A CNC lathe slotting processing control method and system

Abstract

The present invention discloses a CNC lathe slotting processing control method and system, which relates to the technical field of CNC lathes. The system realizes high-precision processing of complex contour slots through intelligent path planning, processing parameter optimization and real-time vibration monitoring, and solves the problem of insufficient precision caused by unreasonable path planning of traditional systems. The system uses three-dimensional model analysis and path optimization algorithms to automatically generate processing paths with optimal precision and improve processing accuracy. At the same time, through the combination of intelligent algorithms and real-time vibration monitoring, the cutting speed, feed rate and depth are dynamically adjusted, which solves the problems of vibration and tool wear caused by traditional parameter settings relying on experience, ensuring smooth processing and improved surface quality. For multiple processes, the system integrates intelligent tool changing and adaptive positioning technologies, realizes automatic identification and precise positioning of workpieces, and determines the timing of tool change based on real-time monitored tool wear data, significantly improving production efficiency and processing continuity.

Description

Technical Field

[0001] The invention relates to the technical field of numerically controlled lathes, and in particular to a numerically controlled lathe slotting processing control method and system. Background Art

[0002] The control method and system for grooving processing of CNC lathes belong to the field of CNC processing technology, and are particularly applied to the field of mold manufacturing. In the mold manufacturing process, many parts involve grooving processing, especially when processing complex curved surfaces and non-standard parts, the grooving operation of CNC lathes is particularly important. These grooving processes directly affect the shape accuracy and structural stability of the mold, and are an important link in mold manufacturing that cannot be ignored. For example, the slider, ejector and other components of the injection mold require precise grooving processing, and the processing accuracy of these components determines the quality and service life of the mold molding.

[0003] However, in the prior art, there are three main problems with groove turning in mold manufacturing:

[0004] 1. First, the traditional control system has unreasonable path planning when processing grooves with complex contours, resulting in insufficient processing accuracy;

[0005] 2. Secondly, due to the cumbersome setting of processing parameters and the lack of intelligent optimization, vibration and tool wear are likely to occur, affecting the surface quality of parts;

[0006] 3. Finally, the existing CNC system has low efficiency in tool change and positioning when performing multi-process slotting, which leads to longer processing time. These problems are mainly caused by the imperfect control algorithm, backward parameter tuning methods and lack of intelligent processing strategy.

[0007] The above technical shortcomings can easily lead to multiple abnormal effects in practical applications, including low processing accuracy, resulting in poor matching between parts and molds, resulting in scrap rate; surface roughness does not meet the requirements, increasing the subsequent grinding and polishing process; and frequent equipment failures during processing, affecting the continuity of the production line. These problems not only increase the cost of mold manufacturing, but also have a negative impact on overall production efficiency, limiting the further optimization and promotion of processing technology. Summary of the invention

[0008] In view of the deficiencies in the prior art, the present invention provides a CNC lathe slotting processing control method and system, which solves the problems mentioned in the background technology.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: a CNC lathe slotting processing control system, including an intelligent path planning module, a processing parameter optimization module, a real-time vibration monitoring and adjustment module, a multi-process automatic tool changing module, a workpiece adaptive positioning module and a processing quality detection feedback module;

[0010] The intelligent path planning module analyzes the workpiece contour and processing requirements through a path planning algorithm, and automatically generates a processing path with the best accuracy;

[0011] The processing parameter optimization module calculates and evaluates the feed speed Jgsd within a fixed period through a built-in intelligent algorithm, combined with the workpiece material and processing conditions; automatically adjusts relevant parameter variables, including cutting speed, feed rate and cutting depth, according to the evaluation result of the feed speed Jgsd;

[0012] The real-time vibration monitoring and adjustment module monitors the vibration conditions during the processing in real time through sensors, and calculates and evaluates the vibration frequency coefficient Zdxs based on the detected data;

[0013] The multi-process automatic tool changing module is used for tool replacement and workpiece positioning; in view of the multi-process requirements in slotting processing, it integrates intelligent tool changing and positioning technology; by automatically collecting tool wear-related data in each process and calculating and evaluating it, the timing of tool change is finally determined based on the wear-related data;

[0014] The workpiece adaptive positioning module is used to automatically identify and accurately position the workpiece using visual recognition and sensing technology;

[0015] The processing quality detection feedback module integrates the functions for online detection and real-time analysis of the quality of the groove after processing, including quality evaluation based on surface roughness and dimensional accuracy data, and finally provides closed-loop feedback to the path planning module.

[0016] Preferably, the intelligent path planning module includes a workpiece contour analysis unit and a path optimization generation unit;

[0017] The workpiece contour analysis unit is responsible for receiving and parsing the geometric model and processing requirements of the workpiece; by analyzing the three-dimensional contour, shape characteristics and processing parameters of the workpiece, identifying the specific location of the key processing area and the groove, and generating a preliminary path framework;

[0018] The path optimization generation unit further adjusts the processing path through a path planning algorithm based on the preliminary path framework output by the workpiece contour analysis unit; generates a path with optimal processing accuracy by comprehensively considering the tool motion trajectory, cutting direction and path length, and dynamically adjusts the path.

[0019] Preferably, the processing parameter optimization module includes a workpiece material and working condition analysis unit, a feed parameter evaluation unit and a processing parameter adjustment unit;

[0020] The workpiece material and working condition analysis unit is responsible for extracting parameters related to the feed speed Jgsd, including material hardness Cyd, cutting force Qxl and workpiece temperature Gwd, based on the input workpiece material information and the current processing conditions; and collecting and acquiring the cutting fluid flow rate Qls in real time through a flow sensor installed near the cutting fluid nozzle.

[0021] Preferably, the feed parameter evaluation unit extracts the cutting fluid flow rate Qls, the material hardness Cyd, the cutting force Qxl and the workpiece temperature Gwd and performs dimensionless processing, and then obtains the feed speed Jgsd by calculating through the following formula:

[0022] , where K represents the overall adjustment coefficient, which is used to adjust the reference value of the feed speed according to the processing environment or equipment performance, and its specific value is adjusted by the user;

[0023] a1, a2, a3 and a4 represent the weight values ​​of cutting fluid flow rate Qls, material hardness Cyd, cutting force Qxl and workpiece temperature Gwd, respectively, and 0≤a1<1, 0≤a2<1, 0≤a3<1, 0≤a4<1, a1+a2+a3+a4=1;

[0024] The feeding parameter evaluation unit compares and evaluates the feeding speed Jgsd by presetting the first feeding speed threshold Q1 and the second feeding speed threshold Q2, and the specific contents are as follows:

[0025] If the preset first feed speed threshold Q1>feed speed Jgsd, the first feed result is generated at this time; it means that the feed speed is abnormal, the processing efficiency is reduced and the friction of the tool in the material is increased; at the same time, the system determines that the current processing stability is qualified, but the cutting efficiency is unqualified;

[0026] If the preset first feed speed threshold Q1≤feed speed Jgsd≤second feed speed threshold Q2, it means that the feed speed is normal, and the cutting efficiency and quality are qualified and balanced;

[0027] If the preset second feed speed threshold Q2 is less than the feed speed Jgsd, the second feed result is generated at this time; it indicates that the feed speed is abnormal, and at the same time causes problems such as rapid tool wear, unstable cutting and reduced workpiece surface quality; the system determines that there is a risk of machining vibration and excessive temperature rise at this time;

[0028] The processing parameter adjustment unit is used to adjust the parameters according to the evaluation result of the feed speed Jgs, and the specific adjustment contents are as follows:

[0029] When the first feed result is generated, the system automatically adjusts the cutting speed, feed rate and cutting depth; including increasing the cutting speed by 10%, increasing the feed rate by 5% and increasing the cutting depth by 5%;

[0030] When the second feed result is generated, the system automatically adjusts the cutting speed, feed rate and cutting depth; including reducing the cutting speed by 10%, reducing the feed rate by 5% and reducing the cutting depth by 5%.

[0031] Preferably, the real-time vibration monitoring and adjustment module includes a vibration data acquisition unit and a vibration frequency evaluation and adjustment unit;

[0032] The vibration data acquisition unit collects vibration-related data in real time during the machining process by installing micro-acceleration sensors and laser displacement sensors at key positions of the tool and the workpiece and integrating high-precision strain gauges in the tool clamping system; including micro-vibration acceleration Wjs, axial offset Zpy and tool force fluctuation rate Dsl. Finally, the micro-vibration acceleration Wjs, axial offset Zpy and tool force fluctuation rate Dsl are dimensionlessly processed to construct a vibration data set.

[0033] Preferably, the vibration frequency evaluation and adjustment sheet extracts the vibration data set, calculates and evaluates the vibration frequency coefficient Zdxs, and the specific contents are as follows:

[0034] Zdxs=\sqrt[{3}] {Wjs*Zpy*Dsl} , by presetting the first vibration frequency threshold W1 and the second vibration frequency threshold W2, and comparing and evaluating with the vibration frequency coefficient Zdxs;

[0035] If the first vibration frequency threshold W1> the vibration frequency coefficient Zdxs, it means that the vibration condition is normal, the machining process is smooth, and no adjustment is required;

[0036] If the first vibration frequency threshold W1≤vibration frequency coefficient Zdxs≤the second vibration frequency threshold W2, the first vibration result is generated, indicating that the vibration condition is abnormal, but the vibration condition is within a controllable range, and the processing conditions are further refined and adjusted at this time;

[0037] If the second vibration frequency threshold W2 is less than the vibration frequency coefficient Zdxs, a second vibration result is generated, indicating that the vibration is abnormal and affects the workpiece surface quality, tool life and machining accuracy, and parameter adjustment is performed immediately.

[0038] Preferably, the multi-process automatic tool changing module includes a tool wear data acquisition unit and a wear assessment and tool changing decision unit;

[0039] The tool wear data acquisition unit is used to collect tool wear related data in real time during the machining process, including the tool cutting edge wear length Qxr, tool temperature Dwd, flank wear width Hms and spindle torque fluctuation rate Zzn; after dimensionless processing of the tool wear related data, the real-time collected data is transmitted to the wear assessment and tool change decision unit by using an integrated sensor and monitoring system;

[0040] The wear assessment and tool change decision unit extracts the cutting edge wear length Qxr, tool temperature Dwd, flank wear width Hms and spindle torque fluctuation rate Zzn, and calculates the tool wear coefficient Dmxs using the following formula:

[0041] , by presetting the first wear threshold E1 and the second wear threshold E2, and comparing and evaluating with the tool wear coefficient Dmxs, the specific contents are as follows:

[0042] If the tool wear coefficient Dmxs is less than the first wear threshold E1, it means that the tool wear state is normal, and the system continues the current processing without changing the tool. At this time, the system still regularly calculates the tool wear coefficient Dmxs within a fixed period to monitor the tool wear condition.

[0043] If the first wear threshold E1≤tool wear coefficient Dmxs<second wear threshold E2, it means that the tool wear state is abnormal, but has not reached an unacceptable wear level; at this time, the system starts an early warning to remind the operator to prepare for the subsequent process; at the same time, the system adjusts some parameters, including adjusting the low cutting speed or feed rate;

[0044] If the tool wear coefficient Dmxs ≥ the second wear threshold E2, it means that the tool wear state is abnormal and the current tool can no longer guarantee the processing quality, and the tool needs to be changed immediately; the system automatically triggers the tool change operation, including stopping the current process, starting the automatic tool change program and repositioning the workpiece.

[0045] Preferably, the workpiece adaptive positioning module includes a visual recognition unit and a sensor positioning unit;

[0046] The visual recognition unit uses a high-resolution camera and an image processing algorithm to obtain the shape features and size data of the workpiece, and matches and compares it with the workpiece model built into the system to achieve automatic recognition of the workpiece;

[0047] The sensing positioning unit combines an optical sensor and a displacement sensor to adjust the position of the workpiece in real time according to the result of automatic identification of the workpiece. In the process of identification and positioning, multiple data collection and feedback are performed to achieve adaptive adjustment and precise positioning of the workpiece.

[0048] Preferably, the processing quality detection feedback module obtains the surface morphology data of the groove after processing through an online sensor, and uses digital signal processing technology to extract the roughness features, and then evaluates the surface quality of the workpiece in real time; secondly, the actual size data of the groove is collected through a high-precision measuring device, and compared with the target size, the processing error and dimensional deviation are analyzed, and finally the detected quality data are summarized and analyzed to generate a quality evaluation result, which is closed-loop fed back to the path planning module.

[0049] A method for controlling slotting processing of a CNC lathe comprises the following steps:

[0050] Step 1: Analyze the workpiece contour and processing requirements through the path planning algorithm, and automatically generate the processing path with the best accuracy;

[0051] Step 2: Calculate and evaluate the feed speed Jgsd within a fixed period through the built-in intelligent algorithm, combined with the workpiece material and processing conditions; automatically adjust the relevant parameter variables, including cutting speed, feed rate and cutting depth, according to the evaluation results of the feed speed Jgsd;

[0052] Step 3: Use sensors to monitor the vibration during the processing in real time, and calculate and evaluate the vibration frequency coefficient Zdxs based on the detected data;

[0053] Step 4: In order to meet the requirements of multiple processes in slotting, intelligent tool changing and positioning technology is integrated; by automatically collecting tool wear-related data in each process and calculating and evaluating it, the timing of tool changing is finally determined based on the wear-related data;

[0054] Step 5: Use visual recognition and sensing technology to automatically identify and accurately locate the workpiece;

[0055] Step 6: Integrate the system for online detection and real-time analysis of the quality of the machined groove, including quality assessment based on surface roughness and dimensional accuracy data, and finally provide closed-loop feedback to the path planning module.

[0056] The present invention provides a CNC lathe slotting control method and system, which has the following beneficial effects:

[0057] (1) This CNC lathe slotting control method and system achieves high-precision processing of complex contour slots through an intelligent path planning module, a processing parameter optimization module, and a real-time vibration monitoring and adjustment module. Traditional path planning methods are often not reasonable in processing complex workpiece contours and are prone to processing errors. This system uses a workpiece contour analysis and path optimization generation unit, combined with a three-dimensional geometric model and processing parameters, to automatically generate a processing path with the best accuracy, fundamentally improving the processing accuracy and solving the shortcomings of traditional systems in terms of path planning rationality.

[0058] (2) A CNC lathe slotting processing control method and system. Traditional processing systems often rely on manual experience when setting processing parameters, which leads to vibration problems and increased tool wear, thereby affecting surface quality. To address this problem, this system introduces a processing parameter optimization module, combined with a real-time vibration monitoring and adjustment module, and analyzes processing parameters through an intelligent algorithm, especially based on the feed speed Jgsd and the vibration frequency coefficient Zdxs, dynamically adjusting the cutting speed, feed rate and depth. By accurately identifying vibration problems during the processing and making corresponding parameter adjustments, the smoothness of the processing and the improvement of the surface quality are ensured.

[0059] (3) A CNC lathe slotting processing control method and system. In the slotting processing of multiple processes, the tool changing and workpiece positioning operations of the traditional CNC system are cumbersome and inefficient, which greatly affects the processing time. To this end, the multi-process automatic tool changing module and workpiece adaptive positioning module of this system integrate intelligent tool changing and positioning technology, and use visual recognition and sensing technology to realize automatic recognition and precise positioning of the workpiece. At the same time, the timing of tool changing is determined by real-time monitoring of the tool wear status, ensuring the continuity and stability of the processing process and greatly improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The present invention is a schematic diagram of the framework structure of a CNC lathe slotting processing control system.

[0061] Figure 2 The present invention is a schematic flow chart of the steps of a CNC lathe slotting processing control method. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Example 1

[0064] See also Figure 1 , a CNC lathe slotting processing control system: including an intelligent path planning module, a processing parameter optimization module, a real-time vibration monitoring and adjustment module, a multi-process automatic tool changing module, a workpiece adaptive positioning module and a processing quality detection feedback module;

[0065] The intelligent path planning module analyzes the workpiece contour and processing requirements through a path planning algorithm, and automatically generates a processing path with the best accuracy;

[0066] The processing parameter optimization module calculates and evaluates the feed speed Jgsd within a fixed period through a built-in intelligent algorithm, combined with the workpiece material and processing conditions; automatically adjusts relevant parameter variables, including cutting speed, feed rate and cutting depth, according to the evaluation result of the feed speed Jgsd;

[0067] The real-time vibration monitoring and adjustment module monitors the vibration conditions during the processing in real time through sensors, and calculates and evaluates the vibration frequency coefficient Zdxs based on the detected data;

[0068] The multi-process automatic tool changing module is used for tool replacement and workpiece positioning; in view of the multi-process requirements in slotting processing, it integrates intelligent tool changing and positioning technology; by automatically collecting tool wear-related data in each process and calculating and evaluating it, the timing of tool change is finally determined based on the wear-related data;

[0069] The workpiece adaptive positioning module is used to automatically identify and accurately position the workpiece using visual recognition and sensing technology;

[0070] The processing quality detection feedback module integrates the functions for online detection and real-time analysis of the quality of the groove after processing, including quality evaluation based on surface roughness and dimensional accuracy data, and finally provides closed-loop feedback to the path planning module.

[0071] In this embodiment, the intelligent path planning module automatically generates the optimal path by analyzing the workpiece contour and processing requirements, thereby solving the problem of insufficient processing accuracy caused by unreasonable path planning in traditional systems; secondly, the processing parameter optimization module can dynamically adjust the cutting speed, feed rate and depth during the processing process, significantly improving the processing efficiency, and realizes vibration control through the real-time monitoring and adjustment module, reducing the risk of tool wear and workpiece surface quality degradation caused by vibration; in addition, the multi-process automatic tool changing module integrates intelligent tool changing and workpiece positioning technology, and optimizes the tool changing timing in combination with the real-time collected tool wear data, thereby improving the processing efficiency and continuity of multiple processes; finally, the workpiece adaptive positioning and processing quality detection feedback module ensures the precise positioning of the workpiece and real-time monitoring of the processing quality, realizes closed-loop feedback adjustment, and improves the overall processing stability and yield rate; in summary, the improvements in path planning, parameter tuning, vibration control and process linkage have significantly improved the accuracy, efficiency and quality of complex grooving processing.

[0072] Example 2

[0073] The intelligent path planning module includes a workpiece contour analysis unit and a path optimization generation unit;

[0074] The workpiece contour analysis unit is responsible for receiving and parsing the geometric model and processing requirements of the workpiece; by analyzing the three-dimensional contour, shape characteristics and processing parameters of the workpiece, identifying the specific location of the key processing area and the groove, and generating a preliminary path framework;

[0075] The path optimization generation unit further adjusts the processing path through a path planning algorithm based on the preliminary path framework output by the workpiece contour analysis unit; generates a path with optimal processing accuracy by comprehensively considering the tool motion trajectory, cutting direction and path length, and dynamically adjusts the path.

[0076] In this embodiment, the cutting path is optimized according to the tool motion trajectory, and the path with the least motion resistance is selected to reduce the processing time; at the same time, the cutting order is dynamically adjusted according to the cutting direction and path length to avoid processing errors caused by repeated paths; the path optimization generation unit also collects the actual deviation data in the workpiece processing in real time, and dynamically adjusts the path through the algorithm to ensure that each cutting is carried out along the optimal path; in addition, by adaptively adjusting the reversing point and the cutting angle of the tool, the path accuracy is further optimized to reduce positioning errors and path overlaps that may occur during processing; this series of dynamic adjustment measures enables path planning to maintain high accuracy and high efficiency in complex processing environments.

[0077] Example 3

[0078] The processing parameter optimization module includes a workpiece material and working condition analysis unit, a feed parameter evaluation unit and a processing parameter adjustment unit;

[0079] The workpiece material and working condition analysis unit is responsible for extracting parameters related to the feed speed Jgsd, including material hardness Cyd, cutting force Qxl and workpiece temperature Gwd, based on the input workpiece material information and the current processing conditions; and collecting and acquiring the cutting fluid flow rate Qls in real time through a flow sensor installed near the cutting fluid nozzle.

[0080] The feed parameter evaluation unit extracts the cutting fluid flow rate Qls, material hardness Cyd, cutting force Qxl and workpiece temperature Gwd and performs dimensionless processing to obtain the feed speed Jgsd by the following formula:

[0081] , where K represents the overall adjustment coefficient, which is used to adjust the reference value of the feed speed according to the processing environment or equipment performance, and its specific value is adjusted by the user;

[0082] a1, a2, a3 and a4 represent the weight values ​​of cutting fluid flow rate Qls, material hardness Cyd, cutting force Qxl and workpiece temperature Gwd, respectively, and 0≤a1<1, 0≤a2<1, 0≤a3<1, 0≤a4<1, a1+a2+a3+a4=1;

[0083] The feeding parameter evaluation unit compares and evaluates the feeding speed Jgsd by presetting the first feeding speed threshold Q1 and the second feeding speed threshold Q2, and the specific contents are as follows:

[0084] If the preset first feed speed threshold Q1>feed speed Jgsd, the first feed result is generated at this time; it means that the feed speed is abnormal, the processing efficiency is reduced and the friction of the tool in the material is increased; at the same time, the system determines that the current processing stability is qualified, but the cutting efficiency is unqualified;

[0085] If the preset first feed speed threshold Q1≤feed speed Jgsd≤second feed speed threshold Q2, it means that the feed speed is normal, and the cutting efficiency and quality are qualified and balanced;

[0086] If the preset second feed speed threshold Q2 is less than the feed speed Jgsd, the second feed result is generated at this time; it indicates that the feed speed is abnormal, and at the same time causes problems such as rapid tool wear, unstable cutting and reduced workpiece surface quality; the system determines that there is a risk of machining vibration and excessive temperature rise at this time;

[0087] The processing parameter adjustment unit is used to adjust the parameters according to the evaluation result of the feed speed Jgs, and the specific adjustment contents are as follows:

[0088] When the first feed result is generated, the system automatically adjusts the cutting speed, feed rate and cutting depth; including increasing the cutting speed by 10%, increasing the feed rate by 5% and increasing the cutting depth by 5%;

[0089] When the second feed result is generated, the system automatically adjusts the cutting speed, feed rate and cutting depth; including reducing the cutting speed by 10%, reducing the feed rate by 5% and reducing the cutting depth by 5%.

[0090] In this embodiment, the cutting fluid flow rate Qls directly affects the cooling and lubrication effect, and then affects the processing temperature and friction conditions. The appropriate cutting fluid flow rate Qls reduces the friction between the tool and the workpiece, reduces heat accumulation, and improves processing stability and surface quality. Material hardness Cyd is an important factor affecting cutting difficulty. The higher the hardness, the greater the tool cutting resistance. Therefore, the tool feed speed usually needs to be appropriately reduced to avoid increased tool wear and processing instability. The cutting force Qxl reflects the resistance of the tool during the cutting process. Excessive cutting force Qxl may cause vibration, reduced processing accuracy and shortened tool life. Therefore, evaluating the cutting force Qxl helps to optimize the feed speed and ensure the balance between processing efficiency and quality. The workpiece temperature Gwd directly affects the workpiece surface quality and tool life. High temperature can easily cause excessive tool wear or workpiece deformation. Therefore, monitoring the workpiece temperature Gwd helps adjust the tool feed speed, reduce the processing temperature rise, and ensure processing stability.

[0091] Example 4

[0092] The real-time vibration monitoring and adjustment module includes a vibration data acquisition unit and a vibration frequency evaluation and adjustment unit;

[0093] The vibration data acquisition unit collects vibration-related data in real time during the machining process by installing micro-acceleration sensors and laser displacement sensors at key positions of the tool and the workpiece and integrating high-precision strain gauges in the tool clamping system; including micro-vibration acceleration Wjs, axial offset Zpy and tool force fluctuation rate Dsl. Finally, the micro-vibration acceleration Wjs, axial offset Zpy and tool force fluctuation rate Dsl are dimensionlessly processed to construct a vibration data set.

[0094] The vibration frequency evaluation and adjustment sheet extracts the vibration data set, calculates and evaluates the vibration frequency coefficient Zdxs, and its specific contents are as follows:

[0095] Zdxs=\sqrt[{3}] {Wjs*Zpy*Dsl} , by presetting the first vibration frequency threshold W1 and the second vibration frequency threshold W2, and comparing and evaluating with the vibration frequency coefficient Zdxs;

[0096] If the first vibration frequency threshold W1> the vibration frequency coefficient Zdxs, it means that the vibration condition is normal, the machining process is smooth, and no adjustment is required;

[0097] If the first vibration frequency threshold W1≤vibration frequency coefficient Zdxs≤the second vibration frequency threshold W2, the first vibration result is generated, indicating that the vibration condition is abnormal, but the vibration condition is within a controllable range, and the processing conditions are further refined and adjusted at this time;

[0098] If the second vibration frequency threshold W2 is less than the vibration frequency coefficient Zdxs, a second vibration result is generated, indicating that the vibration is abnormal and affects the workpiece surface quality, tool life and machining accuracy, and parameter adjustment is performed immediately.

[0099] In this embodiment, the micro-vibration acceleration Wjs reflects the subtle vibration of the workpiece or tool during the machining process. These vibrations may cause ripples or increased roughness on the machined surface. Therefore, monitoring the micro-vibration acceleration Wjs helps to identify unstable factors in the machining process in advance, thereby avoiding the decline in machining accuracy and surface quality; the axial offset Zpy is the degree of offset of the workpiece or tool in the axial direction during machining. The change in the offset will directly affect the accuracy of the machining position and cause dimensional errors. Evaluating the axial offset Zpy helps to adjust the machining path and parameters to ensure the geometric accuracy and consistency of the workpiece; the tool force fluctuation rate Dsl refers to the fluctuation of the force that the tool bears over time during the cutting process. A large fluctuation rate indicates that the cutting force is unstable, which can easily lead to increased tool vibration and accelerated wear. By monitoring the tool force fluctuation rate Dsl, the system can adjust the cutting parameters in time, optimize the machining conditions, and reduce the adverse effects of vibration on the workpiece and tool.

[0100] Example 5

[0101] The multi-process automatic tool changing module includes a tool wear data acquisition unit and a wear assessment and tool changing decision unit;

[0102] The tool wear data acquisition unit is used to collect tool wear related data in real time during the machining process, including the tool cutting edge wear length Qxr, tool temperature Dwd, flank wear width Hms and spindle torque fluctuation rate Zzn; after dimensionless processing of the tool wear related data, the real-time collected data is transmitted to the wear assessment and tool change decision unit by using an integrated sensor and monitoring system;

[0103] The wear assessment and tool change decision unit extracts the cutting edge wear length Qxr, tool temperature Dwd, flank wear width Hms and spindle torque fluctuation rate Zzn, and calculates the tool wear coefficient Dmxs using the following formula:

[0104] , by presetting the first wear threshold E1 and the second wear threshold E2, and comparing and evaluating with the tool wear coefficient Dmxs, the specific contents are as follows:

[0105] If the tool wear coefficient Dmxs is less than the first wear threshold E1, it means that the tool wear state is normal, and the system continues the current processing without changing the tool. At this time, the system still regularly calculates the tool wear coefficient Dmxs within a fixed period to monitor the tool wear condition.

[0106] If the first wear threshold E1≤tool wear coefficient Dmxs<second wear threshold E2, it means that the tool wear state is abnormal, but has not reached an unacceptable wear level; at this time, the system starts an early warning to remind the operator to prepare for the subsequent process; at the same time, the system adjusts some parameters, including adjusting the low cutting speed or feed rate;

[0107] If the tool wear coefficient Dmxs ≥ the second wear threshold E2, it means that the tool wear state is abnormal and the current tool can no longer guarantee the processing quality, and the tool needs to be changed immediately; the system automatically triggers the tool change operation, including stopping the current process, starting the automatic tool change program and repositioning the workpiece.

[0108] In this embodiment, the cutting edge wear length Qxr directly reflects the degree of wear of the tool during the cutting process. As the wear increases, the cutting efficiency of the tool decreases, resulting in a decrease in processing quality. By monitoring the cutting edge wear length Qxr, the progress of tool wear can be judged to help determine the best time to change the tool; the tool temperature Dwd is closely related to wear, and excessive temperature will accelerate tool wear. When the tool works in a high temperature environment, it is easy to produce plastic deformation, resulting in a shortened tool life. By monitoring the tool temperature Dwd, cooling measures can be taken in time or the processing parameters can be adjusted to avoid excessive wear; the back tool wear width Hms is an important indicator of tool wear, which directly affects the surface roughness and dimensional accuracy of the workpiece. When the wear width increases, the contact area between the tool and the workpiece increases, the friction intensifies, and thus affects the processing stability. Monitoring this parameter helps to predict the tool wear state and replace the tool or adjust the processing conditions in a timely manner; the spindle torque fluctuation rate Zzn reflects the stability of the force applied to the tool during the cutting process. A larger fluctuation rate indicates that the cutting force is uneven, which may lead to accelerated tool wear. By monitoring the torque fluctuation rate Zzn, abnormal cutting force can be identified and the processing strategy can be adjusted to extend the tool life.

[0109] Example 6

[0110] The workpiece adaptive positioning module includes a visual recognition unit and a sensor positioning unit;

[0111] The visual recognition unit uses a high-resolution camera and an image processing algorithm to obtain the shape features and size data of the workpiece, and matches and compares it with the workpiece model built into the system to achieve automatic recognition of the workpiece;

[0112] The sensing positioning unit combines an optical sensor and a displacement sensor to adjust the position of the workpiece in real time according to the result of automatic identification of the workpiece. In the process of identification and positioning, multiple data collection and feedback are performed to achieve adaptive adjustment and precise positioning of the workpiece.

[0113] In this embodiment, in the workpiece adaptive positioning module, the visual recognition unit and the sensor positioning unit work together to ensure the accuracy and stability of the workpiece positioning; the visual recognition unit captures the workpiece image through a high-resolution camera, uses an image processing algorithm to extract the outline, size and shape features of the workpiece, and matches these data with the workpiece model built into the system in real time; during the matching process, the accuracy of the recognition result is ensured through multiple iterative comparisons and error corrections; the sensor positioning unit combines optical sensors and displacement sensors to dynamically adjust the workpiece position according to the output data of the visual recognition unit; during the specific adjustment process, the system will collect real-time displacement data multiple times, and make fine adjustments based on error feedback to gradually reduce the deviation between the actual position of the workpiece and the target position; in addition, the sensor positioning unit can also monitor environmental changes in real time, such as vibration or temperature fluctuations, and timely correct positioning errors through adaptive compensation strategies to ensure the stability and accuracy of the workpiece during the processing process; through this multi-level data collection, feedback and adjustment mechanism, the system can achieve high-precision adaptive positioning of workpieces in complex environments.

[0114] Example 7

[0115] The processing quality detection feedback module obtains the surface morphology data of the groove after processing through online sensors, and uses digital signal processing technology to extract roughness features, and then evaluates the surface quality of the workpiece in real time; secondly, the actual size data of the groove is collected through a high-precision measuring device, and compared with the target size, and the processing error and size deviation are analyzed. Finally, the detected quality data is summarized and analyzed to generate a quality evaluation result, and the closed-loop feedback is given to the path planning module.

[0116] In this embodiment, the processing quality detection feedback module obtains the surface morphology data of the groove through an online sensor, and uses digital signal processing technology to extract roughness features to achieve real-time evaluation of the surface quality. After extracting the features, it determines whether the surface finish meets the standard through a preset threshold, and feeds back to the path planning module for adjustment for abnormal situations; at the same time, the high-precision measuring device collects the actual size data of the groove, compares it with the target size, analyzes the processing error and size deviation, and generates a comprehensive evaluation result after multi-dimensional analysis of the quality data. If deviations are found, the system will automatically adjust the cutting path and parameters to optimize the processing quality.

[0117] Example 8

[0118] See also Figure 2 , a CNC lathe slotting processing control method, comprising the following steps:

[0119] Step 1: Analyze the workpiece contour and processing requirements through the path planning algorithm, and automatically generate the processing path with the best accuracy;

[0120] Step 2: Calculate and evaluate the feed speed Jgsd within a fixed period through the built-in intelligent algorithm, combined with the workpiece material and processing conditions; automatically adjust the relevant parameter variables, including cutting speed, feed rate and cutting depth, according to the evaluation results of the feed speed Jgsd;

[0121] Step 3: Use sensors to monitor the vibration during the processing in real time, and calculate and evaluate the vibration frequency coefficient Zdxs based on the detected data;

[0122] Step 4: In order to meet the requirements of multiple processes in slotting, intelligent tool changing and positioning technology is integrated; by automatically collecting tool wear-related data in each process and calculating and evaluating it, the timing of tool changing is finally determined based on the wear-related data;

[0123] Step 5: Use visual recognition and sensing technology to automatically identify and accurately locate the workpiece;

[0124] Step 6: Integrate the system for online detection and real-time analysis of the quality of the machined groove, including quality assessment based on surface roughness and dimensional accuracy data, and finally provide closed-loop feedback to the path planning module.

[0125] In this embodiment, first, the path planning algorithm automatically generates the optimal processing path to ensure the accuracy of the workpiece contour; the feed speed Jgsd is dynamically evaluated through an intelligent algorithm, and the cutting speed, feed amount and cutting depth are optimized in real time to make the processing process more stable and efficient; in the vibration monitoring link, vibration data is collected in real time, the vibration frequency coefficient Zdxs is calculated and dynamically adjusted, and the stability of the processing is improved; the multi-process intelligent tool changing technology is combined with tool wear data to achieve precise tool changing and efficient positioning, extend tool life and reduce downtime; the workpiece identification and positioning module uses vision and sensing technology to achieve precise positioning and reduce processing errors; finally, the integrated online quality inspection module provides real-time feedback on the surface roughness and dimensional accuracy of the groove to ensure that the quality of the finished product always reaches the best level; each step is optimized from path planning to quality evaluation through intelligent algorithms, real-time monitoring and closed-loop feedback, effectively solving the problems of unstable accuracy, untimely tool change and difficult to monitor processing quality in traditional groove processing, and significantly improving the overall processing efficiency and product quality.

[0126] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A CNC lathe slotting processing control system, characterized in that: It includes intelligent path planning module, processing parameter optimization module, real-time vibration monitoring and adjustment module, multi-process automatic tool changing module, workpiece adaptive positioning module and processing quality detection feedback module; The intelligent path planning module analyzes the workpiece contour and processing requirements through a path planning algorithm, and automatically generates a processing path with the best accuracy; The processing parameter optimization module calculates and evaluates the feed speed Jgsd within a fixed period through a built-in intelligent algorithm, combined with the workpiece material and processing conditions; automatically adjusts relevant parameter variables, including cutting speed, feed rate and cutting depth, according to the evaluation result of the feed speed Jgsd; The real-time vibration monitoring and adjustment module monitors the vibration conditions during the processing in real time through sensors, and calculates and evaluates the vibration frequency coefficient Zdxs based on the detected data; The multi-process automatic tool changing module is used for tool replacement and workpiece positioning; in view of the multi-process requirements in slotting processing, it integrates intelligent tool changing and positioning technology; by automatically collecting tool wear-related data in each process and calculating and evaluating it, the timing of tool change is finally determined based on the wear-related data; The workpiece adaptive positioning module is used to automatically identify and accurately position the workpiece using visual recognition and sensing technology; The processing quality detection feedback module integrates the functions for online detection and real-time analysis of the quality of the groove after processing, including quality assessment based on surface roughness and dimensional accuracy data, and finally closed-loop feedback to the path planning module; The intelligent path planning module includes a workpiece contour analysis unit and a path optimization generation unit; The workpiece contour analysis unit is responsible for receiving and parsing the geometric model and processing requirements of the workpiece; by analyzing the three-dimensional contour, shape characteristics and processing parameters of the workpiece, identifying the specific location of the key processing area and the groove, and generating a preliminary path framework; The path optimization generation unit further adjusts the processing path through a path planning algorithm based on the preliminary path framework output by the workpiece contour analysis unit; generates a path with optimal processing accuracy by comprehensively considering the tool motion trajectory, cutting direction and path length, and dynamically adjusts the path; The processing parameter optimization module includes a workpiece material and working condition analysis unit, a feed parameter evaluation unit and a processing parameter adjustment unit; The workpiece material and working condition analysis unit is responsible for extracting parameters related to the feed speed Jgsd, including material hardness Cyd, cutting force Qxl and workpiece temperature Gwd, according to the input workpiece material information and the current processing conditions; and collecting and acquiring the cutting fluid flow rate Qls in real time through a flow sensor installed near the cutting fluid nozzle; the feed parameter evaluation unit extracts the cutting fluid flow rate Qls, material hardness Cyd, cutting force Qxl and workpiece temperature Gwd and performs dimensionless processing, and then calculates the feed speed Jgsd through the following formula: ， In the formula, K represents the overall adjustment coefficient, which is used to adjust the reference value of the feed speed according to the processing environment or equipment performance. Its specific value is adjusted by the user; a1, a2, a3 and a4 represent the weight values ​​of cutting fluid flow rate Qls, material hardness Cyd, cutting force Qxl and workpiece temperature Gwd, respectively, and 0≤a1<1, 0≤a2<1, 0≤a3<1, 0≤a4<1, a1+a2+a3+a4=1; The feeding parameter evaluation unit compares and evaluates the feeding speed Jgsd by presetting the first feeding speed threshold Q1 and the second feeding speed threshold Q2, and the specific contents are as follows: If the preset first feed speed threshold Q1>feed speed Jgsd, the first feed result is generated at this time; it means that the feed speed is abnormal, the processing efficiency is reduced and the friction of the tool in the material is increased; at the same time, the system determines that the current processing stability is qualified, but the cutting efficiency is unqualified; If the preset first feed speed threshold Q1≤feed speed Jgsd≤second feed speed threshold Q2, it means that the feed speed is normal, and the cutting efficiency and quality are qualified and balanced; If the preset second feed speed threshold Q2 is less than the feed speed Jgsd, the second feed result is generated at this time; it indicates that the feed speed is abnormal, and at the same time causes problems such as rapid tool wear, unstable cutting and reduced workpiece surface quality; the system determines that there is a risk of machining vibration and excessive temperature rise at this time; The processing parameter adjustment unit is used to adjust the parameters according to the evaluation result of the feed speed Jgs, and the specific adjustment contents are as follows: When the first feed result is generated, the system automatically adjusts the cutting speed, feed rate and cutting depth; including increasing the cutting speed by 10%, increasing the feed rate by 5% and increasing the cutting depth by 5%; When the second feed result is generated, the system automatically adjusts the cutting speed, feed rate and cutting depth; including reducing the cutting speed by 10%, reducing the feed rate by 5% and reducing the cutting depth by 5%; The real-time vibration monitoring and adjustment module includes a vibration data acquisition unit and a vibration frequency evaluation and adjustment unit; The vibration data acquisition unit collects vibration-related data during the machining process in real time by installing micro acceleration sensors and laser displacement sensors at key positions of the tool and the workpiece and integrating high-precision strain gauges in the tool clamping system; Including micro-vibration acceleration Wjs, axial offset Zpy and tool force fluctuation rate Dsl, finally, the micro-vibration acceleration Wjs, axial offset Zpy and tool force fluctuation rate Dsl are dimensionlessly processed to construct a vibration data set; the vibration frequency evaluation and adjustment sheet extracts the vibration data set, calculates and evaluates the vibration frequency coefficient Zdxs, and its specific contents are as follows: <h2 style=";text-align:left;direction:ltr">Zdxs=\sqrt[{3}] {Wjs*Zpy*Dsl}<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> , By presetting the first vibration frequency threshold W1 and the second vibration frequency threshold W2, a comparison and evaluation is performed with the vibration frequency coefficient Zdxs; If the first vibration frequency threshold W1> the vibration frequency coefficient Zdxs, it means that the vibration condition is normal, the machining process is smooth, and no adjustment is required; If the first vibration frequency threshold W1≤vibration frequency coefficient Zdxs≤the second vibration frequency threshold W2, the first vibration result is generated, indicating that the vibration condition is abnormal, but the vibration condition is within a controllable range, and the processing conditions are further refined and adjusted at this time; If the second vibration frequency threshold W2 is less than the vibration frequency coefficient Zdxs, a second vibration result is generated, indicating that the vibration is abnormal and affects the workpiece surface quality, tool life and machining accuracy, and parameter adjustment is performed immediately.

2. A CNC lathe slotting processing control system according to claim 1, characterized in that: The multi-process automatic tool changing module includes a tool wear data acquisition unit and a wear assessment and tool changing decision unit; The tool wear data acquisition unit is used to collect tool wear related data in real time during the machining process, including the tool cutting edge wear length Qxr, tool temperature Dwd, flank wear width Hms and spindle torque fluctuation rate Zzn; after dimensionless processing of the tool wear related data, the real-time collected data is transmitted to the wear assessment and tool change decision unit by using an integrated sensor and monitoring system; The wear assessment and tool change decision unit extracts the cutting edge wear length Qxr, tool temperature Dwd, flank wear width Hms and spindle torque fluctuation rate Zzn, and calculates the tool wear coefficient Dmxs using the following formula: ， By presetting the first wear threshold E1 and the second wear threshold E2, a comparative evaluation is performed with the tool wear coefficient Dmxs, the specific contents of which are as follows: If the tool wear coefficient Dmxs is less than the first wear threshold E1, it means that the tool wear state is normal, and the system continues the current processing without changing the tool. At this time, the system still regularly calculates the tool wear coefficient Dmxs within a fixed period to monitor the tool wear condition. If the first wear threshold E1≤tool wear coefficient Dmxs<second wear threshold E2, it means that the tool wear state is abnormal, but has not reached an unacceptable wear level; at this time, the system starts an early warning to remind the operator to prepare for the subsequent process; at the same time, the system adjusts some parameters, including adjusting the low cutting speed or feed rate; If the tool wear coefficient Dmxs ≥ the second wear threshold E2, it means that the tool wear state is abnormal and the current tool can no longer guarantee the processing quality, and the tool needs to be changed immediately; the system automatically triggers the tool change operation, including stopping the current process, starting the automatic tool change program and repositioning the workpiece.

3. A CNC lathe slotting processing control system according to claim 1, characterized in that: The workpiece adaptive positioning module includes a visual recognition unit and a sensor positioning unit; The visual recognition unit uses a high-resolution camera and an image processing algorithm to obtain the shape features and size data of the workpiece, and matches and compares it with the workpiece model built into the system to achieve automatic recognition of the workpiece; The sensing positioning unit combines an optical sensor and a displacement sensor to adjust the position of the workpiece in real time according to the result of automatic identification of the workpiece. In the process of identification and positioning, multiple data collection and feedback are performed to achieve adaptive adjustment and precise positioning of the workpiece.

4. A CNC lathe slotting processing control system according to claim 1, characterized in that: The processing quality detection feedback module obtains the surface morphology data of the groove after processing through online sensors, and uses digital signal processing technology to extract roughness features, and then evaluates the surface quality of the workpiece in real time; secondly, the actual size data of the groove is collected through a high-precision measuring device, and compared with the target size, and the processing error and size deviation are analyzed. Finally, the detected quality data is summarized and analyzed to generate a quality evaluation result, and the closed-loop feedback is given to the path planning module.

5. A CNC lathe slotting processing control method, according to the CNC lathe slotting processing control system of claim 1, characterized in that: As claimed in claim 1, comprising the following steps: Step 1: Analyze the workpiece contour and processing requirements through the path planning algorithm, and automatically generate the processing path with the best accuracy; Step 2: Calculate and evaluate the feed speed Jgsd within a fixed period through the built-in intelligent algorithm, combined with the workpiece material and processing conditions; automatically adjust the relevant parameter variables, including cutting speed, feed rate and cutting depth, according to the evaluation results of the feed speed Jgsd; Step 3: Use sensors to monitor the vibration during the processing in real time, and calculate and evaluate the vibration frequency coefficient Zdxs based on the detected data; Step 4: In order to meet the needs of multiple processes in slotting, intelligent tool changing and positioning technology is integrated; by automatically collecting tool wear-related data in each process and calculating and evaluating it, the timing of tool changing is finally determined based on the wear-related data; Step 5: Use visual recognition and sensing technology to automatically identify and accurately locate the workpiece; Step 6: Integrate the system for online detection and real-time analysis of the quality of the machined groove, including quality assessment based on surface roughness and dimensional accuracy data, and finally provide closed-loop feedback to the path planning module.

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