roundness deviation index. The process stability coefficient, product quality coefficient, wall thickness uniformity deviation index, and cross-sectional roundness deviation index are imported into the formula. Obtain the thickness-section roundness deviation adaptability ,in, This is the wall thickness uniformity deviation index. The standard deviation of the wall thickness uniformity deviation. The cross-sectional roundness deviation index. This represents the standard deviation of the cross-sectional roundness deviation.

5. The seamless steel pipe end face chamfering machine according to claim 4, characterized in that, The specific steps for outputting the process stability coefficient based on the spindle vibration acceleration and cutting noise sound pressure level in this machining process using the process stability model are as follows: The spindle vibration acceleration and cutting noise sound pressure level of this machining process are logarithmically transformed and then normalized by maximum-minimum to obtain the spindle vibration acceleration index and the cutting noise sound pressure level index. The parameters characterizing mechanical vibration and acoustic state, namely the spindle vibration acceleration and the cutting noise sound pressure level, are fused together to output a comprehensive process stability coefficient, which decreases as any one or both parameters deteriorate simultaneously. The spindle vibration acceleration index and the cutting noise sound pressure level index are balanced and integrated to generate a process stability coefficient with a value ranging from 0 to 1. The value of the process stability coefficient is negatively correlated with both the spindle vibration acceleration index and the cutting noise sound pressure level index.

6. The seamless steel pipe end face chamfering machine according to claim 4, characterized in that, The specific steps for outputting the product quality coefficient based on the surface roughness and burr height of this processing using the product quality model are as follows: The surface roughness and burr height of this processing are compared with the upper limit of the product specification, and the upper limit is limited to 1 by the min function to obtain the surface roughness index and burr height index. The surface roughness index and the burr height index are balanced and integrated to generate a product quality coefficient with a value ranging from 0 to 1. The product quality coefficient is negatively correlated with both the surface roughness index and the burr height index.

7. The seamless steel pipe end face chamfering machine according to claim 1, characterized in that, The milling cutter feed mechanism includes a guide frame two and a lead screw rotatably connected inside the guide frame two. The feed frame is slidably connected inside the guide frame two and is slidably threadedly connected to the lead screw. A motor three is fixedly mounted at one end of the guide frame two, and the rotating end of the motor three is connected to the lead screw.

8. The seamless steel pipe end face chamfering machine according to claim 7, characterized in that, The loading and unloading mechanism includes a cylindrical housing. The other end of the guide frame two is fixed to one end of the cylindrical housing. A rotating workstation column is rotatably connected inside the cylindrical housing. Three workstation slots are evenly arranged on the side wall of the rotating workstation column. A motor one is fixed to one end of the cylindrical housing. The motor one is connected to one end of the rotating workstation column. A material unloading notch is provided on the rear side of the cylindrical housing. A positioning and clamping assembly for positioning and clamping seamless steel pipes is provided on the front side of the cylindrical housing.

9. The seamless steel pipe end face chamfering machine according to claim 8, characterized in that, The positioning clamping assembly has a guide frame fixed to the front side of the cylindrical shell. A positioning clamping block is slidably connected inside the guide frame along its length. A telescopic cylinder is fixed at the end of the guide frame away from the cylindrical shell, and the telescopic end of the telescopic cylinder is connected to the positioning clamping block.

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