Detection system and method for motor output shaft

Abstract

The invention relates to the technical field of motor output shaft detection, and particularly discloses a detection system and method for a motor output shaft, and the system comprises a sample obtaining and classifying module, a quality performance detection module, a mechanical performance detection module, a dynamic performance detection module, an input-to-use judgment module, and a memory. The comprehensive performance inspection index of the current batch of motor output shaft samples is formed from the three aspects of the quality performance, the mechanical performance and the dynamic performance of the current batch of motor output shaft samples, so that whether the current batch of motor output shaft samples can be put into industrial equipment assembly or not is judged more accurately, online feedback is carried out in time, and the detection accuracy is improved. Decision suggestions and data support are provided for workers, so that the detection process of the motor output shaft is more flexible and efficient, the motor output shaft can continuously adapt to market requirements and continuously changing detection environments, and the overall performance of the motor output shaft can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor output shaft detection, and specifically provides a detection system and method for a motor output shaft. Background Art

[0002] As the core component for a motor to achieve power output, the performance and quality of the motor output shaft directly affect the operation stability and reliability of the entire motor system. During the operation of the motor, the output shaft not only bears the centrifugal force caused by high-speed rotation but also transmits the torque generated by the motor to drive various load devices. Therefore, there are relatively strict requirements for the performance and quality detection of the motor output shaft.

[0003] Currently, the detection technologies and equipment for motor output shafts are uneven, and the detection methods for different types of motor output shafts are also very different. There is an urgent need for a method to perform multi-dimensional detection and analysis on motor output shafts to make the evaluation results of the performance and quality of motor output shafts more meaningful. Summary of the Invention

[0004] The purpose of the present invention is to provide a detection system and method for a motor output shaft to solve the problems raised in the above background.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The first aspect of the present invention provides a detection system for a motor output shaft, including:

[0007] A sample acquisition and classification module, which is used to extract several motor output shaft samples from the motor output shaft samples formed by the current batch of precision forging process and divide them into first-class motor output shaft samples, second-class motor output shaft samples, and third-class motor output shaft samples according to an equal proportion relationship.

[0008] A quality and performance detection module, which is used to detect the quality and performance corresponding to the first-class motor output shaft samples, obtain the quality and performance parameters corresponding to the first-class motor output shaft samples, and analyze the quality and performance evaluation index corresponding to the first-class motor output shaft samples based on this to obtain the quality and performance evaluation index corresponding to the first-class motor output shaft samples.

[0009] Preferably, to detect the quality and performance corresponding to the first-class motor output shaft samples and obtain the quality and performance parameters corresponding to the first-class motor output shaft samples, the specific detection method is:

[0010] Scan each first-class motor output shaft sample through a laser three-dimensional scanner to obtain a contour detection model of each first-class motor output shaft sample, and identify each contour shape feature detection value on the contour detection model of each first-class motor output shaft sample based on an edge detection algorithm, thereby obtaining each contour shape feature detection value on the contour detection model of each first-class motor output shaft sample;

[0011] Uniformly arrange the detection points for each sample of the output shaft of the first-class motor to obtain the detection points of each sample of the output shaft of the first-class motor. Use a roughness measuring instrument to detect the surface roughness of each detection point of each sample of the output shaft of the first-class motor to obtain the surface roughness of each detection point of each sample of the output shaft of the first-class motor;

[0012] Collect the surface images of each sample of the output shaft of the first-class motor through an industrial camera to obtain the surface images of each sample of the output shaft of the first-class motor. Based on image recognition technology, extract the crack areas and scratch areas of each sample of the output shaft of the first-class motor, and then statistically obtain the number of crack areas and the number of scratch areas of each sample of the output shaft of the first-class motor, and extract the length and width of each crack area, the depth and length of each scratch area, and then perform cumulative calculations to obtain the total length, total width of the crack area, total depth of the scratch area, and total length of the scratch area of each sample of the output shaft of the first-class motor, which are respectively used as the crack length, crack width, scratch depth, and scratch length of each sample of the output shaft of the first-class motor.

[0013] Preferably, analyze the quality performance evaluation index corresponding to the sample of the output shaft of the first-class motor to obtain the quality performance evaluation index corresponding to the sample of the output shaft of the first-class motor. The specific analysis method is as follows:

[0014] Extract the detection values of each contour shape feature on the contour detection model of each sample of the output shaft of the first-class motor from the quality performance parameters of each sample of the output shaft of the first-class motor, denoted as At the same time, obtain the standard contour model of the motor output shaft from the memory and extract the standard detection values of each contour shape feature from it, denoted as XZ′ j , where i represents the number of each sample of the output shaft of the first-class motor, i = 1, 2,...., a, a represents the total number of the numbers of the samples of the output shaft of the first-class motor, j represents the number of each contour shape feature, j = 1, 2,...., b, and b represents the total number of the numbers of the contour shape features;

[0015] According to the formula Calculate the production size accuracy JQ of each sample of the output shaft of the first-class motor i , where e represents the natural constant;

[0016] Extract the surface roughness of each detection point of each sample of the output shaft of the first-class motor, and screen out the maximum surface roughness, minimum surface roughness, median surface roughness, and mode surface roughness from it, denoted as According to the formula

[0017]

[0018] Calculate the surface roughness uniformity JY of each sample of the output shaft of the first-class motor i, CX0 represents the set reference surface roughness, and α1, α2, α3, and α4 respectively represent the proportion weights of the set maximum surface roughness, minimum surface roughness, median surface roughness, and mode surface roughness corresponding to the surface roughness uniformity;

[0019] Extract the number of crack regions and the number of scratch regions of each type-I motor output shaft sample, and record them as LN i 、HN i , and at the same time extract the crack length, crack width, scratch depth, and scratch length of each type-I motor output shaft sample, and record them as LL i 、LD i 、HH i 、HL i ;

[0020] According to the formula Calculate the defect degree QX of each type-I motor output shaft sample i , and β1, β2, β3, β4, β5, and β6 respectively represent the proportion weights of the set number of crack regions, number of scratch regions, crack length, crack width, scratch depth, and scratch length corresponding to the defect degree;

[0021] According to the formula Calculate the quality performance evaluation index ZX corresponding to the type-I motor output shaft sample, where w1, w2, and w3 respectively represent the proportion weights of the set production size accuracy, surface roughness uniformity, and defect degree corresponding to the quality performance evaluation index.

[0022] Mechanical property detection module, used to detect the mechanical properties of each type-II motor output shaft sample, obtain the mechanical property parameters corresponding to each type-II motor output shaft sample, and thereby analyze the mechanical property evaluation index corresponding to the type-II motor output shaft sample to obtain the mechanical property evaluation index corresponding to the type-II motor output shaft sample.

[0023] Preferably, detect the mechanical properties of each type-II motor output shaft sample to obtain the mechanical property parameters corresponding to each type-II motor output shaft sample. The specific detection method is:

[0024] Divide each type-II motor output shaft sample into each torque test sample and each fatigue experiment test sample, set the torque test conditions for each working condition gradient, number the torque test conditions in ascending order of the working conditions, and make the numbering order of each working condition torque test sample correspond to the numbering of each working condition gradient torque test condition one by one. Then, sequentially perform torque tests on each torque test sample under its corresponding working condition gradient torque test condition, and at the same time detect the torque magnitude of each torque test sample at each detection time point within the set detection period through a torque sensor to obtain the torque magnitude of each torque test sample at each detection time point within the set detection period;

[0025] Set the fatigue test conditions for each stress level gradient, number the fatigue test conditions in ascending order of stress level, and make the numbering order of each fatigue test sample correspond one-to-one with the numbering of the fatigue test conditions for each stress level gradient. Then, conduct fatigue tests on each fatigue test sample in the fatigue test conditions corresponding to its number in turn. When obvious cracks appear in each fatigue test sample, stop the test and use it as the fatigue failure criterion. At the same time, use a counter to detect the number of cycles when each fatigue test sample reaches the fatigue failure criterion, and obtain the number of cycles of each fatigue test sample, which is used as the fatigue life of each fatigue test sample.

[0026] Preferably, analyze the mechanical property evaluation indices corresponding to each type-II motor output shaft sample to obtain the mechanical property evaluation indices corresponding to each type-II motor output shaft sample. The specific analysis method is as follows:

[0027] Taking each detection time point as the abscissa and the torque magnitude as the ordinate, plot the torque change curves of each torque test sample within the set detection period. At the same time, extract the reference torque change curves of each working condition gradient within the set detection period from the memory; compare the torque change curves of each torque test sample within the set detection period with the reference torque change curves of each working condition gradient within the set detection period to obtain the overlapping length of the torque change curves of each torque test sample within the set detection period, denoted as L x , where x represents the number of each torque test sample, x = 1, 2,...., p, and p represents the total number of torque test sample numbers;

[0028] Obtain the numerical value of the length of the reference torque change curve of each working condition gradient within the set detection period, denoted as L0;

[0029] Extract the maximum torque and minimum torque from the torque change curves of each torque test sample within the set detection period, and denote them respectively as And extract the reference maximum torque and reference minimum torque from the reference torque change curves of each working condition gradient within the set detection period, and denote them respectively as According to the formula Calculate the torque stability coefficient TW corresponding to the torque test sample. γ1, γ2, and γ3 respectively represent the proportion weights of the set torque change curve overlapping length, maximum torque, and minimum torque corresponding to the torque stability coefficient;

[0030] Extract the fatigue life of each fatigue test sample, and at the same time extract the reference fatigue life at each stress level from the memory; compare the fatigue life of each fatigue test sample with the reference fatigue life at each stress level. When the fatigue life of a certain fatigue test sample is greater than or equal to the reference fatigue life at a certain stress level, mark this fatigue test sample as a sample with qualified fatigue strength. When the fatigue life of a certain fatigue test sample is less than the reference fatigue life at a certain stress level, mark this fatigue test sample as a sample with unqualified fatigue strength. Thus, count the number of samples with qualified fatigue strength and the number of samples with unqualified fatigue strength, denoted as PD and PB respectively;

[0031] According to the formula Calculate the fatigue strength coefficient PX corresponding to the fatigue test sample. ε1 and ε2 respectively represent the proportion weights of the fatigue strength coefficients corresponding to the set number of samples with qualified fatigue strength and the set number of samples with unqualified fatigue strength;

[0032] Calculate the mechanical property evaluation index JX corresponding to the output shaft sample of the second-class motor according to the formula JX = TW×z1 + PX×z2. z1 and z2 respectively represent the proportion weights of the mechanical property evaluation index corresponding to the set torque stability coefficient and the fatigue strength coefficient. The dynamic performance detection module is used to detect the dynamic performance of each output shaft sample of the third-class motor, obtain the dynamic performance parameters corresponding to each output shaft sample of the third-class motor, and thus analyze the dynamic performance evaluation index corresponding to the output shaft sample of the third-class motor to obtain the dynamic performance evaluation index corresponding to the output shaft sample of the third-class motor.

[0033] Preferably, detect the dynamic performance of each output shaft sample of the third-class motor to obtain the dynamic performance parameters corresponding to each output shaft sample of the third-class motor. The specific detection method is as follows:

[0034] Divide each output shaft sample of the third-class motor into each radial runout test sample and each axial runout test sample;

[0035] Place and fix each radial runout test sample on the detection platform, apply a periodic simulated external force in the radial horizontal direction of the sample in a set manner to simulate the radial horizontal interference force received by the motor output shaft during actual operation. During the application of the external force, use a high-precision displacement sensor to detect the displacement change values of each radial runout test sample at each detection time point in the radial horizontal direction to obtain the displacement change values of each radial runout test sample at each detection time point in the radial horizontal direction. At the same time, use a vibration sensor to detect the vibration amplitude of each radial runout test sample to obtain the vibration amplitude of each radial runout test sample, and use a sound sensor to detect the noise decibel values of each radial runout test sample at each detection time point to obtain the noise decibel values of each radial runout test sample at each detection time point;

[0036] Place and fix each axial-clearance test sample on the detection platform, and apply a periodic simulated external force in the axial horizontal direction of the sample in a set manner to simulate the axial horizontal interference force on the motor output shaft during actual operation. During the application of the external force, use a high-precision displacement sensor to detect the displacement change values of each axial-clearance test sample at each detection time point in the axial horizontal direction, obtain the displacement change values of each axial-clearance test sample at each detection time point in the axial horizontal direction. At the same time, use a vibration sensor to detect the vibration amplitude of each axial-clearance test sample to obtain the vibration amplitude of each axial-clearance test sample, and use a sound sensor to detect the noise decibel values of each axial-clearance test sample at each detection time point to obtain the noise decibel values of each axial-clearance test sample at each detection time point.

[0037] Preferably, analyze the dynamic performance evaluation indexes corresponding to the three types of motor output shaft samples to obtain the dynamic performance evaluation indexes corresponding to the three types of motor output shaft samples. The specific analysis method is as follows:

[0038] Extract the maximum displacement change value from the displacement change values of each radial-clearance test sample at each detection time point in the radial horizontal direction, denoted as q represents the number of each radial-clearance test sample, q = 1, 2,...., m, m represents the total number of radial-clearance test sample numbers. Extract the vibration amplitude of each radial-clearance test sample, denoted as ZF q ;

[0039] Subtract the noise decibel values of each radial-clearance test sample at each detection time point in the radial horizontal direction from the set permitted noise decibel value to obtain the noise decibel value deviation of each radial-clearance test sample at each detection time point in the radial horizontal direction, and extract the maximum noise decibel value deviation and the minimum noise decibel value deviation from them, and denote them as According to the formula

[0040] Calculate the runout coefficient CD1 of the radial-clearance test sample. ZF', Denote as the set reference maximum radial displacement change value, radial vibration amplitude, maximum noise decibel value deviation, minimum noise decibel value deviation. η1, η2, η3, η4 respectively represent the proportion weights of the runout coefficients corresponding to the set maximum radial displacement change value, vibration amplitude, maximum noise decibel value deviation, minimum noise decibel value deviation.

[0041] Extract the maximum displacement change value from the displacement change values of each axial-clearance test sample at each detection time point in the axial horizontal direction, denoted as Let \(r\) represent the serial number of each axially moving test sample, where \(r = 1, 2,\cdots, n\), and \(n\) represents the total number of serial numbers of axially moving test samples. Extract the vibration amplitude of each axially moving test sample and denote it as \(ZF\). r ;

[0042] Subtract the noise decibel value at each detection time point of each axially moving test sample in the axial horizontal direction from the set permitted noise decibel value to obtain the noise decibel value deviation at each detection time point of each axially moving test sample in the axial horizontal direction, and extract the maximum noise decibel value deviation and the minimum noise decibel value deviation therefrom, and denote them respectively as According to the formula

[0043] Calculate the moving coefficient \(CD2\) of the axially moving test sample. \(ZF'\), Denote the set reference maximum radial displacement change value, radial vibration amplitude, maximum noise decibel value deviation, and minimum noise decibel value deviation. \(\sigma_1\), \(\sigma_2\), \(\sigma_3\), \(\sigma_4\) respectively represent the proportion weights of the set maximum displacement change value, vibration amplitude, maximum noise decibel value deviation, and minimum noise decibel value deviation corresponding to the moving coefficient.

[0044] According to the formula Calculate the dynamic performance evaluation index \(DX\) corresponding to the three types of motor output shaft samples. \(v_1\), \(v_2\) respectively represent the proportion weights of the moving coefficient of the set radial moving test sample and the moving coefficient of the axial test sample corresponding to the dynamic performance evaluation index.

[0045] The put-into-use determination module is used to determine whether the motor output shaft samples after the current batch of precision forging process can be put into industrial equipment for use and give corresponding feedback.

[0046] Preferably, to determine whether the motor output shaft samples after the current batch of precision forging process can be put into industrial equipment for use, the specific determination method is as follows:

[0047] According to the formula Calculate the comprehensive performance inspection index \(KC\) of the motor output shaft samples of the current batch. \(u_1\), \(u_2\), \(u_3\) respectively represent the proportion weights of the quality performance evaluation index of the set first type of motor output shaft samples, the mechanical performance evaluation index of the second type of motor output shaft samples, and the dynamic performance evaluation index of the third type of motor output shaft samples corresponding to the comprehensive performance inspection index.

[0048] Compare the comprehensive performance inspection index of the motor output shaft samples in the current batch with the set threshold of the comprehensive performance inspection index. When the comprehensive performance inspection index of the motor output shaft samples in the current batch is greater than or equal to the preset threshold of the comprehensive performance inspection index, it is determined that the motor output shaft samples in the current batch can be put into industrial equipment for use. When the comprehensive performance inspection index of the motor output shaft samples in the current batch is less than the preset threshold of the comprehensive performance inspection index, it is determined that the motor output shaft samples in the current batch cannot be put into industrial equipment for use.

[0049] The second aspect of the present invention provides a detection method for a motor output shaft, including the following steps:

[0050] S1: Sample acquisition and classification: Extract several motor output shaft samples from the motor output shaft samples formed by the current batch of precision forging process, and divide them into various types of first-class motor output shaft samples, various types of second-class motor output shaft samples, and various types of third-class motor output shaft samples according to an equal proportion relationship;

[0051] S2: Quality performance detection: Detect the quality performance corresponding to each first-class motor output shaft sample, obtain the quality performance parameters corresponding to each first-class motor output shaft sample, and analyze the quality performance evaluation index corresponding to the first-class motor output shaft sample from this to obtain the quality performance evaluation index corresponding to the first-class motor output shaft sample;

[0052] S3: Mechanical performance detection: Detect the mechanical performance corresponding to each second-class motor output shaft sample, obtain the mechanical performance parameters corresponding to each second-class motor output shaft sample, and analyze the mechanical performance evaluation index corresponding to the second-class motor output shaft sample from this to obtain the mechanical performance evaluation index corresponding to the second-class motor output shaft sample;

[0053] S4: Dynamic performance detection: Detect the dynamic performance corresponding to each third-class motor output shaft sample, obtain the dynamic performance parameters corresponding to each third-class motor output shaft sample, and analyze the dynamic performance evaluation index corresponding to the third-class motor output shaft sample from this to obtain the dynamic performance evaluation index corresponding to the third-class motor output shaft sample;

[0054] S5: Determination of being put into use: Determine whether the motor output shaft samples formed by the current batch of precision forging process can be put into industrial equipment for use and give corresponding feedback.

[0055] The beneficial effects of the present invention:

[0056] The present invention detects and analyzes the production dimension accuracy, surface roughness uniformity, and defect degree of each type-I motor output shaft sample, and then comprehensively analyzes to obtain the quality performance evaluation index corresponding to the type-I motor output shaft sample. This multi-dimensional evaluation method can more comprehensively and scientifically reflect the overall quality status of the motor output shaft compared with single-index evaluation, providing a sufficient basis for product quality determination.

[0057] The present invention combines the torque stability coefficient of the torque test sample and the fatigue strength coefficient of the fatigue experiment test sample, comprehensively analyzes the mechanical performance evaluation index corresponding to the type-II motor output shaft sample, simulates the extreme conditions that the motor output shaft sample may face during actual use, and comprehensively examines the performance changes of the motor output shaft sample under different conditions, thus making the evaluation more realistic and reliable.

[0058] The present invention applies periodic simulated external forces to each radial runout test sample and each axial runout test sample respectively, simulates the radial and axial horizontal interference forces received by the motor output shaft during actual operation, and thus analyzes the dynamic performance evaluation index of the type-III motor output shaft sample, comprehensively examines the dynamic performance of the motor output shaft sample. The evaluation results have direct guiding significance for the design optimization and production process improvement of the motor output shaft, helping to improve the overall quality and performance of the motor output shaft and meet the strict requirements of industrial equipment for the dynamic performance of the motor output shaft.

[0059] The present invention forms a comprehensive performance inspection index for the current batch of motor output shaft samples from three aspects: quality performance, mechanical performance, and dynamic performance of the current batch of motor output shaft samples. Based on this, it can more accurately judge whether the current batch of motor output shaft samples can be put into industrial equipment assembly, provide timely online feedback, provide decision-making suggestions and data support for the staff, make the motor output shaft detection process more flexible and efficient, and be able to continuously adapt to market demands and changing detection environments, helping to improve the overall performance of the motor output shaft. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The following further describes the present invention with reference to the drawings.

[0061] Figure 1 It is a schematic diagram of the system module connection of the present invention.

[0062] Figure 2 It is a schematic diagram of the method implementation steps of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0064] Embodiment 1

[0065] Please refer to Figure 1 As shown, the present invention is a detection system for a motor output shaft, including: a sample acquisition and classification module, a quality performance detection module, a mechanical performance detection module, a dynamic performance detection module, a put-into-use determination module, and a memory.

[0066] The memory is connected to the quality performance detection module and the mechanical performance detection module. The sample acquisition and classification module is respectively connected to the quality performance detection module, the mechanical performance detection module, and the dynamic performance detection module. The quality performance detection module, the mechanical performance detection module, and the dynamic performance detection module are respectively connected to the put-into-use determination module.

[0067] The sample acquisition and classification module is used to extract a number of motor output shaft samples from the motor output shaft samples formed by the current batch of precision forging processes, and divide them into first-class motor output shaft samples, second-class motor output shaft samples, and third-class motor output shaft samples according to an equal proportion relationship.

[0068] The quality performance detection module is used to detect the quality performance corresponding to the first-class motor output shaft samples, and obtain the quality performance parameters corresponding to the first-class motor output shaft samples. The specific detection method is:

[0069] Scan the first-class motor output shaft samples through a laser three-dimensional scanner to obtain the contour detection models of the first-class motor output shaft samples. Based on the edge detection algorithm, identify the contour shape feature detection values on the contour detection models of the first-class motor output shaft samples, and thus obtain the contour shape feature detection values on the contour detection models of the first-class motor output shaft samples.

[0070] It should be noted that the contour shape features include but are not limited to: journal, shaft shoulder, keyway contour shape. The contour shape feature values include but are not limited to journal diameter, shaft shoulder diameter, keyway width, keyway depth, surface cylindricity, and chamfer between the output shaft end and the keyway end.

[0071] Uniformly arrange detection points on the first-class motor output shaft samples to obtain the detection points of the first-class motor output shaft samples. Detect the surface roughness of the detection points of the first-class motor output shaft samples through a roughness measuring instrument to obtain the surface roughness of the detection points of the first-class motor output shaft samples.

[0072] Collect the surface images of the output shafts of each type of motor through an industrial camera to obtain the surface images of the output shafts of each type of motor. Based on image recognition technology, extract the crack areas and scratch areas of the output shafts of each type of motor. Then, statistically obtain the number of crack areas and the number of scratch areas of the output shafts of each type of motor sample, and extract the length and width of each crack area, and the depth and length of each scratch area. Then, perform cumulative calculations to obtain the total length, total width of the crack areas, total depth of the scratch areas, and total length of the scratch areas of the output shafts of each type of motor sample, which are used as the crack length, crack width, scratch depth, and scratch length of the output shafts of each type of motor sample respectively;

[0073] And analyze the quality performance evaluation index corresponding to the output shaft sample of the first type of motor to obtain the quality performance evaluation index corresponding to the output shaft sample of the first type of motor. The specific analysis method is as follows:

[0074] Extract the detection values of each contour shape feature on the contour detection model of each output shaft sample of the first type of motor from the quality performance parameters of each output shaft sample of the first type of motor, denoted as At the same time, obtain the standard contour model of the motor output shaft from the memory, and extract the detection values of each standard contour shape feature from it, denoted as XZ′ j , where i represents the number of each output shaft sample of the first type of motor, i = 1, 2,...., a, a represents the total number of output shaft sample numbers of the first type of motor, j represents the number of each contour shape feature, j = 1, 2,...., b, and b represents the total number of contour shape feature numbers;

[0075] According to the formula Calculate the production size accuracy JQ of each output shaft sample of the first type of motor i . Example 1: The total number of motor output shaft samples a = 3, the total number of contour shape features b = 2, the standard contour feature value XZ′1 = 10mm, XZ2′ = 8mm;

[0076] Motor output shaft sample 1 (i = 1): Motor output shaft sample 2 (i = 2): Motor output shaft sample 3 (i = 3): Then the calculation of the production size accuracy is specifically as follows:

[0077] The production size accuracy of sample 1: The production size accuracy of sample 2: The production size accuracy of sample 3: The closer the production size accuracy is to 1, the smaller the deviation between the detection value of the contour shape feature and the detection value of the standard contour shape feature, and the higher the production size accuracy.

[0078] Extract the surface roughness of each detection point of the output shaft samples of each type I motor, and screen out the maximum surface roughness, minimum surface roughness, median surface roughness, and mode surface roughness from them, which are respectively denoted as According to the formula

[0079]

[0080] Calculate the surface roughness uniformity JY of each output shaft sample of type I motor i , CX0 represents the set reference surface roughness, and α1, α2, α3, α4 respectively represent the proportion weights of the surface roughness uniformity corresponding to the set maximum surface roughness, minimum surface roughness, median surface roughness, and mode surface roughness;

[0081] Example 2:

[0082] Set the reference surface roughness CX0 = 3μm, and the weights α1 = α2 = α3 = α4 = 0.25;

[0083] The number of motor output shaft samples i = 3, and the surface roughness data of each sample detection point are as follows:

[0084]

[0085] Then the surface roughness uniformity JY1 of samples 1, 2, and 3 is:

[0086]

[0087]

[0088] The closer the surface roughness uniformity i is to 1, the higher the surface roughness uniformity; furthermore, quantitatively evaluate the surface machining quality of the motor output shaft, assist in optimizing the production process, screen high-quality products, and improve the consistency of surface machining.

[0089] Extract the number of crack areas and the number of scratch areas of each output shaft sample of type I motor, which are respectively denoted as LN i , HN i , and at the same time extract the crack length, crack width, scratch depth, and scratch length of each output shaft sample of type I motor, which are respectively denoted as LL i , LD i , HH i , HL i ;

[0090] According to the formula Calculate the defect degree QX of each output shaft sample of type I motor i, β1, β2, β3, β4, β5, and β6 respectively represent the proportion weights corresponding to the defect degrees of the set number of crack regions, number of scratch regions, crack length, crack width, scratch depth, and scratch length;

[0091] Example 3:

[0092] The proportion weights corresponding to the defect degrees of the set number of crack regions, number of scratch regions, crack length, crack width, scratch depth, and scratch length are respectively: β1 = 0.2, β2 = 0.2, β3 = 0.15, β4 = 0.15

[0093] , β5 = 0.15, β6 = 0.15.

[0094] The sample data of the motor output shaft is as follows:

[0095]

[0096] Then the defect degrees of Samples 1, 2, and 3 are specifically as follows:

[0097]

[0098] According to the formula Calculate the quality performance evaluation index ZX corresponding to a type of motor output shaft sample. w1, w2, and w3 respectively represent the proportion weights corresponding to the quality performance evaluation index of the set production size accuracy, surface roughness uniformity, and defect degree.

[0099] Example 4:

[0100] The proportion weights corresponding to the quality performance evaluation index of the set production size accuracy, surface roughness uniformity, and defect degree are:

[0101] w1 = 0.4, w2 = 0.3, w3 = 0.3; Substitute the production size accuracy, surface roughness uniformity, and defect degree of Examples 1 - 3 into the formula for calculation:

[0102]

[0103] In a specific embodiment, the present invention detects and analyzes the production size accuracy, surface roughness uniformity, and defect degree corresponding to each type of motor output shaft sample, and then comprehensively analyzes to obtain the quality performance evaluation index corresponding to the type of motor output shaft sample. This multi-dimensional evaluation method can more comprehensively and scientifically reflect the overall quality status of the motor output shaft compared with the single-index evaluation, providing a sufficient basis for product quality determination.

[0104] The mechanical property detection module is used to detect the mechanical properties corresponding to each type of motor output shaft sample to obtain the mechanical property parameters corresponding to each type of motor output shaft sample. The specific detection method is:

[0105] Divide the output shaft samples of each type-II motor into torque test samples and fatigue test samples respectively. Set the torque test conditions for each working condition gradient, and number the torque test conditions in ascending order of the working conditions. Make the numbering order of the torque test samples for each working condition correspond one by one to the numbering of the torque test conditions for each working condition gradient. Then, conduct torque tests on each torque test sample under its corresponding torque test conditions for the working condition gradient. At the same time, use a torque sensor to detect the torque magnitude of each torque test sample at each detection time point within the set detection period, and obtain the torque magnitude of each torque test sample at each detection time point within the set detection period.

[0106] It should be noted that the working condition specifically refers to the rotational speed condition, and the working condition gradient means taking the set primary rotational speed as the first rotational speed gradient, and subsequent working condition gradients will increase gradually according to the rotational speed increment. In a specific embodiment, set 1500 rpm as the first working condition gradient, set the rotational speed increment as 500 rpm, then the second working condition gradient is 2000 rpm, and the third working condition gradient is 2500 rpm, and so on. The number of working condition gradients is the same as the number of torque test samples.

[0107] Set the fatigue test conditions for each stress level gradient, and number the fatigue test conditions in ascending order of the stress level. Make the numbering order of the fatigue test samples correspond one by one to the numbering of the fatigue test conditions for each stress level gradient. Then, conduct fatigue tests on the numbered fatigue test samples under their corresponding fatigue test conditions for the stress level gradient. When obvious cracks appear in each fatigue test sample, stop the test and take it as the fatigue failure criterion. At the same time, use a counter to detect the number of cycles of each fatigue test sample reaching the fatigue failure criterion, and obtain the number of cycles of each fatigue test sample, which is used as the fatigue life of each fatigue test sample.

[0108] It should be noted that the stress level gradient means taking the set primary stress level as the first stress level gradient, and subsequent stress level gradients will increase gradually according to the stress level increment. In a specific embodiment, set 100 MPa as the first stress level gradient, set the stress level increment as 50 MPa, then the second stress level gradient is 150 MPa, and the third working condition gradient is 200 MPa, and so on. The number of stress level gradients is the same as the number of fatigue test samples.

[0109] And then analyze the mechanical property evaluation index corresponding to the output shaft sample of the type-II motor to obtain the mechanical property evaluation index corresponding to the output shaft sample of the type-II motor. The specific analysis method is as follows:

[0110] Taking each detection time point as the abscissa and the torque magnitude as the ordinate, plot the torque change curves of each torque test sample within the set detection period. Meanwhile, extract the reference torque change curves of each working condition gradient within the set detection period from the memory; compare the torque change curves of each torque test sample within the set detection period with the reference torque change curves of each working condition gradient within the set detection period to obtain the overlapping length of the torque change curves of each torque test sample within the set detection period, denoted as L x , where x represents the number of each torque test sample, x = 1, 2,...., p, and p represents the total number of torque test sample numbers;

[0111] Obtain the numerical value of the length of the reference torque change curve of each working condition gradient within the set detection period, denoted as L0;

[0112] Extract the maximum torque and minimum torque from the torque change curves of each torque test sample within the set detection period, denoted as and extract the reference maximum torque and reference minimum torque from the reference torque change curves of each working condition gradient within the set detection period, denoted as According to the formula calculate the torque stability coefficient TW corresponding to the torque test sample. γ1, γ2, and γ3 respectively represent the proportion weights of the set torque change curve overlapping length, maximum torque, and minimum torque corresponding to the torque stability coefficient;

[0113] Example 5:

[0114] Set the total number of torque test samples p = 3, weights γ1 = 0.5, γ2 = 0.3, γ3 = 0.2, reference curve length L0 = 100, reference maximum torque and reference minimum torque are respectively:

[0115]

[0116] The data of the torque test sample is as follows:

[0117] Sample number Curve coincidence length Maximum torque of the sample Minimum torque of the sample 1 80 49 31 2 70 51 29 3 85 48 32

[0118] Then the calculation process of the torque stability coefficient corresponding to the torque test sample is:

[0119]

[0120] Extract the fatigue life of each fatigue experiment test sample, and at the same time extract the reference fatigue life at each stress level from the memory; compare the fatigue life of each fatigue experiment test sample with the reference fatigue life at each stress level. When the fatigue life of a certain fatigue experiment test sample is greater than or equal to the reference fatigue life at a certain stress level, mark this fatigue experiment test sample as a sample with qualified fatigue strength. When the fatigue life of a certain fatigue experiment test sample is less than the reference fatigue life at a certain stress level, mark this fatigue experiment test sample as a sample with unqualified fatigue strength. Thus, count the number of samples with qualified fatigue strength and the number of samples with unqualified fatigue strength, which are denoted as PD and PB respectively.

[0121] According to the formula Calculate the fatigue strength coefficient PX corresponding to the fatigue experiment test sample. ε1 and ε2 respectively represent the proportion weights of the fatigue strength coefficients corresponding to the set number of samples with qualified fatigue strength and the set number of samples with unqualified fatigue strength. The determination of the proportion weights can be through: Empirical judgment method: Experts / engineers subjectively set according to industry experience and product requirements. For example, if emphasis is placed on passing the standard, set ε1 high. Data statistics method: Use historical data to analyze the impact of passing / failing the standard on product performance and statistically determine the weights. Analytic Hierarchy Process (AHP): Construct a hierarchical structure, compare the importance of factors, and calculate the weights, which is more systematic. Goal-oriented method: According to the enterprise's quality goal, such as strictly controlling non-compliance and increasing the weight of ε2 to balance the influence of both.

[0122] Example 6:

[0123] Collect 25 sample data, and set the proportion weights of the fatigue strength coefficients corresponding to the set number of samples with qualified fatigue strength and the set number of samples with unqualified fatigue strength as: ε1 = 0.6, ε2 = 0.4.

[0124] Then conduct fatigue experiment tests on the 25 samples. The test results are: the number of samples with qualified fatigue strength is 18, and the number of samples with unqualified fatigue strength is 7.

[0125] Then the calculation process of the fatigue strength coefficient corresponding to the fatigue experiment test sample is:

[0126]

[0127] According to the formula JX = TW×z1 + PX×z2, calculate the mechanical property evaluation index JX corresponding to the output shaft sample of the second-class motor. z1 and z2 respectively represent the proportion weights of the mechanical property evaluation index corresponding to the set torque stability coefficient and the fatigue strength coefficient.

[0128] Example 7:

[0129] Set the proportion weights of the torque stability coefficient and fatigue strength coefficient corresponding to the mechanical property evaluation index to be z1 = 0.5 and z2 = 0.5 respectively; calculate the mechanical property evaluation index for the data in Example 5 and Example 6: JX = 1.044×0.5 + 1.8606×0.5 = 0.522 + 0.9303 = 1.4523.

[0130] In a specific embodiment, the present invention combines the torque stability coefficient of the torque test sample and the fatigue strength coefficient of the fatigue experiment test sample, comprehensively analyzes the mechanical property evaluation index corresponding to the output shaft sample of the second-class motor, simulates the extreme conditions that the output shaft sample of the motor may face during actual use, and comprehensively examines the performance changes of the output shaft sample of the motor under different conditions, thereby making the evaluation more real and reliable.

[0131] The dynamic performance detection module detects the dynamic performance corresponding to each output shaft sample of the third-class motor, and obtains the dynamic performance parameters corresponding to each output shaft sample of the third-class motor. The specific detection method is as follows:

[0132] Divide each output shaft sample of the third-class motor into each radial runout test sample and each axial runout test sample;

[0133] Place and fix each radial runout test sample on the detection platform, apply a periodic simulated external force in the radial horizontal direction of the sample in a set manner to simulate the radial horizontal interference force received by the output shaft of the motor during actual operation. During the application of the external force, use a high-precision displacement sensor to detect the displacement change values of each radial runout test sample at each detection time point in the radial horizontal direction, obtain the displacement change values of each radial runout test sample at each detection time point in the radial horizontal direction, and at the same time use a vibration sensor to detect the vibration amplitude of each radial runout test sample to obtain the vibration amplitude of each radial runout test sample, and use a sound sensor to detect the noise decibel values of each radial runout test sample at each detection time point to obtain the noise decibel values of each radial runout test sample at each detection time point;

[0134] Place and fix each axial-clearance test sample on the detection platform, and apply a periodic simulated external force in the axial horizontal direction of the sample in a set manner to simulate the axial horizontal interference force received by the motor output shaft during actual operation. During the application of the external force, use a high-precision displacement sensor to detect the displacement change values of each axial-clearance test sample at each detection time point in the axial horizontal direction, and obtain the displacement change values of each axial-clearance test sample at each detection time point in the axial horizontal direction. At the same time, use a vibration sensor to detect the vibration amplitude of each axial-clearance test sample to obtain the vibration amplitude of each axial-clearance test sample, and use a sound sensor to detect the noise decibel values of each axial-clearance test sample at each detection time point to obtain the noise decibel values of each axial-clearance test sample at each detection time point;

[0135] And then analyze the dynamic performance evaluation indexes corresponding to the three types of motor output shaft samples to obtain the dynamic performance evaluation indexes corresponding to the three types of motor output shaft samples. The specific analysis method is as follows:

[0136] Extract the maximum displacement change value from the displacement change values of each radial-clearance test sample at each detection time point in the radial horizontal direction, denoted as q represents the number of each radial-clearance test sample, q = 1, 2,...., m, where m represents the total number of radial-clearance test sample numbers. Extract the vibration amplitude of each radial-clearance test sample, denoted as ZF q ;

[0137] Subtract the noise decibel value of each radial-clearance test sample at each detection time point in the radial horizontal direction from the set permitted noise decibel value to obtain the noise decibel value deviation of each radial-clearance test sample at each detection time point in the radial horizontal direction, and extract the maximum noise decibel value deviation and the minimum noise decibel value deviation from it, and denote them as According to the formula

[0138] Calculate the runout coefficient CD1 of the radial-clearance test sample, ZF′, represent the set reference maximum radial displacement change value, radial vibration amplitude, maximum noise decibel value deviation, and minimum noise decibel value deviation. η1, η2, η3, η4 respectively represent the proportion weights of the runout coefficients corresponding to the set maximum radial displacement change value, vibration amplitude, maximum noise decibel value deviation, and minimum noise decibel value deviation;

[0139] Example 8:

[0140] Set the number of samples to 3. The reference radial maximum displacement change value, radial vibration amplitude, maximum noise decibel value deviation, and minimum noise decibel value deviation are 0.1 mm, 14 μm, 7 dB, and -4 dB respectively. The proportion weights η1, η2, η3, and η4 are 0.3, 0.2, 0.3, and 0.2 respectively;

[0141] The basic data of the samples are as follows:

[0142]

[0143]

[0144] Then the crosstalk coefficient of the radial crosstalk test sample is specifically:

[0145] Sum of sub-items of sample 1: Sum of sub-items of sample 2: Sum of sub-items of sample 3: Then Extract the maximum displacement change value from the displacement change values of each detection time point of each axial crosstalk test sample in the axial horizontal direction, and denote it as r represents the number of each axial crosstalk test sample, r = 1, 2,...., n, n represents the total number of axial crosstalk test sample numbers, and extract the vibration amplitude of each axial crosstalk test sample, denoted as ZF r ;

[0146] Subtract the noise decibel value of each detection time point of each axial crosstalk test sample in the axial horizontal direction from the set permitted noise decibel value to obtain the noise decibel value deviation of each detection time point of each axial crosstalk test sample in the axial horizontal direction, and extract the maximum noise decibel value deviation and the minimum noise decibel value deviation from it, and denote them as According to the formula:

[0147] Calculate the crosstalk coefficient CD2 of the axial crosstalk test sample, ZF′, represent the set reference radial maximum displacement change value, radial vibration amplitude, maximum noise decibel value deviation, and minimum noise decibel value deviation. σ1, σ2, σ3, and σ4 respectively represent the proportion weights of the set maximum displacement change value, vibration amplitude, maximum noise decibel value deviation, and minimum noise decibel value deviation corresponding to the crosstalk coefficient;

[0148] Example 9:

[0149] The data of the axial crosstalk test sample are specifically as follows:

[0150]

[0151] Set the reference radial maximum displacement change value, radial vibration amplitude, maximum noise decibel value deviation, and minimum noise decibel value deviation to 0.12 mm, 15 μm, 8 dB, and -5 dB respectively; the proportion weights σ1, σ2, σ3, and σ4 are 0.3, 0.2, 0.3, and 0.2 respectively.

[0152] Sum of sub-items of Sample 1:

[0153] Sum of sub-items of Sample 2:

[0154] Sum of sub-items of Sample 3:

[0155] Then

[0156] According to the formula Calculate the dynamic performance evaluation index DX corresponding to the three types of motor output shaft samples. v1 and v2 respectively represent the proportion weights of the dynamic performance evaluation index corresponding to the runout coefficient of the set radial runout test sample and the runout coefficient of the axial test sample.

[0157] Example 10:

[0158] Set the proportion weights of the dynamic performance evaluation index corresponding to the runout coefficient of the radial runout test sample and the runout coefficient of the axial test sample to: v1 = 0.4, v2 = 0.6; based on the data in Example 8 and Example 9, the calculation process of the dynamic performance evaluation index corresponding to the three types of motor output shaft samples is as follows:

[0159]

[0160] In a specific embodiment, the present invention analyzes the dynamic performance evaluation index of the three types of motor output shaft samples by applying periodic simulated external forces to each radial runout test sample and each axial runout test sample respectively, simulating the radial and axial horizontal interference forces received during the actual operation of the motor output shaft, so as to comprehensively investigate the dynamic performance of the motor output shaft samples. The evaluation results have direct guiding significance for the design optimization and production process improvement of the motor output shaft, which helps to improve the overall quality and performance of the motor output shaft and meet the strict requirements of industrial equipment for the dynamic performance of the motor output shaft.

[0161] The put-into-use determination module is used to determine whether the motor output shaft samples formed by the current batch of precision forging process can be put into industrial equipment for use and give corresponding feedback. The specific determination method is as follows:

[0162] According to the formula Calculate the comprehensive performance inspection index KC of the motor output shaft samples in the current batch. u1, u2, and u3 respectively represent the proportion weights of the comprehensive performance inspection index corresponding to the quality performance evaluation index of the first-class motor output shaft samples, the mechanical performance evaluation index of the second-class motor output shaft samples, and the dynamic performance evaluation index of the third-class motor output shaft samples.

[0163] Example 11: The quality performance evaluation index corresponding to the first-class motor output shaft samples, the mechanical performance evaluation index corresponding to the second-class motor output shaft samples, and the dynamic performance evaluation index of the third-class motor output shaft samples based on Examples 4, 7, and 10;

[0164] Set the proportion weights u1 = 0.4, u2 = 0.3, u3 = 0.3;

[0165] Then

[0166] Compare the comprehensive performance inspection index KC of the motor output shaft samples in the current batch with the set comprehensive performance inspection index threshold. When the comprehensive performance inspection index of the motor output shaft samples in the current batch is greater than or equal to the preset comprehensive performance inspection index threshold, it is determined that the motor output shaft samples in the current batch can be used in industrial equipment. When the comprehensive performance inspection index of the motor output shaft samples in the current batch is less than the preset comprehensive performance inspection index threshold, it is determined that the motor output shaft samples in the current batch cannot be used in industrial equipment. The preset comprehensive performance inspection index threshold is 100; the comprehensive performance inspection index KC of the motor output shaft samples in the current batch is 112.425. Since 112.425 ≥ 100, which meets the condition of "the comprehensive performance inspection index is greater than or equal to the preset threshold", it is determined that "the motor output shaft samples in the current batch can be used in industrial equipment".

[0167] It should be noted that the specific process of the above feedback work is as follows: When it is determined that the motor output shaft samples in the current batch can be used in industrial equipment, the staff is prompted via text message that the motor output shaft samples in the current batch can be used for industrial equipment assembly. When it is determined that the motor output shaft samples in the current batch cannot be used in industrial equipment, the staff is prompted via text message that there are problems with the production conditions of the motor output shaft samples in the current batch and the production conditions need to be changed in time for re-inspection.

[0168] In a specific embodiment, the present invention forms a comprehensive comprehensive performance inspection index for the current batch of motor output shaft samples from three perspectives: the quality performance, mechanical performance, and dynamic performance of the current batch of motor output shaft samples, thereby more accurately judging whether the current batch of motor output shaft samples can be put into industrial equipment assembly, providing timely online feedback, and providing decision-making suggestions and data support for the staff, making the motor output shaft detection process more flexible and efficient, and being able to continuously adapt to market demands and changing detection environments, which helps to improve the overall performance of the motor output shaft.

[0169] A memory for storing a standard profile model of the motor output shaft, a reference torque change curve for each working condition gradient within a set detection period, and a reference fatigue life under each stress level.

[0170] Embodiment 2

[0171] Please refer to Figure 2 As shown, the present invention is a detection method for a motor output shaft, including the following steps:

[0172] S1: Sample acquisition and classification: Extract several motor output shaft samples from the current batch of motor output shaft samples formed by the precision forging process, and divide them into various first-class motor output shaft samples, various second-class motor output shaft samples, and various third-class motor output shaft samples according to an equal proportion relationship;

[0173] S2: Quality performance detection: Detect the quality performance corresponding to each first-class motor output shaft sample, obtain the quality performance parameters corresponding to each first-class motor output shaft sample, and analyze the quality performance evaluation index corresponding to the first-class motor output shaft sample to obtain the quality performance evaluation index corresponding to the first-class motor output shaft sample;

[0174] S3: Mechanical performance detection: Detect the mechanical performance corresponding to each second-class motor output shaft sample, obtain the mechanical performance parameters corresponding to each second-class motor output shaft sample, and analyze the mechanical performance evaluation index corresponding to the second-class motor output shaft sample to obtain the mechanical performance evaluation index corresponding to the second-class motor output shaft sample;

[0175] S4: Dynamic performance detection: Detect the dynamic performance corresponding to each third-class motor output shaft sample, obtain the dynamic performance parameters corresponding to each third-class motor output shaft sample, and analyze the dynamic performance evaluation index corresponding to the third-class motor output shaft sample to obtain the dynamic performance evaluation index corresponding to the third-class motor output shaft sample;

[0176] S5: Judgment for putting into use: Judge whether the current batch of motor output shaft samples formed by the precision forging process can be put into industrial equipment use and give corresponding feedback.

[0177] The above content is only an example and illustration of the structure of the present invention. Those skilled in the art of this technology can make various modifications, supplements, or use similar methods of substitution to the described specific embodiments, as long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should all fall within the protection scope of the present invention.

Claims

1. A detection system for a motor output shaft, characterized in that, Including: A sample acquisition and classification module, which is used to extract several motor output shaft samples from the motor output shaft samples after the current batch of precision forging process, and divide them into various first-class motor output shaft samples, various second-class motor output shaft samples and various third-class motor output shaft samples according to the equal proportion relationship; A quality performance detection module, which is used to detect the quality performance corresponding to each first-class motor output shaft sample, obtain the quality performance parameters corresponding to each first-class motor output shaft sample, and analyze the quality performance evaluation index corresponding to the first-class motor output shaft sample from this, so as to obtain the quality performance evaluation index corresponding to the first-class motor output shaft sample; A mechanical performance detection module, which is used to detect the mechanical performance corresponding to each second-class motor output shaft sample, obtain the mechanical performance parameters corresponding to each second-class motor output shaft sample, and analyze the mechanical performance evaluation index corresponding to the second-class motor output shaft sample from this, so as to obtain the mechanical performance evaluation index corresponding to the second-class motor output shaft sample; A dynamic performance detection module, which is used to detect the dynamic performance corresponding to each third-class motor output shaft sample, obtain the dynamic performance parameters corresponding to each third-class motor output shaft sample, and analyze the dynamic performance evaluation index corresponding to the third-class motor output shaft sample from this, so as to obtain the dynamic performance evaluation index corresponding to the third-class motor output shaft sample; An in-service determination module, which is used to determine whether the motor output shaft samples after the current batch of precision forging process can be put into industrial equipment for use and give corresponding feedback.

2. The detection system for a motor output shaft according to claim 1, wherein, As described above, to detect the quality performance corresponding to each first-class motor output shaft sample and obtain the quality performance parameters corresponding to each first-class motor output shaft sample, the specific detection method is: Scan each first-class motor output shaft sample through a laser three-dimensional scanner to obtain the contour detection model of each first-class motor output shaft sample, and identify each contour shape feature detection value on the contour detection model of each first-class motor output shaft sample based on the edge detection algorithm, so as to obtain each contour shape feature detection value on the contour detection model of each first-class motor output shaft sample; Uniformly arrange detection points for each first-class motor output shaft sample to obtain each detection point of each first-class motor output shaft sample, and detect the surface roughness of each detection point of each first-class motor output shaft sample through a roughness measuring instrument to obtain the surface roughness of each detection point of each first-class motor output shaft sample; Collect the surface images of each first-class motor output shaft sample through an industrial camera to obtain the surface images of each first-class motor output shaft, extract the crack area and scratch area of each first-class motor output shaft sample based on image recognition technology, and then statistically obtain the number of crack areas and scratch areas of each first-class motor output shaft sample, and extract the length and width of each crack area, the depth and length of each scratch area, and then perform cumulative calculation to obtain the total length, total width, total depth and total length of the scratch area of the crack area of each first-class motor output shaft sample, which are respectively used as the crack length, crack width, scratch depth and scratch length of each first-class motor output shaft sample.

3. The detection system for a motor output shaft according to claim 1, characterized in that, Analyze the quality performance evaluation index corresponding to a class of motor output shaft samples to obtain the quality performance evaluation index corresponding to a class of motor output shaft samples. The specific analysis method is as follows: Extract the detection values of each profile shape feature on the profile detection model of the output shaft samples of each type of motor from the quality performance parameters of the output shaft samples of each type of motor, denoted as At the same time, obtain the standard profile model of the motor output shaft from the memory, and extract the standard feature detection values of each profile shape from it, denoted as XZ j ′, i represents the number of the output shaft samples of each type of motor, i = 1, 2,...., a, a represents the total number of the output shaft sample numbers of one type of motor, j represents the number of each profile shape feature, j = 1, 2,...., b, b represents the total number of the profile shape feature numbers; According to the formula calculate the production size accuracy JQ of each sample of the output shaft of the first-class motor i , where e represents the natural constant; Extract the surface roughness of each detection point of the output shaft samples of each type of motor, and screen out the maximum surface roughness, minimum surface roughness, median surface roughness, and mode surface roughness from them, which are respectively denoted as According to the formula Calculate the surface roughness uniformity JY of each sample of the output shaft of the first-class motor i , CX0 represents the set reference surface roughness, and α1, α2, α3, and α4 respectively represent the proportion weights of the corresponding surface roughness uniformity of the set maximum surface roughness, minimum surface roughness, median surface roughness, and mode surface roughness; Extract the number of crack areas and the number of scratch areas of the output shaft samples of each type of motor, denoted as LN i , HN i . At the same time, extract the crack length, crack width, scratch depth, and scratch length of the output shaft samples of each type of motor, denoted as LL i , LD i , HH i , HL i . Through comprehensive analysis, obtain the defect degree QX i ; According to the formula Calculate the mass performance evaluation index ZX corresponding to a class of motor output shaft samples. w1, w2, and w3 respectively represent the proportion weights of the mass performance evaluation indexes corresponding to the set production dimension accuracy, surface roughness uniformity, and defect degree.

4. A detection system for a motor output shaft according to claim 1, characterized in that, Detect the mechanical properties of each second-class motor output shaft sample to obtain the mechanical property parameters corresponding to each second-class motor output shaft sample. The specific detection method is as follows: Divide each second-class motor output shaft sample into each torque test sample and each fatigue experiment test sample. Set the torque test conditions for each working condition gradient, and number each torque test condition in ascending order of the working condition. Make the numbering order of each torque test sample correspond one by one to the numbering of each working condition gradient torque test condition. Then, perform torque tests on each torque test sample under its corresponding working condition gradient torque test conditions in turn. At the same time, use a torque sensor to detect the torque magnitude of each torque test sample at each detection time point within the set detection period to obtain the torque magnitude of each torque test sample at each detection time point within the set detection period; Set the fatigue experiment conditions for each stress level gradient, and number each fatigue experiment condition in ascending order of the stress level. Make the numbering order of each fatigue experiment test sample correspond one by one to the numbering of each stress level gradient fatigue experiment condition. Then, perform fatigue experiments on the numbers of each fatigue experiment test sample under its corresponding stress level gradient fatigue experiment conditions in turn. When obvious cracks appear in each fatigue experiment test sample, stop the test and use it as the fatigue failure criterion. At the same time, use a counter to detect the number of cycles of each fatigue experiment test sample reaching the fatigue failure criterion to obtain the number of cycles of each fatigue experiment test sample, which is used as the fatigue life of each fatigue experiment test sample.

5. The detection system for a motor output shaft according to claim 1, wherein Analyze the mechanical property evaluation index corresponding to each second-class motor output shaft sample to obtain the mechanical property evaluation index corresponding to each second-class motor output shaft sample. The specific analysis method is as follows: Taking each detection time point as the abscissa and the torque magnitude as the ordinate, plot the torque change curves of each torque test sample within the set detection period. At the same time, extract the reference torque change curves of each working condition gradient within the set detection period from the memory; Compare the torque change curves of each torque test sample within the set detection period with the reference torque change curves of each working condition gradient within the set detection period to obtain the overlapping length of the torque change curves of each torque test sample within the set detection period, denoted as L x , where x represents the number of each torque test sample, x = 1, 2,...., p, and p represents the total number of torque test sample numbers; Obtain the numerical value of the length of the reference torque change curve of each working condition gradient within the set detection period, denoted as L0; Extract the maximum torque and minimum torque from the torque change curves of each torque test sample within the set detection period, and denote them respectively as And extract the maximum reference torque and minimum reference torque from the reference torque change curves of each working condition gradient within the set detection period, and denote them respectively as According to the formula Calculate the torque stability coefficient TW corresponding to the torque test sample. γ1, γ2, and γ3 respectively represent the proportion weights of the coincidence length of the set torque change curve, the torque stability coefficients corresponding to the maximum torque and the minimum torque Extract the fatigue life of each fatigue experiment test sample, and at the same time extract the reference fatigue life under each stress level from the memory; Compare the fatigue life of each fatigue experiment test sample with the reference fatigue life under each stress level. When the fatigue life of a certain fatigue experiment test sample is greater than or equal to the reference fatigue life under a certain stress level, mark this fatigue experiment test sample as a fatigue strength qualified sample. When the fatigue life of a certain fatigue experiment test sample is less than the reference fatigue life under a certain stress level, mark this fatigue experiment test sample as a fatigue strength unqualified sample. Thus, count the number of fatigue strength qualified samples and the number of fatigue strength unqualified samples, denoted as PD and PB respectively; According to the formula calculate the fatigue strength coefficient PX corresponding to the fatigue test sample. ε1 and ε2 respectively represent the proportion weights of the fatigue strength coefficients corresponding to the number of samples with qualified set fatigue strength and the number of samples with unqualified fatigue strength Calculate the mechanical performance evaluation index JX corresponding to the output shaft samples of the second-class motor according to the formula JX = TW × z1 + PX × z2, where z1 and z2 represent the proportion weights of the set torque stability coefficient and fatigue strength coefficient corresponding to the mechanical performance evaluation index, respectively.

6. The detection system for a motor output shaft according to claim 1, wherein, Detect the dynamic performance corresponding to the output shaft samples of each third-class motor to obtain the dynamic performance parameters corresponding to the output shaft samples of each third-class motor. The specific detection method is as follows: Divide the output shaft samples of each third-class motor into each radial runout test sample and each axial runout test sample evenly; Place and fix each radial runout test sample on the detection platform, apply a periodic simulated external force in the radial horizontal direction of the sample in a set manner to simulate the radial horizontal interference force received by the motor output shaft during actual operation. During the application of the external force, use a high-precision displacement sensor to detect the displacement change values of each radial runout test sample at each detection time point in the radial horizontal direction to obtain the displacement change values of each radial runout test sample at each detection time point in the radial horizontal direction. At the same time, use a vibration sensor to detect the vibration amplitude of each radial runout test sample to obtain the vibration amplitude of each radial runout test sample, and use a sound sensor to detect the noise decibel values of each radial runout test sample at each detection time point to obtain the noise decibel values of each radial runout test sample at each detection time point; Place and fix each axial runout test sample on the detection platform, apply a periodic simulated external force in the axial horizontal direction of the sample in a set manner to simulate the axial horizontal interference force received by the motor output shaft during actual operation. During the application of the external force, use a high-precision displacement sensor to detect the displacement change values of each axial runout test sample at each detection time point in the axial horizontal direction to obtain the displacement change values of each axial runout test sample at each detection time point in the axial horizontal direction. At the same time, use a vibration sensor to detect the vibration amplitude of each axial runout test sample to obtain the vibration amplitude of each axial runout test sample, and use a sound sensor to detect the noise decibel values of each axial runout test sample at each detection time point to obtain the noise decibel values of each axial runout test sample at each detection time point.

7. A detection system for a motor output shaft according to claim 1, characterized in that, Analyze the dynamic performance evaluation index corresponding to the output shaft samples of the third-class motor to obtain the dynamic performance evaluation index corresponding to the output shaft samples of the third-class motor. The specific analysis method is as follows: Extract the maximum displacement change value from the displacement change values of each radial crosstalk test sample at each detection time point in the radial horizontal direction, and denote it as Let q represent the number of each radial crosstalk test sample, q = 1, 2,...., m, where m represents the total number of radial crosstalk test sample numbers. Extract the vibration amplitude of each radial crosstalk test sample and denote it as ZF q ; Subtract the noise decibel values at each detection time point of each radial runout test sample in the radial horizontal direction from the set permissible noise decibel value to obtain the noise decibel value deviation at each detection time point of each radial runout test sample in the radial horizontal direction, and extract the maximum noise decibel value deviation and the minimum noise decibel value deviation therefrom, and denote them respectively as According to the formula Calculate the crosstalk coefficient CD1 of the radial crosstalk test sample, ZF′, which are expressed as the set reference maximum radial displacement change value, radial vibration amplitude, maximum noise decibel value deviation, and minimum noise decibel value deviation. η1, η2, η3, and η4 are respectively expressed as the proportion weights of the crosstalk coefficients corresponding to the set maximum radial displacement change value, vibration amplitude, maximum noise decibel value deviation, and minimum noise decibel value deviation; Similarly analyze to obtain the runout coefficient CD2 of the axial runout test sample; According to the formula Calculate the dynamic performance evaluation index DX corresponding to the output shaft samples of three types of motors. v1 and v2 respectively represent the proportion weights of the dynamic performance evaluation index corresponding to the runout coefficient of the set radial runout test sample and the runout coefficient of the axial test sample.

8. A detection system for a motor output shaft according to claim 1, characterized in that, Determine whether the output shaft samples of the current batch of motors can be put into industrial equipment for use. The specific determination method is as follows: According to the formula Calculate the comprehensive performance evaluation index KC of the motor output shaft samples of the current batch. u1, u2, and u3 respectively represent the proportion weights of the comprehensive performance evaluation index corresponding to the quality performance evaluation index of the first-class motor output shaft samples, the mechanical performance evaluation index of the second-class motor output shaft samples, and the dynamic performance evaluation index of the third-class motor output shaft samples; Compare the comprehensive performance inspection index of the output shaft samples of the current batch of motors with the set comprehensive performance inspection index threshold. When the comprehensive performance inspection index of the output shaft samples of the current batch of motors is greater than or equal to the preset comprehensive performance inspection index threshold, it is determined that the output shaft samples of the current batch of motors can be put into industrial equipment for use. When the comprehensive performance inspection index of the output shaft samples of the current batch of motors is less than the preset comprehensive performance inspection index threshold, it is determined that the output shaft samples of the current batch of motors cannot be put into industrial equipment for use.

9. A method applied to the detection system for the motor output shaft described in any one of the above claims 1-8, characterized in that, It includes the following steps: S1: Sample acquisition and classification: Extract several motor output shaft samples from the motor output shaft samples after precision forging process in the current batch, and divide them into various first-class motor output shaft samples, various second-class motor output shaft samples, and various third-class motor output shaft samples according to the proportional relationship; S2: Quality performance detection: Detect the quality performance corresponding to each first-class motor output shaft sample to obtain the quality performance parameters corresponding to each first-class motor output shaft sample, and analyze the quality performance evaluation index corresponding to the first-class motor output shaft sample from this to obtain the quality performance evaluation index corresponding to the first-class motor output shaft sample; S3: Mechanical performance detection: Detect the mechanical performance corresponding to each second-class motor output shaft sample to obtain the mechanical performance parameters corresponding to each second-class motor output shaft sample, and analyze the mechanical performance evaluation index corresponding to the second-class motor output shaft sample from this to obtain the mechanical performance evaluation index corresponding to the second-class motor output shaft sample; S4: Dynamic performance detection: Detect the dynamic performance corresponding to each third-class motor output shaft sample to obtain the dynamic performance parameters corresponding to each third-class motor output shaft sample, and analyze the dynamic performance evaluation index corresponding to the third-class motor output shaft sample from this to obtain the dynamic performance evaluation index corresponding to the third-class motor output shaft sample; S5: Judgment of being put into use: Judge whether the motor output shaft samples after precision forging process in the current batch can be put into industrial equipment for use and give corresponding feedback.

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