Pole processing method in cutter large circle interpolation process in numerical control system

Abstract

The invention discloses a pole processing method in a cutter large circle interpolation process in a numerical control system, and the method comprises the following steps: determining a circle center, a radius, a processing parameter and a pole position according to the size of a large circle, generating an actual motion track of a cutter through employing an interpolation algorithm, and carrying out the calculation to obtain the speed data of the pole position; based on historical data of numerical control system processing, a data error in a future period of processing time is predicted, error compensation is performed on data at a pole, so that a cutter can stably pass through the pole, numerical control machine tool simulation software is used for simulating a cutter path and machine tool actions, and preset parameters and error compensation data are used as input. According to the method, error compensation is carried out on the data at the poles, the processing problem caused by errors can be effectively reduced, meanwhile, the data at the large circle and the poles are associated to establish a mathematical model, and pole processing data can be obtained without tedious calculation.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing and analysis, and in particular to a method for processing poles in a tool large circle interpolation process in a numerical control system. Background Art

[0002] Tool great circle interpolation is a path generation technology in CNC machining, which is used to control the tool to move along a circular arc trajectory during machining. Specifically, it is the process of interpolating the tool along a predetermined great circle trajectory in a plane or space through a computer control system. This interpolation method is often used for machining tasks that require arc shapes on the workpiece surface, such as turning and milling, and can effectively realize the machining of complex curves; poles refer to key points on the tool trajectory. The tool may face sudden changes in speed or acceleration at these positions, affecting the smoothness and accuracy of the machining. Therefore, processing poles is one of the key steps to ensure the continuity of tool movement and machining quality.

[0003] However, in the existing large circle interpolation, the poles are usually directly calculated and processed in an ordinary way, ignoring the errors in actual processing. The errors may cause the parameters at the poles to change, thereby affecting the processing of the poles. At the same time, when processing large circles of different sizes, a large number of complex calculations are usually required to obtain the processing data at the poles, making it more complicated to obtain the data at the poles.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of the problems in the related art, the present invention proposes a pole processing method in a tool large circle interpolation process in a numerical control system to overcome the above technical problems existing in the existing related art.

[0006] To this end, the specific technical solution adopted by the present invention is as follows: A method for processing poles in a large circle interpolation process of a tool in a numerical control system, the method comprising the following steps: S1. Determine the center, radius, processing parameters and pole position according to the size of the large circle, and use the interpolation algorithm to generate the actual motion trajectory of the tool, and calculate the speed data of the pole position; S2, based on the historical data of CNC system processing, predict the data error in the future processing time, and compensate the error of the data at the pole, so that the tool can pass through the pole smoothly; S3. Use CNC machine simulation software to simulate tool paths and machine tool movements, and use preset parameters and error compensation data as input to verify the correctness of the pole data. When the pole data value is within the normal range, actual processing can be performed. When the pole data value is within the abnormal range, adjust the parameters and simulate again. S4. After simulation verification, actual processing is performed, and the speed parameters at the pole are monitored by sensors to verify the consistency between the actual data and the simulation data; S5. Associate the big circle data with the pole data and establish a mathematical model. When it is necessary to process big circles of other sizes, directly input the big circle data information and quickly obtain the pole processing data.

[0007] As a preferred embodiment, the method of determining the center, radius, processing parameters and pole position according to the size of the large circle, generating the actual motion trajectory of the tool using an interpolation algorithm, and calculating the speed data of the pole position includes the following steps: S11, determining the size parameters of the large circle, including the diameter and the coordinates of the center of the circle; S12, determining the pole position and processing parameter data, the pole includes the starting point, the maximum speed point, the deceleration point and the end point; S13, using an interpolation algorithm to generate a motion trajectory of the tool, and calculating the speed data of the pole position based on the motion trajectory, including the speed at the maximum speed point and the deceleration at the deceleration point; As a preferred embodiment, the interpolation algorithm is used to generate the motion trajectory of the tool, and the speed data of the extreme point position is calculated in combination with the motion trajectory, including the maximum speed point speed and the deceleration point deceleration, including the following steps: S131, calculate the maximum speed point speed, the specific formula is: ;in, is the maximum speed point speed, is the initial acceleration is the acceleration time; calculate the deceleration at the deceleration point, the specific formula is: ; Where a is the deceleration at the deceleration point, and t is the distance from the maximum speed point to the end point.

[0008] As a preferred implementation, the method of predicting data errors in a future period of processing time based on historical data processed by the numerical control system and performing error compensation on the data at the pole so that the tool can smoothly pass through the pole includes the following steps: S21, collecting historical processing data, including setting parameters of the CNC machine tool, setting parameters at the pole, and actual motion trajectory and actual data at the pole; S22. Analyze historical data to identify parameter errors. Parameter error factors include system response and mechanical wear error. Based on historical data analysis, use statistical models to establish a mathematical model of parameter errors, and use the established error model to predict parameter errors in future processing. Through historical data analysis and modeling, parameter errors in future processing can be accurately predicted.

[0009] S24. According to the predicted parameter error, error compensation is performed on the data at the extreme point: including adjusting the speed at the maximum speed point and the deceleration at the deceleration point.

[0010] As a preferred implementation, the specific steps of establishing a mathematical model of parameter errors using a statistical model based on historical data analysis are: S221. The specific formula of the prediction model is: ;in, is the calculated error parameter, is the system response error, is the mechanical wear error, is the regression coefficient, is the error term.

[0011] As a preferred embodiment, the use of CNC machine simulation software to simulate tool paths and machine tool movements, and using preset parameters and error compensation data as input to verify the correctness of the pole data, when the pole data value is within a normal range, actual processing can be performed, and when the pole data value is within an abnormal range, adjusting the parameters to simulate again includes the following steps: S31, setting the initial parameters of the CNC machine tool simulation software, including tool speed, processing path, processing depth, etc., inputting the preset parameters and error compensation data into the simulation software, running the CNC machine tool simulation software, simulating the tool path and the movement of the machine tool, and observing the parameters during the simulation process; S32. During the simulation process, key extreme point data are recorded, including the speed at the maximum speed point and the deceleration at the deceleration point, to verify the correctness of the extreme point data. When the extreme point data value is within the normal range, actual processing can be performed. When the extreme point data value is within the abnormal range, adjust the parameters in the simulation software and perform the simulation again until the extreme point data is within the normal range.

[0012] Through simulation, we can quickly evaluate the effects of different designs and process solutions, optimize product design and manufacturing processes, and improve product performance and quality.

[0013] As a preferred embodiment, after the simulation verification, actual processing is performed, and the speed parameters at the pole are monitored by sensors, and the consistency of the actual data and the simulation data is verified, which includes the following steps: S41. According to the result of the simulation verification, the speed of the maximum speed point and the speed of the deceleration point are set; S42, installing a speed sensor and receiving speed data at the pole; S43: Compare the parameter data actually collected with the data verified by simulation to verify the consistency between the actual data and the simulation data.

[0014] As a preferred implementation, the process of associating the great circle data with the pole data and establishing a mathematical model, directly inputting the great circle data information when processing great circles of other sizes, and quickly obtaining the pole processing data includes the following steps: S51. First, the standard equation of a circle is used to describe the shape and size of the great circle. At the same time, the geometric parameters of the great circle are associated with the pole data, and a mathematical model is established to calculate the data at the pole; S52. Use historical data sets to train and verify the pole data processing model to ensure that the model can accurately output the data at the pole. When it is necessary to process large circles of other sizes, directly input new large circle data information. The model will quickly calculate the processing data at the new pole based on the established mathematical model and the trained pole data processing model. There is no need to perform a lot of tedious calculations, which is convenient for pole processing.

[0015] Training with historical data sets can make the extreme point data processing model more accurate and reliable.

[0016] The beneficial effects of the present invention are: 1. The present invention collects historical data, predicts parameter errors of future processing, and performs error compensation on parameters at the extreme points, which can effectively reduce the reduction in processing accuracy due to error accumulation, thereby ensuring the processing quality and consistency of products in different time periods. At the same time, by compensating for the errors at the extreme points, it can reduce processing interruptions or reprocessing caused by parameter fluctuations, thereby improving the continuity and efficiency of processing.

[0017] 2. The present invention associates the data of the big circle, the parameter data set at the pole and the pole data, and establishes a mathematical model. When it is necessary to process big circles of other sizes, the big circle data information can be directly input to quickly obtain the parameter settings at the pole and the pole data. This method greatly simplifies the calculation steps and avoids tedious calculation processes. The operator only needs to focus on inputting and confirming the new big circle parameters without recalculating the detailed parameters required for each size.

[0018] 3. The present invention can discover and solve potential problems before actual processing by verifying the correctness of the pole data in the simulation software. At the same time, the simulation software can accurately simulate the tool path and machine tool action, and verify it in combination with preset parameters and error compensation data. It can not only improve the accuracy of the pole data, but also quickly adjust the parameters and verify the effect after the simulation experiment, helping to optimize the parameter settings and error compensation strategies at the pole, so that the tool can pass through the pole smoothly and reduce tool oscillation at the pole. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 The present invention is a flowchart of a method for processing poles in a tool great circle interpolation process in a numerical control system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0022] According to an embodiment of the present invention, a method for processing poles in a tool large circle interpolation process in a numerical control system is provided.

[0023] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to a method for processing poles in a tool great circle interpolation process in a numerical control system according to an embodiment of the present invention, the method comprises the following steps: S1. Determine the center, radius, processing parameters and pole position according to the size of the large circle, and use the interpolation algorithm to generate the actual motion trajectory of the tool, and calculate the speed data of the pole position; Furthermore, the method of determining the center, radius, machining parameters and pole position according to the size of the large circle, generating the actual motion trajectory of the tool using an interpolation algorithm, and calculating the speed data of the pole position includes the following steps: S11, determining the size parameters of the large circle, including the diameter and the coordinates of the center of the circle; S12, determining the pole position and processing parameter data, the pole includes the starting point, the maximum speed point, the deceleration point and the end point; S13, using an interpolation algorithm to generate a motion trajectory of the tool, and calculating the speed data of the pole position based on the motion trajectory, including the speed at the maximum speed point and the deceleration at the deceleration point; It should be noted that the interpolation algorithm is a commonly used calculation method in CNC machining, which is used to generate the motion trajectory of the tool to ensure that the tool moves accurately along the expected path. Specifically, the interpolation algorithm calculates the numerical control system instructions under the set processing speed and accuracy requirements, so that the tool can move and cut accurately along the required trajectory.

[0024] In actual processing, there are too many shapes of workpieces to be processed. In order to meet the requirements of geometric dimensional accuracy, the tool center trajectory should be accurately generated according to the contour shape of the workpiece. For simple curves, the CNC system can be easily implemented, but for more complex shapes, direct generation requires complex algorithms and will also increase the workload of the computer. Therefore, in actual applications, a small straight line or arc is often used for fitting to meet the accuracy requirements. This fitting method is called "interpolation".

[0025] Interpolation is the work of completing data densification based on limited information. The actual trajectory of the interpolation movement cannot be exactly the same as its ideal trajectory. Therefore, the interpolation points generally will not fall on the ideal trajectory.

[0026] Furthermore, the use of an interpolation algorithm to generate a motion trajectory of the tool, and calculating the speed data of the extreme point position in combination with the motion trajectory, including the maximum speed point speed and the deceleration point deceleration, comprises the following steps: S131, calculate the maximum speed point speed, the specific formula is: ;in, is the maximum speed point speed, is the initial acceleration is the acceleration time; calculate the deceleration at the deceleration point, the specific formula is: ;Wherein, a is the deceleration at the deceleration point, and t is the distance from the maximum speed point to the end point. It should be noted that controlling the speed at the maximum speed point can ensure that the processing quality will not be affected by excessive speed during the processing, such as avoiding vibration, increased tool wear or increased surface roughness, thereby ensuring the accuracy and surface quality of the product; the deceleration is the speed change rate when the tool slows down from the maximum speed point to a lower speed. By reasonably controlling the deceleration, the tool can be prevented from generating impact or vibration during the deceleration process, ensuring the safety of the processing process and the stable operation of the equipment.

[0027] S2, based on the historical data of CNC system processing, predict the data error in the future processing time, and compensate the error of the data at the pole, so that the tool can pass through the pole smoothly; Furthermore, the method of predicting the data error in a future period of processing time based on the historical data of the CNC system processing and performing error compensation on the data at the pole so that the tool can smoothly pass through the pole includes the following steps: S21, collecting historical processing data, including setting parameters of the CNC machine tool, setting parameters at the pole, and actual motion trajectory and actual data at the pole; S22, analyzing historical data to identify parameter errors, the parameter error factors include system response and mechanical wear error, based on the historical data analysis, using statistical models to establish a mathematical model of parameter errors, and using the established error model to predict parameter errors in future processing; Furthermore, the specific steps of establishing a mathematical model of parameter errors using a statistical model based on historical data analysis are: S221. The specific formula of the prediction model is: ;in, is the calculated error parameter, is the system response error, is the mechanical wear error, is the regression coefficient, is the error term. It should be noted that statistical models can help analyze and predict the distribution and trend of parameter errors. By establishing mathematical models, the impact of different parameters on system output can be quantified, the main sources of errors can be identified, and targeted optimization and control can be performed. S24. According to the predicted parameter error, the data at the extreme point is compensated for the error: including adjusting the maximum speed point speed and the deceleration point deceleration. It should be noted that by performing error compensation on the maximum speed point speed and the deceleration point deceleration, the overshoot and oscillation in the dynamic response process of the system can be reduced, the stability of the system can be improved, and the dynamic performance of the system can be effectively optimized.

[0028] S3. Use CNC machine simulation software to simulate tool paths and machine tool movements, and use preset parameters and error compensation data as input to verify the correctness of the pole data. When the pole data value is within the normal range, actual processing can be performed. When the pole data value is within the abnormal range, adjust the parameters and simulate again. Furthermore, the use of CNC machine simulation software to simulate tool paths and machine tool movements, and using preset parameters and error compensation data as input to verify the correctness of the pole data, when the pole data value is within a normal range, actual processing can be performed, and when the pole data value is within an abnormal range, adjusting the parameters and simulating again includes the following steps: S31, setting the initial parameters of the CNC machine tool simulation software, including tool speed, processing path, processing depth, etc., inputting the preset parameters and error compensation data into the simulation software, running the CNC machine tool simulation software, simulating the tool path and the movement of the machine tool, and observing the parameters during the simulation process; S32. During the simulation process, key extreme point data are recorded, including the speed at the maximum speed point and the deceleration at the deceleration point, to verify the correctness of the extreme point data. When the extreme point data value is within the normal range, actual processing can be performed. When the extreme point data value is within the abnormal range, adjust the parameters in the simulation software and perform the simulation again until the extreme point data is within the normal range.

[0029] It should be noted that when performing simulation, VERICUT simulation software is used for CNC machining simulation. This simulation software can not only simulate the tool path and machine tool movement in detail, but also has the ability to view the workpiece processing process in real time.

[0030] S4. After simulation verification, actual processing is performed, and the speed parameters at the pole are monitored by sensors to verify the consistency between the actual data and the simulation data; Furthermore, after the simulation verification, actual processing is performed, and the speed parameters at the pole are monitored by sensors to verify the consistency between the actual data and the simulation data, including the following steps: S41. According to the result of the simulation verification, the speed of the maximum speed point and the speed of the deceleration point are set; S42, installing a speed sensor and receiving speed data at the pole; S43: Compare the parameter data actually collected with the data verified by simulation to verify the consistency between the actual data and the simulation data.

[0031] It should be noted that by comparing actual data with simulation data, possible deviations or errors in the model can be discovered and adjusted, and parameter settings can be further optimized. At the same time, accurate simulation data verification can provide a basis for improving the actual production process.

[0032] S5. Associate the big circle data with the pole data and establish a mathematical model. When it is necessary to process big circles of other sizes, directly input the big circle data information and quickly obtain the pole processing data.

[0033] Furthermore, the process of associating the big circle data with the pole data and establishing a mathematical model, directly inputting the big circle data information when processing big circles of other sizes, and quickly obtaining the pole processing data includes the following steps: S51. First, the standard equation of a circle is used to describe the shape and size of the great circle. At the same time, the geometric parameters of the great circle are associated with the pole data, and a mathematical model is established to calculate the data at the pole; S52. Use historical data sets to train and verify the pole data processing model to ensure that the model can accurately output the data at the pole. When it is necessary to process large circles of other sizes, directly input new large circle data information. The model will quickly calculate the processing data at the new pole based on the established mathematical model and the trained pole data processing model. There is no need to perform a lot of tedious calculations, which is convenient for pole processing.

[0034] It should be noted that pole processing data usually requires a lot of calculations to obtain. Associating the large circle data with the pole data can effectively reduce the calculation steps. When large circles of other sizes need to be processed, the calculation efficiency of the pole data can be improved and the processing data at the pole can be quickly obtained.

[0035] In summary, the present invention collects historical data, predicts parameter errors of future processing, and compensates for the errors of the parameters at the poles, which can effectively reduce the reduction in processing accuracy caused by error accumulation, thereby ensuring the processing quality and consistency of the product in different time periods. At the same time, by compensating for the errors at the poles, it is possible to reduce processing interruptions or reprocessing caused by parameter fluctuations, thereby improving the continuity and efficiency of processing; the present invention associates the data of the large circle, the parameter data set at the poles, and the pole data, and establishes a mathematical model. When it is necessary to process large circles of other sizes, the large circle data information is directly input to quickly obtain the parameter settings at the poles and the pole data. This method greatly simplifies the calculation steps and avoids tedious calculation processes. The operator only needs to focus on inputting and confirming the new great circle parameters without recalculating the detailed parameters required for each size. The present invention can find and solve potential problems before actual processing by verifying the correctness of the pole data in the simulation software. At the same time, the simulation software can accurately simulate the tool path and machine tool action, and verify it in combination with preset parameters and error compensation data. It can not only improve the accuracy of the pole data, but also quickly adjust the parameters and verify the effect after the simulation experiment, which helps to optimize the parameter settings and error compensation strategies at the pole, so that the tool can pass through the pole smoothly and reduce the tool vibration at the pole.

[0036] 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, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for processing poles in a large circle interpolation process of a tool in a numerical control system, characterized in that: The method comprises the following steps: S1. Determine the center, radius, processing parameters and pole position according to the size of the large circle, and use the interpolation algorithm to generate the actual motion trajectory of the tool, and calculate the speed data of the pole position; S2, based on the historical data of CNC system processing, predict the data error in the future processing time, and compensate the error of the data at the pole, so that the tool can pass through the pole smoothly; S3. Use CNC machine simulation software to simulate tool paths and machine tool movements, and use preset parameters and error compensation data as input to verify the correctness of the pole data. When the pole data value is within the normal range, actual processing can be performed. When the pole data value is within the abnormal range, adjust the parameters and simulate again. S4. After simulation verification, actual processing is performed, and the speed parameters at the pole are monitored by sensors to verify the consistency between the actual data and the simulation data; S5. Associate the big circle data with the pole data and establish a mathematical model. When it is necessary to process big circles of other sizes, directly input the big circle data information and quickly obtain the pole processing data.

2. The method for processing poles in a tool great circle interpolation process in a numerical control system according to claim 1, characterized in that: Determining the center, radius, machining parameters and pole position according to the size of the large circle, generating the actual motion trajectory of the tool using an interpolation algorithm, and calculating the speed data of the pole position at the same time include the following steps: S11, determining the size parameters of the large circle, including the diameter and the coordinates of the center of the circle; S12, determining the pole position and processing parameter data, the pole includes the starting point, the maximum speed point, the deceleration point and the end point; S13, using an interpolation algorithm to generate a motion trajectory of the tool, and calculating the speed data of the pole position based on the motion trajectory, including the speed at the maximum speed point and the deceleration at the deceleration point.

3. The method for processing poles in a tool large circle interpolation process in a numerical control system according to claim 2, characterized in that: The method of using an interpolation algorithm to generate a motion trajectory of a tool and calculating the speed data of the extreme point position in combination with the motion trajectory, including the speed at the maximum speed point and the deceleration at the deceleration point, comprises the following steps: S131, calculate the maximum speed point speed, the specific formula is: ;in, is the maximum speed point speed, is the initial acceleration is the acceleration time; Calculate the deceleration at the deceleration point. The specific formula is: ; Where a is the deceleration at the deceleration point, and t is the distance from the maximum speed point to the end point.

4. The method for processing poles in a tool great circle interpolation process in a numerical control system according to claim 1, characterized in that: The method of predicting the data error in a future processing time based on the historical data processed by the numerical control system and performing error compensation on the data at the pole so that the tool can smoothly pass through the pole includes the following steps: S21, collecting historical processing data, including setting parameters of the CNC machine tool, setting parameters at the pole, and actual motion trajectory and actual data at the pole; S22, analyzing historical data to identify parameter errors, the parameter error factors include system response and mechanical wear error, based on the historical data analysis, using statistical models to establish a mathematical model of parameter errors, and using the established error model to predict parameter errors in future processing; S24. According to the predicted parameter error, error compensation is performed on the data at the extreme point: including adjusting the speed at the maximum speed point and the deceleration at the deceleration point.

5. The method for processing poles in a tool large circle interpolation process in a numerical control system according to claim 4, characterized in that: The specific steps of establishing a mathematical model of parameter errors using a statistical model based on historical data analysis are: S221. The specific formula of the prediction model is: ;in, is the calculated error parameter, is the system response error, is the mechanical wear error, is the regression coefficient, is the error term.

6. The method for processing poles in a tool great circle interpolation process in a numerical control system according to claim 1, characterized in that: The method of using CNC machine simulation software to simulate tool paths and machine tool movements, and taking preset parameters and error compensation data as input to verify the correctness of the pole data, and when the pole data value is within a normal range, actual processing can be performed, and when the pole data value is within an abnormal range, adjusting the parameters and simulating again includes the following steps: S31, setting the initial parameters of the CNC machine tool simulation software, including tool speed, processing path, and processing depth, inputting the preset parameters and error compensation data into the simulation software, running the CNC machine tool simulation software, simulating the tool path and the movement of the machine tool, and observing the parameters during the simulation process; S32. During the simulation process, key extreme point data are recorded, including the speed at the maximum speed point and the deceleration at the deceleration point, to verify the correctness of the extreme point data. When the extreme point data value is within the normal range, actual processing can be performed. When the extreme point data value is within the abnormal range, adjust the parameters in the simulation software and perform the simulation again until the extreme point data is within the normal range.

7. The method for processing poles in a tool great circle interpolation process in a numerical control system according to claim 1, characterized in that: After the simulation verification, actual processing is performed, and the speed parameters at the pole are monitored by sensors to verify the consistency between the actual data and the simulation data, including the following steps: S41. According to the result of the simulation verification, the speed of the maximum speed point and the speed of the deceleration point are set; S42, installing a speed sensor and receiving speed data at the pole; S43: Compare the parameter data actually collected with the data verified by simulation to verify the consistency between the actual data and the simulation data.

8. The method for processing poles in a tool great circle interpolation process in a numerical control system according to claim 1, characterized in that: The data of the big circle is associated with the pole data, and a mathematical model is established. When it is necessary to process big circles of other sizes, the big circle data information is directly input, and the pole processing data is quickly obtained. The following steps are involved: S51. First, the standard equation of a circle is used to describe the shape and size of the great circle. At the same time, the geometric parameters of the great circle are associated with the pole data, and a mathematical model is established to calculate the data at the pole; S52. Use historical data sets to train and verify the pole data processing model to ensure that the model can accurately output the data at the pole. When it is necessary to process large circles of other sizes, directly input the new large circle data information. The model will quickly calculate the processing data at the new pole based on the established mathematical model and the trained pole data processing model.

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