Motor shell finished product detection method and system based on multi-source data analysis

Abstract

The invention discloses a motor shell finished product detection method and system based on multi-source data analysis, and relates to the technical field of quality detection, and the main scheme is as follows: collecting multiple data of a motor shell; respectively calculating a size conformity comprehensive index, an internal structure stability index and a material performance evaluation value, further calculating a finished product comprehensive evaluation index, presetting a threshold value and judging the grade of the motor shell; a shell quality reliability index is calculated, a shell optimization potential index is calculated based on the shell quality reliability index, then a detection optimization effect index is calculated, a threshold value is preset, and the optimization effect of current motor shell finished product detection is judged; the method can comprehensively and accurately evaluate the quality of the finished product, find potential problems in time, optimize the production and detection process, and improve the overall reliability and production efficiency of the motor shell product.

Description

Technical Field

[0001] The present invention relates to the technical field of quality inspection, and in particular to a method and system for inspecting finished motor housings using multi-source data analysis. Background Art

[0002] At a time when the new energy vehicle industry is booming, the performance of the motor, as the core component that drives the vehicle, is directly related to the power, stability and endurance of the vehicle. As new energy vehicles develop towards high performance and long endurance, higher requirements are placed on the quality and performance of the motor housing. However, in the current production and manufacturing process of motor housings, there are many factors that affect their quality, such as fluctuations in material properties and differences in processing technology. At the same time, the quality inspection link of the motor housing faces problems such as insufficient inspection accuracy, low efficiency, and difficulty in comprehensively evaluating the overall performance, which seriously restrict the high-quality development of the new energy vehicle industry.

[0003] The existing motor housing quality inspection technology mainly adopts a single indicator inspection method. For example, in terms of dimensional accuracy inspection, high-precision calipers, micrometers and other traditional measuring tools, or advanced equipment such as three-coordinate measuring machines, are used to measure the external dimensions and wall thickness of the motor housing to determine whether it meets the design standards. For material performance testing, chemical analysis and other methods are used to determine the composition of the motor housing material. These technical means operate independently and inspect the quality of the motor housing from different angles, providing a certain basis for product quality control.

[0004] Although the existing detection technology has played a certain role in the quality control of motor housings, it still has obvious limitations. First, each detection link is independent of each other, lacks the integration and correlation analysis of multi-source data, and cannot fully reflect the comprehensive quality status of the motor housing. Summary of the invention

[0005] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides a motor housing finished product inspection method and system based on multi-source data analysis, which collects multiple data of the motor housing; respectively calculates the comprehensive index of dimensional conformity, the internal structure stability index and the material performance evaluation value, further calculates the comprehensive evaluation index of the finished product, presets the threshold and judges the motor housing grade; calculates the housing quality reliability index, calculates the housing optimization potential index based on the housing quality reliability index, and then calculates the inspection optimization effectiveness index, presets the threshold and judges the optimization effectiveness of the current motor housing finished product inspection, thereby solving the problems of insufficient inspection accuracy and low efficiency.

[0006] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a motor housing finished product detection method based on multi-source data analysis, comprising: Collect geometric dimension data, internal structure data and process parameter data of the motor housing; Calculate the dimension deviation value SDF based on the geometric dimension data, and calculate the dimension conformity comprehensive index CS based on the dimension deviation value SDF; calculate the internal structure characteristic value PM based on the internal structure data, and calculate the internal structure stability index II based on the internal structure characteristic value PM; calculate the material performance evaluation value SQ based on the process parameter data; Calculate the finished product comprehensive evaluation index MSK based on the dimensional conformity comprehensive index CS, the internal structure stability index II and the material performance evaluation value SQ; preset the finished product comprehensive evaluation index threshold, compare the finished product comprehensive evaluation index MSK with the finished product comprehensive evaluation index threshold, and determine the motor housing grade based on the comparison result; if the motor housing is of unqualified grade, isolate and store the product; If the motor housing is of qualified grade or high-quality grade, the housing quality reliability index MRI is calculated based on the finished product comprehensive evaluation index MSK; the housing optimization potential index POI is calculated based on the housing quality reliability index MRI, and then the detection optimization effectiveness index TEO is calculated; a detection optimization effectiveness index threshold is preset, and the detection optimization effectiveness index TEO is compared with the detection optimization effectiveness index threshold, and the optimization effectiveness of the current motor housing finished product detection is judged according to the comparison result; if the optimization effectiveness of the current motor housing finished product detection is insufficient, the detection strategy is changed.

[0007] In the preferred embodiment of the motor housing finished product detection method of the multi-source data analysis, the method for calculating the dimensional conformity comprehensive index CS is: The geometric dimension data include the shell length LK, shell width WK, shell height HK and wall thickness TK; The dimensional deviation SDF is calculated based on the shell length LK, shell width WK, shell height HK and wall thickness TK according to the following formula: ; Among them, LS is the standard length of the shell; WS is the standard width of the shell; HS is the standard height of the shell; TS is the standard wall thickness; ω1 is The weight coefficient is 0.2~0.4; ω2 is The weight coefficient is 0.1~0.3; ω3 is The weight coefficient is 0.3~0.5; ω4 is The weight coefficient is 0.2~0.4; and ω1+ω2+ω3+ω4=1; Based on the dimension deviation value SDF, the dimension conformity comprehensive index CS is calculated. The calculation formula is: .

[0008] In the preferred embodiment of the motor housing finished product detection method of the multi-source data analysis, the method for calculating the internal structure stability index II is: The internal structure data includes the stator core outer diameter DS, the stator core inner diameter DF, the rotor outer diameter FR, the rotor shaft diameter FG, the number of winding turns NS and the winding wire diameter DW; According to the stator core outer diameter DS, the stator core inner diameter DF, the rotor outer diameter FR, the rotor shaft diameter FG, the number of winding turns NS and the winding wire diameter DW, the internal structure characteristic value PM is calculated using the following formula: ; Based on the internal structure characteristic value PM, the internal structure stability index II is calculated according to the formula: ; Among them, RS is the stator winding resistance; RR is the rotor winding resistance.

[0009] In the preferred embodiment of the motor housing finished product detection method of the multi-source data analysis, the method for calculating the material performance evaluation value SQ is: The process parameter data include die casting temperature TM, die casting pressure YL, holding time PI, mold preheating temperature TP and cooling speed LQ; The material performance evaluation value SQ is calculated based on the die casting temperature TM, die casting pressure YL, holding time PI, mold preheating temperature TP and cooling rate LQ. The calculation formula is: ; Among them, TM min is the lower limit of die casting temperature, TM max Is the upper limit of die casting temperature; YL min Is the lower limit of die casting pressure, YL max Is the upper limit of die casting pressure; PI min is the lower limit of the holding time, PI max is the upper limit of the holding time; TP min is the lower limit of mold preheating temperature, TP max Is the upper limit of mold preheating temperature; LQ min is the lower limit of cooling rate, LQ max is the upper limit of cooling rate; ɑ1 is The weight coefficient of The weight coefficient of The weight coefficient of The weight coefficient of The weight coefficient of .

[0010] In the preferred solution of the above-mentioned finished product detection method for the motor housing based on multi-source data analysis: The method for calculating the comprehensive evaluation index MSK of the finished product is as follows: Based on the dimensional compliance comprehensive index CS, the internal structure stability index II, and the material property evaluation value SQ, calculate the comprehensive evaluation index MSK of the finished product. The calculation formula is: ; Among them, φ1 is the weight coefficient of the dimensional compliance comprehensive index CS, and its value ranges from 0.1 to 0.3; φ2 is the weight coefficient of the internal structure stability index II, and its value ranges from 0.3 to 0.4; φ3 is the weight coefficient of the material property evaluation value SQ, and its value ranges from 0.3 to 0.5; and φ1 + φ2 + φ3 = 1.

[0011] In the preferred solution of the above-mentioned finished product detection method for the motor housing based on multi-source data analysis: The method for judging the grade of the motor housing is as follows: The comprehensive evaluation index threshold of the finished product includes the first comprehensive evaluation index threshold MSK1 and the second comprehensive evaluation index threshold MSK2 of the finished product, and MSK1 < MSK2; When MSK ≤ MSK1, it is judged that the motor housing is of high-quality grade and directly flows into the finished product warehouse; When MSK1 < MSK ≤ MSK2, it is judged that the motor housing is of qualified grade and subject to secondary sampling inspection; When MSK > MSK2, it is judged that the motor housing is of unqualified grade, and the product is stored separately.

[0012] In the preferred solution of the above-mentioned finished product detection method for the motor housing based on multi-source data analysis: The method for calculating the housing quality reliability index MRI is as follows: Based on the comprehensive evaluation index MSK of the finished product, calculate the housing quality reliability index MRI. The formula is: ; Among them, EV is the expected number of failures of the motor housing in a specific use environment; CA is the expected maintenance cost of the motor housing during its service life; CB is the manufacturing cost of the motor housing; LR is the expected service life of the motor housing; LB is the designed service life of the motor housing; FR is the failure rate of the motor housing in a specific use environment; TA is the mean time between failures of the motor housing.

[0013] In the preferred solution of the above-mentioned finished product detection method for the motor housing based on multi-source data analysis: The method for calculating the housing optimization potential index POI is as follows: Based on the housing quality reliability index MRI, calculate the housing optimization potential index POI. The formula is: ; Among them, MRImax is the maximum quality reliability index that the motor housing can achieve; C is the optimizable coefficient in the current production process; V is the weight coefficient of production optimization on quality reliability; HS is the time required for production optimization.

[0014] In the preferred embodiment of the motor housing finished product detection method of the multi-source data analysis, the method for judging the optimization effect of the current motor housing finished product detection is: Based on the shell optimization potential index POI, the detection optimization effectiveness index TEO is calculated based on the formula: ; Among them, ETR is the expected detection efficiency improvement rate; SDC is the time cost ratio of technology improvement; TCF is the complexity coefficient of technology improvement; ERI is the equipment reliability index; Preset detection optimization effectiveness index threshold Tth; When TEO≤Tth, it is judged that the optimization effect of the current motor housing finished product inspection is insufficient, and the inspection strategy is changed; When TEO>Tth, it is judged that the optimization effect of the current motor housing finished product inspection is good.

[0015] The present invention also discloses a motor housing finished product detection system for multi-source data analysis, which is used to implement the motor housing finished product detection method for multi-source data analysis, including: A data acquisition module is used to collect geometric dimension data, internal structure data and process parameter data of the motor housing; A data processing module is used to calculate a dimension deviation value SDF based on geometric dimension data, and calculate a dimension conformity comprehensive index CS based on the dimension deviation value SDF; calculate an internal structure characteristic value PM based on internal structure data, and calculate an internal structure stability index II based on the internal structure characteristic value PM; and calculate a material performance evaluation value SQ based on process parameter data; The grade determination module is used to calculate the finished product comprehensive evaluation index MSK according to the dimensional conformity comprehensive index CS, the internal structure stability index II and the material performance evaluation value SQ; preset the finished product comprehensive evaluation index threshold, compare the finished product comprehensive evaluation index MSK with the finished product comprehensive evaluation index threshold, and determine the motor housing grade according to the comparison result; if the motor housing is of unqualified grade, the product will be isolated and stored; Efficiency improvement module, if the motor housing is of qualified grade or high-quality grade, calculate the housing quality reliability index MRI based on the finished product comprehensive evaluation index MSK; calculate the housing optimization potential index POI based on the housing quality reliability index MRI, and then calculate the detection optimization effectiveness index TEO; preset the detection optimization effectiveness index threshold, compare the detection optimization effectiveness index TEO with the detection optimization effectiveness index threshold, and judge the optimization effectiveness of the current motor housing finished product detection based on the comparison result; if the optimization effectiveness of the current motor housing finished product detection is insufficient, change the detection strategy.

[0016] (III) Beneficial effects The present invention provides a motor housing finished product detection method and system based on multi-source data analysis, which has the following beneficial effects: (1) By collecting the geometric dimension data, internal structure data and process parameter data of the motor housing, we can obtain comprehensive basic information about the motor housing. These data provide a rich and accurate basis for subsequent analysis and evaluation, making the quality control and performance judgment of the motor housing more scientific and reliable, helping to discover potential problems in a timely manner, and laying a solid foundation for optimizing production processes and improving product quality.

[0017] (2) Calculating the comprehensive index of dimensional conformity, the internal structure stability index and the material performance evaluation value can quantitatively evaluate the quality and performance of the motor housing from multiple dimensions. Different indicators reflect the status of the motor housing in terms of size, internal structure and material performance, making the evaluation results more comprehensive and objective, and providing strong support for accurately judging the grade of the motor housing.

[0018] (3) Calculate the comprehensive evaluation index of finished products, preset the threshold of the comprehensive evaluation index of finished products and compare it with the calculated index to determine the grade of the motor housing, which can effectively distinguish the product quality levels. For unqualified products, they should be isolated and stored in time to prevent them from entering the market and reduce the risks and losses caused by defective products; for qualified and high-quality products, a more in-depth evaluation can be carried out to improve resource utilization efficiency and product quality control level; (4) Calculate the shell quality reliability index, shell optimization potential index, and detection optimization effectiveness index, and compare them with the preset threshold to determine the detection optimization effectiveness, so as to continuously monitor and optimize the quality and detection methods of the motor shell. If the optimization effect is insufficient, the detection strategy can be replaced in time, which will help to continuously improve the accuracy and effectiveness of the detection, thereby improving the overall quality and production efficiency of the motor shell. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the steps of the motor housing finished product detection method based on multi-source data analysis of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1 The present invention provides a motor housing finished product detection method based on multi-source data analysis, comprising: Step 1: Collect the geometric dimension data, internal structure data and process parameter data of the motor housing.

[0022] Comprehensive step one: Collecting the geometric dimension data of the motor housing can accurately grasp its appearance specifications, ensure that it is in line with the design standards and meet assembly requirements; obtaining internal structure data can provide insights into the layout and coordination of key components to ensure stable motor performance; collecting process parameter data can help trace the production process and analyze the impact on material properties and product quality. The combination of the three provides solid data support for comprehensively evaluating the quality of motor housings, optimizing production processes, and improving product performance.

[0023] Step 2: Calculate the dimensional deviation value SDF based on the geometric dimension data, and calculate the dimensional conformity comprehensive index CS based on the dimensional deviation value SDF; calculate the internal structure characteristic value PM based on the internal structure data, and calculate the internal structure stability index II based on the internal structure characteristic value PM; calculate the material performance evaluation value SQ based on the process parameter data.

[0024] Step 201: Calculate the comprehensive index CS of size conformity, the specific method is: The geometric dimension data includes the housing length LK, the housing width WK, the housing height HK and the wall thickness TK.

[0025] It should be noted that by placing the motor housing on the workbench of the image measuring instrument, photographing the housing through the optical imaging system, and then using VMS, QV and other measurement software to process and analyze the image, the size data of the housing can be accurately measured to obtain the housing length LK, housing width WK and housing height HK. The thickness of the motor housing is directly measured using an electromagnetic eddy current thickness gauge to obtain the wall thickness TK.

[0026] The dimensional deviation SDF is calculated based on the shell length LK, shell width WK, shell height HK and wall thickness TK according to the following formula: ; Among them, LS is the standard length of the shell; WS is the standard width of the shell; HS is the standard height of the shell; TS is the standard wall thickness; ω1 is The weight coefficient is 0.2~0.4; ω2 is The weight coefficient is 0.1~0.3; ω3 is The weight coefficient is 0.3~0.5; ω4 is The weight coefficient is between 0.2 and 0.4; and ω1+ω2+ω3+ω4=1.

[0027] It should be noted that the standard length LS, standard width WS, standard height HS and standard wall thickness TS of the shell are obtained by looking up the design drawings or specification product manuals of the motor shell during the design stage and obtaining the standard dimensions of the motor shell.

[0028] It should be noted that the dimensional deviation value SDF is obtained by calculating the square of the difference between the actual measured geometric dimensions of the motor housing and the standard dimensions, assigning corresponding weight coefficients according to the differences in the importance of different dimensions in the overall performance of the motor housing, and finally adding the weighted results of each part.

[0029] Based on the dimension deviation value SDF, the dimension conformity comprehensive index CS is calculated. The calculation formula is: .

[0030] It should be noted that Indicates the absolute value of the dimensional deviation, reflecting the deviation between the actual size and the standard size. It is the sum of the standard length, standard width, standard height and standard wall thickness of the shell, representing the standard size scale of the entire shell. The comprehensive dimensional conformity index CS is obtained by subtracting the ratio of the dimensional deviation value to the sum of the standard dimensions from 1.

[0031] Step 202: Calculate the internal structure stability index II, the specific method is: The internal structure data includes the stator core outer diameter DS, the stator core inner diameter DF, the rotor outer diameter FR, the rotor shaft diameter FG, the number of winding turns NS and the winding wire diameter DW.

[0032] It should be noted that by using a vernier caliper, a micrometer or a three-dimensional coordinate measuring machine, measurements are taken at multiple different positions along the inner diameter and outer diameter directions of the stator core, and then the average values ​​are taken as the stator core outer diameter DS and the stator core inner diameter DF.

[0033] The rotor outer diameter is measured by using a vernier caliper, micrometer or three-dimensional coordinate measuring machine. Multiple measuring points are selected on the outer circumference of the rotor, and their diameters are measured and the average value is taken to obtain the rotor outer diameter FR. For the rotor shaft diameter, measurements are taken at different parts of the shaft, and the average value is taken as the rotor shaft diameter FG.

[0034] The number of winding turns NS is determined by disassembling the motor winding and counting the number of winding turns one by one.

[0035] By using a vernier caliper, micrometer or three-dimensional coordinate measuring machine, select multiple different positions on the winding wire for measurement, and then take the average value as the winding wire diameter DW.

[0036] According to the stator core outer diameter DS, the stator core inner diameter DF, the rotor outer diameter FR, the rotor shaft diameter FG, the number of winding turns NS and the winding wire diameter DW, the internal structure characteristic value PM is calculated using the following formula: ; It should be noted that By calculating the square root of the sum of the squares of the difference between the inner and outer diameters of the stator core and the difference between the outer diameter of the rotor and the shaft diameter, the comprehensive difference between the size and structure of the stator and rotor is reflected. The larger the difference, the more obvious the difference between the size and structure of the stator and rotor, and the greater the impact on the motor performance. Based on the stator core outer diameter DS, the denominator is adjusted through a logarithmic function. It combines the relevant dimensional parameters of the stator and rotor. This part comprehensively considers the influence of winding parameters and stator and rotor size parameters on the internal structure characteristic values ​​through multiplication and division operations. It is the absolute value of the difference between the rotor outer diameter and the shaft diameter, which is used to normalize the numerator calculation results. The overall formula constructs a numerical PM that can comprehensively reflect the internal structural characteristics of the motor housing through the combined operation of these parameters.

[0037] Based on the internal structure characteristic value PM, the internal structure stability index II is calculated according to the formula: ; Among them, RS is the stator winding resistance; RR is the rotor winding resistance.

[0038] It should be noted that the stator winding resistance RS and the rotor winding resistance RR can be obtained by directly measuring the resistance values ​​of the stator winding and the rotor winding using an ohmmeter or a resistance meter when the motor is powered off and in a cold state.

[0039] It should be noted that It reflects the combined influence of internal structural characteristics and the total winding resistance on the stability index. It regulates the overall exponential operation, thereby affecting the size of the internal structure stability index II. The formula uses an exponential function to construct a nonlinear relationship between the stability index and the internal structure characteristic value and the winding resistance. Through this operation, the formula combines the internal structure characteristics of the motor with the winding resistance parameters to obtain an index II that can reflect the stability of the internal structure of the motor.

[0040] Step 203: Calculate the material performance evaluation value SQ, the specific method is: The process parameter data include die casting temperature TM, die casting pressure YL, holding time PI, mold preheating temperature TP and cooling speed LQ; It should be noted that the die casting temperature TM, die casting pressure YL, holding time PI, mold preheating temperature TP and cooling rate LQ are measured values ​​in the actual production process, which can be obtained in real time by installing corresponding sensors on the die casting equipment. For example, a thermocouple is used to measure the die casting temperature, a pressure sensor is used to measure the die casting pressure, a timer is used to measure the holding time, a temperature sensor is used to measure the mold preheating temperature, and the cooling rate is calculated by means of a temperature-time curve.

[0041] The material performance evaluation value SQ is calculated based on the die casting temperature TM, die casting pressure YL, holding time PI, mold preheating temperature TP and cooling rate LQ. The calculation formula is: ; It should be noted that TM min is the lower limit of die casting temperature, TM max Is the upper limit of die casting temperature; YL min Is the lower limit of die casting pressure, YL max Is the upper limit of die casting pressure; PI min is the lower limit of the holding time, PI max is the upper limit of the holding time; TP min is the lower limit of mold preheating temperature, TP max Is the upper limit of mold preheating temperature; LQ min is the lower limit of cooling rate, LQ max It is the upper limit of cooling rate; these limit values ​​can refer to relevant industry standards and technical specifications to determine the reasonable upper and lower limits of each parameter.

[0042] It should be noted that ɑ1 is The weight coefficient is 0.2-0.3, ɑ2 is The weight coefficient is 0.2-0.3, ɑ3 is The weight coefficient is 0.1-0.2, ɑ4 is The weight coefficient of The weight coefficient is 0.15-0.25.

[0043] It should be noted that The die-casting temperature is normalized so that the relative position of the actual die-casting temperature within the temperature range is between 0 and 1. max When the value is close to 1, the influence on SQ is greater when the weight coefficient is constant, which means that the die casting temperature factor has a more significant effect on improving material properties; on the contrary, if it is close to TM min , the smaller the impact on SQ; and so on, the other parts of the formula represent the normalized results of die-casting pressure YL, holding time PI, mold preheating temperature TP and cooling rate LQ, as well as their contribution to SQ. The weight coefficient of each parameter part reflects the importance of each parameter on the material properties. The larger the weight coefficient, the greater the impact of the normalized item of the corresponding parameter on SQ; since the appropriate die-casting temperature can ensure the fluidity of the molten metal, too high or too low temperature will affect the molding quality and internal structure of the casting, and thus affect the material properties. If the temperature is too low, the molten metal will have poor fluidity, which may lead to incomplete filling of the casting; if the temperature is too high, it may cause problems such as molten metal inhalation and oxidation; sufficient die-casting pressure can enable the molten metal to better fill the mold cavity, increase the density of the casting, and thus improve the strength and other properties of the material. If the pressure is insufficient, the casting may have defects such as looseness; sufficient holding time can compensate for the shrinkage of the molten metal when it solidifies and shrinks, reducing defects such as shrinkage cavities and shrinkage, which is crucial to the stability of material performance; the appropriate mold preheating temperature helps the molten metal to fill evenly, reduce thermal stress, avoid deformation or cracking of the casting, and has a positive effect on material performance. The cooling rate affects the crystallization process of the metal. Rapid cooling can refine the grains and improve the strength and hardness of the material, but too fast may produce internal stress. This formula uses the weight coefficient to weight the influence of each parameter and comprehensively considers the synergistic effect of all process parameters on material performance, so that the final SQ value can fully reflect the material performance status under the comprehensive effect of various process parameters, and can more comprehensively and reasonably reflect the material performance.

[0044] Comprehensive steps 201 to 203: This method can refine the evaluation of various aspects of the motor housing performance, from geometric dimensions to internal structure to material properties, and comprehensively control product quality. By accurately calculating various indicators, problems in the production process can be discovered in a timely manner, such as excessive dimensional deviation, unstable internal structure, or substandard material performance, etc., which facilitates targeted process optimization, parameter adjustment, and improved product quality and production efficiency.

[0045] Step 3: Calculate the comprehensive evaluation index MSK of the finished product based on the comprehensive index of dimensional compliance CS, the internal structure stability index II, and the material property evaluation value SQ; preset the threshold of the comprehensive evaluation index of the finished product, compare the comprehensive evaluation index MSK of the finished product with the threshold of the comprehensive evaluation index of the finished product, and judge the grade of the motor housing according to the comparison result; if the motor housing is of unqualified grade, store the product separately.

[0046] Step 301: Calculate the comprehensive evaluation index MSK of the finished product. The specific method is as follows: Calculate the comprehensive evaluation index MSK of the finished product based on the comprehensive index of dimensional compliance CS, the internal structure stability index II, and the material property evaluation value SQ. The calculation formula is: ; Among them, φ1 is the weight coefficient of the comprehensive index of dimensional compliance CS, and its value ranges from 0.1 to 0.3; φ2 is the weight coefficient of the internal structure stability index II, and its value ranges from 0.3 to 0.4; φ3 is the weight coefficient of the material property evaluation value SQ, and its value ranges from 0.3 to 0.5; and φ1 + φ2 + φ3 = 1.

[0047] It should be noted that this formula comprehensively considers the three important parameters of the comprehensive index of dimensional compliance CS, the internal structure stability index II, and the material property evaluation value SQ. Each parameter has a corresponding weight coefficient, indicating the degree of importance attached to different parameters when calculating the comprehensive evaluation index MSK of the finished product. By multiplying each parameter by its corresponding weight coefficient and then summing, a comprehensive evaluation index MSK of the finished product that can comprehensively describe the motor housing is obtained.

[0048] Step 302: Judge the grade of the motor housing. The specific method is as follows: Preset the threshold of the comprehensive evaluation index of the finished product; the threshold of the comprehensive evaluation index of the finished product includes the first threshold of the comprehensive evaluation index of the finished product MSK1 and the second threshold of the comprehensive evaluation index of the finished product MSK2, and MSK1 < MSK2; by collecting the quality data of each link of a large number of historical motor housing finished products, including the comprehensive index of dimensional compliance CS, the internal structure stability index II, and the material property evaluation value SQ, calculate the comprehensive evaluation index MSK of each motor housing finished product, and further calculate the average value MSKavg and the standard deviation σ of the comprehensive evaluation index of the finished product. Take MSKavg - σ as the first threshold of the comprehensive evaluation index of the finished product MSK1; take MSKavg + σ as the second threshold of the comprehensive evaluation index of the finished product MSK2.

[0049] When MSK ≤ MSK1, judge that the motor housing is of high-quality grade and directly flow into the finished product warehouse; When MSK1 < MSK ≤ MSK2, judge that the motor housing is of qualified grade and conduct a second random inspection; When MSK>MSK2, the motor housing is judged to be unqualified and the product is isolated and stored.

[0050] Comprehensive step 301~step 302: This method can comprehensively and accurately evaluate the quality of motor housings. According to the classification of evaluation results, it can ensure that high-quality products flow directly into the market, qualified products are sampled again to ensure quality, and unqualified products are isolated to prevent them from entering the market. This method can also optimize the production process and improve overall production efficiency and quality control level.

[0051] Step 4: If the motor housing is of qualified grade or high-quality grade, calculate the housing quality reliability index MRI based on the finished product comprehensive evaluation index MSK; calculate the housing optimization potential index POI based on the housing quality reliability index MRI, and then calculate the detection optimization effectiveness index TEO; preset the detection optimization effectiveness index threshold, compare the detection optimization effectiveness index TEO with the detection optimization effectiveness index threshold, and judge the optimization effectiveness of the current motor housing finished product detection based on the comparison result; if the optimization effectiveness of the current motor housing finished product detection is insufficient, change the detection strategy.

[0052] Step 401: Calculate the shell quality reliability index MRI, the specific method is: Based on the finished product comprehensive evaluation index MSK, the shell quality reliability index MRI is calculated based on the formula: ; Among them, EV is the expected number of failures of the motor housing under a specific use environment; CA is the expected maintenance cost of the motor housing during its service life; CB is the manufacturing cost of the motor housing; LR is the expected service life of the motor housing; LB is the design service life of the motor housing; FR is the failure rate of the motor housing under a specific use environment; TA is the average trouble-free working time of the motor housing.

[0053] It should be noted that the expected number of failures EV of the motor housing under a specific usage environment is obtained by collecting a large number of historical operating data of the same type of motor housings under the same or similar specific usage environments, and counting the number of failure events, so as to derive the expected number of failures EV of the motor housing under a specific usage environment.

[0054] Obtain the estimated maintenance cost CA of the motor housing during its service life, check the historical maintenance records of motor housings of the same type, and calculate the cost of each maintenance, including the cost of repair and replacement parts, labor costs, consumables costs, etc., and calculate the total of these costs to obtain the estimated maintenance cost CA.

[0055] The manufacturing cost CB of the motor housing is obtained by obtaining detailed cost accounting information during the production process of the motor housing, including the sum of various expenses such as raw material cost, processing cost, equipment depreciation, energy consumption, and labor cost, and adding them together to obtain the manufacturing cost CB of the motor housing.

[0056] To obtain the estimated service life LR of the motor housing, simulate the actual operating conditions of the motor housing in a laboratory environment, such as temperature, humidity, vibration, load and other conditions, and conduct long-term operation tests. Record the time from the start of the test to the time when the key performance indicators of the motor housing drop to the specified limit, such as serious deformation or cracking of the housing, to obtain the estimated service life LR.

[0057] Obtain the design service life LB of the motor housing by referring to the design drawings, technical specifications and other design documents of the motor housing to obtain the design service life.

[0058] Obtain the failure rate FR of the motor housing in a specific use environment, perform real-time monitoring on a certain number of motor housings in the specific use environment, record the number of failures QG within the operating time T, and calculate the failure rate FR of the motor housing in the specific use environment, FR=QG / T.

[0059] The average trouble-free working time TA of the motor housing is obtained by collecting the fault records of the motor housing during actual use, calculating the continuous working time before each fault occurs, and taking the average of these working times as the average trouble-free working time TA.

[0060] It should be noted that It means taking the cube root of the expected number of failures to appropriately reduce its influence on the final index. This part reflects the impact of the ratio of maintenance cost to manufacturing cost on quality reliability. The lower the maintenance cost is relative to the manufacturing cost, the more beneficial it is to quality reliability. The natural logarithm function is used to reflect the impact of the ratio of expected service life to design service life on quality reliability. When the expected service life is greater than the design service life, the value is positive, and the larger the ratio, the more obvious the effect of improving the quality reliability index. It indicates the impact of failure rate on quality reliability. The higher the failure rate, the larger the value of this part, and the more significant the effect of reducing the quality reliability index. It reflects the impact of the mean trouble-free working time on quality reliability. The longer the mean trouble-free working time, the closer the value of this part is to 1, and the smaller the negative impact on the quality reliability index.

[0061] Step 402: Calculate the shell optimization potential index POI, the specific method is: Based on the shell quality reliability index MRI, the shell optimization potential index POI is calculated according to the formula: ; Among them, MRImax is the maximum quality reliability index that the motor housing can achieve; C is the optimizable coefficient in the current production process; V is the weight coefficient of production optimization on quality reliability; HS is the time required for production optimization.

[0062] It should be noted that to obtain the maximum quality reliability index MRImax that the motor housing can achieve, the theoretical maximum quality grade QK of the material used for the motor housing, the maximum dimensional deviation DVmax allowed in the manufacturing process of the motor housing, and the optimal performance index EXmax that the motor housing can achieve under ideal design and operating conditions are obtained through technical specifications or research literature; the maximum quality reliability index MRImax is calculated using the following formula: Among them, QKmin is the lower limit of the material quality grade that meets the basic performance requirements of the motor housing; EXact is the actual performance index achieved by the current motor housing; a is The weight coefficient is 0.2~0.3; b is The weight coefficient is 0.3~0.4; c is The weight coefficient is between 0.4 and 0.5, and a+b+c=1.

[0063] Obtain the optimizable coefficient C in the current production process. By obtaining the best production efficiency EBI and the current actual production efficiency ECU of similar motor housing production, calculate the optimizable coefficient C in the current production process, C=1-(ECU / EBI).

[0064] Obtain the time HS required for production optimization by formulating a production optimization plan, including the specific tasks and time arrangements of each link, such as equipment procurement, installation and commissioning, process improvement testing, and personnel training. Add up the time required for each link to obtain the time HS required for production optimization.

[0065] It should be noted that It indicates the gap between the current quality reliability index and the maximum quality reliability index. The larger the gap, the greater the room for improvement and the more obvious the effect on POI improvement. It takes into account both the current quality reliability and the room for improvement. By multiplying the two, POI will have a larger value only when the current quality reliability has a certain foundation and there is still a lot of room for improvement. It represents the combined effect of the optimizability coefficient and the weight of the optimization on quality reliability, reflecting the comprehensive potential of production optimization in improving quality reliability. It shows the influence of the time required for production optimization on the shell optimization potential index POI.

[0066] Step 403: Determine the optimization effect of the current motor housing finished product inspection, the specific method is: Based on the shell optimization potential index POI, the detection optimization effectiveness index TEO is calculated based on the formula: ; Among them, ETR is the expected detection efficiency improvement rate; SDC is the time cost ratio of technology improvement; TCF is the technology improvement complexity coefficient; ERI is the equipment reliability index.

[0067] It should be noted that to obtain the expected detection efficiency improvement rate (ETR), in a laboratory environment, simulate the application of a new detection method or optimization solution and record the time required to detect a certain number of motor housings. Compare it with the time taken by the existing detection method and calculate the efficiency improvement ratio, which is used as the expected detection efficiency improvement rate (ETR). For example, it originally took 10 hours to detect 100 motor housings, but only 8 hours after simulating the new solution, then ETR = (10-8) / 10 = 0.2.

[0068] Obtain the time cost ratio of technology improvement (SDC), and make a detailed calculation of all time costs involved in the implementation of detection optimization, including equipment commissioning time, personnel training time, new detection method verification time, etc. Compare the sum of these time costs with the time of the entire production cycle to calculate the time cost ratio of technology improvement (SDC). For example, if the technology improvement takes a total of 50 hours, and the entire production cycle is expected to be 500 hours, then SDC=50 / 500=0.1.

[0069] Obtain the technical improvement complexity coefficient TCF, obtain the total number NL of technical elements such as detection software modules, sensor types and hardware interfaces involved in the production optimization process and the number NZ of technical elements that need to be modified, and calculate the technical improvement complexity coefficient TCF, TCF=NZ / NF.

[0070] Obtain the equipment reliability index ERI by counting the number of equipment failures s within a certain time period TU, and obtaining the equipment's operating time Ti and actual operating time Ta under ideal conditions, and calculating the equipment reliability index ERI. The calculation formula is: .

[0071] It should be noted that represents the shell optimization potential index; This is to make the impact of the expected detection efficiency improvement rate on the result present a nonlinear relationship. As the improvement rate increases, the growth rate of its impact on the optimization value of the detection efficiency gradually slows down. It reflects the regulatory effect of technical complexity on the proportion of time cost. The higher the technical complexity, the smaller the negative impact of the proportion of time cost on the optimization value of detection efficiency. It reflects the impact of equipment reliability on the optimization value of detection efficiency. The higher the equipment reliability, the more obvious the effect on improving the optimization value of detection efficiency. It directly reflects the negative impact of technical complexity on the optimization value of detection efficiency. The higher the technical complexity, the lower the optimization value of detection efficiency.

[0072] A detection optimization effectiveness index threshold Tth is preset; by collecting historical data of multiple motor housing finished product monitoring optimization projects in the past, including the housing optimization potential index POI, the expected detection efficiency improvement rate ETR, the technology improvement time cost ratio SDC, the technology improvement complexity coefficient TCF and the equipment reliability index ERI, the detection optimization effectiveness index TEO of each motor housing finished product is calculated, and the detection optimization effectiveness index average value TEOavg is further calculated, and the detection optimization effectiveness index average value TEOavg is used as the detection optimization effectiveness index threshold Tth.

[0073] When TEO≤Tth, it is judged that the optimization effect of the current motor housing finished product inspection is insufficient, and the inspection strategy is changed; When TEO>Tth, it is judged that the optimization effect of the current motor housing finished product inspection is good.

[0074] Comprehensive steps 401 to 403: This method can accurately quantify the reliability of shell quality, optimization potential and detection optimization results. After comparing with the preset threshold, the detection optimization results can be judged in time. If the results are insufficient, the detection strategy can be changed. This helps to find problems in time, improve detection methods, improve product quality and production efficiency, and reduce defective rates and costs.

[0075] On the other hand, the present invention also discloses a motor housing finished product detection system for multi-source data analysis, which is used to implement the motor housing finished product detection method for multi-source data analysis, including: A data acquisition module is used to collect geometric dimension data, internal structure data and process parameter data of the motor housing; A data processing module is used to calculate a dimension deviation value SDF based on geometric dimension data, and calculate a dimension conformity comprehensive index CS based on the dimension deviation value SDF; calculate an internal structure characteristic value PM based on internal structure data, and calculate an internal structure stability index II based on the internal structure characteristic value PM; and calculate a material performance evaluation value SQ based on process parameter data; The grade determination module is used to calculate the finished product comprehensive evaluation index MSK according to the dimensional conformity comprehensive index CS, the internal structure stability index II and the material performance evaluation value SQ; preset the finished product comprehensive evaluation index threshold, compare the finished product comprehensive evaluation index MSK with the finished product comprehensive evaluation index threshold, and determine the motor housing grade according to the comparison result; if the motor housing is of unqualified grade, the product will be isolated and stored; Efficiency improvement module, if the motor housing is of qualified grade or high-quality grade, calculate the housing quality reliability index MRI based on the finished product comprehensive evaluation index MSK; calculate the housing optimization potential index POI based on the housing quality reliability index MRI, and then calculate the detection optimization effectiveness index TEO; preset the detection optimization effectiveness index threshold, compare the detection optimization effectiveness index TEO with the detection optimization effectiveness index threshold, and judge the optimization effectiveness of the current motor housing finished product detection based on the comparison result; if the optimization effectiveness of the current motor housing finished product detection is insufficient, change the detection strategy.

[0076] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware or in combination with computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0077] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A motor housing finished product detection method based on multi-source data analysis, characterized in that: include: Collect geometric dimension data, internal structure data and process parameter data of the motor housing; Calculate the dimension deviation value SDF based on the geometric dimension data, and calculate the dimension conformity comprehensive index CS based on the dimension deviation value SDF; calculate the internal structure characteristic value PM based on the internal structure data, and calculate the internal structure stability index II based on the internal structure characteristic value PM; Calculate the material performance evaluation value SQ based on the process parameter data; The finished product comprehensive evaluation index MSK is calculated based on the dimensional conformity comprehensive index CS, the internal structure stability index II and the material performance evaluation value SQ; Preset the finished product comprehensive evaluation index threshold, compare the finished product comprehensive evaluation index MSK with the finished product comprehensive evaluation index threshold, and determine the motor housing grade based on the comparison result; if the motor housing is of unqualified grade, isolate and store the product; If the motor housing is of qualified grade or high-quality grade, the housing quality reliability index MRI is calculated based on the finished product comprehensive evaluation index MSK; the housing optimization potential index POI is calculated based on the housing quality reliability index MRI, and then the detection optimization effectiveness index TEO is calculated; A detection optimization effectiveness index threshold is preset, and the detection optimization effectiveness index TEO is compared with the detection optimization effectiveness index threshold. The optimization effectiveness of the current motor housing finished product detection is determined based on the comparison result; if the optimization effectiveness of the current motor housing finished product detection is insufficient, the detection strategy is changed.

2. The motor housing finished product detection method based on multi-source data analysis according to claim 1 is characterized in that: The method for calculating the comprehensive index CS of dimensional conformity is: The geometric dimension data include the shell length LK, shell width WK, shell height HK and wall thickness TK; The dimensional deviation SDF is calculated based on the shell length LK, shell width WK, shell height HK and wall thickness TK according to the following formula: ； Among them, LS is the standard length of the shell; WS is the standard width of the shell; HS is the standard height of the shell; TS is the standard wall thickness; ω1 is The weight coefficient is 0.2~0.4; ω2 is The weight coefficient is 0.1~0.3; ω3 is The weight coefficient is 0.3~0.5; ω4 is The weight coefficient is 0.2~0.4; and ω1+ω2+ω3+ω4=1; Based on the dimension deviation value SDF, the dimension conformity comprehensive index CS is calculated. The calculation formula is: 。 3. The motor housing finished product detection method based on multi-source data analysis according to claim 2 is characterized in that: The method for calculating the internal structure stability index II is: The internal structure data includes the stator core outer diameter DS, the stator core inner diameter DF, the rotor outer diameter FR, the rotor shaft diameter FG, the number of winding turns NS and the winding wire diameter DW; According to the stator core outer diameter DS, the stator core inner diameter DF, the rotor outer diameter FR, the rotor shaft diameter FG, the number of winding turns NS and the winding wire diameter DW, the internal structure characteristic value PM is calculated using the following formula: ； Based on the internal structure characteristic value PM, the internal structure stability index II is calculated according to the formula: ； Among them, RS is the stator winding resistance; RR is the rotor winding resistance.

4. The motor housing finished product detection method based on multi-source data analysis according to claim 3 is characterized in that: The method for calculating the material performance evaluation value SQ is: The process parameter data include die casting temperature TM, die casting pressure YL, holding time PI, mold preheating temperature TP and cooling speed LQ; The material performance evaluation value SQ is calculated based on the die casting temperature TM, die casting pressure YL, holding time PI, mold preheating temperature TP and cooling rate LQ. The calculation formula is: ； Among them, TM min is the lower limit of die casting temperature, TM max Is the upper limit of die casting temperature; YL min Is the lower limit of die casting pressure, YL max Is the upper limit of die casting pressure; PI min is the lower limit of the holding time, PI max is the upper limit of the holding time; TP min is the lower limit of mold preheating temperature, TP max Is the upper limit of mold preheating temperature; LQ min is the lower limit of cooling rate, LQ max is the upper limit of cooling rate; ɑ1 is The weight coefficient of The weight coefficient of The weight coefficient of The weight coefficient of The weight coefficient of .

5. The motor housing finished product detection method based on multi-source data analysis according to claim 4 is characterized in that: The method for calculating the finished product comprehensive evaluation index MSK is: According to the comprehensive index of dimensional conformity CS, the internal structure stability index II and the material performance evaluation value SQ, the comprehensive evaluation index MSK of the finished product is calculated. The calculation formula is: ； Among them, φ1 is the weight coefficient of the comprehensive index CS of dimensional compliance, with a value range of 0.1 to 0.3; φ2 is the weight coefficient of the internal structure stability index II, with a value range of 0.3 to 0.4; φ3 is the weight coefficient of the material property evaluation value SQ, with a value range of 0.3 to 0.5; and φ1 + φ2 + φ3 = 1.

6. The motor housing finished product detection method based on multi-source data analysis according to claim 5 is characterized in that: The method for judging the grade of the motor housing is as follows: The threshold values of the finished product comprehensive evaluation index include the first threshold value MSK1 and the second threshold value MSK2 of the finished product comprehensive evaluation index, and MSK1 < MSK2; When MSK ≤ MSK1, it is judged that the motor housing is of high-quality grade and directly flows into the finished product warehouse; When MSK1 < MSK ≤ MSK2, it is judged that the motor housing is of qualified grade and undergoes a second random inspection; When MSK > MSK2, it is judged that the motor housing is of unqualified grade and the product is stored separately.

7. The motor housing finished product detection method based on multi-source data analysis according to claim 6 is characterized in that: The method for calculating the housing mass reliability index MRI is as follows: Based on the finished product comprehensive evaluation index MSK, the housing mass reliability index MRI is calculated, and the formula relied on is: ； Among them, EV is the expected number of failures of the motor housing in a specific use environment; CA is the estimated maintenance cost of the motor housing during its service life; CB is the manufacturing cost of the motor housing; LR is the estimated service life of the motor housing; LB is the designed service life of the motor housing; FR is the failure rate of the motor housing in a specific use environment; TA is the mean time between failures of the motor housing.

8. The motor housing finished product detection method based on multi-source data analysis according to claim 7 is characterized in that: The method for calculating the housing optimization potential index POI is as follows: Based on the housing mass reliability index MRI, the housing optimization potential index POI is calculated, and the formula relied on is: ； Among them, MRImax is the maximum mass reliability index that the motor housing can achieve; C is the optimizable coefficient in the current production process; V is the weight coefficient of production optimization on mass reliability; HS is the time required for production optimization.

9. The motor housing finished product detection method based on multi-source data analysis according to claim 8, characterized in that: The method for judging the optimization effect of the current finished product inspection of the motor housing is as follows: Based on the housing optimization potential index POI, the inspection optimization effect index TEO is calculated, and the formula relied on is: ； Among them, ETR is the expected detection efficiency improvement rate; SDC is the proportion of the technical improvement time cost; TCF is the technical improvement complexity coefficient; ERI is the equipment reliability index; Preset the threshold value Tth of the inspection optimization effect index; When TEO ≤ Tth, it is judged that the optimization effect of the current finished product inspection of the motor housing is insufficient, and the detection strategy is replaced; When TEO > Tth, it is judged that the optimization effect of the current finished product inspection of the motor housing is good.

10. The motor housing finished product inspection system with multi-source data analysis is characterized by: Including: A data acquisition module for collecting geometric dimension data, internal structure data, and process parameter data of the motor housing; A data processing module for calculating the dimensional deviation value SDF based on the geometric dimension data, calculating the comprehensive index CS of dimensional compliance based on the dimensional deviation value SDF; calculating the internal structure characteristic value PM based on the internal structure data, calculating the internal structure stability index II based on the internal structure characteristic value PM; calculating the material property evaluation value SQ based on the process parameter data; The grade determination module is used to calculate the finished product comprehensive evaluation index MSK based on the dimensional conformity comprehensive index CS, the internal structure stability index II and the material performance evaluation value SQ; Preset the finished product comprehensive evaluation index threshold, compare the finished product comprehensive evaluation index MSK with the finished product comprehensive evaluation index threshold, and determine the motor housing grade based on the comparison result; if the motor housing is of unqualified grade, isolate and store the product; Efficiency improvement module: if the motor housing is of qualified grade or high-quality grade, the housing quality reliability index MRI is calculated based on the finished product comprehensive evaluation index MSK; the housing optimization potential index POI is calculated based on the housing quality reliability index MRI, and then the detection optimization effectiveness index TEO is calculated; A detection optimization effectiveness index threshold is preset, and the detection optimization effectiveness index TEO is compared with the detection optimization effectiveness index threshold. The optimization effectiveness of the current motor housing finished product detection is determined based on the comparison result; if the optimization effectiveness of the current motor housing finished product detection is insufficient, the detection strategy is changed.

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