A method for detecting installation consistency of a batch machine tool and related devices

Abstract

The application provides a batch machine tool installation consistency detection method and related device, and belongs to the technical field of installation consistency detection. The application obtains the amplitude-frequency curve, the phase-frequency curve and the coherence function of each detection point based on the excitation force signal and the vibration acceleration response signal of the batch machine tool detection point; when the coherence function of each detection point is greater than or equal to the set threshold value, it indicates that the amplitude-frequency curve and the phase-frequency curve of each detection point are effective, and the effective FRF data is obtained; based on the effective FRF data, the multi-dimensional difference index is calculated, the multi-dimensional difference index matrix is constructed based on the multi-dimensional difference index, the outlier statistics of the multi-dimensional difference index matrix is carried out, and the outlier statistics result is obtained; based on the outlier statistics result, the installation consistency of the batch machine tool is detected, and the detection result is obtained. The application solves the problem that the detection precision of the installation consistency of the batch machine tool is not high due to the dependence on artificial experience.

Description

Technical Field

[0001] This invention belongs to the field of installation consistency detection technology, specifically relating to a method and related apparatus for detecting the installation consistency of batch machine tools. Background Technology

[0002] Machine tools are complex electromechanical systems assembled from numerous components through mating surfaces. Extensive research and practice have shown that the contact stiffness and damping of these mating surfaces contribute a significant percentage (usually over 60%) to the overall static and dynamic characteristics of a machine tool. Fixed mating surfaces, primarily connected by bolts (such as the bed-column mating surface and the column-beam mating surface), are particularly crucial. Even machine tools from the same batch can exhibit variations in assembly consistency due to variations in bolt preload, contact surface flatness errors, and the presence of minute impurities. These microscopic assembly differences are difficult to detect directly through static geometric accuracy (such as laser interferometers), but they significantly affect the machine tool's frequency response characteristics under cutting chatter, ultimately leading to a divergence in the machining performance of different machine tools.

[0003] Currently, the industry's inspection of machine tool assembly quality largely relies on manual experience, such as judging by the operator's auditory experience or using static dial indicators, or performing modal testing on only a single machine. There is a lack of a comprehensive, rapid, quantitative, and automated method for comparing the assembly consistency of multiple machine tools in the same batch. Moreover, existing modal analysis methods are mostly used for research testing of single prototypes, with fixed and singular diagnostic rules that cannot achieve data-driven self-learning optimization. Summary of the Invention

[0004] The purpose of this invention is to provide a method and related apparatus for detecting the consistency of assembly of batch machine tools, in order to solve the problem that the existing technology relies on manual experience, resulting in low detection accuracy of the consistency of assembly of batch machine tools.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for detecting the consistency of assembly of batch machine tools, comprising the following steps: Select batch of machine tool inspection points; The excitation force signal and vibration acceleration response signal of the batch machine tool test points are obtained. Based on the excitation force signal and vibration acceleration response signal of the batch machine tool test points, the amplitude frequency curve, phase frequency curve and coherence function of each test point are obtained. When the coherence function of each detection point is greater than or equal to the set threshold, it indicates that the amplitude-frequency curve and phase-frequency curve of each detection point are valid, and valid FRF (Frequency Response Function) data is obtained. Based on valid FRF data, multidimensional difference indicators are calculated, and a multidimensional difference indicator matrix is ​​constructed based on these indicators. Outlier statistics were performed on the multidimensional dissimilarity index matrix to obtain the outlier statistics results; Based on the outlier statistics, the consistency of machine tool installation in batches was tested, and the test results were obtained.

[0006] A further improvement of the present invention is that the multidimensional difference index includes complex frequency response confidence, root mean square value of phase difference, and root mean square value of logarithmic amplitude weighted.

[0007] A further improvement of this invention is that the formula for calculating the complex frequency response confidence level is:

[0008] in, For complex frequency response confidence levels, For the FRF of the machine tool being tested, The base FRF is used, and * represents the conjugate complex number; The formula for calculating the root mean square value of the phase difference is:

[0009] in, This is the root mean square value of the phase difference. For the machine tool under test in the first i FRF phase angle at each frequency point As a benchmark FRF in the i Phase angle at each frequency point N To analyze the total number of frequency points involved in the calculation within the frequency band; The formula for calculating the logarithmic magnitude-weighted root mean square value is as follows:

[0010] in, The root mean square value is the logarithmic magnitude weighted average. and The tested machine tool and the reference FRF are respectively in the 1st... i The amplitude at each frequency point For the first i The coherence function values ​​at each frequency point N This is to determine the total number of frequency points involved in the calculation within the analysis band.

[0011] A further improvement of this invention is that, before performing outlier statistics on the multidimensional difference index matrix and obtaining the outlier statistics results, the multidimensional difference index matrix is ​​standardized.

[0012] A further improvement of this invention is that the outlier statistics of the multidimensional dissimilarity index matrix are obtained as follows: The interquartile range method was used to perform outlier statistics on the multidimensional difference index matrix, and the outlier statistics results were obtained.

[0013] A further improvement of this invention is that, after obtaining the detection results, an alarm is triggered for the detected outlier machine tools.

[0014] A further improvement of this invention is that, after obtaining the test results, the fault data of the machine tools with unqualified installation are stored in the database as valid positive samples.

[0015] Secondly, the present invention provides a detection system for batch machine tool installation consistency, comprising: The inspection point selection module is used to select inspection points for a batch of machine tools; The frequency response analysis module is used to acquire the excitation force signal and vibration acceleration response signal of the batch machine tool test points. Based on the excitation force signal and vibration acceleration response signal of the batch machine tool test points, the amplitude frequency curve, phase frequency curve and coherence function of each test point are obtained. The effective FRF data acquisition module is used to obtain effective FRF data when the coherence function of each detection point is greater than or equal to a set threshold, indicating that the amplitude-frequency curve and phase-frequency curve of each detection point are effective. The multidimensional matrix construction module is used to calculate multidimensional dissimilarity indicators based on valid FRF data, and to construct a multidimensional dissimilarity indicator matrix based on the multidimensional dissimilarity indicators. The outlier statistics module is used to perform outlier statistics on a multidimensional dissimilarity index matrix and obtain the outlier statistics results. The detection module is used to detect the installation consistency of batch machine tools based on outlier statistics and obtain the detection results.

[0016] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the batch machine tool installation consistency detection method described above.

[0017] Fourthly, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the batch machine tool installation consistency detection method described above.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The proposed method for detecting the installation consistency of batch machine tools has two aspects. First, it calculates multidimensional difference indices based on effective FRF data. This operation can comprehensively quantify the differences in effective FRF data, thereby improving the detection accuracy of batch machine tool installation consistency. Second, it performs outlier statistics on the multidimensional difference index matrix to obtain outlier statistics results. This operation can achieve objective judgment of installation consistency differences, avoiding subjective bias and experience error caused by manual evaluation, thereby improving the detection accuracy of batch machine tool installation consistency.

[0019] Furthermore, this invention discloses multidimensional difference indicators including complex frequency response confidence, root mean square value of phase difference, and logarithmic amplitude weighted root mean square value. The complex frequency response confidence, root mean square value of phase difference, and logarithmic amplitude weighted root mean square value can simultaneously capture the stiffness attenuation and damping change of the joint surface, and are highly sensitive to the micro loosening of the joint surface. Attached Figure Description

[0020] Figure 1 This is a flowchart of the batch machine tool installation consistency detection method of the present invention; Figure 2 This is a schematic diagram of the batch machine tool installation consistency detection system of the present invention; Figure 3 This is a flowchart of the batch machine tool installation consistency detection method in Embodiment 4 of the present invention; Figure 4 Schematic diagram of the arrangement of measuring points on the key mating surfaces of the machine tool in Embodiment 4 of the present invention; Figure 5 This is a schematic diagram illustrating the outlier detection principle based on the IQR (Interquartile Range) method in Embodiment 4 of the present invention. Figure 6 This is a schematic diagram of the consistency determination sample in Embodiment 4 of the present invention; Figure 7 This is a flowchart of the self-learning iterative optimization mechanism in Embodiment 4 of the present invention; Figure 8 This is a schematic diagram of the structure of the electronic device of the present invention; In the diagram: 1. Crossbeam; 2. Left column; 3. Worktable; 4. Z-axis connecting plate; 5. Spindle; 6. Left column; 7. Bed. Detailed Implementation

[0021] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0022] Example 1: The flowchart of the batch machine tool installation consistency detection method of the present invention is as follows: Figure 1As shown, the batch machine tool installation consistency detection method of the present invention includes the following steps: S1. Select batch of machine tool inspection points; S2. Obtain the excitation force signal and vibration acceleration response signal of the batch machine tool detection points. Based on the excitation force signal and vibration acceleration response signal of the batch machine tool detection points, obtain the amplitude frequency curve, phase frequency curve and coherence function of each detection point. S3. When the coherence function of each detection point is greater than or equal to the set threshold, it indicates that the amplitude-frequency curve and phase-frequency curve of each detection point are valid, and valid FRF data is obtained; S4. Based on valid FRF data, calculate multidimensional difference indicators, and construct a multidimensional difference indicator matrix based on the multidimensional difference indicators; S5. Perform outlier statistics on the multidimensional difference index matrix to obtain the outlier statistics results; S6. Based on the outlier statistics, the consistency of the installation of batch machine tools is tested, and the test results are obtained.

[0023] Example 2: A schematic diagram of the batch machine tool installation consistency detection system of the present invention is shown below. Figure 2 As shown, the batch machine tool installation consistency detection system of the present invention includes: The inspection point selection module is used to select inspection points for a batch of machine tools; The frequency response analysis module is used to acquire the excitation force signal and vibration acceleration response signal of the batch machine tool test points. Based on the excitation force signal and vibration acceleration response signal of the batch machine tool test points, the amplitude frequency curve, phase frequency curve and coherence function of each test point are obtained. The effective FRF data acquisition module is used to obtain effective FRF data when the coherence function of each detection point is greater than or equal to a set threshold, indicating that the amplitude-frequency curve and phase-frequency curve of each detection point are effective. The multidimensional matrix construction module is used to calculate multidimensional dissimilarity indicators based on valid FRF data, and to construct a multidimensional dissimilarity indicator matrix based on the multidimensional dissimilarity indicators. The outlier statistics module is used to perform outlier statistics on a multidimensional dissimilarity index matrix and obtain the outlier statistics results. The detection module is used to detect the installation consistency of batch machine tools based on outlier statistics and obtain the detection results.

[0024] Example 3: The method for detecting the consistency of batch machine tool installation according to the present invention includes the following steps: S1. Select batch of machine tool inspection points.

[0025] S2. Obtain the excitation force signal and vibration acceleration response signal of the batch of machine tool test points. Based on the excitation force signal and vibration acceleration response signal of the batch of machine tool test points, obtain the amplitude frequency curve, phase frequency curve and coherence function of each test point.

[0026] S3. When the coherence function of each detection point is greater than or equal to the set threshold, it indicates that the amplitude-frequency curve and phase-frequency curve of each detection point are valid, and valid FRF data is obtained.

[0027] S4. Based on valid FRF data, calculate multidimensional difference indicators, and construct a multidimensional difference indicator matrix based on the multidimensional difference indicators.

[0028] The multidimensional difference indicators in this step include complex frequency response confidence, root mean square value of phase difference, and root mean square value of logarithmic magnitude weighted.

[0029] The formula for calculating the confidence level of the complex frequency response in this step is:

[0030] in, For complex frequency response confidence levels, For the FRF of the machine tool being tested, is the baseline FRF, and * represents the conjugate complex number.

[0031] The formula for calculating the root mean square value of the phase difference in this step is:

[0032] in, This is the root mean square value of the phase difference. For the machine tool under test in the first i FRF phase angle at each frequency point As a benchmark FRF in the i Phase angle at each frequency point N This is to determine the total number of frequency points involved in the calculation within the analysis band.

[0033] The formula for calculating the weighted root mean square value of the logarithmic magnitude in this step is:

[0034] in, The root mean square value is the logarithmic magnitude weighted average. and The tested machine tool and the reference FRF are respectively in the 1st... i The amplitude at each frequency point For the first i The coherence function values ​​at each frequency point N This is to determine the total number of frequency points involved in the calculation within the analysis band.

[0035] S5. Perform outlier statistics on the multidimensional difference index matrix to obtain the outlier statistics results.

[0036] In this step, outlier statistics are performed on the multidimensional difference index matrix. Before obtaining the outlier statistics results, the multidimensional difference index matrix is ​​standardized.

[0037] This step involves outlier statistics on the multidimensional dissimilarity index matrix to obtain the outlier statistics results, specifically: The interquartile range method was used to perform outlier statistics on the multidimensional difference index matrix, and the outlier statistics results were obtained.

[0038] S6. Based on the outlier statistics, the consistency of the installation of batch machine tools is tested, and the test results are obtained.

[0039] After obtaining the detection results in this step, an alarm is triggered for any outlier machine tools detected.

[0040] After obtaining the test results in this step, the fault data of the unqualified machine tools detected are stored in the database as valid positive samples.

[0041] Example 4: The method of the present invention will be described in detail below. The batch machine tool installation consistency detection method of the present invention is implemented by a batch machine tool installation consistency detection system (hereinafter referred to as the detection system). The flowchart of the batch machine tool installation consistency detection method of the present invention is as follows: Figure 3 As shown, the batch machine tool installation consistency detection method of the present invention includes the following steps: Step S1: Experimental Design and Data Acquisition For machine tools of the same model and batch to be inspected, based on the tree structure, determine the key fixed mating surfaces (especially the bed-column mating surface, column-beam mating surface, headstock transmission mating surface along the Z-axis, worktable mating surface, and spindle mating surface, etc.). A schematic diagram of the measurement point layout for the key mating surfaces of the machine tool is shown below. Figure 4 As shown. Figure 4In this diagram, label 1 represents the crossbeam, label 2 represents the left column, label 3 represents the worktable, label 4 represents the Z-axis connecting plate, label 5 represents the spindle, label 6 represents the right column, and label 7 represents the bed. High-sensitivity accelerometers are installed at locations with good rigidity on both sides of these mating surfaces. Subsequently, a force hammer with a force sensor is used to perform fixed-point excitation or traverse excitation at identical locations on each machine tool. The excitation force signal and vibration acceleration response signal are synchronously recorded using a data acquisition instrument, and the frequency response function (FRF) (also known as the frequency response function, including amplitude-frequency curves and phase-frequency curves) and coherence function of all measuring points are calculated. This invention emphasizes the quality control role of the coherence function; only analysis frequency bands with a coherence function higher than 0.9 are considered valid data, avoiding the misleading influence of noise interference on subsequent statistical analysis.

[0042] Step S2: Calculation of multidimensional difference index We designed multidimensional difference indicators to comprehensively measure the differences in FRF curves. These multidimensional difference indicators specifically include: 1. Complex Frequency Response Confidence Ratio (also known as Complex FRAC, FRAC stands for Frequency Response Assurance Criterion) Complex domain correlation indices, which fully preserve phase information, are extremely sensitive to changes in mode shapes. The formula for calculating the confidence level of the complex frequency response is:

[0043] in, For complex frequency response confidence levels, For the FRF of the machine tool being tested, For the baseline FRF, * denotes the conjugate complex number.

[0044] 2. Root Mean Square (RMS) value of phase difference Within the analysis frequency band, the phase angle difference between the tested machine tool's FRF and the reference FRF is calculated point by point, and then the root mean square (RMS) value is calculated. The formula for calculating the RMS value of the phase difference is:

[0045] in, This is the root mean square value of the phase difference. For the machine tool under test in the first i FRF phase angle at each frequency point As a benchmark FRF in the i Phase angle at each frequency point N This is to determine the total number of frequency points involved in the calculation within the analysis band.

[0046] 3. Log-weighted root mean square (RMS) Taking the base-10 logarithm of the FRF amplitude, calculate the logarithmic amplitude difference at each frequency point, and using the coherence function value at that frequency point as a weighting factor, the formula for calculating the weighted root mean square value of the logarithmic amplitude is:

[0047] in, The root mean square value is the logarithmic magnitude weighted average. and The tested machine tool and the reference FRF are respectively in the 1st... i The amplitude at each frequency point For the first i The coherence function values ​​at each frequency point N This is to determine the total number of frequency points involved in the calculation within the analysis band. The higher the coherence function, the greater the contribution weight of the amplitude difference at that frequency point in the overall detection.

[0048] Based on multidimensional difference indicators, a multidimensional difference indicator matrix (also known as a multidimensional feature matrix) is constructed.

[0049] Step S3: Statistical outlier identification First, Z-score is used to standardize all differential indicators (multidimensional differential indicator matrix) of different dimensions (such as a dynamic database containing current batch and historical data) so that different indicators can be compared comprehensively.

[0050] Subsequently, a robust statistical outlier detection algorithm (specifically, the interquartile range method) is executed. A schematic diagram of the outlier detection principle based on the IQR method is shown below. Figure 5 As shown, the specific process is as follows: For a specific measurement point of a certain model, a certain index (such as the complex FRAC difference) of all historical qualified data will form a distribution; calculate the third quartile (Q3) and the first quartile (Q1) of this distribution. If the index value of a certain machine tool is lower than Q1-1.5×IQR or higher than Q3+1.5×IQR, it will be marked as a strong outlier.

[0051] Step S4: Consistency Comprehensive Judgment The final judgment adopts a strict "AND" judgment logic, that is, only when the key mating surface measurement points of the same machine tool are judged as abnormal by statistical rules in multiple dimensions such as large difference in complex FRAC, phase difference exceeding the limit, and severe decrease in weighted amplitude, will an "installation failure" alarm be issued to the quality management personnel, thereby minimizing the false alarm rate.

[0052] A schematic diagram of the consistency determination sample is shown below. Figure 6 As shown, from Figure 6It can be seen that when testing the same test point of the same batch of machine tools, the test points with large assembly deviations will have differences in frequency response functions. By comprehensively identifying the test points with large deviations through multi-dimensional indicators (multi-dimensional difference indicators), the test points with large deviations are marked as outliers.

[0053] Step S5: Self-learning iterative optimization The flowchart of the self-learning iterative optimization mechanism is as follows: Figure 7 As shown below, the process of the self-learning iterative optimization mechanism is explained: When a machine tool deemed "incompatible with installation" is disassembled and reassembled, and physical problems such as abnormal bolt torque or scratches on mating surfaces are confirmed, the inspection system can store typical fault data of that equipment as valid positive samples in the database. Over time, during subsequent batch inspections, the diagnostic algorithm automatically references the characteristics of historical fault samples, dynamically increasing the weighting coefficients of these sensitive feature dimensions when calculating the comprehensive difference score. This upgrades the system from "general threshold judgment" to "customized experience evolution," making the inspection system increasingly accurate with use.

[0054] Compared with the prior art, the present invention has the following advantages: 1. Group statistical analysis replaces stand-alone judgment. It abandons the traditional single-machine, one-by-one judgment mode and introduces the concept of "group statistical analysis". Only one round of stimulation experiment is needed to complete the horizontal comparison of a batch, realizing rapid batch screening.

[0055] 2. Multi-dimensional combined diagnosis is more sensitive By combining complex FRAC, phase difference RMS, and logarithmic amplitude weighted RMS for diagnosis, it is possible to simultaneously capture the stiffness attenuation and damping changes of the joint surface, and is highly sensitive to micro-loosening of the joint surface.

[0056] 3. Self-learning mechanism overcomes the limitations of fixed thresholds. Through self-learning and iterative optimization, the shortcomings of fixed threshold limitation in generality are overcome. The model can continuously adapt and optimize as data accumulates, achieving an upgrade from "general threshold judgment" to "customized experience evolution", making the system more and more accurate with use, which is very suitable for end-of-line inspection scenarios in industrial production lines.

[0057] Example 5: Please see Figure 8 As shown, the present invention also provides an electronic device 100 for a batch machine tool installation consistency detection method; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0058] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the batch machine tool installation consistency detection method described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0059] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0060] The memory 101 in the electronic device 100 stores multiple instructions to implement a batch machine tool installation consistency detection method, and the processor 102 can execute the multiple instructions to achieve the following: Select batch of machine tool inspection points; The excitation force signal and vibration acceleration response signal of the batch machine tool test points are obtained. Based on the excitation force signal and vibration acceleration response signal of the batch machine tool test points, the amplitude frequency curve, phase frequency curve and coherence function of each test point are obtained. When the coherence function of each detection point is greater than or equal to the set threshold, it indicates that the amplitude-frequency curve and phase-frequency curve of each detection point are valid, and valid FRF data is obtained. Based on valid FRF data, multidimensional difference indicators are calculated, and a multidimensional difference indicator matrix is ​​constructed based on these indicators. Outlier statistics were performed on the multidimensional dissimilarity index matrix to obtain the outlier statistics results; Based on the outlier statistics, the consistency of machine tool installation in batches was tested, and the test results were obtained.

[0061] Example 6: If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0062] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for detecting the consistency of assembly in batches of machine tools, characterized in that, Includes the following steps: Select batch of machine tool inspection points; The excitation force signal and vibration acceleration response signal of the batch machine tool test points are obtained. Based on the excitation force signal and vibration acceleration response signal of the batch machine tool test points, the amplitude frequency curve, phase frequency curve and coherence function of each test point are obtained. When the coherence function of each detection point is greater than or equal to the set threshold, it indicates that the amplitude-frequency curve and phase-frequency curve of each detection point are valid, and valid FRF data is obtained. Based on valid FRF data, multidimensional difference indicators are calculated, and a multidimensional difference indicator matrix is ​​constructed based on these indicators. Outlier statistics were performed on the multidimensional dissimilarity index matrix to obtain the outlier statistics results; Based on the outlier statistics, the consistency of machine tool installation in batches was tested, and the test results were obtained.

2. The method for detecting the consistency of batch machine tool installation according to claim 1, characterized in that, The multidimensional difference indicators include complex frequency response confidence, root mean square value of phase difference, and root mean square value of logarithmic amplitude weighted.

3. The method for detecting the consistency of batch machine tool installation according to claim 2, characterized in that, The formula for calculating the confidence level of the complex frequency response is as follows: in, For complex frequency response confidence levels, For the FRF of the machine tool being tested, The base FRF is used, and * represents the conjugate complex number; The formula for calculating the root mean square value of the phase difference is: in, This is the root mean square value of the phase difference. For the machine tool under test in the first i FRF phase angle at each frequency point As a benchmark FRF in the i Phase angle at each frequency point N To analyze the total number of frequency points involved in the calculation within the frequency band; The formula for calculating the logarithmic magnitude-weighted root mean square value is as follows: in, The root mean square value is the logarithmic magnitude weighted average. and The tested machine tool and the reference FRF are respectively in the 1st... i The amplitude at each frequency point For the first i coherence function values ​​at each frequency point N This is to determine the total number of frequency points involved in the calculation within the analysis band.

4. The method for detecting the consistency of batch machine tool installation according to claim 1, characterized in that, Before obtaining the outlier statistics results, the multidimensional difference index matrix is ​​standardized.

5. The method for detecting the consistency of batch machine tool installation according to claim 1, characterized in that, The outlier statistics of the multidimensional dissimilarity index matrix are obtained as follows: The interquartile range method was used to perform outlier statistics on the multidimensional difference index matrix, and the outlier statistics results were obtained.

6. The method for detecting the consistency of batch machine tool installation according to claim 1, characterized in that, After obtaining the test results, an alarm is triggered for the outlier machine tools detected.

7. The method for detecting the consistency of batch machine tool installation according to claim 1, characterized in that, After obtaining the test results, the fault data of the machine tools with unqualified installation are stored in the database as valid positive samples.

8. A batch machine tool installation consistency detection system, characterized in that, include: The inspection point selection module is used to select inspection points for a batch of machine tools; The frequency response analysis module is used to acquire the excitation force signal and vibration acceleration response signal of the batch machine tool test points. Based on the excitation force signal and vibration acceleration response signal of the batch machine tool test points, the amplitude frequency curve, phase frequency curve and coherence function of each test point are obtained. The effective FRF data acquisition module is used to obtain effective FRF data when the coherence function of each detection point is greater than or equal to a set threshold, indicating that the amplitude-frequency curve and phase-frequency curve of each detection point are effective. The multidimensional matrix construction module is used to calculate multidimensional dissimilarity indicators based on valid FRF data, and to construct a multidimensional dissimilarity indicator matrix based on the multidimensional dissimilarity indicators. The outlier statistics module is used to perform outlier statistics on a multidimensional dissimilarity index matrix and obtain the outlier statistics results. The detection module is used to detect the installation consistency of batch machine tools based on outlier statistics and obtain the detection results.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the batch machine tool installation consistency detection method according to any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the batch machine tool installation consistency detection method according to any one of claims 1 to 7.

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