Machining path analysis and optimization method and device based on machine dynamic characteristics

Abstract

The invention relates to a method and a device for analyzing and optimizing a processing path based on dynamic characteristics of a machine, and the device for analyzing and optimizing the processing path based on the dynamic characteristics of the machine comprises a machine motion path error module which comprises a virtual interpolator and a processing path analyzer, the virtual interpolator can be used for inputting a numerical control programming language to output a corresponding position signal and outputting a corresponding acceleration signal after the position signal is subjected to acceleration conversion processing; machine tool dynamic model data constructed through dynamic tracking measurement is stored in the machining path analyzer, and the machining path analyzer comprises a reversing angle calculation module and an error calculation module, and at least one reversing angle numerical value and at least one error numerical value can be obtained through calculation by means of the machining path analyzer. By judging whether the reversing angle value is greater than a reversing angle threshold value or not and whether the error value is greater than an error tolerance value or not, processing path analysis and optimization are facilitated.

Description

Technical Field

[0001] The present invention relates to a processing path analysis and optimization method and device based on the dynamic characteristics of a machine, which refers to a processing path analysis and optimization method and device that can analyze and optimize the dynamic characteristics of an individual machine and adaptively adjust the optimal processing path. Background Art

[0002] In the metal parts processing / products industry, currently, Taiwanese businesses need to use CAM software to convert CAD into processing programs to process more complex products. CAM can only rely on allowable errors to control accuracy, but cannot consider and measure controller parameters and machine characteristics, so it is difficult to match a suitable processing program. When the processing path does not match the controller parameters, it is easy to lead to poor processing efficiency. In addition, if the dynamic characteristics of the machine are not considered, the error may exceed the allowable range, resulting in the need for rework, time and cost. The 3D model of the workpiece will have errors in each stage of CAM, controller path interpolation, servo system and machine processing, and the final workpiece contour will be the result of the superposition of all errors.

[0003] Moreover, a complete processing path is usually composed of many different line segments, and each line segment has a different commutation angle. When the commutation angle is too large, in order to smoothly complete the commutation action, the machine must repeatedly decelerate and change the angle and then accelerate after completing the angle change. In this way, if there is an excessively large commutation angle between the line segments of the processing path, the overall processing time will increase due to the repeated deceleration and acceleration of the machine. Therefore, the optimization of the commutation angle is also a major focus of processing path optimization.

[0004] The present invention considers and measures the dynamic characteristics of the machine and reads the current controller parameters. Without changing the controller parameters, the optimal processing path is found based on them and outputted as a processing program. This can automatically help the industry shorten the processing program time and improve the overall processing efficiency. Summary of the invention

[0005] In order to solve the above-mentioned problems, the present invention provides a processing path analysis and optimization method based on the dynamic characteristics of a machine and a device thereof. The end user only needs to select a numerical control program language (NC CODE) through the processing path analysis and optimization method based on the dynamic characteristics of a machine and a device thereof. The present invention can consider and measure the dynamic characteristics of the machine and adjust the processing path by increasing or decreasing interpolation points in an artificial intelligence manner according to the existing controller motion control parameter conditions. The optimized processing path can not only meet the allowable error requirements, but also reduce unnecessary acceleration and deceleration caused by the change in the commutation angle, thereby shortening the processing time under stable processing quality, so that the processed product meets the needs of users.

[0006] To achieve the above-mentioned purpose, an embodiment of the present invention provides a processing path analysis method based on the dynamic characteristics of a machine, which includes the following steps: Step 1: input a numerical control program language (NC CODE), calculate and output a corresponding position signal, and output a corresponding acceleration signal after the position signal is converted into acceleration processing; Step 2: through a dynamic model data of a machine tool constructed by dynamic tracking measurement, the acceleration signal is used to obtain a processing path data, and an error value is obtained through the processing path data, and a commutation angle value is obtained by analyzing the processing path, feed rate and a machine parameter of the numerical control program language; and Step 3: determine whether the commutation angle value is greater than a commutation angle threshold and whether the error value is greater than an error tolerance value, so as to perform processing path analysis.

[0007] In a preferred embodiment, the dynamic model data of the machine tool is established by diagnosing the machining performance of a target machine tool, and the machining performance diagnosis is targeted at linear path measurement to obtain the dynamic characteristic value of the machine.

[0008] In a preferred embodiment, the linear path measurement may be a grid encoder dynamic test (KGM).

[0009] In a preferred embodiment, the machine parameter is a corner deceleration parameter of a controller of the target machine tool.

[0010] Another embodiment of the present invention provides a machining path optimization method based on the dynamic characteristics of a machine, which further includes the following steps: Step 4: When the commutation angle value obtained in Step 3 is greater than the commutation angle threshold and / or the error value is greater than an error tolerance value, it is selected as a modified path data; Step 5: A control point group is inferred from the modified path data using the Bezier curve formula, and a particle swarm optimization (PSO) boundary condition data is established accordingly; Step 6: The modified path data is re-arranged using the Bezier curve formula to generate a new line segment, and the points are increased or decreased according to the comparison between the angle of the new line segment and the commutation angle threshold, and an optimized machining path with the shortest machining time is generated under the condition of meeting the error tolerance value, and the optimized machining path is then sent back to the particle swarm optimization (PSO) boundary condition builder to generate an interpolation point data; and Step 7: At least one optimized numerical control program language (NC CODE) is generated according to the interpolation point data.

[0011] Another embodiment of the present invention provides a machining path analysis and optimization device based on the dynamic characteristics of a machine platform, which includes a machine platform motion path error module, the machine platform motion path error module including: a virtual interpolator, the virtual interpolator can be used to input a numerical control program language (NC CODE), the virtual interpolator can be based on the numerical control program language (NC CODE) CODE) outputs a corresponding position signal, and outputs a corresponding acceleration signal after the position signal is converted into acceleration processing; a machining path analyzer, which stores a machine tool dynamic model data constructed by dynamic tracking measurement, and the machining path analyzer can calculate a machining path data based on the acceleration signal and the machine tool dynamic model data, and the machining path analyzer includes a commutation angle calculation module and an error calculation module, the commutation angle calculation module can calculate a commutation angle value by analyzing the machining path, feed rate and machine parameters of the CNC program language, and the error calculation module can calculate at least one error value through the machining path data, and the machining path analyzer can perform machining path analysis by judging whether the commutation angle value is greater than a commutation angle threshold and whether the error value is greater than an error tolerance value.

[0012] In a preferred embodiment, the machine tool dynamic model data is established by diagnosing the machine processing performance of a target machine tool, and the machine processing performance diagnosis is targeted at linear path measurement to obtain the dynamic characteristic value of the machine, and the linear path measurement can be a grid encoder dynamic test (KGM).

[0013] In a preferred embodiment, the machine parameter is a corner deceleration parameter of a controller of the target machine tool.

[0014] In a preferred embodiment, the commutation angle threshold is obtained according to the machining path of the numerical control program language, the feed rate and the corner deceleration parameter of the controller of the target machine tool.

[0015] In a preferred embodiment, a path optimization module coupled to the machine motion path error module is also included, which includes: a path modification selector, which is coupled to the processing path analyzer and can receive the commutation angle value and the error value. When the commutation angle value and the error value are greater than the commutation angle threshold value and / or the error tolerance value, it is determined to perform path modification to select a modified path data; a particle swarm optimization (PSO) boundary condition builder, which is coupled to the path modification selector and can receive the modified path data to reversely infer a control point group using the Bezier curve formula, and establish a particle swarm optimization based on the modified path data. an interpolation point update strategist, which is coupled to the particle swarm optimization (PSO) boundary condition builder, and can receive the modified path data to generate a new line segment after re-arranging points using the Bezier curve formula, increase or decrease the points according to the comparison between the angle of the new line segment and the commutation angle threshold, and generate an optimized processing path with the shortest processing time under the condition of meeting the error tolerance, and then return the optimized processing path to the particle swarm optimization (PSO) boundary condition builder to generate an interpolation point data; and a numerical control program language (NC CODE) generator, which is coupled to the particle swarm optimization (PSO) boundary condition builder, and can generate at least one optimized numerical control program language (NC CODE) according to the interpolation point data.

[0016] In a preferred embodiment, the interpolation point update strategy can be coupled to the machine motion path error module to compare the commutation angle value of the new line segment with the commutation angle threshold.

[0017] In a preferred embodiment, the interpolation point update strategy can be coupled to the machine motion path error module to compare the error value of the new line segment with the error tolerance value.

[0018] The present invention takes into account the dynamic characteristics of the machine and the current controller parameters. Without changing the controller parameters, the processing path is adjusted according to the existing parameter settings. Within the allowable error range of the end user, the optimal processing program with the shortest processing time as the goal is provided, thereby helping the industry to shorten the processing program time and thus achieve the purpose of improving processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the composition of the machine motion path error module of the present invention; Figure 2 It is a schematic diagram of the composition of the path optimization module of the present invention; Figure 3 It is a schematic diagram of the composition of the present invention; Figure 4 is a schematic diagram of calculating the commutation angle threshold value of the present invention; Figure 5 It is a schematic diagram of the action flow of the machine motion path error module of the present invention; Figure 6 It is a schematic diagram of the action flow of the path modification selector of the present invention; Figure 7 It is a schematic diagram of the action flow of the path optimization module of the present invention; Figure 8A to Figure 8C This is a schematic diagram of the particle swarm algorithm combined with the cubic Bezier curve processing path; Fig. 9 It is a schematic diagram of the action flow of the interpolation point update strategy device of the present invention.

[0020] Description of Reference Numerals 100: Processing path analysis and optimization device 10: Machine motion path error module 11: Virtual interpolator 12: Processing path analyzer 121: Commutation angle calculation module 122: Error calculation module 20: Path optimization module 21: Path modification selector 22: Particle Swarm Optimization (PSO) Boundary Condition Builder 23: Interpolation point update strategy 24: Numerical control program language (NC CODE) generator. DETAILED DESCRIPTION

[0021] In order to facilitate the description of the central idea of ​​the present invention in the above invention content column, specific embodiments are now used for expression. Various objects in the embodiments are described according to the proportions, sizes, deformations or displacements suitable for the description, rather than being drawn according to the proportions of the actual elements.

[0022] See also Figure 1 to Figure 3 As shown, the present invention provides a machining path analysis and optimization device 100 based on the dynamic characteristics of a machine platform, which includes a machine platform motion path error module 10, and the machine platform motion path error module 10 includes: A virtual interpolator 11 can be used to input a numerical control program language (NC CODE). The virtual interpolator 11 can output a corresponding position signal according to the numerical control program language (NC CODE), and output a corresponding acceleration signal after converting the position signal into an acceleration signal; wherein the numerical control program language (NC CODE) at least includes data such as a processing path and a feed rate.

[0023] A machining path analyzer 12 has a machine tool dynamic model data constructed by dynamic tracking measurement stored therein. The machining path analyzer 12 can calculate a machining path data based on the acceleration signal and the machine tool dynamic model data. The machine tool dynamic model data is established by diagnosing the machine processing performance of a target machine tool, and the machine processing performance diagnosis is for linear path measurement to obtain the dynamic characteristic value of the machine. In a possible embodiment, the linear path measurement can be a grid encoder dynamic test (KGM). The machining path analyzer 12 includes a commutation angle calculation module 121 and an error calculation module 122. The commutation angle calculation module 121 can calculate a commutation angle value by parsing the machining path, feed rate and machine parameters of the numerical control program language. The machine parameters are the corner deceleration parameters of the controller of the target machine tool. The error calculation module 122 can calculate at least one error value through the machining path data, wherein the error value is a contour error value. The machining path analyzer 12 can perform machining path analysis by determining whether the commutation angle value is greater than a commutation angle threshold and whether the error value is greater than an error tolerance value.

[0024] And, if Figure 4 As shown, it is a schematic diagram of the relationship between the path angle and the commutation angle, which is obtained according to the processing path of the CNC program language, the feed rate and the parameters of the controller of the machine tool (corner deceleration parameters). The commutation angle threshold is calculated by the processing path, feed rate and the corner deceleration parameters of the CNC program language.

[0025] Furthermore, if Figure 2 and Figure 3 As shown, the machining path analysis and optimization device 100 further includes a path optimization module 20 coupled to the machine motion path error module 10, which includes: A path modification selector 21 is coupled to the machining path analyzer 12, and can receive the commutation angle value and the error value, and when the commutation angle value and the error value are greater than the commutation angle threshold value and / or the error tolerance value, it is determined to perform path modification to select a modified path data; A particle swarm optimization (PSO) boundary condition builder 22, coupled to the path modification selector 21, can receive the modified path data to infer a control point group using a Bezier curve formula, and accordingly build a particle swarm optimization (PSO) boundary condition data; an interpolation point update strategist 23, which is coupled to the particle swarm optimization (PSO) boundary condition builder 22, and can receive the modified path data to generate a new line segment after re-arranging points using the Bezier curve formula, increase or decrease the number of points according to the comparison between the angle of the new line segment and the commutation angle threshold, and generate an optimized processing path with the shortest processing time under the condition of meeting the error tolerance, and then return the optimized processing path to the particle swarm optimization (PSO) boundary condition builder 22 to generate an interpolation point data; and A numerical control program language (NC CODE) generator 24 is coupled to the particle swarm optimization (PSO) boundary condition creator 22 and can generate at least one optimized numerical control program language (NC CODE) according to the interpolation point data.

[0026] And, if Figure 5 As shown, the present invention provides a processing path analysis method based on the dynamic characteristics of a machine platform, which includes the following steps: Step 1: Input a numerical control program language (NC CODE), calculate and output a corresponding position signal, and output a corresponding acceleration signal after converting the position signal into acceleration signal; wherein the numerical control program language (NC CODE) at least includes data such as a processing path and a feed rate.

[0027] Step 2: Use a machine tool dynamic model data constructed through dynamic tracking measurement, the machine tool dynamic model data is established through a machine processing performance diagnosis of a target machine tool, and the machine processing performance diagnosis is for straight path measurement to obtain the dynamic characteristic value of the machine. The straight path measurement can be a grid encoder dynamic test (KGM). The acceleration signal is calculated and processed to obtain a processing path data, and then the processing path data is calculated and processed to obtain an error value, wherein the error value is a contour error value. And a commutation angle value is obtained by analyzing the processing path, feed rate and a machine parameter of the CNC program language; the machine parameter is the corner deceleration parameter of the controller of the target machine tool; and Step three: Determine whether the commutation angle value is greater than a commutation angle threshold and whether the error value is greater than an error tolerance value. The commutation angle threshold is obtained based on the processing path of the CNC program language, the feed rate and the corner deceleration parameters of the controller of the target machine tool to perform processing path analysis.

[0028] like Figure 5~Figure 9 As shown, the present invention further provides a machining path optimization method based on the dynamic characteristics of the machine platform using the aforementioned steps 1 to 3, which also includes the following steps: Step 4: When the commutation angle value obtained in step 3 is greater than the commutation angle threshold value and / or the error value is greater than an error tolerance value, it is selected as a modified path data; Step 5: Use the Bezier curve formula to infer a control point group from the modified path data, and establish a particle swarm optimization (PSO) boundary condition data accordingly; Step 6: Re-arrange the modified path data using the Bezier curve formula to generate a new line segment, increase or decrease the number of points according to the comparison between the angle of the new line segment and the commutation angle threshold, and generate an optimized processing path with the shortest processing time under the condition of meeting the error tolerance, and then generate interpolation point data based on the optimized processing path; and Step 7: Generate at least one optimized numerical control program language (NC CODE) according to the interpolation point data.

[0029] After understanding the detailed structure of the present invention, the following describes the working principle of the present invention in detail: like Figure 5 As shown, the virtual interpolator 11 of the machine motion path error module 10 can obtain a position signal after inputting the numerical control program language (NCCODE), and then process the position signal into an acceleration signal, which is then input into the processing path analyzer 12.

[0030] The machining path analyzer 12 stores the dynamic model data of the machine tool constructed by dynamic tracking measurement, and calculates the machining path data by the acceleration signal through the commutation angle calculation module 121 and the error calculation module 122, and then calculates the commutation angle value and the error value by the commutation angle calculation module 121 and the error calculation module 122, and can judge whether the current path is greater than the commutation angle threshold and whether the allowable error is reached. In this way, the user can easily find the unit node that causes the corner deceleration and exceeds the allowable error through the machine motion path error module 10 according to the machine parameters.

[0031] like Figure 6 As shown, the path modification selector 21 can determine whether the path needs to be modified. The path modification selector 21 is coupled to the processing path analyzer 12 and can receive the commutation angle value and the error value. When it is greater than the commutation angle threshold and / or the error tolerance value, it is determined that the path needs to be modified, and the path modification data is selected. If it is less than the commutation angle threshold and / or the error tolerance value, there is no need to perform subsequent path optimization processing.

[0032] like Figure 7As shown, the particle swarm optimization (PSO) boundary condition establisher 22 is coupled to the path modification selector 21, which can receive the modified path data to reversely infer a control point group using the Bezier curve formula, and establish a particle swarm optimization (PSO) boundary condition data accordingly. When the control point group does not meet the particle swarm optimization (PSO) exploration number, the interpolation point update strategy device 23 establishes an interpolation point update strategy.

[0033] like Figure 8A to Figure 8C and Fig. 9 As shown, the interpolation point update strategy 23 uses particle swarm optimization (PSO) to find control points again and uses Bezier curves to re-distribute points. For example, Figure 8A to Figure 8C As shown, after the interpolation point update strategy device 23 uses the particle swarm algorithm with the cubic Bezier curve, the re-planned Bezier curve can be quite close to the ideal processing path. In this way, the interpolation point update strategy device 23 can generate new line segment data, and then decide whether to increase or decrease the control points based on the comparison between the new line segment commutation angle value and the commutation angle threshold value. When the new line segment commutation angle value and the number of control points can meet the requirements, the new line segment error value is compared with the error tolerance value. When the new line segment error value meets the allowable error condition, the processing time required for the new line segment is evaluated, and it is determined whether to update it to a new line segment. Repeat the operation to find the optimized processing path with the shortest processing time. When the new line segment error value is greater than the error tolerance value, the processing time can be +999, which means that the new line segment data is eliminated. Then the optimized processing path is returned to the particle swarm optimization (PSO) boundary condition builder 22 to re-check whether the particle swarm optimization (PSO) exploration times are met. In a possible embodiment, the interpolation point update strategy 23 can be coupled to the machine motion path error module 10, and the machine motion path error module 10 can compare the commutation angle value of the new line segment with the commutation angle threshold, and compare the error value of the new line segment with the error tolerance value.

[0034] See also Figure 7 As shown, when the particle swarm optimization (PSO) exploration times are met, the particle swarm optimization (PSO) boundary condition builder 22 generates corresponding interpolation point data, and transmits the interpolation point data to the NC CODE generator 24, and generates optimized NC CODE according to the interpolation point data.

[0035] The user of the present invention only needs to select the numerical control programming language (NC CODE) through the machine motion path error module 10 and the path optimization module 20 based on the dynamic characteristics of the machine, and can consider and measure the dynamic characteristics of the machine and adjust the processing path by increasing or decreasing the interpolation points according to the motion control parameter conditions of the controller of the existing machine through artificial intelligence, so that the processing path can reach the allowable error and reduce unnecessary acceleration and deceleration, thereby providing the best processing program with the shortest time as the goal under the stable processing quality, so as to help the industry shorten the processing program time and improve the processing efficiency.

[0036] The above embodiments are only used to illustrate the present invention, and are not used to limit the scope of the present invention. All modifications or changes made according to the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A machining path analysis method based on machine dynamic characteristics, characterized in that: The machining path analysis method based on the dynamic characteristics of the machine platform includes the following steps: Step 1: input a numerical control program language, calculate and output a corresponding position signal, and output a corresponding acceleration signal after converting the position signal into acceleration signal; Step 2: Obtain a machining path data from the acceleration signal through a dynamic model data of a machine tool constructed by dynamic tracking measurement, obtain an error value through the machining path data, and obtain a commutation angle value by analyzing the machining path, feed rate and a machine parameter of the numerical control program language; and Step 3: Determine whether the commutation angle value is greater than a commutation angle threshold and whether the error value is greater than an error tolerance value, so as to perform a machining path analysis.

2. The processing path analysis method based on the dynamic characteristics of the machine according to claim 1, characterized in that: The dynamic model data of the machine tool is established by diagnosing the machine processing performance of a target machine tool, and the machine processing performance diagnosis is aimed at linear path measurement to obtain the dynamic characteristic value of the target machine tool.

3. The processing path analysis method based on the dynamic characteristics of the machine according to claim 2 is characterized in that: This straight path measurement can be used as a dynamic test of the grid encoder.

4. The processing path analysis method based on machine dynamic characteristics according to claim 2 is characterized in that: The machine parameter is a corner deceleration parameter of a controller of the target machine tool.

5. A processing path optimization method based on the dynamic characteristics of a machine, comprising the analysis method as claimed in claim 1, characterized in that: The machining path optimization method based on the dynamic characteristics of the machine platform also includes the following steps: Step 4: When the commutation angle value obtained in step 3 is greater than the commutation angle threshold value and / or the error value is greater than the error tolerance value, it is selected as a modified path data; Step 5: Use the Bezier curve formula to infer a control point group from the modified path data, and establish a particle swarm optimization boundary condition data accordingly; Step 6: Re-arrange the modified path data using the Bezier curve formula to generate a new line segment, increase or decrease the number of points according to the comparison between the angle of the new line segment and the commutation angle threshold, and generate an optimized processing path with the shortest processing time under the condition of meeting the error tolerance, and then generate interpolation point data based on the optimized processing path; and Step 7: Generate at least one optimized numerical control program language according to the interpolation point data.

6. A processing path analysis and optimization device based on the dynamic characteristics of a machine, characterized in that: The machining path analysis and optimization device based on the dynamic characteristics of the machine platform includes a machine platform motion path error module, and the machine platform motion path error module includes: A virtual interpolator, which can be used to input a numerical control program language, and can output a corresponding position signal according to the numerical control program language, and output a corresponding acceleration signal after converting the position signal into an acceleration signal; A machining path analyzer stores therein a machine tool dynamic model data constructed by dynamic tracking measurement. The machining path analyzer can calculate a machining path data based on the acceleration signal and the machine tool dynamic model data. The machining path analyzer includes a commutation angle calculation module and an error calculation module. The commutation angle calculation module can calculate a commutation angle value by analyzing the machining path, feed rate and a machine parameter of the numerical control program language, and the error calculation module can calculate at least one error value through the machining path data. The machining path analyzer can perform machining path analysis by determining whether the commutation angle value is greater than a commutation angle threshold and whether the error value is greater than an error tolerance value.

7. The processing path analysis and optimization device based on the dynamic characteristics of the machine according to claim 6, characterized in that: The dynamic model data of the machine tool is established by diagnosing the machine processing performance of a target machine tool, and the machine processing performance diagnosis is aimed at straight path measurement to obtain the dynamic characteristic value of the target machine tool. The straight path measurement can be a grid encoder dynamic test.

8. The processing path analysis and optimization device based on the dynamic characteristics of the machine according to claim 7, characterized in that: The machine parameter is a corner deceleration parameter of a controller of the target machine tool.

9. The processing path analysis and optimization device based on the dynamic characteristics of the machine according to claim 7, characterized in that: The commutation angle threshold is obtained according to a machining path of the numerical control program language, a feed rate, and a rotation angle deceleration parameter of a controller of the target machine tool.

10. The processing path analysis and optimization device based on machine platform dynamic characteristics according to claim 6, characterized in that: The machining path analysis and optimization device based on the dynamic characteristics of the machine platform also includes a path optimization module coupled to the machine platform motion path error module, which includes: A path modification selector is coupled to the processing path analyzer, and can receive the commutation angle value and the error value, and when the commutation angle value and the error value are greater than the commutation angle threshold value and / or the error tolerance value, it is determined to perform path modification to select a modified path data; A particle swarm optimization boundary condition builder is coupled to the path modification selector and can receive the modified path data to infer a control point group using a Bezier curve formula and thereby build a particle swarm optimization boundary condition data; an interpolation point update strategist, coupled to the particle swarm optimization boundary condition builder, capable of receiving the modified path data to generate a new line segment by re-arranging points using a Bezier curve formula, increasing or decreasing points according to a comparison between the angle of the new line segment and the commutation angle threshold, and generating an optimized machining path with the shortest machining time under the condition of meeting the error tolerance, and then returning the optimized machining path to the particle swarm optimization boundary condition builder to generate an interpolation point data; and A numerical control program language generator is coupled to the particle swarm optimization boundary condition builder and can generate at least one optimized numerical control program language according to the interpolation point data.

11. The processing path analysis and optimization device based on machine platform dynamic characteristics according to claim 10, characterized in that: The interpolation point update strategy device is coupled to the machine motion path error module to compare the commutation angle value of the new line segment with the commutation angle threshold.

12. The processing path analysis and optimization device based on machine platform dynamic characteristics according to claim 10, characterized in that: The interpolation point update strategy device is coupled to the machine motion path error module to compare the error value of the new line segment with the error tolerance value.

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