Error compensation method, device, equipment and storage medium for shaft parts processing

Abstract

The present invention relates to the technical field of data processing, and provides an error compensation method, device, equipment and storage medium for machining shaft parts, including obtaining measurement data of the actual size of shaft parts, and calculating deviation data, inputting the measurement data and deviation data into a preset neural network compensation model, obtaining compensation parameters and processing stability parameters, adjusting the path of the machining tool based on the compensation parameters, obtaining target path machining instructions and performing spectrum analysis, obtaining corrected instructions and checking them through machining stability parameters, and machining the shaft parts after obtaining the target machining instructions. By measuring the actual size of shaft parts and calculating deviations, adjusting the machining path in combination with the neural network compensation model, optimizing the machining instructions using spectrum analysis technology, improving the inability to cope with errors that are dynamically changing during machining and the tremor that is prone to occur during machining, the difficulty of part compensation is aggravated, and there is a problem of insufficient real-time error correction.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to an error compensation method, device, equipment and storage medium for machining shaft parts. Background Art

[0002] Shaft parts are an important part of the mechanical manufacturing field and are widely used in the automotive, aerospace, industrial equipment and other industries. Such parts usually have high precision requirements, and their processing quality directly affects the operating performance and service life of the whole machine. In modern manufacturing, in order to meet the needs of high-performance equipment, the dimensional accuracy and geometric tolerance requirements of shaft parts are gradually increasing. How to efficiently and accurately process shaft parts has become the focus of the industry.

[0003] Among the related technical means, error compensation technology usually analyzes the sources of errors in the processing process and takes measures such as manually adjusting processing parameters and correcting processing paths using offline measurement data to correct errors. These methods can effectively reduce dimensional deviations caused by factors such as tool wear, thermal deformation or vibration during the processing process by gradually optimizing the processing technology, thereby improving the processing accuracy and stability of parts. For example, some systems will use CNC programs to re-optimize the tool path after processing to achieve error compensation. This type of technical means has improved the processing accuracy of shaft parts to a certain extent and significantly reduced the scrap rate.

[0004] Regarding the above technical solutions, although the existing methods can improve the processing accuracy to a certain extent, in the existing solutions, the error compensation methods are mostly based on offline measurement data, and the real-time performance of the compensation path adjustment is poor. It is unable to cope with the dynamically changing errors in the processing process and the vibration that is prone to occur during the processing process, which aggravates the difficulty of part compensation and there is a problem of insufficient real-time error correction. Summary of the invention

[0005] In order to improve the inability to cope with errors that are dynamically changing during machining and the tremors that are prone to occur during machining, which increase the difficulty of parts compensation and the problem of insufficient real-time error correction, the present application provides an error compensation method, device, equipment and storage medium for shaft parts machining.

[0006] The present invention provides an error compensation method for machining shaft parts, comprising: obtaining measurement data of actual dimensions of shaft parts, calculating the deviation between the actual dimensions of the shaft parts and preset design dimensions using the measurement data, and obtaining deviation data; inputting the measurement data and the deviation data into a preset neural network compensation model, obtaining compensation parameters and processing stability parameters, adjusting the path of a machining tool based on the compensation parameters, and obtaining target path processing instructions; performing spectrum analysis on the target path processing instructions through fast Fourier transform to obtain an amplitude spectrum and a phase spectrum, filtering the amplitude spectrum to obtain filtered amplitude information, performing phase correction on the filtered amplitude information using the phase spectrum, and obtaining corrected instructions; checking the corrected instructions using the processing stability parameters to obtain target processing instructions, and machining the shaft parts based on the target processing instructions to obtain compensated shaft parts.

[0007] As a preferred embodiment, the step of obtaining the measurement data of the actual size of the shaft parts, calculating the deviation between the actual size of the shaft parts and the preset design size using the measurement data, and obtaining the deviation data includes: using a three-coordinate measuring machine to measure the dimensions of multiple key parts of the shaft parts to obtain measurement data of several parts; comparing the measurement data of all key parts with the preset design size to obtain deviation data of each part.

[0008] As a preferred scheme, the step of inputting the measurement data and the deviation data into a preset neural network compensation model to obtain compensation parameters and processing stability parameters, adjusting the path of the processing tool based on the compensation parameters, and obtaining target path processing instructions includes: inputting the measurement data and the deviation data into the neural network compensation model to obtain initial compensation parameters and initial processing stability parameters; optimizing the initial compensation parameters through a genetic algorithm to obtain optimized compensation parameters and parameter sensitivity information, adjusting the initial processing stability parameters using the parameter sensitivity information to obtain corrected processing stability parameters; simulating the processing path using the optimized compensation parameters and the corrected processing stability parameters to obtain path error correction values ​​and path feasibility evaluation results, and generating initial path processing instructions and processing path feasibility parameters based on the path feasibility evaluation results; verifying the processing path feasibility parameters using the path error correction value to obtain path optimization suggestions, adjusting the initial path processing instructions according to the path optimization suggestions to obtain target path processing instructions.

[0009] As a preferred solution, the step of performing spectrum analysis on the target path processing instruction by fast Fourier transform to obtain an amplitude spectrum and a phase spectrum, filtering the amplitude spectrum to obtain filtered amplitude information, and performing phase correction on the filtered amplitude information using the phase spectrum to obtain the corrected instruction includes: performing fast Fourier transform processing on the target path processing instruction to obtain an amplitude spectrum and a phase spectrum, decomposing the amplitude spectrum into different frequency bands to obtain low-frequency amplitude information, intermediate-frequency amplitude information and high-frequency amplitude information; filtering the low-frequency amplitude information and the intermediate-frequency amplitude information to obtain filtered amplitude information; wherein the filtered amplitude information includes the filtered low-frequency amplitude information and the intermediate-frequency amplitude information. value information and filtered intermediate frequency amplitude information; processing the phase spectrum by a phase unwrapping algorithm to obtain continuous phase information; parsing the phase information by Hilbert transform to obtain phase correction parameters and phase error data, combining the filtered low-frequency amplitude information with the phase correction parameters to obtain a corrected low-frequency instruction, fusing the filtered intermediate frequency amplitude information with the phase error data to obtain a corrected intermediate frequency instruction; integrating the corrected low-frequency instruction, the corrected intermediate frequency instruction and the high-frequency amplitude information to obtain a comprehensive correction instruction and a high-frequency variation parameter, and fine-tuning the comprehensive correction instruction by using the high-frequency variation parameter to obtain a corrected instruction.

[0010] As a preferred solution, the steps of combining the filtered low-frequency amplitude information with the phase correction parameters to obtain a corrected low-frequency instruction, and fusing the filtered intermediate frequency amplitude information with the phase error data to obtain a corrected intermediate frequency instruction include: establishing a corresponding relationship between the filtered low-frequency amplitude information and the phase correction parameters, generating a phase compensation matrix, using the phase compensation matrix to adjust the phase of the filtered low-frequency amplitude information to obtain phase-corrected low-frequency amplitude information; converting the phase-corrected low-frequency amplitude information into a low-frequency time domain information by an inverse fast Fourier transform. signal, generate a corrected low-frequency instruction based on the low-frequency time domain signal; perform phase error analysis on the filtered intermediate frequency amplitude information through wavelet transform, generate intermediate frequency phase error data based on the analysis result, perform weighted average of the intermediate frequency phase error data and the phase error data to obtain an intermediate frequency phase compensation value; perform phase correction on the filtered intermediate frequency amplitude information using the intermediate frequency phase compensation value to obtain phase-corrected intermediate frequency amplitude information, convert the phase-corrected intermediate frequency amplitude information into an intermediate frequency time domain signal through inverse fast Fourier transform, and generate a corrected intermediate frequency instruction based on the intermediate frequency time domain signal.

[0011] As a preferred scheme, the step of using the processing stability parameters to check the corrected instructions to obtain target processing instructions, and processing the shaft parts based on the target processing instructions to obtain compensated shaft parts includes: using the processing stability parameters to perform stability check on the corrected instructions to obtain processing reliability data, comparing the processing reliability data with preset path smoothness information to obtain processing consistency evaluation results, and using the processing consistency evaluation results to adjust the corrected instructions to obtain target processing instructions; inputting the target processing instructions into CNC machining equipment to generate processing path data and tool motion trajectory, and processing the shaft parts based on the processing path data and the tool motion trajectory to obtain compensated shaft parts.

[0012] As a preferred scheme, the step of using the processing stability parameters to perform stability check on the corrected instructions to obtain processing reliability data, and comparing the processing reliability data with preset path smoothness information to obtain processing consistency evaluation results includes: establishing a stability evaluation model based on processing stability parameters, using the stability evaluation model to perform numerical simulation on the corrected instructions, extracting dynamic response parameters during the processing, and obtaining processing reliability data; matching and analyzing the vibration amplitude, cutting force change and temperature gradient indicators in the processing reliability data with the preset path smoothness information, evaluating the smoothness of the processing path, and obtaining path smoothness matching; comprehensively analyzing the processing reliability data and the path smoothness matching, identifying key factors that may cause processing instability based on the analysis results, and obtaining processing consistency evaluation results.

[0013] The present application also provides an error compensation device for machining shaft parts, including: an acquisition module, used to acquire measurement data of actual dimensions of shaft parts, and use the measurement data to calculate the deviation between the actual dimensions of the shaft parts and preset design dimensions to obtain deviation data; an input module, used to input the measurement data and the deviation data into a preset neural network compensation model to obtain compensation parameters and processing stability parameters, and adjust the path of the machining tool based on the compensation parameters to obtain target path machining instructions; an analysis module, used to perform spectrum analysis on the target path machining instructions through fast Fourier transform to obtain amplitude spectrum and phase spectrum, filter the amplitude spectrum to obtain filtered amplitude information, and use the phase spectrum to perform phase correction on the filtered amplitude information to obtain corrected instructions; a verification module, used to verify the corrected instructions using the processing stability parameters to obtain target machining instructions, and machine the shaft parts based on the target machining instructions to obtain compensated shaft parts.

[0014] The present application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements any of the above-mentioned error compensation methods for shaft parts processing.

[0015] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor executes the error compensation method for machining shaft parts as described in any one of the above.

[0016] Compared with the prior art, the present application has the following beneficial effects: accurate compensation and high stability. By accurately measuring the actual size of shaft parts and calculating the deviation, adjusting the processing path in combination with the neural network compensation model, and optimizing the processing instructions using spectrum analysis technology, the processing error can be compensated in real time, further improving the processing accuracy and stability; by integrating the intelligent compensation and spectrum analysis technology of neural networks, the problems of error accumulation and path instability in traditional processing are overcome, more accurate tool path adjustment and processing process optimization are achieved, the accuracy and real-time performance of error compensation are improved, the inability to cope with dynamic changes in the processing process and the proneness to tremor during the processing process are improved, the difficulty of part compensation is aggravated, and there is a problem of insufficient real-time error correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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 or the description of the prior art 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 labor.

[0018] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0019] Figure 1 It is a flow chart of an error compensation method for machining shaft parts provided by an embodiment of the present invention;

[0020] Figure 2 is a schematic block diagram of the structure of an error compensation device for machining shaft parts provided by an embodiment of the present invention;

[0021] Figure 3It is a schematic block diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0022] Description of reference numerals:

[0023] 10. Error compensation device for shaft parts processing; 11. Acquisition module; 12. Input module; 13. Analysis module; 14. Verification module; 20. Electronic equipment; 21. Memory; 22. Processor. DETAILED DESCRIPTION

[0024] 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 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.

[0025] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0026] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0027] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0028] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0029] Embodiment 1:

[0030] like Figure 1 As shown, the error compensation method for shaft parts processing provided in the embodiment of the present application includes steps S100 to S400.

[0031] Step S100, obtaining measurement data of the actual size of the shaft part, and using the measurement data to calculate the deviation between the actual size of the shaft part and the preset design size to obtain deviation data.

[0032] In this step, the actual dimensions of shaft parts are accurately measured by high-precision measuring equipment (such as laser measuring instruments, three-dimensional measuring machines, etc.) to obtain a series of actual dimension data; specifically, the length, diameter, roundness and other important parameters of shaft parts are obtained during the measurement process, and these parameters are compared with the preset dimensions in the design drawings to calculate the deviation between the actual dimensions of each measurement point and the preset design dimensions. The deviation data is obtained based on the difference between the measured data and the design dimensions, and usually forms a set of two-dimensional or three-dimensional deviation values ​​for subsequent compensation processing.

[0033] For example, for a shaft part, if the design requires an outer diameter of 50mm, but the measured outer diameter is 50.05mm, the deviation data will record the difference of 0.05mm; if it is the end face of a circular shaft, the end face roundness measurement value will also be compared with the design value to obtain the deviation of each measuring position.

[0034] Step S200: input the measurement data and deviation data into a preset neural network compensation model to obtain compensation parameters and processing stability parameters, adjust the path of the processing tool based on the compensation parameters, and obtain the target path processing instructions.

[0035] In this step, the measurement data and deviation data are used as inputs, combined with the pre-trained neural network compensation model, to calculate the processing compensation parameters. Specifically, the neural network model extracts the corresponding compensation values ​​according to different types of deviations (such as size, shape or position deviations), and generates processing stability parameters. The processing stability parameters are used to evaluate the impact of factors such as vibration and tool wear during the processing process on the processing quality of parts, thereby providing additional compensation. Subsequently, based on these compensation parameters, the processing path of the tool is adjusted in real time to ensure that the tool can dynamically compensate for the deviation during the processing process, thereby generating the target path processing instructions.

[0036] For example, if it is found during the measurement process that the outer diameter of a shaft part is larger than the design value, the neural network model will output a path adjustment value to reduce the outer diameter based on the compensation rule, thereby ensuring that the tool is processed along the adjusted path and making the outer diameter of the part closer to the design requirements.

[0037] Step S300, performing spectrum analysis on the target path processing instruction by fast Fourier transform to obtain an amplitude spectrum and a phase spectrum, filtering the amplitude spectrum to obtain filtered amplitude information, and performing phase correction on the filtered amplitude information using the phase spectrum to obtain a corrected instruction.

[0038] In this step, the target path processing instruction is subjected to a fast Fourier transform (FFT) to analyze its spectral characteristics and obtain the amplitude spectrum and phase spectrum. Specifically, FFT converts the target path processing instruction from the time domain to the frequency domain to obtain the amplitude and phase information of each frequency component. Then, the amplitude spectrum is filtered to remove high-frequency noise or interference to obtain smoother filtered amplitude information. Next, the phase information in the phase spectrum is used to perform phase correction on the filtered amplitude information to ensure that the phase of the path instruction is consistent with the dynamic changes in the actual processing process, and the corrected processing instruction is obtained.

[0039] For example, assuming that the spectrum analysis results of the target path show that some high-frequency signals are interference signals caused by tool vibration, filtering processing can effectively remove these irrelevant high-frequency components, thereby generating a more stable and accurate processing path.

[0040] Step S400: Use the processing stability parameter to check the corrected instruction to obtain the target processing instruction, and process the shaft part based on the target processing instruction to obtain the compensated shaft part.

[0041] In this step, the corrected processing instructions are further checked through the processing stability parameters to ensure that processing stability, such as vibration, thermal deformation, tool wear and other factors, are taken into account during the processing. Specifically, the processing stability parameters will correct each correction instruction to ensure that the tool path during the processing is more stable, thereby reducing the adverse effects of chatter and vibration on the processing quality. Finally, the shaft parts are processed based on the checked target processing instructions to ensure that various parameters in the processing process are reasonably controlled, and the compensated shaft parts have higher processing accuracy and stability.

[0042] For example, during the machining process, if the tool wear is severe, the machining stability parameters will prompt the adjustment of the tool's cutting parameters to avoid dimensional deviations due to decreased tool performance.

[0043] In this embodiment, by obtaining the measurement data of the actual size of the shaft parts, the measurement data is used to calculate the deviation between the actual size of the shaft parts and the preset design size, and the deviation data is obtained. Subsequently, the measurement data and the deviation data are input into the preset neural network compensation model to obtain the compensation parameters and the processing stability parameters, and the path of the processing tool is adjusted based on the compensation parameters to generate the target path processing instructions. Next, the target path processing instructions are subjected to spectrum analysis by fast Fourier transform to obtain the amplitude spectrum and the phase spectrum, and the amplitude spectrum is filtered to obtain the filtered amplitude information, and then the filtered amplitude information is phase corrected by the phase spectrum to generate the corrected instructions. Finally, the corrected instructions are checked by the processing stability parameters to obtain the target processing instructions, and the shaft parts are processed based on the target processing instructions to obtain the compensated shaft parts. A comprehensive analysis of the measurement data of the actual size of the shaft parts is achieved, and the neural network compensation model and the spectrum analysis are combined to improve the accuracy and real-time performance of the error compensation, improve the inability to cope with the dynamic changes in the processing process and the easy occurrence of tremors in the processing process, aggravate the difficulty of part compensation, and have the problem of insufficient real-time error correction.

[0044] Embodiment 2:

[0045] In step S100, a three-coordinate measuring machine is used to measure the dimensions of multiple key parts of the shaft part to obtain measurement data of several parts.

[0046] The high-precision sensors of the three-dimensional coordinate measuring machine are used to scan and measure the length, diameter, roundness, positional relationship and other key parts of shaft parts point by point. Specifically, the three-dimensional coordinate measuring machine will select multiple key parts that need to be tested for measurement according to the preset design drawings of the parts, including the spindle radial surface, end face roundness, threaded area and cylindrical segment size, etc. The measurement results are recorded in the form of digital point cloud data to form measurement data that can be analyzed.

[0047] For example, when measuring a shaft part with a complex structure, the three-coordinate measuring machine will measure the symmetry of the supporting surfaces at both ends of the shaft part, collect the diameter and length data of the cylindrical section, and measure the tooth profile angle of the threaded area multiple times to ensure the integrity and accuracy of the data.

[0048] Compare the measurement data of all key parts with the preset design dimensions to obtain the deviation data of each part.

[0049] The collected measurement data is input into the data processing software and compared with the preset dimensions in the design drawings. Specifically, the deviation between the actual size and the theoretical size of each key part is calculated, including radial deviation, axial deviation and position deviation, etc., and the results are output in the form of deviation diagram or deviation table for subsequent analysis.

[0050] For example, if the design of a shaft part requires the cylindrical section diameter to be 50mm, and the measured value is 50.03mm, the deviation data will show a radial deviation of +0.03mm; for end face roundness, if the design requires a tolerance of 0.02mm and the actual measured deviation is 0.03mm, the deviation data will be recorded as out of tolerance.

[0051] In step S200, the measurement data and the deviation data are input into the neural network compensation model to obtain initial compensation parameters and initial processing stability parameters.

[0052] By taking the measurement data and deviation data as input and loading the pre-trained neural network compensation model, the model will generate corresponding initial compensation parameters according to different deviation types, and generate initial processing stability parameters by analyzing the dynamic change trend in the measurement data. Specifically, the compensation parameters are used to adjust the processing path, and the processing stability parameters are used to evaluate the dynamic adaptability and vibration possibility of the tool path.

[0053] For example, in the input deviation data, the diameter deviation of the cylindrical segment is +0.03mm. The neural network compensation model will output the initial compensation parameters for reducing the tool path radius, and generate initial processing stability parameters for the vibration trend, indicating the need to reduce the cutting speed during processing.

[0054] The initial compensation parameters are optimized by genetic algorithm to obtain the optimized compensation parameters and parameter sensitivity information. The initial machining stability parameters are adjusted by using the parameter sensitivity information to obtain the corrected machining stability parameters.

[0055] The initial compensation parameters are iteratively adjusted through optimization algorithms (such as genetic algorithms) to screen out the optimal compensation parameters, and the sensitivity of the compensation parameters to changes in the machining path is analyzed. Specifically, during the optimization process, sensitivity indicators are generated according to the stability influencing factors of different machining paths, and based on these indicators, the initial machining stability parameters are adaptively adjusted to ensure the smoothness and stability of the machining path.

[0056] For example, if the optimization algorithm analysis finds that the cutting speed is highly sensitive to vibration, the cutting speed in the initial processing stability parameters will be optimized and adjusted, such as adjusting the cutting speed from 1000rpm to 800rpm to reduce the vibration risk.

[0057] The machining path simulation is performed using the optimized compensation parameters and the corrected machining stability parameters to obtain the path error correction value and the path feasibility evaluation result, and the initial path machining instructions and machining path feasibility parameters are generated based on the path feasibility evaluation result.

[0058] By inputting the optimized compensation parameters and corrected machining stability parameters into the simulation software, the dynamic changes and error distribution of the machining path are simulated; specifically, the simulation will analyze the tool interference, overcutting or chattering problems in the path, generate path error correction values, evaluate the feasibility of the machining path, and generate initial path machining instructions.

[0059] For example, in the simulation, it was found that the surface roughness of a certain section of the processing path exceeded the standard due to the large tool angle. The path error correction value would prompt to reduce the tool feed depth. At the same time, the path feasibility assessment result showed that the path starting point needed to be adjusted.

[0060] The feasibility parameters of the machining path are verified using the path error correction value to obtain path optimization suggestions. The initial path machining instructions are adjusted according to the path optimization suggestions to obtain the target path machining instructions.

[0061] By verifying the feasibility parameters of the machining path and analyzing the necessity of path adjustment in combination with the path error correction value; specifically, generating path optimization suggestions based on the verification results, and applying the suggestions to the initial path machining instructions to ensure the accuracy and reliability of the target path machining instructions.

[0062] For example, if the verification results show that the curvature of a certain machining path changes too quickly, the optimization suggestion will prompt you to smooth the curvature change by reducing the tool path radius, thereby reducing machining chatter.

[0063] In step S300, the target path processing instruction is subjected to fast Fourier transform processing to obtain an amplitude spectrum and a phase spectrum, and the amplitude spectrum is decomposed into different frequency bands to obtain low-frequency amplitude information, medium-frequency amplitude information and high-frequency amplitude information.

[0064] The target path machining instructions are converted from the time domain to the frequency domain through fast Fourier transform, and the vibration characteristics in the frequency domain signal are analyzed. Specifically, the amplitude spectrum is divided into three frequency bands: low frequency, medium frequency and high frequency according to the frequency distribution, which respectively represent the influence of smoothness, local changes and high-frequency noise in the machining process.

[0065] For example, the low-frequency amplitude information represents the overall smoothness of the machining path, the medium-frequency amplitude information reflects the local trajectory adjustment, and the high-frequency amplitude information shows the interference signal caused by tool vibration.

[0066] The low-frequency amplitude information and the intermediate-frequency amplitude information are filtered to obtain filtered amplitude information; wherein the filtered amplitude information includes filtered low-frequency amplitude information and filtered intermediate-frequency amplitude information.

[0067] The low-frequency and medium-frequency signals are processed by designing appropriate filters to remove noise components and retain valid signals; specifically, the low-frequency signal filter focuses on smoothing the processing path, while the medium-frequency signal filter focuses on removing vibration interference.

[0068] For example, after filtering the low-frequency signal, the filtered low-frequency amplitude information generated can reflect the global smoothness of the processing path, and the intermediate frequency amplitude information obtained after filtering the intermediate frequency signal can more accurately reflect the local adjustment requirements of the processing.

[0069] The phase spectrum is processed by the phase unwrapping algorithm to obtain continuous phase information. The phase information is analyzed by Hilbert transform to obtain phase correction parameters and phase error data. The filtered low-frequency amplitude information is combined with the phase correction parameters to obtain the corrected low-frequency instructions. The filtered intermediate frequency amplitude information is fused with the phase error data to obtain the corrected intermediate frequency instructions.

[0070] The discrete phase spectrum is processed continuously through the phase unwrapping algorithm to solve the phase jump problem and ensure the smoothness of the phase information in the frequency domain; specifically, the phase data is gradually unfolded through the algorithm to form a continuous distribution of the phase information. Then, the processed phase information is analyzed using the Hilbert transform to separate the phase correction parameters and phase error data for path correction. The filtered low-frequency amplitude information is linearly combined with the phase correction parameters to correct the low-frequency path instructions; at the same time, the filtered intermediate frequency amplitude information is fused with the phase error data to generate the corrected intermediate frequency path instructions.

[0071] For example, if there is a 0.1mm deviation in the low-frequency amplitude information of a certain machining path, the path phase can be adjusted after the phase correction parameter is obtained through Hilbert transform. The generated corrected low-frequency instruction can make the machining path more in line with the design requirements. At the same time, the error data of the intermediate frequency instruction can be fused and corrected to remove the phase deviation caused by path vibration.

[0072] The corrected low-frequency instruction, the corrected medium-frequency instruction and the high-frequency amplitude information are integrated to obtain a comprehensive correction instruction and a high-frequency variation parameter. The high-frequency variation parameter is used to fine-tune the comprehensive correction instruction to obtain a corrected instruction.

[0073] The corrected low-frequency instructions, medium-frequency instructions, and high-frequency amplitude information are integrated to form complete processing path correction data; specifically, high-frequency variation parameters are used to accurately correct small fluctuations in the processing path to ensure the smoothness and dynamic response of the instructions. The integrated path instructions are fine-tuned using high-frequency variation parameters to obtain the corrected processing path instructions.

[0074] For example, in a certain path segment, the high-frequency amplitude information shows a rapid vibration fluctuation of 0.02 mm. Fine-tuning the path by changing the high-frequency parameters can effectively eliminate this tiny vibration, making the processing path smoother and more stable.

[0075] Among them, the steps of combining the filtered low-frequency amplitude information with the phase correction parameters to obtain the corrected low-frequency instructions, and fusing the filtered intermediate frequency amplitude information with the phase error data to obtain the corrected intermediate frequency instructions include: establishing a corresponding relationship between the filtered low-frequency amplitude information and the phase correction parameters, generating a phase compensation matrix, and using the phase compensation matrix to adjust the phase of the filtered low-frequency amplitude information to obtain the phase-corrected low-frequency amplitude information.

[0076] A phase compensation matrix is ​​generated by establishing a mapping relationship between the filtered low-frequency amplitude information and the phase correction parameters. Specifically, the low-frequency amplitude information is phase-adjusted point by point based on the phase compensation matrix to ensure the consistency of the path low-frequency signal in the time domain and the frequency domain, and the low-frequency amplitude information after phase correction is obtained.

[0077] For example, if there is a phase offset of 0.05 mm in the low-frequency amplitude information, after adjusting the offset through the phase compensation matrix, the generated phase-corrected low-frequency information can strictly conform to the target path.

[0078] The phase-corrected low-frequency amplitude information is converted into a low-frequency time-domain signal through inverse fast Fourier transform, and a corrected low-frequency instruction is generated based on the low-frequency time-domain signal.

[0079] The phase-corrected low-frequency amplitude information is restored from the frequency domain to the time domain signal through the inverse fast Fourier transform (IFFT); specifically, the frequency domain data is converted into the time series signal required for the actual processing path to generate more accurate corrected low-frequency instructions.

[0080] For example, after phase correction, the low-frequency amplitude information is restored to time-domain path data through inverse fast Fourier transform, which can guide the machining tool to perform cutting along a smoother path.

[0081] The phase error analysis is performed on the filtered intermediate frequency amplitude information through wavelet transform, and the intermediate frequency phase error data is generated based on the analysis result. The intermediate frequency phase error data and the phase error data are weighted averaged to obtain the intermediate frequency phase compensation value.

[0082] The filtered intermediate frequency amplitude information is decomposed into multiple scales through wavelet transform to extract the phase error information in different frequency bands; specifically, the phase error is quantitatively analyzed to obtain the intermediate frequency phase error data. Then, the intermediate frequency phase error data is weighted averaged with the previously obtained phase error data to generate the intermediate frequency phase compensation value to optimize the path accuracy of the intermediate frequency signal.

[0083] For example, if there is a slight path deviation in the intermediate frequency amplitude information, its main error frequency component can be analyzed through wavelet transform, and combined with the weighted calculation of the previous error data, a more accurate intermediate frequency phase compensation value can be obtained.

[0084] The intermediate frequency amplitude information after filtering is phase-corrected using the intermediate frequency phase compensation value to obtain the intermediate frequency amplitude information after phase correction, the intermediate frequency amplitude information after phase correction is converted into an intermediate frequency time domain signal through inverse fast Fourier transform, and the corrected intermediate frequency instruction is generated based on the intermediate frequency time domain signal.

[0085] The phase error is gradually corrected by combining the intermediate frequency phase compensation value with the filtered intermediate frequency amplitude information; specifically, the corrected intermediate frequency amplitude information is restored to an intermediate frequency time domain signal through an inverse fast Fourier transform, and a corrected intermediate frequency path instruction is generated.

[0086] For example, if there is a path error of 0.02mm in the intermediate frequency amplitude, after correction through the phase compensation value, error-free intermediate frequency time domain path data can be generated, further ensuring the accuracy of the processing path.

[0087] In step S400, the corrected instructions are stability checked using processing stability parameters to obtain processing reliability data, the processing reliability data is compared with preset path smoothness information to obtain processing consistency evaluation results, and the corrected instructions are adjusted using the processing consistency evaluation results to obtain target processing instructions.

[0088] The corrected instructions are checked by using machining stability parameters to simulate the stability characteristics of the machining process. Specifically, the checking step includes performing dynamic analysis on the machining path, extracting dynamic indicators such as the vibration response, cutting force distribution, and temperature change of the path during machining, and generating machining reliability data. Subsequently, the machining reliability data is compared with the preset path smoothness information to evaluate the dynamic stability and precision consistency of the path, and output the machining consistency evaluation results.

[0089] For example, if there is an error of 0.02mm in the machining path due to tool vibration, the vibration amplitude in the machining reliability data is too high. After comparing with the path smoothness setting standard, the evaluation results show that the path curvature needs to be further optimized or the cutting speed needs to be reduced to eliminate the impact of vibration.

[0090] The target processing instructions are input into the CNC processing equipment to generate processing path data and tool motion trajectory, and the shaft parts are processed based on the processing path data and tool motion trajectory to obtain compensated shaft parts.

[0091] By inputting the optimized target processing instructions into the CNC processing equipment, the CNC system automatically generates processing path data and tool motion trajectory; specifically, the equipment will accurately control the tool position, speed, acceleration and other parameters according to the corrected instructions to ensure that the processing path meets the design requirements and completes high-precision processing of shaft parts.

[0092] For example, when the radius and vibration parameters of the tool path are optimized in the input target processing instructions, the equipment will complete the processing of the cylindrical segment of shaft parts with minimal vibration and optimal path, with the error controlled within 0.005mm, meeting the high-precision processing requirements.

[0093] Among them, the steps of using processing stability parameters to perform stability check on the corrected instructions to obtain processing reliability data, and comparing the processing reliability data with preset path smoothness information to obtain processing consistency evaluation results include: establishing a stability evaluation model based on processing stability parameters, using the stability evaluation model to perform numerical simulation on the corrected instructions, extracting dynamic response parameters in the processing process, and obtaining processing reliability data.

[0094] By establishing a stability evaluation model based on machining stability parameters, the corrected instructions are subjected to multi-dimensional numerical simulation. Specifically, the dynamic response parameters in the machining process are analyzed through numerical simulation, including key indicators such as tool displacement, vibration amplitude, cutting force distribution and temperature gradient, to generate machining reliability data, providing a basis for stability verification.

[0095] For example, in a certain machining path, numerical simulation shows that the vibration frequency reaches 300 Hz and the cutting force fluctuates greatly. The vibration response in the machining reliability data deviates from the standard, indicating that this path needs to be further smoothed to improve machining stability.

[0096] The vibration amplitude, cutting force change and temperature gradient indicators in the machining reliability data are matched and analyzed with the preset path smoothness information to evaluate the smoothness of the machining path and obtain the path smoothness matching degree.

[0097] The key dynamic indicators in the processing reliability data (such as vibration amplitude, cutting force change, temperature gradient) are matched and analyzed with the preset standards of path smoothness; specifically, each indicator is compared item by item, and the deviation between the path smoothness and the preset standard is calculated to obtain the path smoothness matching degree.

[0098] For example, the vibration amplitude of a certain processing path is 0.03mm, while the path smoothness standard requires the vibration amplitude to be less than 0.01mm. The matching analysis results show that the path smoothness matching degree is 70%, and the processing path or cutting parameters need to be further adjusted to meet the smoothness requirements.

[0099] A comprehensive analysis is performed on the processing reliability data and path smoothness matching degree. Based on the analysis results, the key factors leading to processing instability are identified to obtain the processing consistency evaluation results.

[0100] By comprehensively analyzing the processing reliability data and path smoothness matching, the key factors leading to processing instability are identified; specifically, the dynamic parameters such as tool motion trajectory, path curvature, cutting load, etc. are analyzed to screen the root causes of vibration, thermal deformation or tool wear, and output the processing consistency evaluation results.

[0101] For example, the analysis results show that there is an error offset of 0.02mm at the starting point of the machining path, and the high cutting speed causes the tool vibration to increase. The output recommends optimizing the starting point path and reducing the cutting speed to improve the consistency of the path and the machining quality.

[0102] In this embodiment, the dimensions of the key parts of the shaft parts are measured by a three-coordinate measuring machine to obtain accurate measurement data, and compared with the preset design dimensions to obtain the deviation data of each part. These data provide a basis for the subsequent compensation model, and then the initial compensation parameters and processing stability parameters are optimized and corrected by the neural network model to ensure that the errors in the processing process are effectively compensated and the stability is improved. Then, the initial compensation parameters are optimized by genetic algorithm, and adjusted in combination with the processing stability parameters, so as to obtain a more accurate and reliable processing path. Through simulation, based on the optimized compensation parameters and stability parameters, the initial path processing instructions are generated, and error correction and path feasibility evaluation are performed on them, and the processing instructions are further optimized to ensure the feasibility of the path and processing accuracy. The signal is analyzed in the frequency domain by fast Fourier transform, and the phase information is corrected by methods such as phase unwrapping algorithm and Hilbert transform, which further improves the accuracy and stability of the path instructions. In addition, combined with wavelet transform and phase correction technology, the low-frequency and medium-frequency instructions are finely adjusted to obtain more accurate correction instructions. The stability of the corrected instructions is checked by machining stability parameters, and the stability of the machining path is further evaluated by comparing the machining reliability data with the path smoothness information, ensuring the consistency and high reliability of the machining. By analyzing the dynamic response of the machining path, the factors that cause machining instability can be identified, and the target machining instructions can be adjusted accordingly to achieve high-quality machining of shaft parts.

[0103] Embodiment 3:

[0104] like Figure 2 As shown, the present application also provides an error compensation device 10 for machining shaft parts, including an acquisition module 11, an input module 12, an analysis module 13 and a verification module 14.

[0105] The acquisition module 11 is mainly used to acquire the measurement data of the actual size of the shaft parts, and use the measurement data to calculate the deviation between the actual size of the shaft parts and the preset design size to obtain the deviation data.

[0106] The input module 12 is mainly used to input the measurement data and deviation data into the preset neural network compensation model to obtain compensation parameters and processing stability parameters, adjust the path of the processing tool based on the compensation parameters, and obtain the target path processing instructions.

[0107] The analysis module 13 is mainly used to perform spectrum analysis on the target path processing instructions through fast Fourier transform to obtain amplitude spectrum and phase spectrum, filter the amplitude spectrum to obtain filtered amplitude information, and use the phase spectrum to perform phase correction on the filtered amplitude information to obtain corrected instructions.

[0108] The verification module 14 is mainly used to verify the corrected instructions using the processing stability parameters to obtain the target processing instructions, and to process the shaft parts based on the target processing instructions to obtain the compensated shaft parts.

[0109] In this embodiment, the acquisition module 11 accurately collects the measurement data of the actual size of the shaft parts, calculates the deviation between the actual size and the preset design size, generates accurate deviation data, and provides a data basis for subsequent compensation. The input module 12 obtains the compensation parameters and processing stability parameters by inputting the measurement data and deviation data into the preset neural network compensation model, and adjusts the processing tool path accordingly to generate the target path processing instructions to ensure the accuracy of error compensation. The analysis module 13 performs spectrum analysis on the target path processing instructions through fast Fourier transform, decomposes the amplitude spectrum and phase spectrum, uses the filtering method to remove irrelevant noise, thereby obtaining the filtered amplitude information, and then uses the phase spectrum to perform phase correction on the amplitude information to generate a smoother and more stable correction instruction. The verification module 14 performs stability verification on the corrected instructions based on the processing stability parameters, evaluates the dynamic response characteristics during the processing process, ensures the reliability of the instructions, and executes the target processing instructions through the CNC processing equipment to obtain the compensated high-precision shaft parts. Through modular design, this device greatly improves the automation level and processing quality of the error compensation process, and ensures the dimensional accuracy and stability of shaft parts.

[0110] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described device and each module can refer to the corresponding process in the aforementioned error compensation method embodiment for shaft parts processing, and will not be repeated here.

[0111] Embodiment 4:

[0112] like Figure 3As shown, the present application also provides an electronic device 20, including a memory 21 and a processor 22, wherein the memory 21 stores a computer program that can be run on the processor 22, and when the processor 22 executes the computer program, the error compensation method for shaft parts processing of Example 1 is implemented.

[0113] In this embodiment, the error compensation method for shaft parts processing is implemented through the memory 21 and the processor 22 in the electronic device 20, further improving the intelligence and operability. The memory 21 stores an executable computer program, and when the processor 22 executes the program, the error compensation method for shaft parts processing described in Example 1 is accurately implemented. Specifically, under the control of the program, the processor 22 sequentially completes the acquisition of measurement data, calculation of deviation data, generation of compensation parameters and processing stability parameters, spectrum analysis, filtering processing, phase correction, stability verification, and generation and execution of the target path to achieve high-precision error compensation processing. The electronic device 20 can automatically execute the entire error compensation process without human intervention, reduce human operation errors, and ensure the accuracy and stability of processing through data analysis and path optimization. In addition, the device can be seamlessly integrated with existing CNC processing equipment, further improving processing efficiency and part quality, and is suitable for large-scale, automated high-precision manufacturing scenarios.

[0114] Embodiment 5:

[0115] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by the processor 22, the processor 22 executes the error compensation method for shaft part processing as described in Example 1.

[0116] In this embodiment, a computer program is stored in a computer-readable storage medium, so that the processor 22 executes the shaft parts processing error compensation method described in Example 1, and realizes automatic optimization and error compensation of high-precision processing paths. The computer program is stored in a computer-readable storage medium. When the processor 22 runs the program, it accurately completes the steps of measuring the actual size of shaft parts, calculating deviation data, generating path compensation parameters, spectrum analysis and filtering processing, phase correction, stability verification, etc., generates target processing instructions and executes processing. The program effectively compensates for errors in the processing process and improves the accuracy and stability of the processing path by integrating advanced technologies such as neural network models, genetic algorithms and fast Fourier transforms. In addition, the storage medium can be deployed in various electronic devices 20 and CNC systems, supports multi-platform compatibility, realizes efficient and automated error compensation processing, further reduces the complexity and cost of operation, and is suitable for the fields of intelligent manufacturing and high-precision processing.

[0117] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An error compensation method for machining shaft parts, characterized in that: include: Acquire measurement data of the actual size of the shaft part, and use the measurement data to calculate the deviation between the actual size of the shaft part and the preset design size to obtain deviation data; Inputting the measurement data and the deviation data into a neural network compensation model to obtain initial compensation parameters and initial processing stability parameters; optimizing the initial compensation parameters by a genetic algorithm to obtain optimized compensation parameters and parameter sensitivity information, and adjusting the initial processing stability parameters by using the parameter sensitivity information to obtain corrected processing stability parameters; The processing path simulation is performed using the optimized compensation parameters and the corrected processing stability parameters to obtain a path error correction value and a path feasibility evaluation result, and an initial path processing instruction and a processing path feasibility parameter are generated based on the path feasibility evaluation result; the processing path feasibility parameter is verified using the path error correction value to obtain a path optimization suggestion, and the initial path processing instruction is adjusted according to the path optimization suggestion to obtain a target path processing instruction; Performing fast Fourier transform processing on the target path processing instruction to obtain an amplitude spectrum and a phase spectrum, decomposing the amplitude spectrum into different frequency bands to obtain low-frequency amplitude information, intermediate-frequency amplitude information and high-frequency amplitude information; filtering the low-frequency amplitude information and the intermediate-frequency amplitude information to obtain filtered amplitude information; wherein the filtered amplitude information includes filtered low-frequency amplitude information and filtered intermediate-frequency amplitude information; processing the phase spectrum by a phase unwrapping algorithm to obtain continuous phase information; parsing the phase information by Hilbert transform to obtain phase correction parameters and phase error data, combining the filtered low-frequency amplitude information with the phase correction parameters to obtain a corrected low-frequency instruction, fusing the filtered intermediate-frequency amplitude information with the phase error data to obtain a corrected intermediate-frequency instruction; integrating the corrected low-frequency instruction, the corrected intermediate-frequency instruction and the high-frequency amplitude information to obtain a comprehensive correction instruction and a high-frequency variation parameter, and fine-tuning the comprehensive correction instruction by using the high-frequency variation parameter to obtain a corrected instruction; The corrected instructions are checked using the processing stability parameters to obtain target processing instructions, and the shaft parts are processed based on the target processing instructions to obtain compensated shaft parts.

2. The error compensation method for shaft parts processing according to claim 1 is characterized in that: The step of obtaining the measurement data of the actual size of the shaft part, and using the measurement data to calculate the deviation between the actual size of the shaft part and the preset design size to obtain the deviation data includes: Using a three-coordinate measuring machine to measure the dimensions of multiple key parts of the shaft parts to obtain measurement data of several parts; The measurement data of all key parts are compared with the preset design dimensions to obtain the deviation data of each part.

3. The error compensation method for shaft parts processing according to claim 1 is characterized in that: The steps of combining the filtered low-frequency amplitude information with the phase correction parameter to obtain a corrected low-frequency instruction, and fusing the filtered intermediate frequency amplitude information with the phase error data to obtain a corrected intermediate frequency instruction include: Establishing a corresponding relationship between the filtered low-frequency amplitude information and the phase correction parameter, generating a phase compensation matrix, and using the phase compensation matrix to perform phase adjustment on the filtered low-frequency amplitude information to obtain phase-corrected low-frequency amplitude information; Converting the phase-corrected low-frequency amplitude information into a low-frequency time-domain signal through an inverse fast Fourier transform, and generating a corrected low-frequency instruction based on the low-frequency time-domain signal; Performing phase error analysis on the filtered intermediate frequency amplitude information by wavelet transform, generating intermediate frequency phase error data based on the analysis result, and performing weighted averaging on the intermediate frequency phase error data and the phase error data to obtain an intermediate frequency phase compensation value; The filtered intermediate frequency amplitude information is phase-corrected using the intermediate frequency phase compensation value to obtain phase-corrected intermediate frequency amplitude information, the phase-corrected intermediate frequency amplitude information is converted into an intermediate frequency time domain signal through an inverse fast Fourier transform, and a corrected intermediate frequency instruction is generated based on the intermediate frequency time domain signal.

4. The error compensation method for machining shaft parts according to claim 1, characterized in that: The step of using the processing stability parameter to check the corrected instruction to obtain a target processing instruction, and processing the shaft part based on the target processing instruction to obtain a compensated shaft part includes: Using the processing stability parameter to perform stability check on the corrected instruction to obtain processing reliability data, comparing the processing reliability data with preset path smoothness information to obtain a processing consistency evaluation result, and using the processing consistency evaluation result to adjust the corrected instruction to obtain a target processing instruction; The target processing instruction is input into a numerical control processing device to generate processing path data and a tool motion trajectory, and the shaft parts are processed based on the processing path data and the tool motion trajectory to obtain compensated shaft parts.

5. The error compensation method for shaft parts processing according to claim 4 is characterized in that: The step of using the processing stability parameter to perform stability check on the corrected instruction to obtain processing reliability data, and comparing the processing reliability data with preset path smoothness information to obtain a processing consistency evaluation result includes: Establishing a stability evaluation model based on processing stability parameters, using the stability evaluation model to perform numerical simulation on the corrected instructions, extracting dynamic response parameters during the processing, and obtaining processing reliability data; Matching and analyzing the vibration amplitude, cutting force change and temperature gradient index in the processing reliability data with the preset path smoothness information, evaluating the stability of the processing path, and obtaining the path smoothness matching degree; A comprehensive analysis is performed on the processing reliability data and the path smoothness matching degree, and based on the analysis results, key factors that may cause processing instability are identified to obtain a processing consistency evaluation result.

6. An error compensation device for machining shaft parts, characterized in that: include: An acquisition module is used to acquire measurement data of the actual size of the shaft part, and calculate the deviation between the actual size of the shaft part and the preset design size using the measurement data to obtain deviation data; An input module is used to input the measurement data and the deviation data into a neural network compensation model to obtain initial compensation parameters and initial processing stability parameters; optimize the initial compensation parameters by a genetic algorithm to obtain optimized compensation parameters and parameter sensitivity information, and adjust the initial processing stability parameters by using the parameter sensitivity information to obtain corrected processing stability parameters; The processing path simulation is performed using the optimized compensation parameters and the corrected processing stability parameters to obtain a path error correction value and a path feasibility evaluation result, and an initial path processing instruction and a processing path feasibility parameter are generated based on the path feasibility evaluation result; the processing path feasibility parameter is verified using the path error correction value to obtain a path optimization suggestion, and the initial path processing instruction is adjusted according to the path optimization suggestion to obtain a target path processing instruction; an analysis module, for performing fast Fourier transform processing on the target path processing instruction to obtain an amplitude spectrum and a phase spectrum, decomposing the amplitude spectrum into different frequency bands to obtain low-frequency amplitude information, intermediate-frequency amplitude information and high-frequency amplitude information; filtering the low-frequency amplitude information and the intermediate-frequency amplitude information to obtain filtered amplitude information; wherein the filtered amplitude information includes filtered low-frequency amplitude information and filtered intermediate-frequency amplitude information; processing the phase spectrum by a phase unwrapping algorithm to obtain continuous phase information; parsing the phase information by Hilbert transform to obtain phase correction parameters and phase error data, combining the filtered low-frequency amplitude information with the phase correction parameters to obtain a corrected low-frequency instruction, fusing the filtered intermediate-frequency amplitude information with the phase error data to obtain a corrected intermediate-frequency instruction; integrating the corrected low-frequency instruction, the corrected intermediate-frequency instruction and the high-frequency amplitude information to obtain a comprehensive correction instruction and a high-frequency variation parameter, and fine-tuning the comprehensive correction instruction by using the high-frequency variation parameter to obtain a corrected instruction; A verification module is used to verify the corrected instructions using the processing stability parameters to obtain target processing instructions, and to process the shaft parts based on the target processing instructions to obtain compensated shaft parts.

7. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor implements the error compensation method for shaft part processing as described in any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the processor is enabled to execute the error compensation method for machining shaft parts as described in any one of claims 1 to 5.

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